 MLB Spin Rate Investigation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "## The \"sticky\" Background\n",
    "Over the last several years spin rate has become a much talked about statistic amongst baseball analysts and fans. As spin rates rose, pitches got nastier and nastier, and strikeouts rose, the hardest thing to do in professional sports, hitting a baseball, got a little too hard. Curveballs, sliders, and other breaking pitches unsurprisingly got sharper, but fastballs also benefitted from the introduction of sticky stuff. An increased spin rate on a four-seam fastball caused fastballs to have the appearance of rising, in reality they were just falling less than a lifetime of knowledge told the hitter it should. All the while there was an understanding throughout the sport that one of the primary factors in this rise was the use of \"sticky stuff\" by pitchers to get an unnaturally strong grip on the ball. That may or may not seem like a huge issue to some, after all isn't having a good grip on something you're trying to throw 100 mph important? However, it is a direct violation of rules 3.01, 6.02(c), and 6.02(d) regarding the use of outside substances to influence the flight of the baseball. This rule was never enforced for a variety of reasons, but on June 05 2021 reports [emerged](https://www.espn.com/mlb/story/_/id/31572769/mlb-plans-enforcement-foreign-substance-rules-being-finalized-rollout-pending-sources-say) that the league started investigating the issue and warned players change was coming. On June 21, 2021 Major League Baseball officially started enforcing it by checking pitchers gloves, hats, and belts (where they would often store patches of sticky stuff for use) and ejecting them and suspending them if they find sticky stuff.  \n",
    "\n",
    "## Questions/Goals\n",
    "1. Did this change in rule enforcement cause a drop in spin rate and if so which pitch types were most impacted?\n",
    "2. If there was a change in spin rate when did it occur? On the threat of enforcement, or the actual enforcement itself. \n",
    "3. Did some teams see a larger drop in spin rate than others?\n",
    "4. Has there been a change in xwOBA amongst pitchers since spin rate has dropped.\n",
    "\n",
    "\n",
    "## The Data\n",
    "Every pitch, from every game in the MLB is tracked by a camera system installed at every MLB stadium called StatCast. Statcast records an enormous number of data points on every single pitch and batted ball in every single game in the major leagues. This data is made available to the public and can be downloaded via the Python library [pybaseball](https://github.com/jldbc/pybaseball). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from datetime import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style\n",
    "from matplotlib import ticker\n",
    "import pybaseball\n",
    "from pybaseball import statcast\n",
    "import plotly.express as px\n",
    "import os\n",
    "style.use(style='fivethirtyeight')\n",
    "%matplotlib inline\n",
    "pybaseball.cache.enable()\n",
    "from pybaseball import cache\n",
    "cache.enable()\n",
    "if not os.path.exists(\"images\"):\n",
    "    os.mkdir(\"images\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a large query, it may take a moment to complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 121/121 [00:05<00:00, 23.55it/s]\n"
     ]
    }
   ],
   "source": [
    "# Collect data on every pitch thrown through July 12 using pybaseball library\n",
    "df = statcast(start_dt='2021-04-01', end_dt='2021-07-30')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pitch_type</th>\n",
       "      <th>game_date</th>\n",
       "      <th>release_speed</th>\n",
       "      <th>release_pos_x</th>\n",
       "      <th>release_pos_z</th>\n",
       "      <th>player_name</th>\n",
       "      <th>batter</th>\n",
       "      <th>pitcher</th>\n",
       "      <th>events</th>\n",
       "      <th>description</th>\n",
       "      <th>...</th>\n",
       "      <th>fld_score</th>\n",
       "      <th>post_away_score</th>\n",
       "      <th>post_home_score</th>\n",
       "      <th>post_bat_score</th>\n",
       "      <th>post_fld_score</th>\n",
       "      <th>if_fielding_alignment</th>\n",
       "      <th>of_fielding_alignment</th>\n",
       "      <th>spin_axis</th>\n",
       "      <th>delta_home_win_exp</th>\n",
       "      <th>delta_run_exp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>FF</td>\n",
       "      <td>2021-07-29</td>\n",
       "      <td>92.9</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>6.28</td>\n",
       "      <td>Davis, Wade</td>\n",
       "      <td>657757</td>\n",
       "      <td>451584</td>\n",
       "      <td>field_out</td>\n",
       "      <td>hit_into_play</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Infield shift</td>\n",
       "      <td>Standard</td>\n",
       "      <td>182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>FC</td>\n",
       "      <td>2021-07-29</td>\n",
       "      <td>89.7</td>\n",
       "      <td>-1.76</td>\n",
       "      <td>6.12</td>\n",
       "      <td>Davis, Wade</td>\n",
       "      <td>657757</td>\n",
       "      <td>451584</td>\n",
       "      <td>None</td>\n",
       "      <td>ball</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Infield shift</td>\n",
       "      <td>Standard</td>\n",
       "      <td>141</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>KC</td>\n",
       "      <td>2021-07-29</td>\n",
       "      <td>81.9</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>6.25</td>\n",
       "      <td>Davis, Wade</td>\n",
       "      <td>657757</td>\n",
       "      <td>451584</td>\n",
       "      <td>None</td>\n",
       "      <td>foul</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Infield shift</td>\n",
       "      <td>Strategic</td>\n",
       "      <td>28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2087</th>\n",
       "      <td>FF</td>\n",
       "      <td>2021-07-29</td>\n",
       "      <td>94.9</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>Davis, Wade</td>\n",
       "      <td>657757</td>\n",
       "      <td>451584</td>\n",
       "      <td>None</td>\n",
       "      <td>ball</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Infield shift</td>\n",
       "      <td>Strategic</td>\n",
       "      <td>194</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2121</th>\n",
       "      <td>FF</td>\n",
       "      <td>2021-07-29</td>\n",
       "      <td>92.7</td>\n",
       "      <td>-1.56</td>\n",
       "      <td>6.27</td>\n",
       "      <td>Davis, Wade</td>\n",
       "      <td>657757</td>\n",
       "      <td>451584</td>\n",
       "      <td>None</td>\n",
       "      <td>swinging_strike</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Infield shift</td>\n",
       "      <td>Strategic</td>\n",
       "      <td>195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pitch_type  game_date  release_speed  release_pos_x  release_pos_z  \\\n",
       "1923         FF 2021-07-29           92.9           -1.4           6.28   \n",
       "1967         FC 2021-07-29           89.7          -1.76           6.12   \n",
       "2003         KC 2021-07-29           81.9           -1.5           6.25   \n",
       "2087         FF 2021-07-29           94.9           -1.5            6.3   \n",
       "2121         FF 2021-07-29           92.7          -1.56           6.27   \n",
       "\n",
       "      player_name  batter  pitcher     events      description  ...  \\\n",
       "1923  Davis, Wade  657757   451584  field_out    hit_into_play  ...   \n",
       "1967  Davis, Wade  657757   451584       None             ball  ...   \n",
       "2003  Davis, Wade  657757   451584       None             foul  ...   \n",
       "2087  Davis, Wade  657757   451584       None             ball  ...   \n",
       "2121  Davis, Wade  657757   451584       None  swinging_strike  ...   \n",
       "\n",
       "      fld_score  post_away_score  post_home_score  post_bat_score  \\\n",
       "1923          5                0                5               0   \n",
       "1967          5                0                5               0   \n",
       "2003          5                0                5               0   \n",
       "2087          5                0                5               0   \n",
       "2121          5                0                5               0   \n",
       "\n",
       "      post_fld_score if_fielding_alignment of_fielding_alignment spin_axis  \\\n",
       "1923               5         Infield shift              Standard       182   \n",
       "1967               5         Infield shift              Standard       141   \n",
       "2003               5         Infield shift             Strategic        28   \n",
       "2087               5         Infield shift             Strategic       194   \n",
       "2121               5         Infield shift             Strategic       195   \n",
       "\n",
       "     delta_home_win_exp delta_run_exp  \n",
       "1923                0.0        -0.088  \n",
       "1967                0.0         0.018  \n",
       "2003                0.0           0.0  \n",
       "2087                0.0         0.012  \n",
       "2121                0.0        -0.026  \n",
       "\n",
       "[5 rows x 92 columns]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There have been 448284 pitches thrown as of July 13th.\n"
     ]
    }
   ],
   "source": [
    "print('There have been', len(df.index), 'pitches thrown as of July 13th.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Look at columns, datatypes, and null values. Then Clean the data by looking at missing data and removing unecessary columns and missing data. </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pitch_type', 'game_date', 'release_speed', 'release_pos_x',\n",
       "       'release_pos_z', 'player_name', 'batter', 'pitcher', 'events',\n",
       "       'description', 'spin_dir', 'spin_rate_deprecated',\n",
       "       'break_angle_deprecated', 'break_length_deprecated', 'zone', 'des',\n",
       "       'game_type', 'stand', 'p_throws', 'home_team', 'away_team', 'type',\n",
       "       'hit_location', 'bb_type', 'balls', 'strikes', 'game_year', 'pfx_x',\n",
       "       'pfx_z', 'plate_x', 'plate_z', 'on_3b', 'on_2b', 'on_1b',\n",
       "       'outs_when_up', 'inning', 'inning_topbot', 'hc_x', 'hc_y',\n",
       "       'tfs_deprecated', 'tfs_zulu_deprecated', 'fielder_2', 'umpire', 'sv_id',\n",
       "       'vx0', 'vy0', 'vz0', 'ax', 'ay', 'az', 'sz_top', 'sz_bot',\n",
       "       'hit_distance_sc', 'launch_speed', 'launch_angle', 'effective_speed',\n",
       "       'release_spin_rate', 'release_extension', 'game_pk', 'pitcher.1',\n",
       "       'fielder_2.1', 'fielder_3', 'fielder_4', 'fielder_5', 'fielder_6',\n",
       "       'fielder_7', 'fielder_8', 'fielder_9', 'release_pos_y',\n",
       "       'estimated_ba_using_speedangle', 'estimated_woba_using_speedangle',\n",
       "       'woba_value', 'woba_denom', 'babip_value', 'iso_value',\n",
       "       'launch_speed_angle', 'at_bat_number', 'pitch_number', 'pitch_name',\n",
       "       'home_score', 'away_score', 'bat_score', 'fld_score', 'post_away_score',\n",
       "       'post_home_score', 'post_bat_score', 'post_fld_score',\n",
       "       'if_fielding_alignment', 'of_fielding_alignment', 'spin_axis',\n",
       "       'delta_home_win_exp', 'delta_run_exp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A number of the columns are from a previous system from or not useful for our analysis, so I have dropped them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "relevent_col = ['pitch_type', 'game_date', 'release_speed',\n",
    "                'release_pos_x', 'release_pos_z', 'player_name',\n",
    "                'zone', 'des', 'p_throws', 'home_team', 'away_team',\n",
    "                'plate_x', 'plate_z', 'inning_topbot', 'launch_speed',\n",
    "                'launch_angle', 'effective_speed', 'release_spin_rate',\n",
    "                'release_extension', 'estimated_ba_using_speedangle',\n",
    "                'estimated_woba_using_speedangle', 'woba_value', 'woba_denom',\n",
    "                'babip_value', 'iso_value', 'launch_speed_angle',\n",
    "                'at_bat_number', 'pitch_number', 'pitch_name', 'spin_axis']\n",
    "df = df[relevent_col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 448284 entries, 1923 to 4081\n",
      "Data columns (total 30 columns):\n",
      " #   Column                           Non-Null Count   Dtype         \n",
      "---  ------                           --------------   -----         \n",
      " 0   pitch_type                       447934 non-null  object        \n",
      " 1   game_date                        448284 non-null  datetime64[ns]\n",
      " 2   release_speed                    448201 non-null  Float64       \n",
      " 3   release_pos_x                    448219 non-null  Float64       \n",
      " 4   release_pos_z                    448219 non-null  Float64       \n",
      " 5   player_name                      448284 non-null  object        \n",
      " 6   zone                             448219 non-null  Int64         \n",
      " 7   des                              448284 non-null  object        \n",
      " 8   p_throws                         448284 non-null  object        \n",
      " 9   home_team                        448284 non-null  object        \n",
      " 10  away_team                        448284 non-null  object        \n",
      " 11  plate_x                          448219 non-null  Float64       \n",
      " 12  plate_z                          448219 non-null  Float64       \n",
      " 13  inning_topbot                    448284 non-null  object        \n",
      " 14  launch_speed                     144859 non-null  Float64       \n",
      " 15  launch_angle                     144859 non-null  Int64         \n",
      " 16  effective_speed                  448238 non-null  Float64       \n",
      " 17  release_spin_rate                446357 non-null  Int64         \n",
      " 18  release_extension                446214 non-null  Float64       \n",
      " 19  estimated_ba_using_speedangle    75835 non-null   Float64       \n",
      " 20  estimated_woba_using_speedangle  75835 non-null   Float64       \n",
      " 21  woba_value                       114400 non-null  Float64       \n",
      " 22  woba_denom                       114400 non-null  Int64         \n",
      " 23  babip_value                      114400 non-null  Int64         \n",
      " 24  iso_value                        114400 non-null  Int64         \n",
      " 25  launch_speed_angle               75835 non-null   Int64         \n",
      " 26  at_bat_number                    448284 non-null  Int64         \n",
      " 27  pitch_number                     448284 non-null  Int64         \n",
      " 28  pitch_name                       447934 non-null  object        \n",
      " 29  spin_axis                        446377 non-null  Int64         \n",
      "dtypes: Float64(11), Int64(10), datetime64[ns](1), object(8)\n",
      "memory usage: 115.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pitch_type                            350\n",
       "game_date                               0\n",
       "release_speed                          83\n",
       "release_pos_x                          65\n",
       "release_pos_z                          65\n",
       "player_name                             0\n",
       "zone                                   65\n",
       "des                                     0\n",
       "p_throws                                0\n",
       "home_team                               0\n",
       "away_team                               0\n",
       "plate_x                                65\n",
       "plate_z                                65\n",
       "inning_topbot                           0\n",
       "launch_speed                       303425\n",
       "launch_angle                       303425\n",
       "effective_speed                        46\n",
       "release_spin_rate                    1927\n",
       "release_extension                    2070\n",
       "estimated_ba_using_speedangle      372449\n",
       "estimated_woba_using_speedangle    372449\n",
       "woba_value                         333884\n",
       "woba_denom                         333884\n",
       "babip_value                        333884\n",
       "iso_value                          333884\n",
       "launch_speed_angle                 372449\n",
       "at_bat_number                           0\n",
       "pitch_number                            0\n",
       "pitch_name                            350\n",
       "spin_axis                            1907\n",
       "dtype: int64"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the batted ball stats have null values which makes sense because not every pitch is hit. However, there are some nulls in the release spin, pitch_type, pitch_name, and zone columns I will remove."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove rows with nulls indicating a lack of a reading\n",
    "df = df[~df['release_spin_rate'].isnull()]\n",
    "df = df[~df['pitch_type'].isnull()]\n",
    "df = df[~df['pitch_name'].isnull()]\n",
    "df = df[~df['zone'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to compare pitchers spin rates who throw different velocities (spin rate generally increases with velocity, but it's the ratio of spin rate to velocity to creates movement) we will add a column for the spin rate divided by the pitch velocity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Bauer Units (b units) column\n",
    "df['b_units'] = (df['release_spin_rate']\n",
    "                 / df['release_speed'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To look at weekly and monthly averages we extract the week and month from the datetime object that is game_date into seperate columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df['month'] = df['game_date'].dt.month\n",
    "df['week'] = df['game_date'].dt.isocalendar().week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1923   2021-07-26\n",
       "1967   2021-07-26\n",
       "2003   2021-07-26\n",
       "2087   2021-07-26\n",
       "2121   2021-07-26\n",
       "2235   2021-07-26\n",
       "2242   2021-07-26\n",
       "2323   2021-07-26\n",
       "2441   2021-07-26\n",
       "2545   2021-07-26\n",
       "Name: week of, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Update week numbers with date of Monday of that week\n",
    "week_date_mapper = {\n",
    "                    13: 'March 29',\n",
    "                    14: 'April 05',\n",
    "                    15: 'April 12',\n",
    "                    16: 'April 19',\n",
    "                    17: 'April 26',\n",
    "                    18: 'May 03',\n",
    "                    19: 'May 10',\n",
    "                    20: 'May 17',\n",
    "                    21: 'May 24',\n",
    "                    22: 'May 31',\n",
    "                    23: 'June 07',\n",
    "                    24: 'June 14',\n",
    "                    25: 'June 21',\n",
    "                    26: 'June 28',\n",
    "                    27: 'July 05',\n",
    "                    28: 'July 12',\n",
    "                    29: 'July 19',\n",
    "                    30: 'July 26',\n",
    "                    31: 'August 2',\n",
    "                    32: 'August 9',\n",
    "                    31: 'August 16',\n",
    "                    32: 'August 23',\n",
    "                    32: 'August 30',\n",
    "                    33: 'September 6',\n",
    "                    34: 'September 13',\n",
    "                    35: 'September 20',\n",
    "                    36: 'September 27',\n",
    "                    37: 'October 4',\n",
    "                    38: 'October 11',\n",
    "                    39: 'October 18',\n",
    "                    40: 'October 25',\n",
    "                    41: 'November 1',\n",
    "                    42: 'November 8',\n",
    "                    43: 'November 15'\n",
    "                    }\n",
    "df['week'] = df['week'].replace(week_date_mapper)\n",
    "df['week'] = df['week'] + ', 2021'\n",
    "df['week of'] = df['week'].apply(lambda x: dt.strptime(x, '%B %d, %Y'))\n",
    "df.drop('week', axis= 'columns', inplace = True)\n",
    "df['week of'].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Add column for the pitching team</b><br>\n",
    "To breakdown the data by team we will have to create a column for the pitching team as this is not included in the dataset. However, we know that if it is the top of the inning while the pitcher is pitching they are on the home team and visa versa with the bottom of the inning. We do have info on the home team and top or bottom of inning, so we can utilize that to deduce the pitching team. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_team</th>\n",
       "      <th>inning_topbot</th>\n",
       "      <th>pitch_team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>CWS</td>\n",
       "      <td>KC</td>\n",
       "      <td>Top</td>\n",
       "      <td>KC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>CWS</td>\n",
       "      <td>KC</td>\n",
       "      <td>Top</td>\n",
       "      <td>KC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>CWS</td>\n",
       "      <td>KC</td>\n",
       "      <td>Top</td>\n",
       "      <td>KC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2087</th>\n",
       "      <td>CWS</td>\n",
       "      <td>KC</td>\n",
       "      <td>Top</td>\n",
       "      <td>KC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2121</th>\n",
       "      <td>CWS</td>\n",
       "      <td>KC</td>\n",
       "      <td>Top</td>\n",
       "      <td>KC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     away_team home_team inning_topbot pitch_team\n",
       "1923       CWS        KC           Top         KC\n",
       "1967       CWS        KC           Top         KC\n",
       "2003       CWS        KC           Top         KC\n",
       "2087       CWS        KC           Top         KC\n",
       "2121       CWS        KC           Top         KC"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If pitching during bottom of inning then away team\n",
    "df['pitch_team'] = df['home_team']\n",
    "df.loc[df['inning_topbot'] == 'Bot', 'pitch_team'] = df.loc[\n",
    "    df['inning_topbot'] == 'Bot', 'away_team']\n",
    "df[['away_team', 'home_team', 'inning_topbot', 'pitch_team']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Overall Pitch Frequency</b><br>\n",
    "We'll be looking at the spin of different pitches, so we'll see what pitches we have information on and how often they each occur. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pitch_name</th>\n",
       "      <th>count</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4-Seam Fastball</td>\n",
       "      <td>155389</td>\n",
       "      <td>34.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Slider</td>\n",
       "      <td>85648</td>\n",
       "      <td>19.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sinker</td>\n",
       "      <td>70192</td>\n",
       "      <td>15.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Changeup</td>\n",
       "      <td>50910</td>\n",
       "      <td>11.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Curveball</td>\n",
       "      <td>36887</td>\n",
       "      <td>8.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cutter</td>\n",
       "      <td>30371</td>\n",
       "      <td>6.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Knuckle Curve</td>\n",
       "      <td>9391</td>\n",
       "      <td>2.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Split-Finger</td>\n",
       "      <td>6627</td>\n",
       "      <td>1.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fastball</td>\n",
       "      <td>541</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Knuckleball</td>\n",
       "      <td>69</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eephus</td>\n",
       "      <td>43</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Screwball</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         pitch_name   count  frequency\n",
       "0   4-Seam Fastball  155389      34.84\n",
       "10           Slider   85648      19.20\n",
       "9            Sinker   70192      15.74\n",
       "1          Changeup   50910      11.41\n",
       "2         Curveball   36887       8.27\n",
       "3            Cutter   30371       6.81\n",
       "6     Knuckle Curve    9391       2.11\n",
       "11     Split-Finger    6627       1.49\n",
       "5          Fastball     541       0.12\n",
       "7       Knuckleball      69       0.02\n",
       "4            Eephus      43       0.01\n",
       "8         Screwball       1       0.00"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Investigate pitch frequency\n",
    "pitch_count = (df.groupby('pitch_name')['release_spin_rate']\n",
    "               .count().reset_index().sort_values(\n",
    "                'release_spin_rate',\n",
    "                ascending=False))\n",
    "pitch_count.rename(\n",
    "                   mapper={'release_spin_rate': 'count'},\n",
    "                   axis='columns',\n",
    "                   inplace=True\n",
    "                   )\n",
    "pitch_count['frequency'] = round((pitch_count['count']\n",
    "                                 / pitch_count['count'].sum())*100, 2)\n",
    "pitch_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have five pitch names that occur less than 0.2% of the time. Two of these, fastball and 2-seam Fastball are likely from a legacy system or misnamed as there are a high volume of the common names for the three main types of fastballs: 4-Seam Fastball, Sinker, and Cutter. Knuckleball, Eephus, and Screwball occur at a very low rate in the modern game. I will remove all rows of these five types. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[~df['pitch_name'].isin(\n",
    "     ['Fastball', 'Knuckleball', 'Eephus', '2-Seam Fastball', 'Screwball']\n",
    "     )]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pitch_name</th>\n",
       "      <th>count</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4-Seam Fastball</td>\n",
       "      <td>155389</td>\n",
       "      <td>34.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Slider</td>\n",
       "      <td>85648</td>\n",
       "      <td>19.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sinker</td>\n",
       "      <td>70192</td>\n",
       "      <td>15.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Changeup</td>\n",
       "      <td>50910</td>\n",
       "      <td>11.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Curveball</td>\n",
       "      <td>36887</td>\n",
       "      <td>8.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cutter</td>\n",
       "      <td>30371</td>\n",
       "      <td>6.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Knuckle Curve</td>\n",
       "      <td>9391</td>\n",
       "      <td>2.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Split-Finger</td>\n",
       "      <td>6627</td>\n",
       "      <td>1.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pitch_name   count  frequency\n",
       "0  4-Seam Fastball  155389      34.89\n",
       "6           Slider   85648      19.23\n",
       "5           Sinker   70192      15.76\n",
       "1         Changeup   50910      11.43\n",
       "2        Curveball   36887       8.28\n",
       "3           Cutter   30371       6.82\n",
       "4    Knuckle Curve    9391       2.11\n",
       "7     Split-Finger    6627       1.49"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pitch_count = (df.groupby('pitch_name')['release_spin_rate']\n",
    "               .count().reset_index().sort_values('release_spin_rate',\n",
    "                                                  ascending=False))\n",
    "pitch_count.rename(mapper={'release_spin_rate': 'count'},\n",
    "                   axis='columns', inplace=True)\n",
    "pitch_count['frequency'] = round((pitch_count['count']\n",
    "                                 / pitch_count['count'].sum()\n",
    "                                  )*100, 2)\n",
    "pitch_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If there was a drop in spinrate, when did it occur?\n",
    "Group by day, week, and month and average spin rate for each, then visualize. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create daily, weekly, and month average spin rate dataframes\n",
    "daily_avg_spin = (df.groupby('game_date')\n",
    "                  ['release_spin_rate'].mean().reset_index())\n",
    "daily_avg_spin['game_date_str'] = (daily_avg_spin['game_date']\n",
    "                                   .apply(lambda x: x.strftime('%Y-%m-%d')))\n",
    "week_avg_spin = df.groupby('week of')['release_spin_rate'].mean().reset_index()\n",
    "monthly_avg_spin = (df.groupby('month')\n",
    "                    ['release_spin_rate'].mean().reset_index()\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_monthly_line = sns.lineplot(data=monthly_avg_spin,\n",
    "                                x='month',\n",
    "                                y='release_spin_rate')\n",
    "plt.title('Average Spin Rate Per Month - All Pitches (2021)',\n",
    "          fontsize=16, fontweight=600)\n",
    "plt.ylabel('Average Spin Rate', fontsize=12, labelpad=10)\n",
    "plt.xlabel('Month', fontsize=12, labelpad=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the decline in spin rate started between May and June. Let's see if weekly can identify when it happened. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "positions = [week for week in week_avg_spin['week of']]\n",
    "labels = [week_date_mapper[key] for key in week_date_mapper]\n",
    "\n",
    "all_weekly_line = sns.lineplot(data=week_avg_spin, x='week of',\n",
    "                               y='release_spin_rate')\n",
    "plt.title('Average Spin Rate Per Week - All Pitches (2021)',\n",
    "          fontsize=16, fontweight=600)\n",
    "plt.ylabel('Average Spin Rate', fontsize=12, labelpad=10)\n",
    "plt.xlabel('Week of', fontsize=12, labelpad=10)\n",
    "plt.xticks(rotation=70)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the drop in spin started occuring after the first week in June. Was there a specific day? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adjust x axis labels\n",
    "# Retrieve index in dataframe of first of each month for labeling x axis\n",
    "positions = [daily_avg_spin['game_date_str'][daily_avg_spin['game_date_str']\n",
    "                                             == '2021-04-01'].index[0],\n",
    "             daily_avg_spin['game_date_str'][daily_avg_spin['game_date_str']\n",
    "                                             == '2021-05-01'].index[0],\n",
    "             daily_avg_spin['game_date_str'][daily_avg_spin['game_date_str']\n",
    "                                             == '2021-06-01'].index[0],\n",
    "             daily_avg_spin['game_date_str'][daily_avg_spin['game_date_str']\n",
    "                                             == '2021-07-01'].index[0]]\n",
    "\n",
    "june_5 = daily_avg_spin['game_date_str'][daily_avg_spin['game_date_str']\n",
    "                                             == '2021-06-05'].index[0]\n",
    "june_21 = daily_avg_spin['game_date_str'][daily_avg_spin['game_date_str']\n",
    "                                             == '2021-06-21'].index[0]\n",
    "labels = ['April 1', 'May 1', 'June 1', 'July 1']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 10))\n",
    "\n",
    "sns.lineplot(data=daily_avg_spin,\n",
    "                      x='game_date_str',\n",
    "                      y='release_spin_rate',\n",
    "                      ax=ax)\n",
    "\n",
    "plt.title('Average Spin Rate Per Day - All Pitches (2021)',\n",
    "          fontsize=24, fontweight=600)\n",
    "plt.ylabel('Average Spin Rate', fontsize=24, labelpad=10)\n",
    "plt.xlabel('', fontsize=12, labelpad=10)\n",
    "# Set x axis tick labels\n",
    "ax.xaxis.set_major_locator(ticker.FixedLocator(positions))\n",
    "ax.xaxis.set_major_formatter(ticker.FixedFormatter(labels))\n",
    "# x and y tick fontsize\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "# Line showing June 5\n",
    "plt.axvline(june_5, color='yellow')\n",
    "plt.text(june_5-10, 2340, 'Threat of Enforcement', fontsize=16)\n",
    "# Line showing June 21\n",
    "plt.axvline(june_21, color='red')\n",
    "plt.text(june_21+1, 2300, 'Enforcement', fontsize=16)\n",
    "plt.savefig(\"images/daily_spin.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can we find a specific day or couple of days where the spin dropped off a cliff in early June as it appears? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>game_date</th>\n",
       "      <th>release_spin_rate</th>\n",
       "      <th>game_date_str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2021-05-31</td>\n",
       "      <td>2298.575392</td>\n",
       "      <td>2021-05-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>2307.880662</td>\n",
       "      <td>2021-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2021-06-02</td>\n",
       "      <td>2291.261733</td>\n",
       "      <td>2021-06-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2021-06-03</td>\n",
       "      <td>2269.909204</td>\n",
       "      <td>2021-06-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>2262.941162</td>\n",
       "      <td>2021-06-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2021-06-05</td>\n",
       "      <td>2273.894604</td>\n",
       "      <td>2021-06-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2021-06-06</td>\n",
       "      <td>2303.079512</td>\n",
       "      <td>2021-06-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2021-06-07</td>\n",
       "      <td>2330.453362</td>\n",
       "      <td>2021-06-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2021-06-08</td>\n",
       "      <td>2229.080305</td>\n",
       "      <td>2021-06-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2021-06-09</td>\n",
       "      <td>2218.096081</td>\n",
       "      <td>2021-06-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2021-06-10</td>\n",
       "      <td>2216.897626</td>\n",
       "      <td>2021-06-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2021-06-11</td>\n",
       "      <td>2263.218308</td>\n",
       "      <td>2021-06-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2021-06-12</td>\n",
       "      <td>2241.47175</td>\n",
       "      <td>2021-06-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2021-06-13</td>\n",
       "      <td>2178.553444</td>\n",
       "      <td>2021-06-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2021-06-14</td>\n",
       "      <td>2228.762507</td>\n",
       "      <td>2021-06-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>2021-06-15</td>\n",
       "      <td>2183.915185</td>\n",
       "      <td>2021-06-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    game_date  release_spin_rate game_date_str\n",
       "60 2021-05-31        2298.575392    2021-05-31\n",
       "61 2021-06-01        2307.880662    2021-06-01\n",
       "62 2021-06-02        2291.261733    2021-06-02\n",
       "63 2021-06-03        2269.909204    2021-06-03\n",
       "64 2021-06-04        2262.941162    2021-06-04\n",
       "65 2021-06-05        2273.894604    2021-06-05\n",
       "66 2021-06-06        2303.079512    2021-06-06\n",
       "67 2021-06-07        2330.453362    2021-06-07\n",
       "68 2021-06-08        2229.080305    2021-06-08\n",
       "69 2021-06-09        2218.096081    2021-06-09\n",
       "70 2021-06-10        2216.897626    2021-06-10\n",
       "71 2021-06-11        2263.218308    2021-06-11\n",
       "72 2021-06-12         2241.47175    2021-06-12\n",
       "73 2021-06-13        2178.553444    2021-06-13\n",
       "74 2021-06-14        2228.762507    2021-06-14\n",
       "75 2021-06-15        2183.915185    2021-06-15"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create dataframe of average spin rate per day June 1 - June 15\n",
    "june_1_15_daily_spin = daily_avg_spin[\n",
    "                       (daily_avg_spin['game_date'] >= '2021-05-31')\n",
    "                       &\n",
    "                       (daily_avg_spin['game_date'] <= '2021-06-15')\n",
    "                                     ]\n",
    "june_1_15_daily_spin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the the day after which there is a significant drop in spin is June 7th. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "june_daily_line = sns.lineplot(data=june_1_15_daily_spin,\n",
    "                               x='game_date_str',\n",
    "                               y='release_spin_rate')\n",
    "plt.title('Average Spin Rate Per Day - All Pitches (Early June)',\n",
    "          fontsize=16,\n",
    "          fontweight=600)\n",
    "plt.ylabel('Average Spin Rate', fontsize=12, labelpad=10)\n",
    "plt.xlabel('', fontsize=12, labelpad=10)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Takeaways from spin over time analysis</b><br>\n",
    "When initial reports started coming out that a change was coming, players immediately stopped using outside sticky substances to increase their spin on the ball rather than waiting for official punishment (which started June 21) to come through. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Impact on the frequency with which certain pitches are thrown</b><br>\n",
    "At this point it is clear that spin rate across the board went down, but how much of that is due to pitches losing rpm of spin and how much is due to pitchers throwing more of their pitches that rely less on having high spin? To better understand where the biggest changes are happening we'll look at the change in frequency of each pitch being thrown before and after June 7th and which pitches are seeing the biggest change in spin frequency. \n",
    "\n",
    "1. Calculate and compare the frequency at which each pitch is thrown pre and post June 7\n",
    "2. Calculate and compare average spin rate on all pitches pre and post June 7\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pitch_name</th>\n",
       "      <th>pre_count</th>\n",
       "      <th>pre_frequency</th>\n",
       "      <th>post_count</th>\n",
       "      <th>post_frequency</th>\n",
       "      <th>freq_diff</th>\n",
       "      <th>freq_diff_pct_freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4-Seam Fastball</td>\n",
       "      <td>89438</td>\n",
       "      <td>35.00</td>\n",
       "      <td>65951</td>\n",
       "      <td>34.74</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Slider</td>\n",
       "      <td>49027</td>\n",
       "      <td>19.18</td>\n",
       "      <td>36621</td>\n",
       "      <td>19.29</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sinker</td>\n",
       "      <td>39368</td>\n",
       "      <td>15.40</td>\n",
       "      <td>30824</td>\n",
       "      <td>16.24</td>\n",
       "      <td>0.84</td>\n",
       "      <td>5.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Changeup</td>\n",
       "      <td>29680</td>\n",
       "      <td>11.61</td>\n",
       "      <td>21230</td>\n",
       "      <td>11.18</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Curveball</td>\n",
       "      <td>20981</td>\n",
       "      <td>8.21</td>\n",
       "      <td>15906</td>\n",
       "      <td>8.38</td>\n",
       "      <td>0.17</td>\n",
       "      <td>2.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cutter</td>\n",
       "      <td>17706</td>\n",
       "      <td>6.93</td>\n",
       "      <td>12665</td>\n",
       "      <td>6.67</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Knuckle Curve</td>\n",
       "      <td>5816</td>\n",
       "      <td>2.28</td>\n",
       "      <td>3575</td>\n",
       "      <td>1.88</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-17.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Split-Finger</td>\n",
       "      <td>3540</td>\n",
       "      <td>1.39</td>\n",
       "      <td>3087</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0.24</td>\n",
       "      <td>17.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pitch_name  pre_count  pre_frequency  post_count  post_frequency  \\\n",
       "0  4-Seam Fastball      89438          35.00       65951           34.74   \n",
       "1           Slider      49027          19.18       36621           19.29   \n",
       "2           Sinker      39368          15.40       30824           16.24   \n",
       "3         Changeup      29680          11.61       21230           11.18   \n",
       "4        Curveball      20981           8.21       15906            8.38   \n",
       "5           Cutter      17706           6.93       12665            6.67   \n",
       "6    Knuckle Curve       5816           2.28        3575            1.88   \n",
       "7     Split-Finger       3540           1.39        3087            1.63   \n",
       "\n",
       "   freq_diff  freq_diff_pct_freq  \n",
       "0      -0.26               -0.74  \n",
       "1       0.11                0.57  \n",
       "2       0.84                5.45  \n",
       "3      -0.43               -3.70  \n",
       "4       0.17                2.07  \n",
       "5      -0.26               -3.75  \n",
       "6      -0.40              -17.54  \n",
       "7       0.24               17.27  "
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create df of frequency thrown for each pitch before June 7\n",
    "pre_freq = (df[\n",
    "           df['game_date'] <= '2021-06-7'\n",
    "             ].groupby('pitch_name')['release_spin_rate'].count()\n",
    "            .reset_index().sort_values('release_spin_rate', ascending=False))\n",
    "pre_freq.rename(mapper={'release_spin_rate': 'pre_count'},\n",
    "                axis='columns',\n",
    "                inplace=True)\n",
    "pre_freq['pre_frequency'] = round((pre_freq['pre_count']\n",
    "                                  / pre_freq['pre_count'].sum()\n",
    "                                   )*100, 2)\n",
    "# Create df of frequency thrown for each pitch after June 7\n",
    "post_freq = (df[df['game_date'] > '2021-06-7']\n",
    "             .groupby('pitch_name')['release_spin_rate'].count().reset_index()\n",
    "             .rename(mapper={'release_spin_rate': 'post_count'},\n",
    "                     axis='columns'))\n",
    "post_freq['post_frequency'] = round((post_freq['post_count']\n",
    "                                    / post_freq['post_count'].sum()\n",
    "                                     )*100, 2)\n",
    "# Merge before and after dataframes and calculate difference\n",
    "pre_post_freq = pre_freq.merge(post_freq,\n",
    "                               how='left',\n",
    "                               left_on='pitch_name', right_on='pitch_name')\n",
    "pre_post_freq['freq_diff'] = (pre_post_freq['post_frequency']\n",
    "                              - pre_post_freq['pre_frequency'])\n",
    "pre_post_freq['freq_diff_pct_freq'] = round((pre_post_freq['freq_diff']\n",
    "                                             / pre_post_freq['pre_frequency']\n",
    "                                             ) * 100, 2)\n",
    "pre_post_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The largest drop in frequency of a pitch thrown as a percent of it's frequency thrown in the Knuckle Curve, followed by the cutter and changeup. Let's take a look at the spin rates of these different pitches. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pitch_name</th>\n",
       "      <th>pre_spin</th>\n",
       "      <th>post_spin</th>\n",
       "      <th>spin_diff</th>\n",
       "      <th>spin_diff_pct_spin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Knuckle Curve</td>\n",
       "      <td>2562.247421</td>\n",
       "      <td>2499.199161</td>\n",
       "      <td>-63.04826</td>\n",
       "      <td>-2.460662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Curveball</td>\n",
       "      <td>2554.558505</td>\n",
       "      <td>2473.727084</td>\n",
       "      <td>-80.831421</td>\n",
       "      <td>-3.164203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Slider</td>\n",
       "      <td>2462.570359</td>\n",
       "      <td>2395.065509</td>\n",
       "      <td>-67.50485</td>\n",
       "      <td>-2.741235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cutter</td>\n",
       "      <td>2418.781035</td>\n",
       "      <td>2331.874299</td>\n",
       "      <td>-86.906735</td>\n",
       "      <td>-3.592997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4-Seam Fastball</td>\n",
       "      <td>2317.880286</td>\n",
       "      <td>2241.684993</td>\n",
       "      <td>-76.195292</td>\n",
       "      <td>-3.287283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sinker</td>\n",
       "      <td>2160.116338</td>\n",
       "      <td>2099.904879</td>\n",
       "      <td>-60.211459</td>\n",
       "      <td>-2.787417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Changeup</td>\n",
       "      <td>1784.691408</td>\n",
       "      <td>1718.875412</td>\n",
       "      <td>-65.815996</td>\n",
       "      <td>-3.687808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Split-Finger</td>\n",
       "      <td>1481.199435</td>\n",
       "      <td>1314.332685</td>\n",
       "      <td>-166.86675</td>\n",
       "      <td>-11.26565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pitch_name     pre_spin    post_spin  spin_diff  spin_diff_pct_spin\n",
       "0    Knuckle Curve  2562.247421  2499.199161  -63.04826           -2.460662\n",
       "1        Curveball  2554.558505  2473.727084 -80.831421           -3.164203\n",
       "2           Slider  2462.570359  2395.065509  -67.50485           -2.741235\n",
       "3           Cutter  2418.781035  2331.874299 -86.906735           -3.592997\n",
       "4  4-Seam Fastball  2317.880286  2241.684993 -76.195292           -3.287283\n",
       "5           Sinker  2160.116338  2099.904879 -60.211459           -2.787417\n",
       "6         Changeup  1784.691408  1718.875412 -65.815996           -3.687808\n",
       "7     Split-Finger  1481.199435  1314.332685 -166.86675           -11.26565"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create df of each pitch spin rate pre June 7\n",
    "pre_spin = (df[df['game_date'] <= '2021-06-7']\n",
    "            .groupby('pitch_name')['release_spin_rate'].mean()\n",
    "            .reset_index().sort_values('release_spin_rate', ascending=False))\n",
    "pre_spin.rename(mapper={'release_spin_rate': 'pre_spin'},\n",
    "                axis='columns', inplace=True)\n",
    "# Create df of each pitch spin rate post June 7\n",
    "post_spin = (df[df['game_date'] > '2021-06-7']\n",
    "             .groupby('pitch_name')['release_spin_rate'].mean()\n",
    "             .reset_index().rename(mapper={'release_spin_rate': 'post_spin'},\n",
    "                                   axis='columns'))\n",
    "# Merge df and calc differences\n",
    "pre_post_spin = pre_spin.merge(post_spin,how='left',\n",
    "                               left_on='pitch_name',\n",
    "                               right_on='pitch_name')\n",
    "pre_post_spin['spin_diff'] = (pre_post_spin['post_spin']\n",
    "                              - pre_post_spin['pre_spin'])\n",
    "pre_post_spin['spin_diff_pct_spin'] = (pre_post_spin['spin_diff']\n",
    "                                       / pre_post_spin['pre_spin']\n",
    "                                       ) * 100\n",
    "\n",
    "pre_post_spin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Across the board we see between a 2.5 and 3.5% drop in spin rate, so it appears as though likely no matter what players are throwing they would be effected by this rule change if they are using sticky stuff. The split-finger saw the largest drop in spin rate as a percent of spin, but it is also the least thrown and slowest spinning pitching, so it is likely highly suceptable to questionable spin readings and a low sample size. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which Teams are Impacted the Most? \n",
    "Was this a problem equally spread throughout the league, or did it vary team to team? I'll calclate the difference in average spin per team before and after June 7. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pitch_team</th>\n",
       "      <th>pre_spin</th>\n",
       "      <th>post_spin</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LAD</td>\n",
       "      <td>2587.406326</td>\n",
       "      <td>2391.237916</td>\n",
       "      <td>-196.16841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DET</td>\n",
       "      <td>2229.332754</td>\n",
       "      <td>2077.445437</td>\n",
       "      <td>-151.887317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BOS</td>\n",
       "      <td>2296.015298</td>\n",
       "      <td>2176.769348</td>\n",
       "      <td>-119.24595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CIN</td>\n",
       "      <td>2363.140974</td>\n",
       "      <td>2246.669412</td>\n",
       "      <td>-116.471562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BAL</td>\n",
       "      <td>2286.482153</td>\n",
       "      <td>2182.971379</td>\n",
       "      <td>-103.510774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARI</td>\n",
       "      <td>2289.833294</td>\n",
       "      <td>2187.577785</td>\n",
       "      <td>-102.255509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>OAK</td>\n",
       "      <td>2151.546676</td>\n",
       "      <td>2054.615565</td>\n",
       "      <td>-96.931111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHC</td>\n",
       "      <td>2278.52168</td>\n",
       "      <td>2181.860635</td>\n",
       "      <td>-96.661045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SEA</td>\n",
       "      <td>2246.818991</td>\n",
       "      <td>2153.507203</td>\n",
       "      <td>-93.311789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MIL</td>\n",
       "      <td>2313.117987</td>\n",
       "      <td>2220.28747</td>\n",
       "      <td>-92.830516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CLE</td>\n",
       "      <td>2297.63354</td>\n",
       "      <td>2217.498614</td>\n",
       "      <td>-80.134926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NYM</td>\n",
       "      <td>2275.230858</td>\n",
       "      <td>2195.625524</td>\n",
       "      <td>-79.605334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>PIT</td>\n",
       "      <td>2368.872057</td>\n",
       "      <td>2290.097044</td>\n",
       "      <td>-78.775013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NYY</td>\n",
       "      <td>2374.319572</td>\n",
       "      <td>2301.116935</td>\n",
       "      <td>-73.202637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>TB</td>\n",
       "      <td>2293.519633</td>\n",
       "      <td>2227.071969</td>\n",
       "      <td>-66.447664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LAA</td>\n",
       "      <td>2239.459011</td>\n",
       "      <td>2173.160411</td>\n",
       "      <td>-66.2986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>STL</td>\n",
       "      <td>2270.57631</td>\n",
       "      <td>2206.763758</td>\n",
       "      <td>-63.812552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ATL</td>\n",
       "      <td>2303.806588</td>\n",
       "      <td>2242.330041</td>\n",
       "      <td>-61.476547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SF</td>\n",
       "      <td>2143.783624</td>\n",
       "      <td>2084.017168</td>\n",
       "      <td>-59.766455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MIA</td>\n",
       "      <td>2236.89508</td>\n",
       "      <td>2178.473484</td>\n",
       "      <td>-58.421596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>TEX</td>\n",
       "      <td>2205.987758</td>\n",
       "      <td>2149.90481</td>\n",
       "      <td>-56.082947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>WSH</td>\n",
       "      <td>2235.782061</td>\n",
       "      <td>2181.781701</td>\n",
       "      <td>-54.00036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>PHI</td>\n",
       "      <td>2139.236519</td>\n",
       "      <td>2094.750233</td>\n",
       "      <td>-44.486286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HOU</td>\n",
       "      <td>2297.344572</td>\n",
       "      <td>2253.068259</td>\n",
       "      <td>-44.276313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SD</td>\n",
       "      <td>2361.679092</td>\n",
       "      <td>2317.682424</td>\n",
       "      <td>-43.996668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KC</td>\n",
       "      <td>2323.144787</td>\n",
       "      <td>2280.180755</td>\n",
       "      <td>-42.964032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CWS</td>\n",
       "      <td>2263.646974</td>\n",
       "      <td>2231.294887</td>\n",
       "      <td>-32.352086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>TOR</td>\n",
       "      <td>2245.143202</td>\n",
       "      <td>2213.32077</td>\n",
       "      <td>-31.822432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>COL</td>\n",
       "      <td>2302.583483</td>\n",
       "      <td>2279.297405</td>\n",
       "      <td>-23.286078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MIN</td>\n",
       "      <td>2169.073904</td>\n",
       "      <td>2151.156202</td>\n",
       "      <td>-17.917702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pitch_team     pre_spin    post_spin  Difference\n",
       "13        LAD  2587.406326  2391.237916  -196.16841\n",
       "9         DET  2229.332754  2077.445437 -151.887317\n",
       "3         BOS  2296.015298  2176.769348  -119.24595\n",
       "5         CIN  2363.140974  2246.669412 -116.471562\n",
       "2         BAL  2286.482153  2182.971379 -103.510774\n",
       "0         ARI  2289.833294  2187.577785 -102.255509\n",
       "19        OAK  2151.546676  2054.615565  -96.931111\n",
       "4         CHC   2278.52168  2181.860635  -96.661045\n",
       "23        SEA  2246.818991  2153.507203  -93.311789\n",
       "15        MIL  2313.117987   2220.28747  -92.830516\n",
       "6         CLE   2297.63354  2217.498614  -80.134926\n",
       "17        NYM  2275.230858  2195.625524  -79.605334\n",
       "21        PIT  2368.872057  2290.097044  -78.775013\n",
       "18        NYY  2374.319572  2301.116935  -73.202637\n",
       "26         TB  2293.519633  2227.071969  -66.447664\n",
       "12        LAA  2239.459011  2173.160411    -66.2986\n",
       "25        STL   2270.57631  2206.763758  -63.812552\n",
       "1         ATL  2303.806588  2242.330041  -61.476547\n",
       "24         SF  2143.783624  2084.017168  -59.766455\n",
       "14        MIA   2236.89508  2178.473484  -58.421596\n",
       "27        TEX  2205.987758   2149.90481  -56.082947\n",
       "29        WSH  2235.782061  2181.781701   -54.00036\n",
       "20        PHI  2139.236519  2094.750233  -44.486286\n",
       "10        HOU  2297.344572  2253.068259  -44.276313\n",
       "22         SD  2361.679092  2317.682424  -43.996668\n",
       "11         KC  2323.144787  2280.180755  -42.964032\n",
       "8         CWS  2263.646974  2231.294887  -32.352086\n",
       "28        TOR  2245.143202   2213.32077  -31.822432\n",
       "7         COL  2302.583483  2279.297405  -23.286078\n",
       "16        MIN  2169.073904  2151.156202  -17.917702"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate pre, post, and difference in spin for each team\n",
    "team_pre_spin = (df[df['game_date'] <= '2021-06-07']\n",
    "                 .groupby(by='pitch_team')['release_spin_rate'].mean()\n",
    "                 .reset_index().rename(\n",
    "                                      mapper={'release_spin_rate': 'pre_spin'},\n",
    "                                      axis='columns')\n",
    "                 )\n",
    "team_post_spin = (df[df['game_date'] > '2021-06-07']\n",
    "                  .groupby(by='pitch_team')['release_spin_rate'].mean()\n",
    "                  .reset_index().rename(\n",
    "                             mapper={'release_spin_rate': 'post_spin'},\n",
    "                             axis='columns'))\n",
    "team_spin_comp = team_pre_spin.merge(team_post_spin,\n",
    "                                     how='left',\n",
    "                                     left_on='pitch_team',\n",
    "                                     right_on='pitch_team')\n",
    "team_spin_comp['Difference'] = (team_spin_comp['post_spin']\n",
    "                                - team_spin_comp['pre_spin'])\n",
    "team_spin_comp = team_spin_comp.sort_values('Difference')\n",
    "team_spin_comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2587.406326034063,
           2391.2379158819836
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "LAD"
         ],
         "legendgroup": "LAD",
         "marker": {
          "color": "#005a9c",
          "pattern": {
           "shape": ""
          }
         },
         "name": "LAD",
         "offsetgroup": "LAD",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "LAD"
         ],
         "xaxis": "x",
         "y": [
          -196.16841015207956
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2229.3327544286212,
           2077.44543695102
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "DET"
         ],
         "legendgroup": "DET",
         "marker": {
          "color": "#f26722",
          "pattern": {
           "shape": ""
          }
         },
         "name": "DET",
         "offsetgroup": "DET",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "DET"
         ],
         "xaxis": "x",
         "y": [
          -151.8873174776013
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2296.015298087739,
           2176.769348191116
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "BOS"
         ],
         "legendgroup": "BOS",
         "marker": {
          "color": "#bd3039",
          "pattern": {
           "shape": ""
          }
         },
         "name": "BOS",
         "offsetgroup": "BOS",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "BOS"
         ],
         "xaxis": "x",
         "y": [
          -119.24594989662319
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2363.1409742120345,
           2246.669412257434
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "CIN"
         ],
         "legendgroup": "CIN",
         "marker": {
          "color": "#c6011f",
          "pattern": {
           "shape": ""
          }
         },
         "name": "CIN",
         "offsetgroup": "CIN",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "CIN"
         ],
         "xaxis": "x",
         "y": [
          -116.47156195460047
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2286.482153039115,
           2182.9713794182044
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "BAL"
         ],
         "legendgroup": "BAL",
         "marker": {
          "color": "#df4601",
          "pattern": {
           "shape": ""
          }
         },
         "name": "BAL",
         "offsetgroup": "BAL",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "BAL"
         ],
         "xaxis": "x",
         "y": [
          -103.51077362091064
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2289.833294337857,
           2187.5777849427695
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "ARI"
         ],
         "legendgroup": "ARI",
         "marker": {
          "color": "#e3d4ad",
          "pattern": {
           "shape": ""
          }
         },
         "name": "ARI",
         "offsetgroup": "ARI",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "ARI"
         ],
         "xaxis": "x",
         "y": [
          -102.2555093950873
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2151.5466759323604,
           2054.6155652465613
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "OAK"
         ],
         "legendgroup": "OAK",
         "marker": {
          "color": "#003831",
          "pattern": {
           "shape": ""
          }
         },
         "name": "OAK",
         "offsetgroup": "OAK",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "OAK"
         ],
         "xaxis": "x",
         "y": [
          -96.93111068579901
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2278.521679909194,
           2181.860635316101
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "CHC"
         ],
         "legendgroup": "CHC",
         "marker": {
          "color": "#0e3386",
          "pattern": {
           "shape": ""
          }
         },
         "name": "CHC",
         "offsetgroup": "CHC",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "CHC"
         ],
         "xaxis": "x",
         "y": [
          -96.66104459309327
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2246.8189914411287,
           2153.5072027861324
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "SEA"
         ],
         "legendgroup": "SEA",
         "marker": {
          "color": "#005c5c",
          "pattern": {
           "shape": ""
          }
         },
         "name": "SEA",
         "offsetgroup": "SEA",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "SEA"
         ],
         "xaxis": "x",
         "y": [
          -93.31178865499623
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2313.11798679868,
           2220.2874704491724
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "MIL"
         ],
         "legendgroup": "MIL",
         "marker": {
          "color": "#ffc52f",
          "pattern": {
           "shape": ""
          }
         },
         "name": "MIL",
         "offsetgroup": "MIL",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "MIL"
         ],
         "xaxis": "x",
         "y": [
          -92.83051634950743
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2297.6335403726707,
           2217.498614055112
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "CLE"
         ],
         "legendgroup": "CLE",
         "marker": {
          "color": "#e31937",
          "pattern": {
           "shape": ""
          }
         },
         "name": "CLE",
         "offsetgroup": "CLE",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "CLE"
         ],
         "xaxis": "x",
         "y": [
          -80.1349263175589
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2275.230857800806,
           2195.6255242660277
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "NYM"
         ],
         "legendgroup": "NYM",
         "marker": {
          "color": "#002d72",
          "pattern": {
           "shape": ""
          }
         },
         "name": "NYM",
         "offsetgroup": "NYM",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "NYM"
         ],
         "xaxis": "x",
         "y": [
          -79.60533353477831
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2368.872057074911,
           2290.097043801269
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "PIT"
         ],
         "legendgroup": "PIT",
         "marker": {
          "color": "#fdb827",
          "pattern": {
           "shape": ""
          }
         },
         "name": "PIT",
         "offsetgroup": "PIT",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "PIT"
         ],
         "xaxis": "x",
         "y": [
          -78.77501327364189
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2374.3195718654433,
           2301.1169348150606
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "NYY"
         ],
         "legendgroup": "NYY",
         "marker": {
          "color": "#132448",
          "pattern": {
           "shape": ""
          }
         },
         "name": "NYY",
         "offsetgroup": "NYY",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "NYY"
         ],
         "xaxis": "x",
         "y": [
          -73.20263705038269
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2293.519632899628,
           2227.0719690600304
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "TB"
         ],
         "legendgroup": "TB",
         "marker": {
          "color": "#8fbce6",
          "pattern": {
           "shape": ""
          }
         },
         "name": "TB",
         "offsetgroup": "TB",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "TB"
         ],
         "xaxis": "x",
         "y": [
          -66.44766383959768
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2239.459010989011,
           2173.160411150911
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "LAA"
         ],
         "legendgroup": "LAA",
         "marker": {
          "color": "#ba0021",
          "pattern": {
           "shape": ""
          }
         },
         "name": "LAA",
         "offsetgroup": "LAA",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "LAA"
         ],
         "xaxis": "x",
         "y": [
          -66.29859983810002
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2270.576310295572,
           2206.763758389262
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "STL"
         ],
         "legendgroup": "STL",
         "marker": {
          "color": "#c41e3a",
          "pattern": {
           "shape": ""
          }
         },
         "name": "STL",
         "offsetgroup": "STL",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "STL"
         ],
         "xaxis": "x",
         "y": [
          -63.81255190631009
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2303.806587738835,
           2242.33004076513
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "ATL"
         ],
         "legendgroup": "ATL",
         "marker": {
          "color": "#13274f",
          "pattern": {
           "shape": ""
          }
         },
         "name": "ATL",
         "offsetgroup": "ATL",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "ATL"
         ],
         "xaxis": "x",
         "y": [
          -61.4765469737049
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2143.783623564283,
           2084.0171681115075
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "SF"
         ],
         "legendgroup": "SF",
         "marker": {
          "color": "#fd5a1e",
          "pattern": {
           "shape": ""
          }
         },
         "name": "SF",
         "offsetgroup": "SF",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "SF"
         ],
         "xaxis": "x",
         "y": [
          -59.76645545277552
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2236.8950803819653,
           2178.4734842226994
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "MIA"
         ],
         "legendgroup": "MIA",
         "marker": {
          "color": "#00A3E0",
          "pattern": {
           "shape": ""
          }
         },
         "name": "MIA",
         "offsetgroup": "MIA",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "MIA"
         ],
         "xaxis": "x",
         "y": [
          -58.421596159265846
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2205.987757878032,
           2149.904810470848
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "TEX"
         ],
         "legendgroup": "TEX",
         "marker": {
          "color": "#003278",
          "pattern": {
           "shape": ""
          }
         },
         "name": "TEX",
         "offsetgroup": "TEX",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "TEX"
         ],
         "xaxis": "x",
         "y": [
          -56.08294740718384
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2235.7820609725372,
           2181.781700721154
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "WSH"
         ],
         "legendgroup": "WSH",
         "marker": {
          "color": "#ab0003",
          "pattern": {
           "shape": ""
          }
         },
         "name": "WSH",
         "offsetgroup": "WSH",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "WSH"
         ],
         "xaxis": "x",
         "y": [
          -54.00036025138343
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2139.236519461969,
           2094.7502329916124
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "PHI"
         ],
         "legendgroup": "PHI",
         "marker": {
          "color": "#e81828",
          "pattern": {
           "shape": ""
          }
         },
         "name": "PHI",
         "offsetgroup": "PHI",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "PHI"
         ],
         "xaxis": "x",
         "y": [
          -44.48628647035639
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2297.3445722747465,
           2253.0682593856654
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "HOU"
         ],
         "legendgroup": "HOU",
         "marker": {
          "color": "#eb6e1f",
          "pattern": {
           "shape": ""
          }
         },
         "name": "HOU",
         "offsetgroup": "HOU",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "HOU"
         ],
         "xaxis": "x",
         "y": [
          -44.276312889081055
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2361.679092204802,
           2317.682423857868
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "SD"
         ],
         "legendgroup": "SD",
         "marker": {
          "color": "#2F241D",
          "pattern": {
           "shape": ""
          }
         },
         "name": "SD",
         "offsetgroup": "SD",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "SD"
         ],
         "xaxis": "x",
         "y": [
          -43.99666834693426
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2323.1447865400023,
           2280.180754834685
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "KC"
         ],
         "legendgroup": "KC",
         "marker": {
          "color": "#174885",
          "pattern": {
           "shape": ""
          }
         },
         "name": "KC",
         "offsetgroup": "KC",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "KC"
         ],
         "xaxis": "x",
         "y": [
          -42.96403170531721
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2263.646973715939,
           2231.2948874010954
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "CWS"
         ],
         "legendgroup": "CWS",
         "marker": {
          "color": "#000000",
          "pattern": {
           "shape": ""
          }
         },
         "name": "CWS",
         "offsetgroup": "CWS",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "CWS"
         ],
         "xaxis": "x",
         "y": [
          -32.352086314843746
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2245.1432015429123,
           2213.3207699773534
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "TOR"
         ],
         "legendgroup": "TOR",
         "marker": {
          "color": "#134a8e",
          "pattern": {
           "shape": ""
          }
         },
         "name": "TOR",
         "offsetgroup": "TOR",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "TOR"
         ],
         "xaxis": "x",
         "y": [
          -31.822431565558873
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2302.5834825158136,
           2279.297404529674
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "COL"
         ],
         "legendgroup": "COL",
         "marker": {
          "color": "#33006f",
          "pattern": {
           "shape": ""
          }
         },
         "name": "COL",
         "offsetgroup": "COL",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "COL"
         ],
         "xaxis": "x",
         "y": [
          -23.28607798613939
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           2169.073903818953,
           2151.156202143951
          ]
         ],
         "hovertemplate": "<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>",
         "hovertext": [
          "MIN"
         ],
         "legendgroup": "MIN",
         "marker": {
          "color": "#002b5c",
          "pattern": {
           "shape": ""
          }
         },
         "name": "MIN",
         "offsetgroup": "MIN",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "MIN"
         ],
         "xaxis": "x",
         "y": [
          -17.917701675002263
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "autosize": true,
        "barmode": "relative",
        "legend": {
         "title": {
          "text": "Team"
         },
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "showlegend": false,
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Change in Team Avg Spin Rate Before and After June 5"
        },
        "xaxis": {
         "anchor": "y",
         "autorange": true,
         "categoryarray": [
          "LAD",
          "DET",
          "BOS",
          "CIN",
          "BAL",
          "ARI",
          "OAK",
          "CHC",
          "SEA",
          "MIL",
          "CLE",
          "NYM",
          "PIT",
          "NYY",
          "TB",
          "LAA",
          "STL",
          "ATL",
          "SF",
          "MIA",
          "TEX",
          "WSH",
          "PHI",
          "HOU",
          "SD",
          "KC",
          "CWS",
          "TOR",
          "COL",
          "MIN"
         ],
         "categoryorder": "array",
         "domain": [
          0,
          1
         ],
         "range": [
          -0.5,
          29.5
         ],
         "title": {
          "text": "Team"
         },
         "type": "category"
        },
        "yaxis": {
         "anchor": "x",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          -206.49306331797848,
          0
         ],
         "title": {
          "text": "Spin Rate Change"
         },
         "type": "linear"
        }
       }
      },
      "image/png": "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",
      "text/html": [
       "<div>                            <div id=\"17a8498d-df23-42b6-8758-882ff4ed7fef\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"17a8498d-df23-42b6-8758-882ff4ed7fef\")) {                    Plotly.newPlot(                        \"17a8498d-df23-42b6-8758-882ff4ed7fef\",                        [{\"alignmentgroup\":\"True\",\"customdata\":[[2587.406326034063,2391.2379158819836]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"LAD\"],\"legendgroup\":\"LAD\",\"marker\":{\"color\":\"#005a9c\",\"pattern\":{\"shape\":\"\"}},\"name\":\"LAD\",\"offsetgroup\":\"LAD\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"LAD\"],\"xaxis\":\"x\",\"y\":[-196.16841015207956],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2229.3327544286212,2077.44543695102]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"DET\"],\"legendgroup\":\"DET\",\"marker\":{\"color\":\"#f26722\",\"pattern\":{\"shape\":\"\"}},\"name\":\"DET\",\"offsetgroup\":\"DET\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"DET\"],\"xaxis\":\"x\",\"y\":[-151.8873174776013],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2296.015298087739,2176.769348191116]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"BOS\"],\"legendgroup\":\"BOS\",\"marker\":{\"color\":\"#bd3039\",\"pattern\":{\"shape\":\"\"}},\"name\":\"BOS\",\"offsetgroup\":\"BOS\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"BOS\"],\"xaxis\":\"x\",\"y\":[-119.24594989662319],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2363.1409742120345,2246.669412257434]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"CIN\"],\"legendgroup\":\"CIN\",\"marker\":{\"color\":\"#c6011f\",\"pattern\":{\"shape\":\"\"}},\"name\":\"CIN\",\"offsetgroup\":\"CIN\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"CIN\"],\"xaxis\":\"x\",\"y\":[-116.47156195460047],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2286.482153039115,2182.9713794182044]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"BAL\"],\"legendgroup\":\"BAL\",\"marker\":{\"color\":\"#df4601\",\"pattern\":{\"shape\":\"\"}},\"name\":\"BAL\",\"offsetgroup\":\"BAL\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"BAL\"],\"xaxis\":\"x\",\"y\":[-103.51077362091064],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2289.833294337857,2187.5777849427695]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"ARI\"],\"legendgroup\":\"ARI\",\"marker\":{\"color\":\"#e3d4ad\",\"pattern\":{\"shape\":\"\"}},\"name\":\"ARI\",\"offsetgroup\":\"ARI\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"ARI\"],\"xaxis\":\"x\",\"y\":[-102.2555093950873],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2151.5466759323604,2054.6155652465613]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"OAK\"],\"legendgroup\":\"OAK\",\"marker\":{\"color\":\"#003831\",\"pattern\":{\"shape\":\"\"}},\"name\":\"OAK\",\"offsetgroup\":\"OAK\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"OAK\"],\"xaxis\":\"x\",\"y\":[-96.93111068579901],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2278.521679909194,2181.860635316101]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"CHC\"],\"legendgroup\":\"CHC\",\"marker\":{\"color\":\"#0e3386\",\"pattern\":{\"shape\":\"\"}},\"name\":\"CHC\",\"offsetgroup\":\"CHC\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"CHC\"],\"xaxis\":\"x\",\"y\":[-96.66104459309327],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2246.8189914411287,2153.5072027861324]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"SEA\"],\"legendgroup\":\"SEA\",\"marker\":{\"color\":\"#005c5c\",\"pattern\":{\"shape\":\"\"}},\"name\":\"SEA\",\"offsetgroup\":\"SEA\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"SEA\"],\"xaxis\":\"x\",\"y\":[-93.31178865499623],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2313.11798679868,2220.2874704491724]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"MIL\"],\"legendgroup\":\"MIL\",\"marker\":{\"color\":\"#ffc52f\",\"pattern\":{\"shape\":\"\"}},\"name\":\"MIL\",\"offsetgroup\":\"MIL\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"MIL\"],\"xaxis\":\"x\",\"y\":[-92.83051634950743],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2297.6335403726707,2217.498614055112]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"CLE\"],\"legendgroup\":\"CLE\",\"marker\":{\"color\":\"#e31937\",\"pattern\":{\"shape\":\"\"}},\"name\":\"CLE\",\"offsetgroup\":\"CLE\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"CLE\"],\"xaxis\":\"x\",\"y\":[-80.1349263175589],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2275.230857800806,2195.6255242660277]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"NYM\"],\"legendgroup\":\"NYM\",\"marker\":{\"color\":\"#002d72\",\"pattern\":{\"shape\":\"\"}},\"name\":\"NYM\",\"offsetgroup\":\"NYM\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"NYM\"],\"xaxis\":\"x\",\"y\":[-79.60533353477831],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2368.872057074911,2290.097043801269]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"PIT\"],\"legendgroup\":\"PIT\",\"marker\":{\"color\":\"#fdb827\",\"pattern\":{\"shape\":\"\"}},\"name\":\"PIT\",\"offsetgroup\":\"PIT\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"PIT\"],\"xaxis\":\"x\",\"y\":[-78.77501327364189],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2374.3195718654433,2301.1169348150606]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"NYY\"],\"legendgroup\":\"NYY\",\"marker\":{\"color\":\"#132448\",\"pattern\":{\"shape\":\"\"}},\"name\":\"NYY\",\"offsetgroup\":\"NYY\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"NYY\"],\"xaxis\":\"x\",\"y\":[-73.20263705038269],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2293.519632899628,2227.0719690600304]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"TB\"],\"legendgroup\":\"TB\",\"marker\":{\"color\":\"#8fbce6\",\"pattern\":{\"shape\":\"\"}},\"name\":\"TB\",\"offsetgroup\":\"TB\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"TB\"],\"xaxis\":\"x\",\"y\":[-66.44766383959768],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2239.459010989011,2173.160411150911]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"LAA\"],\"legendgroup\":\"LAA\",\"marker\":{\"color\":\"#ba0021\",\"pattern\":{\"shape\":\"\"}},\"name\":\"LAA\",\"offsetgroup\":\"LAA\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"LAA\"],\"xaxis\":\"x\",\"y\":[-66.29859983810002],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2270.576310295572,2206.763758389262]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"STL\"],\"legendgroup\":\"STL\",\"marker\":{\"color\":\"#c41e3a\",\"pattern\":{\"shape\":\"\"}},\"name\":\"STL\",\"offsetgroup\":\"STL\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"STL\"],\"xaxis\":\"x\",\"y\":[-63.81255190631009],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2303.806587738835,2242.33004076513]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"ATL\"],\"legendgroup\":\"ATL\",\"marker\":{\"color\":\"#13274f\",\"pattern\":{\"shape\":\"\"}},\"name\":\"ATL\",\"offsetgroup\":\"ATL\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"ATL\"],\"xaxis\":\"x\",\"y\":[-61.4765469737049],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2143.783623564283,2084.0171681115075]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"SF\"],\"legendgroup\":\"SF\",\"marker\":{\"color\":\"#fd5a1e\",\"pattern\":{\"shape\":\"\"}},\"name\":\"SF\",\"offsetgroup\":\"SF\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"SF\"],\"xaxis\":\"x\",\"y\":[-59.76645545277552],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2236.8950803819653,2178.4734842226994]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"MIA\"],\"legendgroup\":\"MIA\",\"marker\":{\"color\":\"#00A3E0\",\"pattern\":{\"shape\":\"\"}},\"name\":\"MIA\",\"offsetgroup\":\"MIA\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"MIA\"],\"xaxis\":\"x\",\"y\":[-58.421596159265846],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2205.987757878032,2149.904810470848]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"TEX\"],\"legendgroup\":\"TEX\",\"marker\":{\"color\":\"#003278\",\"pattern\":{\"shape\":\"\"}},\"name\":\"TEX\",\"offsetgroup\":\"TEX\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"TEX\"],\"xaxis\":\"x\",\"y\":[-56.08294740718384],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2235.7820609725372,2181.781700721154]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"WSH\"],\"legendgroup\":\"WSH\",\"marker\":{\"color\":\"#ab0003\",\"pattern\":{\"shape\":\"\"}},\"name\":\"WSH\",\"offsetgroup\":\"WSH\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"WSH\"],\"xaxis\":\"x\",\"y\":[-54.00036025138343],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2139.236519461969,2094.7502329916124]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"PHI\"],\"legendgroup\":\"PHI\",\"marker\":{\"color\":\"#e81828\",\"pattern\":{\"shape\":\"\"}},\"name\":\"PHI\",\"offsetgroup\":\"PHI\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"PHI\"],\"xaxis\":\"x\",\"y\":[-44.48628647035639],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2297.3445722747465,2253.0682593856654]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"HOU\"],\"legendgroup\":\"HOU\",\"marker\":{\"color\":\"#eb6e1f\",\"pattern\":{\"shape\":\"\"}},\"name\":\"HOU\",\"offsetgroup\":\"HOU\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"HOU\"],\"xaxis\":\"x\",\"y\":[-44.276312889081055],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2361.679092204802,2317.682423857868]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"SD\"],\"legendgroup\":\"SD\",\"marker\":{\"color\":\"#2F241D\",\"pattern\":{\"shape\":\"\"}},\"name\":\"SD\",\"offsetgroup\":\"SD\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"SD\"],\"xaxis\":\"x\",\"y\":[-43.99666834693426],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2323.1447865400023,2280.180754834685]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"KC\"],\"legendgroup\":\"KC\",\"marker\":{\"color\":\"#174885\",\"pattern\":{\"shape\":\"\"}},\"name\":\"KC\",\"offsetgroup\":\"KC\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"KC\"],\"xaxis\":\"x\",\"y\":[-42.96403170531721],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2263.646973715939,2231.2948874010954]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"CWS\"],\"legendgroup\":\"CWS\",\"marker\":{\"color\":\"#000000\",\"pattern\":{\"shape\":\"\"}},\"name\":\"CWS\",\"offsetgroup\":\"CWS\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"CWS\"],\"xaxis\":\"x\",\"y\":[-32.352086314843746],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2245.1432015429123,2213.3207699773534]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"TOR\"],\"legendgroup\":\"TOR\",\"marker\":{\"color\":\"#134a8e\",\"pattern\":{\"shape\":\"\"}},\"name\":\"TOR\",\"offsetgroup\":\"TOR\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"TOR\"],\"xaxis\":\"x\",\"y\":[-31.822431565558873],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2302.5834825158136,2279.297404529674]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"COL\"],\"legendgroup\":\"COL\",\"marker\":{\"color\":\"#33006f\",\"pattern\":{\"shape\":\"\"}},\"name\":\"COL\",\"offsetgroup\":\"COL\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"COL\"],\"xaxis\":\"x\",\"y\":[-23.28607798613939],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"customdata\":[[2169.073903818953,2151.156202143951]],\"hovertemplate\":\"<b>%{hovertext}</b><br><br>Team=%{x}<br>Spin Rate Change=%{y}<br>pre_spin=%{customdata[0]}<br>post_spin=%{customdata[1]}<extra></extra>\",\"hovertext\":[\"MIN\"],\"legendgroup\":\"MIN\",\"marker\":{\"color\":\"#002b5c\",\"pattern\":{\"shape\":\"\"}},\"name\":\"MIN\",\"offsetgroup\":\"MIN\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"MIN\"],\"xaxis\":\"x\",\"y\":[-17.917701675002263],\"yaxis\":\"y\"}],                        {\"barmode\":\"relative\",\"legend\":{\"title\":{\"text\":\"Team\"},\"tracegroupgap\":0},\"margin\":{\"t\":60},\"showlegend\":false,\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"title\":{\"text\":\"Change in Team Avg Spin Rate Before and After June 5\"},\"xaxis\":{\"anchor\":\"y\",\"categoryarray\":[\"LAD\",\"DET\",\"BOS\",\"CIN\",\"BAL\",\"ARI\",\"OAK\",\"CHC\",\"SEA\",\"MIL\",\"CLE\",\"NYM\",\"PIT\",\"NYY\",\"TB\",\"LAA\",\"STL\",\"ATL\",\"SF\",\"MIA\",\"TEX\",\"WSH\",\"PHI\",\"HOU\",\"SD\",\"KC\",\"CWS\",\"TOR\",\"COL\",\"MIN\"],\"categoryorder\":\"array\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Team\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Spin Rate Change\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('17a8498d-df23-42b6-8758-882ff4ed7fef');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "team_color_map = {'LAD': '#005a9c',\n",
    "                  'DET': '#f26722',\n",
    "                  'BOS': '#bd3039',\n",
    "                  'CIN': '#c6011f',\n",
    "                  'BAL': '#df4601',\n",
    "                  'ARI': '#e3d4ad',\n",
    "                  'OAK': '#003831',\n",
    "                  'CHC': '#0e3386',\n",
    "                  'SEA': '#005c5c',\n",
    "                  'MIL': '#ffc52f',\n",
    "                  'CLE': '#e31937',\n",
    "                  'NYM': '#002d72',\n",
    "                  'PIT': '#fdb827',\n",
    "                  'NYY': '#132448',\n",
    "                  'TB': '#8fbce6',\n",
    "                  'LAA': '#ba0021',\n",
    "                  'STL': '#c41e3a',\n",
    "                  'ATL': '#13274f',\n",
    "                  'SF': '#fd5a1e',\n",
    "                  'MIA': '#00A3E0',\n",
    "                  'TEX': '#003278',\n",
    "                  'WSH': '#ab0003',\n",
    "                  'PHI': '#e81828',\n",
    "                  'HOU': '#eb6e1f',\n",
    "                  'SD': '#2F241D',\n",
    "                  'KC': '#174885',\n",
    "                  'CWS': '#000000',\n",
    "                  'TOR': '#134a8e',\n",
    "                  'COL': '#33006f',\n",
    "                  'MIN': '#002b5c'}\n",
    "# Use plotly to visualize difference for each team\n",
    "fig = px.bar(team_spin_comp, x='pitch_team', y='Difference',\n",
    "             hover_name='pitch_team', hover_data=['pre_spin', 'post_spin'],\n",
    "             labels={\n",
    "                 'Difference': 'Spin Rate Change',\n",
    "                 'pitch_team': 'Team'\n",
    "             }, color = 'pitch_team', color_discrete_map = team_color_map)\n",
    "fig.update_layout(showlegend=False,\n",
    "                  title='Change in Team Avg Spin Rate Before and After June 5')\n",
    "fig.write_image(\"images/team_spin_change.png\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Tigers and Dodgers have easily seen the biggest drop in spin rate, whereas the Blue Jays and Twins have hardly seen a difference. Clearly this was not an issue equally on every team and certain teams might have had more pitchers using sticky stuff than others. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Has a Change in Spin had an Impact on Results?\n",
    "\n",
    "It's clear that across the league the spin rate of pitches has decreased. However, is this equally effecting all pitchers or are some seeing a higher drop in spin rate than others? Since we saw that spin rate across the league started to drop from June 7 on I'll compare players spin rate and Bauer units before and after June 7. Then I'll look at how the [xwOBA (expected weighted on base average)](https://www.mlb.com/glossary/statcast/expected-woba#:~:text=Definition,of%20batted%20balls%2C%20Sprint%20Speed.&text=For%20the%20majority%20of%20batted,exit%20velocity%20and%20launch%20angle.) for the pitchers has changed and weather it is correlated with a change in spin rate or Bauer units. xWoba looks at the expected value a batted ball based on the speed and angle from which it leaves the bat. By doing this it attempts to be agnostic of defense and luck to a degree and attempts to distill how much value the hit had in a vacuum. If a pitcher has a high xwOBA they are giving up a lot of quality contact. If their xwOBA goes up after June 7 we might guess that the use of sticky stuff had been helping them induce weak contact in some at bats.\n",
    "\n",
    "I will take a look at starting pitchers and relievers to see if there is a difference. I will also only used pitchers who have made appearances both before and after June 7. To qualify as a starter you will have to have made 5 appearances before and 5 after June 7th and have thrown at least an average of 75 pitchers per appearance. To quality as a reliever you will have to have made 5 appearances before and after June 7th and thrown less than 50 pitches per appearance. \n",
    "\n",
    "I'll create a dataframe that contains the following values for a pitcher before and after Juen 7: spin rate, xwOBA, Bauer units, and appearances. I'll also include their total pitches thrown, total appearances, and pitches per appearance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_name</th>\n",
       "      <th>Pre_Spin</th>\n",
       "      <th>Post_Spin</th>\n",
       "      <th>spin_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Bumgarner, Madison</td>\n",
       "      <td>2532.236663</td>\n",
       "      <td>2150.942149</td>\n",
       "      <td>-381.294515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Richards, Garrett</td>\n",
       "      <td>2783.718924</td>\n",
       "      <td>2433.959029</td>\n",
       "      <td>-349.759895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>Ríos, Yacksel</td>\n",
       "      <td>2200.5</td>\n",
       "      <td>1861.804734</td>\n",
       "      <td>-338.695266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Bender, Anthony</td>\n",
       "      <td>2580.624339</td>\n",
       "      <td>2256.549479</td>\n",
       "      <td>-324.074859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Borucki, Ryan</td>\n",
       "      <td>2514.561224</td>\n",
       "      <td>2218.59375</td>\n",
       "      <td>-295.967474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>Yajure, Miguel</td>\n",
       "      <td>2313.65812</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>Yamamoto, Jordan</td>\n",
       "      <td>2495.563636</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>Ynoa, Huascar</td>\n",
       "      <td>2282.012857</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>Zamora, Daniel</td>\n",
       "      <td>2700.230769</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>Zimmermann, Jordan</td>\n",
       "      <td>2277.890244</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>635 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            player_name     Pre_Spin    Post_Spin   spin_diff\n",
       "70   Bumgarner, Madison  2532.236663  2150.942149 -381.294515\n",
       "456   Richards, Garrett  2783.718924  2433.959029 -349.759895\n",
       "483       Ríos, Yacksel       2200.5  1861.804734 -338.695266\n",
       "41      Bender, Anthony  2580.624339  2256.549479 -324.074859\n",
       "54        Borucki, Ryan  2514.561224   2218.59375 -295.967474\n",
       "..                  ...          ...          ...         ...\n",
       "619      Yajure, Miguel   2313.65812         <NA>        <NA>\n",
       "620    Yamamoto, Jordan  2495.563636         <NA>        <NA>\n",
       "624       Ynoa, Huascar  2282.012857         <NA>        <NA>\n",
       "626      Zamora, Daniel  2700.230769         <NA>        <NA>\n",
       "630  Zimmermann, Jordan  2277.890244         <NA>        <NA>\n",
       "\n",
       "[635 rows x 4 columns]"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create df to compare spin pre and post June 7\n",
    "pitcher_pre_spin = (df[df['game_date'] < '2021-06-07']\n",
    "                    .groupby(by='player_name')['release_spin_rate'].mean()\n",
    "                    .reset_index().rename(\n",
    "                    mapper={'release_spin_rate': 'Pre_Spin'},\n",
    "                    axis='columns'))\n",
    "pitcher_post_spin = (df[df['game_date'] >= '2021-06-07']\n",
    "                     .groupby(by='player_name')['release_spin_rate'].mean()\n",
    "                     .reset_index().rename(\n",
    "                     mapper={'release_spin_rate': 'Post_Spin'},\n",
    "                     axis='columns'))\n",
    "pitcher_spin_comp = pitcher_pre_spin.merge(pitcher_post_spin,\n",
    "                                           how='left',\n",
    "                                           left_on='player_name',\n",
    "                                           right_on='player_name')\n",
    "pitcher_spin_comp['spin_diff'] = (pitcher_spin_comp['Post_Spin']\n",
    "                                  - pitcher_spin_comp['Pre_Spin'])\n",
    "pitcher_spin_comp = pitcher_spin_comp.sort_values('spin_diff')\n",
    "pitcher_spin_comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_name</th>\n",
       "      <th>pitches_thrown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbott, Cory</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abreu, Albert</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abreu, Bryan</td>\n",
       "      <td>652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Acevedo, Domingo</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adam, Jason</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>Zimmermann, Jordan</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>Zuber, Tyler</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>de Geus, Brett</td>\n",
       "      <td>626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>deGrom, Jacob</td>\n",
       "      <td>1226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>Álvarez, José</td>\n",
       "      <td>558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>748 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            player_name  pitches_thrown\n",
       "0          Abbott, Cory             267\n",
       "1         Abreu, Albert             259\n",
       "2          Abreu, Bryan             652\n",
       "3      Acevedo, Domingo              51\n",
       "4           Adam, Jason             172\n",
       "..                  ...             ...\n",
       "743  Zimmermann, Jordan              82\n",
       "744        Zuber, Tyler             350\n",
       "745      de Geus, Brett             626\n",
       "746       deGrom, Jacob            1226\n",
       "747       Álvarez, José             558\n",
       "\n",
       "[748 rows x 2 columns]"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total pitches thrown by each pitcher\n",
    "pitcher_grouper = df.groupby('player_name')\n",
    "pitches_thrown = pitcher_grouper['pitch_name'].count()\n",
    "pitches_thrown = pitches_thrown.reset_index()\n",
    "pitches_thrown = pitches_thrown.rename(mapper={\n",
    "                'pitch_name': 'pitches_thrown'\n",
    "                }, axis='columns')\n",
    "pitches_thrown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_name</th>\n",
       "      <th>appearances_june_7_later</th>\n",
       "      <th>appearances_june_7_before</th>\n",
       "      <th>total_apps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbott, Cory</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abreu, Albert</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abreu, Bryan</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adams, Austin</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Akin, Keegan</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>Zimmermann, Bruce</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>Zuber, Tyler</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>de Geus, Brett</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>deGrom, Jacob</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>Álvarez, José</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>520 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           player_name  appearances_june_7_later  appearances_june_7_before  \\\n",
       "0         Abbott, Cory                         5                          1   \n",
       "1        Abreu, Albert                         4                          4   \n",
       "2         Abreu, Bryan                         9                         21   \n",
       "3        Adams, Austin                        21                         24   \n",
       "4         Akin, Keegan                         7                          6   \n",
       "..                 ...                       ...                        ...   \n",
       "515  Zimmermann, Bruce                         2                         10   \n",
       "516       Zuber, Tyler                         1                         20   \n",
       "517     de Geus, Brett                        15                         16   \n",
       "518      deGrom, Jacob                         6                          9   \n",
       "519      Álvarez, José                        18                         18   \n",
       "\n",
       "     total_apps  \n",
       "0             6  \n",
       "1             8  \n",
       "2            30  \n",
       "3            45  \n",
       "4            13  \n",
       "..          ...  \n",
       "515          12  \n",
       "516          21  \n",
       "517          31  \n",
       "518          15  \n",
       "519          36  \n",
       "\n",
       "[520 rows x 4 columns]"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dfs of appearances by each pitcher before and since June 7\n",
    "apps_after_grouper = (df[df['game_date'] >= '2021-06-07']\n",
    "                      .groupby(by='player_name'))\n",
    "apps_after = apps_after_grouper.agg({'game_date': 'nunique'}).reset_index()\n",
    "apps_after = apps_after.rename(mapper={\n",
    "                'game_date': 'appearances_june_7_later'\n",
    "                }, axis='columns')\n",
    "apps_before_grouper = (df[df['game_date'] < '2021-06-07']\n",
    "                       .groupby(by='player_name'))\n",
    "apps_before = apps_before_grouper.agg({'game_date': 'nunique'}).reset_index()\n",
    "apps_before = apps_before.rename(mapper={\n",
    "                'game_date': 'appearances_june_7_before'\n",
    "                                        }, axis='columns')\n",
    "# merge together with inner join to only get players with appearances\n",
    "# before and after\n",
    "apps = apps_after.merge(apps_before,\n",
    "                        how='inner',\n",
    "                        left_on='player_name',\n",
    "                        right_on='player_name')\n",
    "apps['total_apps'] = (apps['appearances_june_7_later']\n",
    "                      + apps['appearances_june_7_before'])\n",
    "apps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_name</th>\n",
       "      <th>appearances_june_7_later</th>\n",
       "      <th>appearances_june_7_before</th>\n",
       "      <th>total_apps</th>\n",
       "      <th>pitches_thrown</th>\n",
       "      <th>pitches_per_app</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbott, Cory</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>267</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abreu, Albert</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>259</td>\n",
       "      <td>32.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abreu, Bryan</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>652</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adams, Austin</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>45</td>\n",
       "      <td>636</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Akin, Keegan</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>936</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>Zimmermann, Bruce</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>999</td>\n",
       "      <td>83.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>Zuber, Tyler</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>350</td>\n",
       "      <td>16.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>de Geus, Brett</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>31</td>\n",
       "      <td>626</td>\n",
       "      <td>20.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>deGrom, Jacob</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>1226</td>\n",
       "      <td>81.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>Álvarez, José</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>558</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>520 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           player_name  appearances_june_7_later  appearances_june_7_before  \\\n",
       "0         Abbott, Cory                         5                          1   \n",
       "1        Abreu, Albert                         4                          4   \n",
       "2         Abreu, Bryan                         9                         21   \n",
       "3        Adams, Austin                        21                         24   \n",
       "4         Akin, Keegan                         7                          6   \n",
       "..                 ...                       ...                        ...   \n",
       "515  Zimmermann, Bruce                         2                         10   \n",
       "516       Zuber, Tyler                         1                         20   \n",
       "517     de Geus, Brett                        15                         16   \n",
       "518      deGrom, Jacob                         6                          9   \n",
       "519      Álvarez, José                        18                         18   \n",
       "\n",
       "     total_apps  pitches_thrown  pitches_per_app  \n",
       "0             6             267             44.5  \n",
       "1             8             259             32.4  \n",
       "2            30             652             21.7  \n",
       "3            45             636             14.1  \n",
       "4            13             936             72.0  \n",
       "..          ...             ...              ...  \n",
       "515          12             999             83.2  \n",
       "516          21             350             16.7  \n",
       "517          31             626             20.2  \n",
       "518          15            1226             81.7  \n",
       "519          36             558             15.5  \n",
       "\n",
       "[520 rows x 6 columns]"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge apps and pitches dataframes\n",
    "pitches_apps = apps.merge(pitches_thrown,\n",
    "                          how='left',\n",
    "                          left_on='player_name',\n",
    "                          right_on='player_name')\n",
    "pitches_apps['pitches_per_app'] = round(pitches_apps['pitches_thrown']\n",
    "                                        / pitches_apps['total_apps'], 1)\n",
    "pitches_apps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge dataframe with spin difference data frame created previously. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_name</th>\n",
       "      <th>appearances_june_7_later</th>\n",
       "      <th>appearances_june_7_before</th>\n",
       "      <th>total_apps</th>\n",
       "      <th>pitches_thrown</th>\n",
       "      <th>pitches_per_app</th>\n",
       "      <th>Pre_Spin</th>\n",
       "      <th>Post_Spin</th>\n",
       "      <th>spin_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbott, Cory</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>267</td>\n",
       "      <td>44.5</td>\n",
       "      <td>2390.809524</td>\n",
       "      <td>2320.062222</td>\n",
       "      <td>-70.747302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abreu, Albert</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>259</td>\n",
       "      <td>32.4</td>\n",
       "      <td>2167.448819</td>\n",
       "      <td>2071.265152</td>\n",
       "      <td>-96.183667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abreu, Bryan</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>652</td>\n",
       "      <td>21.7</td>\n",
       "      <td>2439.801762</td>\n",
       "      <td>2415.792929</td>\n",
       "      <td>-24.008833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adams, Austin</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>45</td>\n",
       "      <td>636</td>\n",
       "      <td>14.1</td>\n",
       "      <td>2864.226629</td>\n",
       "      <td>2781.925795</td>\n",
       "      <td>-82.300834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Akin, Keegan</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>936</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2289.4573</td>\n",
       "      <td>2236.649215</td>\n",
       "      <td>-52.808086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     player_name  appearances_june_7_later  appearances_june_7_before  \\\n",
       "0   Abbott, Cory                         5                          1   \n",
       "1  Abreu, Albert                         4                          4   \n",
       "2   Abreu, Bryan                         9                         21   \n",
       "3  Adams, Austin                        21                         24   \n",
       "4   Akin, Keegan                         7                          6   \n",
       "\n",
       "   total_apps  pitches_thrown  pitches_per_app     Pre_Spin    Post_Spin  \\\n",
       "0           6             267             44.5  2390.809524  2320.062222   \n",
       "1           8             259             32.4  2167.448819  2071.265152   \n",
       "2          30             652             21.7  2439.801762  2415.792929   \n",
       "3          45             636             14.1  2864.226629  2781.925795   \n",
       "4          13             936             72.0    2289.4573  2236.649215   \n",
       "\n",
       "   spin_diff  \n",
       "0 -70.747302  \n",
       "1 -96.183667  \n",
       "2 -24.008833  \n",
       "3 -82.300834  \n",
       "4 -52.808086  "
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge apps and pitches dataframe with spin dataframe\n",
    "apps_spin_diff = pitches_apps.merge(pitcher_spin_comp,\n",
    "                                    how='left',\n",
    "                                    left_on='player_name',\n",
    "                                    right_on='player_name')\n",
    "apps_spin_diff.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last two dataframes I will make is a comparison of xwOBA and Bauer units (spin rate/velocity)) before and after June 7th for each pitcher. xwOBA is up first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_name</th>\n",
       "      <th>Pre_xwOBA</th>\n",
       "      <th>Post_xwOBA</th>\n",
       "      <th>xwOBA_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>Mattson, Isaac</td>\n",
       "      <td>1.071</td>\n",
       "      <td>0.233667</td>\n",
       "      <td>-0.837333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>Poppen, Sean</td>\n",
       "      <td>0.551105</td>\n",
       "      <td>0.139</td>\n",
       "      <td>-0.412105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>Ramirez, Yohan</td>\n",
       "      <td>0.497778</td>\n",
       "      <td>0.164846</td>\n",
       "      <td>-0.332932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>Scrubb, Andre</td>\n",
       "      <td>0.513975</td>\n",
       "      <td>0.237</td>\n",
       "      <td>-0.276975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Cahill, Trevor</td>\n",
       "      <td>0.374726</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.274726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        player_name  Pre_xwOBA  Post_xwOBA  xwOBA_diff\n",
       "344  Mattson, Isaac      1.071    0.233667   -0.837333\n",
       "435    Poppen, Sean   0.551105       0.139   -0.412105\n",
       "450  Ramirez, Yohan   0.497778    0.164846   -0.332932\n",
       "499   Scrubb, Andre   0.513975       0.237   -0.276975\n",
       "79   Cahill, Trevor   0.374726         0.1   -0.274726"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create df to compare xwOBA pre and post June 7\n",
    "pitcher_pre_xwoba = (df[\n",
    "                     df['game_date'] < '2021-06-07']\n",
    "                     .groupby('player_name')['estimated_woba_using_speedangle']\n",
    "                     .mean().reset_index().rename(\n",
    "                     mapper={'estimated_woba_using_speedangle': 'Pre_xwOBA'},\n",
    "                     axis='columns'))\n",
    "pitcher_post_xwoba = (df[\n",
    "                      df['game_date'] >= '2021-06-07']\n",
    "                      .groupby('player_name')\n",
    "                      ['estimated_woba_using_speedangle'].mean().reset_index()\n",
    "                      .rename(\n",
    "                      mapper={'estimated_woba_using_speedangle': 'Post_xwOBA'},\n",
    "                      axis='columns'))\n",
    "pitcher_xwoba_comp = pitcher_pre_xwoba.merge(pitcher_post_xwoba,\n",
    "                                             how='left',\n",
    "                                             left_on='player_name',\n",
    "                                             right_on='player_name')\n",
    "pitcher_xwoba_comp['xwOBA_diff'] = (pitcher_xwoba_comp['Post_xwOBA']\n",
    "                                    - pitcher_xwoba_comp['Pre_xwOBA'])\n",
    "pitcher_xwoba_comp = pitcher_xwoba_comp.sort_values('xwOBA_diff')\n",
    "pitcher_xwoba_comp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge this df with the appearances and spin diff df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_name</th>\n",
       "      <th>appearances_june_7_later</th>\n",
       "      <th>appearances_june_7_before</th>\n",
       "      <th>total_apps</th>\n",
       "      <th>pitches_thrown</th>\n",
       "      <th>pitches_per_app</th>\n",
       "      <th>Pre_Spin</th>\n",
       "      <th>Post_Spin</th>\n",
       "      <th>spin_diff</th>\n",
       "      <th>Pre_xwOBA</th>\n",
       "      <th>Post_xwOBA</th>\n",
       "      <th>xwOBA_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbott, Cory</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>267</td>\n",
       "      <td>44.5</td>\n",
       "      <td>2390.809524</td>\n",
       "      <td>2320.062222</td>\n",
       "      <td>-70.747302</td>\n",
       "      <td>0.1775</td>\n",
       "      <td>0.420462</td>\n",
       "      <td>0.242962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abreu, Albert</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>259</td>\n",
       "      <td>32.4</td>\n",
       "      <td>2167.448819</td>\n",
       "      <td>2071.265152</td>\n",
       "      <td>-96.183667</td>\n",
       "      <td>0.320647</td>\n",
       "      <td>0.5244</td>\n",
       "      <td>0.203753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abreu, Bryan</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>652</td>\n",
       "      <td>21.7</td>\n",
       "      <td>2439.801762</td>\n",
       "      <td>2415.792929</td>\n",
       "      <td>-24.008833</td>\n",
       "      <td>0.349086</td>\n",
       "      <td>0.364821</td>\n",
       "      <td>0.015736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adams, Austin</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>45</td>\n",
       "      <td>636</td>\n",
       "      <td>14.1</td>\n",
       "      <td>2864.226629</td>\n",
       "      <td>2781.925795</td>\n",
       "      <td>-82.300834</td>\n",
       "      <td>0.297703</td>\n",
       "      <td>0.301828</td>\n",
       "      <td>0.004125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Akin, Keegan</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>936</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2289.4573</td>\n",
       "      <td>2236.649215</td>\n",
       "      <td>-52.808086</td>\n",
       "      <td>0.351262</td>\n",
       "      <td>0.47801</td>\n",
       "      <td>0.126748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     player_name  appearances_june_7_later  appearances_june_7_before  \\\n",
       "0   Abbott, Cory                         5                          1   \n",
       "1  Abreu, Albert                         4                          4   \n",
       "2   Abreu, Bryan                         9                         21   \n",
       "3  Adams, Austin                        21                         24   \n",
       "4   Akin, Keegan                         7                          6   \n",
       "\n",
       "   total_apps  pitches_thrown  pitches_per_app     Pre_Spin    Post_Spin  \\\n",
       "0           6             267             44.5  2390.809524  2320.062222   \n",
       "1           8             259             32.4  2167.448819  2071.265152   \n",
       "2          30             652             21.7  2439.801762  2415.792929   \n",
       "3          45             636             14.1  2864.226629  2781.925795   \n",
       "4          13             936             72.0    2289.4573  2236.649215   \n",
       "\n",
       "   spin_diff  Pre_xwOBA  Post_xwOBA  xwOBA_diff  \n",
       "0 -70.747302     0.1775    0.420462    0.242962  \n",
       "1 -96.183667   0.320647      0.5244    0.203753  \n",
       "2 -24.008833   0.349086    0.364821    0.015736  \n",
       "3 -82.300834   0.297703    0.301828    0.004125  \n",
       "4 -52.808086   0.351262     0.47801    0.126748  "
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge xwOBA dataframe with with full comp dataframe\n",
    "apps_spin_xwoba = apps_spin_diff.merge(pitcher_xwoba_comp,\n",
    "                                       how='left',\n",
    "                                       left_on='player_name',\n",
    "                                       right_on='player_name')\n",
    "apps_spin_xwoba.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_name</th>\n",
       "      <th>Pre_b_units</th>\n",
       "      <th>Post_b_units</th>\n",
       "      <th>bu_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Bumgarner, Madison</td>\n",
       "      <td>29.469197</td>\n",
       "      <td>25.483292</td>\n",
       "      <td>-3.985905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Richards, Garrett</td>\n",
       "      <td>31.203562</td>\n",
       "      <td>27.257577</td>\n",
       "      <td>-3.945984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>Ríos, Yacksel</td>\n",
       "      <td>23.581219</td>\n",
       "      <td>19.8891</td>\n",
       "      <td>-3.692119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Bender, Anthony</td>\n",
       "      <td>28.417144</td>\n",
       "      <td>24.989715</td>\n",
       "      <td>-3.427428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>Maples, Dillon</td>\n",
       "      <td>31.826984</td>\n",
       "      <td>28.513456</td>\n",
       "      <td>-3.313528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            player_name  Pre_b_units  Post_b_units   bu_diff\n",
       "70   Bumgarner, Madison    29.469197     25.483292 -3.985905\n",
       "456   Richards, Garrett    31.203562     27.257577 -3.945984\n",
       "483       Ríos, Yacksel    23.581219       19.8891 -3.692119\n",
       "41      Bender, Anthony    28.417144     24.989715 -3.427428\n",
       "335      Maples, Dillon    31.826984     28.513456 -3.313528"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create df to compare Bauer units pre and post June 7\n",
    "pitcher_pre_bu = (df[df['game_date'] < '2021-06-07'].groupby(by='player_name')\n",
    "                  ['b_units'].mean().reset_index()\n",
    "                  .rename(mapper={'b_units': 'Pre_b_units'}, axis='columns'))\n",
    "pitcher_post_bu = (df[df['game_date'] >= '2021-06-07'].groupby('player_name')\n",
    "                   ['b_units'].mean().reset_index()\n",
    "                   .rename(mapper={'b_units': 'Post_b_units'}, axis='columns'))\n",
    "pitcher_bu_comp = pitcher_pre_bu.merge(pitcher_post_bu,\n",
    "                                       how='left',\n",
    "                                       left_on='player_name',\n",
    "                                       right_on='player_name')\n",
    "pitcher_bu_comp['bu_diff'] = (pitcher_bu_comp['Post_b_units']\n",
    "                              - pitcher_bu_comp['Pre_b_units'])\n",
    "pitcher_bu_comp = pitcher_bu_comp.sort_values('bu_diff')\n",
    "pitcher_bu_comp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge b unit df with our growing comparison dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 519 entries, 0 to 519\n",
      "Data columns (total 16 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   player_name                519 non-null    object \n",
      " 1   appearances_june_7_later   519 non-null    int64  \n",
      " 2   appearances_june_7_before  519 non-null    int64  \n",
      " 3   total_apps                 519 non-null    int64  \n",
      " 4   pitches_thrown             519 non-null    int64  \n",
      " 5   pitches_per_app            519 non-null    float64\n",
      " 6   Pre_Spin                   519 non-null    Float64\n",
      " 7   Post_Spin                  519 non-null    Float64\n",
      " 8   spin_diff                  519 non-null    Float64\n",
      " 9   Pre_xwOBA                  519 non-null    Float64\n",
      " 10  Post_xwOBA                 519 non-null    Float64\n",
      " 11  xwOBA_diff                 519 non-null    Float64\n",
      " 12  Pre_b_units                519 non-null    Float64\n",
      " 13  Post_b_units               519 non-null    Float64\n",
      " 14  bu_diff                    519 non-null    Float64\n",
      " 15  pct_spin_change            519 non-null    Float64\n",
      "dtypes: Float64(10), float64(1), int64(4), object(1)\n",
      "memory usage: 74.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Merge b unit comparison to ful dataframe\n",
    "apps_spin_xwoba_bu = apps_spin_xwoba.merge(pitcher_bu_comp,\n",
    "                                           how='left',\n",
    "                                           left_on='player_name',\n",
    "                                           right_on='player_name')\n",
    "apps_spin_xwoba_bu['pct_spin_change'] = (apps_spin_xwoba_bu['spin_diff'] \n",
    "                                         / apps_spin_xwoba_bu['Pre_Spin'])\n",
    "apps_spin_xwoba_bu = apps_spin_xwoba_bu[apps_spin_xwoba_bu['Post_xwOBA'].notna()]\n",
    "apps_spin_xwoba_bu.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 10))\n",
    "sns.scatterplot(data=apps_spin_xwoba_bu, x='pct_spin_change', y='xwOBA_diff')\n",
    "plt.axvline(0, color='black', lw=2)\n",
    "plt.axhline(0, color='black', lw=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It doesn't appear as though there is a correlation between spin change and xwOBA, but we'll confirm and look at a few more variables.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyIAAAKuCAYAAABdSKhJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAACulUlEQVR4nOzdd3wU1dfH8c8hQBK6SpWOgBQBxQ6oKGLvBRBFsaCCvfuzYsHH3lGxooIVRFGxCxYE7KKAAtI7SicQIDnPHzOJm80mJJDshuT79jUvd2fuzNzZyS579t5zr7k7IiIiIiIi8VQu0RUQEREREZGyR4GIiIiIiIjEnQIRERERERGJOwUiIiIiIiISdwpEREREREQk7hSIiIiIiIhI3CkQEREpIcxsoJl5xDIuwfUZGlWfoYmsj4CZ9Y26J3OitjeJ2u5m1iRBdZ0TVY++CapH9OvRNRH1EJHcyie6AiJliZk1AvoChwItgF2ALcA/wG/AZ8Ar7r42n2MMBc7JY/MWYB0wH/gVeMvdPyhg3eqF+yVFbXrb3XsU5BgFZWY7AX0IXoc9CV6HVGA1MAMYD4x09wlFeV4JvsgCTSJWjXP3cQmpTBlmZk8B/WNsauPu0+Jdn0jhF/Wx+RTZAqwC/gK+AJ5z9wXbcJ4mBJ+H2dx9YGGPIyI7LgUiInFgZhWB+4BLgAoxilQh+HJ4InCnmV3v7i9sw6nKAzXCpR3Qx8w+Bk52941b2fcMcgchAMebWTV3X7MN9cnBzMoBNwL/I7jmaLuEywHANWb2sLtfs73nlRz6AodErRsX/2qUXWZWAcgruO8D3BTH6myL8kDNcOlM8F4d4O6vFPI4TYDbo9YN3O7aicgOQ12zRIqZmSUTtHRcSewgJNrOwPNmdm8RVeEogi//W3NWHutTgNO2txLhl6/RwCBiByGx7LK955Xtci3QNGK5NrHVKTWOJu+/7d5mZvGsTBGoDLxkZt2i1nch59/PiHhXTERKNgUiIsXvSeDgqHVTgDOBtsBeBF/wVkSVucHMzijA8Rfy3z/0rYDzgA1RZfI9jpm1DuuRl7yClMJ4Cjg2at0agsDkYKAlsDdwLvAukFEE55Tt4O7/uPuciOWfRNeplMjv/dQYOCheFSmEMwg+Y5oRtIJEt36UI6o1w90XRP39rItLTUVkx+HuWrTs0AvQiCC3wCOWs6PKpALTo8o8EG67N2r9fVH7do/afl3U9t5R2z+O2LYHkBm1fSKQGuM6dicIRiLLzgcqRpUbGlVmToxjPRNVZuNWXsN7osp/AmyOeJ4BNNiOe7R/1PEdmA00y2eflsC5eWxrBTwOTCboq74JWErQX/1KoEoe+w2MqsO4cH1f4LvwWA50zeO1HkrQLeUq4Gdgbbi+SdR56oTn+o4g/2cTsJyg3/1lQEph6hexvSJBftBgYEL4Gq4O79UK4CfgMYI8g+hjj4txD2ItcyL2yXX9edS7IkEA/AFBYJxOEGT+CTwP7J/PfY4+f1eCLj8PAX+Hx1oKDAd2y+MY5Qi+3H9A8J7ZGC7zge+BIQQBbq0S8HlVjeCHgshr/iDq+bP57N83r/sVbm8S4zVtUsg6do11X2KU+yqqTAaQHLF9TtT2vvkcP6+lb4zz7kXwHvgNWEnw/lpE8H67i6jPlTz+xmoA9xPkpG3c2t9YxLGaAQ8QvNdWhOdeAnwEnA0k5bHf0Kg6DGUrnyXA8QStSLOBNIL3wkLgl3D/i4HGif6b1qJle5aEV0CLlqJYwn8AIj/k/yHiS0f4D0fk9slZ/2ASdJOI3PZN1LEHRm1/N2r74KjtN0ZseyrGP4Lt87mOq2KU7xFVJvoftDkxjvN0VJkl+ZzTyP2F4bjwH9bIdddvx/15K8Z1HbgNxzHgDoIvPPl9eVkEdIqxf/S9/Irgl92YX7pivNavAZ/HKN8k4hxnAeu3Ur8ZQOsC1G9c1Pa6Wzlu1rIZGBC177gC7jsnYp/o6x8ao86tgGkFOO6zRAXV4f7R5a4CluVxjOVA06j9y5H7i3xeS98S8Fl1XlSdfiR3oL6CiC/0Ufv3zet+hdub5Pf3WcA6do1xjK4xykV/rjpQL2L7nFivfx7H3+o9I+gm+mwB9rlyK39jFxIEqQX6G4s4znXk/IEm1vI9sGuMfYdGlcv3s4TY/3bEWgYm+m9ai5btWdQ1S0oFD5IkR0as2oXgF3PMbD+CLzdZNgFnuXt6+Hw8ObsB7RMml2fpHHW6TlHPu0Q9/yricXSf6V/dfXLMiwhkfSmOdFg+5QHKh0N2NjGzlmZ2DkFgFumjfPY/iKA7SJYVBC0ir0eV26buWWaWRNCqFGm8b9uIWLcCt7H1bqX1gI/NrM1WynUmSA4uqB7kvqfZzOx0gntYaSvHaQ58ZmZ1C3HuwigPPGFmHYvp+ACE9f+CIBjZmn4EAfLWPATUymNbTYIWzEinkbvLX0kW/T563d0nAbMi1u3EjnFNrWOsW1UcJwrzZoYR/B1tryeBBnlsi/U3hpldS9CCsrVBfvYFPjKzrX0G5PlZEv6bFWtENZFSR4GIlCYXETSRZ+llZicDL5JzNKhbIoMBD0aD+iViewrQEbK/RB8QdZ5aZtYy3F6NoPtVljSCXzgxs/IE3Ysi/ZTfBbj7vwTN8JG29mW6frjPbILhNIeS84vwX+SfrB79xWiku28myNOIHGmrnZm130pdYmlE0A0i0tjCHsTMmhIEIpGmAicQDAF8NUHXhSxVCYPRfCQRdJ37P4LuHh0Ifi1dkk/5DcANBKOS7R2ed52ZVSH4FTMy0XgkQSDZiqCbxW8R2+oT5McUhhPkFw0iGGHtAIK/sY4Ev7TPjShbDrg04nkvgj7+k6KO+Rg5E4qjA+v8DAJ2jarfIGAfguDz66jy55nZ1o5vwChgP4Ig+Zeo7SdF/VAQnU/xKkELQwuC+3MGQavlnK2ct9iZWQNyjljmwJvh4zejihdFXlaRs0A9M7uN3MHSL+4enZ8Wy0SCv7VYuWtNiZ3gfmq4RPqboMtdG4LP4bOALwtw/goU4m8sHHY9+r06hOCHjNYE7605EdvaA1sb7S/PzxJy/01/HK5rQfBZdyrwIMHnn8iOLdFNMlq0FOUCHEPuLiqRz78CysXY78GocleH6/eKWLc04vG54fajovb7LOKYtcndjH5PAa5hQtQ+U6O2D41x3LyW1cCh+Zwrmdx5KYdGbB8Zte3+bbgn+8Wo18XbcJw7o46xAagTVeaKGOdqGrF9YIztd+RzzlivdV55K+dElfsOsKgyzaLKpAOV8qnfuEK+RqdE7f9XjDLjosoMLMT1D43YlkoQqEZufzjG39fiqDIvR5WJfn3/IqKfPUGQFV1mj4jt0d0QY+ajEAQ4lYvqs2ZbFuD6qLp+FbGtXdS2jUCNGMfoG1VuTtT2JjFeryaFrGfXGMcoyNIr6jhzorb33dp58qnTl1FlVwC18yhbvYj/xm6P2vZ6jHMeFlVm/lbeS/l9ltwQVa5nPq9LzHw4LVp2lEUtIlKquPsYgl+qskQ2o68hSGLPjLHrV1HPO0f9H+CJrWyPdZxEqwZ8aWbX5bH9WIJuIFkWk/Ma3ogqf0Y4F0giRP+S/pG7L41a91IB9ou0CXi0EHVYTPCLeyzRv2IeCGRGzuhM8AtupIoEXTkKzMyqm9k1ZvaZmS0ws7SI44+MKr5rrGMUkX0JAo1IQyOfeND9MbqL39ZaRF5098iuktNjlKkR8fiXqG3vm9lLZnadmR1vZo3Duri7r9/KuXOI6PIYaynoENSRcnXLynrg7r+T8xfuZPKea6SkceBOd4/+vCgSYct09GftE+6+LGZl3Fdv5ZCF/RuLfm/3ip6tnaCLYqQGWX97ecjvsyT6b/o5M3vNzG4ys1OyWuQBXCORyQ5OgYiURteQs4tKlmvdPdZ6gG8IuuhkOTD8f2Q+yEsESfDw3z+K+eWHRA/HC0ErydZEl9nakKlz3d3c3QgCrxYEo79Euj+PblXRX4zeigrUPiDoKpClAcGvmIWxPMa6+oU8BgSJ2pFmRxfwoJvdyqjV9fI55nx3jy6fn6nuviWPbdv6pb/AeSJmtgfBSFQPAocTvI6p+eyyLV+WCypWvXPdE3J3icrvfkCQyB8pVlefyB8YhgG/RzyvRdBqcD/BvDVzzGxWGJgUZB6fSLPzWQo1t074/msXsWoLuefVKJK8rDhaTPD6H+jutxfjeXYhCNoj5ZdrtzWF/Rsrjvd2fp8ln4VLlqoE3dgGEfzY8JeZLTazQWZWeRvrJlIiKBCR0qgJsf8ByPOXWHdfRc7++/XCnISsgGOuuy8k6G4DsHuYqLtfxD4bCEZMyTrmFnL/0rZ3fhU3s10I+kVHKnA/YHfPcPeZBP2mo38tPDfqXDUIurJFuiLqV740cn+ZLeyXo3nkTmA9tJDHKC6Lirl8QeQXSER7mUIELjuoHAF81C/Xubh7GsEPB7cQBCQeo1hTgsDkqSKq47aIft+UB5ZHvd/uiirTZSu/qsdT1jwiTQl+kKjq7ru6ex8Pku13JIX6G9sO+b238/wscXcnaK2+jODflFj1qwvcRJDLJ7LDUiAipUr4i+cwcncZATjbzE7JZ/foblU9CRKtIRhZK/L/BlxCzqTwif7fSFxZopvr9zSzduTtbHImO0PBki9z8CDZPLr1p3nU8x7Efp225lQzSylEXTLI+eseQGczix4EYGuiE8ijAzbMrDo5u5pB8KttXmJ108tPfuWj6/cuuRNvYy0Fmm06DIyjR8F6k2AyyObhsfoW5FhFJFZCf657QvDDQKT87sc2cff17j7I3dsTdEfMSlIfQs7A5Lww2I+rsDtjQSYnzbUrwcSnJcES/29iwoVx7hL0L0E3ykj5fY4Wtei/9Scp2Ht7Yj7HzPezx903u/uT7r4/wY9BHQha4R4k52txuJnlNxmtSImmQERKmzsIRhXJMi9q+xAzq5PHvtGByOURj7+L+j8EgUh++8N/ibSRnjWzXL+Uhf1+o0eFWsA2/OIV9l9vEbU6+le1be32UY1gpKrCeCjGuuH5/dprZs3NrG/Eqm+jihwd4172Jbfo/YrLN1HPDwa2eM6ZpbMXggnM9irEF7pYXZoudvdv3P3v8JgdCnCczVHPC9MiE+lHco5SBlGvv5klk/sLeJHeDzOrFeYQAEGfeXf/2d3fcPeLydnSWY7c74s8ZXV5zGMZWohqdiXv4WK3pqQEIsUh+m+RWJ+N4Y8Z46NWX2ZmMYd5Dn+QKErR7+2jgBX5vLe3AK3cfWOuIxWAmdWI/LHH3Te6+2R3H+nu1wFjonbZfVvOI1ISKBCRUsPMOhGMSpNlA0E/+rcj1tUkmOk5lm/IGTREfvHL+kfwR/77NSr6l/dcgUiYgPpC1OoDgO/NrLeZtTazDmZ2NcFoWdHHvMHdo38JjBY5j8huZnY4Qd/4GlHlsr+QhQFAdFe1ruT9y95zUWULFcSEXTeiX/dmwG9mdqeZdTazFma2l5n1MbO3CSbJ6xpRfijBP/BZUoAvzOw4M2tvZlcC90Wd4wt3j5W3UBxGEPxym2Vn4Gszu8jM9rZgjpfOZtbfzN4lCDKvKMTxY+UKDQqvfR8zu7+Ax4vO2TkxrFfT8G+oRkEqE3aJis5FutLM7jazjmbWDfiU3F3Jov+WtldPYJ6ZDTazXuFr3Tx8Xa4n5/DaEEw2GW/R75d3yfu91p6cv5a3sWKeDyaBYuWPXWdmu2d9pkWsj56DZmdgvJmdHX6OtjGznmb2MVHdUIvAi+RshWgOfGNmZ4Wf37ub2SFmdqWZfUqQQ9RrO87XBVhoZi+G17d/+PnY1swuJPh3LVIi/qZFikaih+3SoqUoFoKm65nkHPLw2nBbLXLP1Nwvj+NMjirnBKNtRQ71GD28btZQmyl5HDOFIEgp7FCY9+VxvKHbcKz1QOOIY9wUtX3WVl7f6NnnNwG7FPIeVaDgM2BnLUOjjnFbIfZdA7SJ2n9gVJlxW6lz9Gs9dCvlexXy+sYVpn4Eier5HW9J9LoYdbx8K8e4u6DXTxCsLyzE9b4Qoz7RZboWpgzBXCkFPf9cYgzfXcyfTSkEw2hH1uPsrewzKar8wxHb+kZtmxO1b5MY192kkHXuWpD7UoDjzIk6Rt+o7eUIAuz87ln5sKwB7xTwPl9ZlH9j4fbooZe3tkS/V4bmtz2q7HGFOM86YgzzrEXLjrKoRURKi4eB3SKefw88AuDuy4EB0eXNrFmM48TqXjXJcyYzRncRAPje82iGD9d3J5hcL69RUiKtBC5w9xsKULYgVgOne84Rw6K7e+Q38zoEc09EXl8Fgl+iC8yDvJUTCJKKC9od6d+o53eFi29lv8XA0e4e1wm/PBi+tA8Fv74FhTzFheS8D5GmEyS3bs2rFFHSvbsvJvh19q8CFH+exM4WvZa8h+8uTicQdGfM4sAnW9nn46jnvSK7n5UW4b14oIBlneBz66VirVTe57+fIBjJ1Z0sDwuLsTpZNgPnezDYisgOSYGI7PDM7FigX8SqTQQTRWUHD+4+AngrokwV4BXLPSdGrEDku6jnsQKRfOcPcfdN7n4FQZP+QIIZpxcT9LFfT/BL7WiCL5KN3T26O1dhpBP8Mj6WoOWjpQfzqwAQdvOInq0930DEg9mSo6+x0Dkm7p7p7oMIBgG4guCa5xJ8cd9CEHhMJAgsO7v7NVH7u7vfRtDd5kngD4IvmFsIunmMI5iduKW7x7pPxc7dhxF0O7uZ4DVbTvCFYQPBtX4Ubmvv7oXt4vY1wazh7xCM/JNOMDfJAwTzesTq6hJ9jJUEo8ENBeZT8C9WeR1vGkF3ogsI+q4vIXgPricYJvVFguFd+/nWuxlui1cIvuw/SNC98m/++3taQdC6MIigz34i5vmJvsc/e+75b6JFvx/rAd2Krkolh7vfR/C3M5HgvZxf2Q3ufh6wD0FXrT8IfmjZTPB3N5HgXr9fTHV9gCDH6J7wXP8S/J2lEfzdvUfw+dPc3W/ejlN9DhxJcC1fEryPVhPk+a0mmGfkUaCtu7+5HecRSTgLfmQQERERERGJH7WIiIiIiIhI3CkQERERERGRuFMgIiIiIiIicadARERERERE4k6BiIiIiIiIxJ0CERERERERiTsFIiIiIiIiEncKREREREREJO4UiIiIiIiISNwpEBERERERkbhTICIiIiIiUoaZ2YtmtszM/shju5nZ42Y208wmm1nHojivAhERERERkbJtKHBUPtuPBlqEy4XA00VxUgUiIiIiIiJlmLt/DazIp8iJwCsemAjUMLN623teBSIiIiIiIpKf+sD8iOcLwnXbpfz2HkCKx+Z/Znmi6yDF44J9rkt0FaSY7eYpia6CFKNffE2iqyAi22HUvPct0XWIJZ7f/SrUbFYiXgO1iIiIiIiISH4WAg0jnjcI120XtYiIiIiIiCRaZkaia5Cf0cClZvYGsD+w2t0Xb+9BFYiIiIiIiJRhZvY60BWoaWYLgNuBCgDu/gwwBjgGmAmkAecWxXkViIiIiIiIJJpnJu7U7mdsZbsDlxT1eZUjIiIiIiIicadARERERERE4k5ds0REREREEi0zcV2zEkUtIiIiIiIiEndqERERERERSTBPYLJ6oqhFRERERERE4k4tIiIiIiIiiaYcERERERERkeKnFhERERERkURTjoiIiIiIiEjxU4uIiIiIiEiiZWYkugZxpxYRERERERGJO7WIiIiIiIgkmnJEREREREREip9aREREREREEk3ziIiIiIiIiBQ/tYiIiIiIiCSYK0dERERERESk+CkQERERERGRuFPXLBERERGRRFOyuoiIiIiISPFTi4iIiIiISKIpWV1ERERERKT4qUVERERERCTRMjMSXYO4U4uIiIiIiIjEnVpEREREREQSTTkiIiIiIiIixU8tIiIiIiIiiaZ5RERERERERIqfWkRERERERBJNOSIiIiIiIiLFTy0iIiIiIiKJphwRERERERGR4qcWERERERGRBHPXzOoiIiIiIiLFToGIiIiIiIjEnbpmiYiIiIgkmobvFRERERERKX5qERERERERSTQN3ysiIiIiIlL81CIiIiIiIpJoyhEREREREREpfmoRERERERFJtExNaCgiIiIiIlLs1CIiCfPaiNG8O+ZzZsyazTGHd2XQLdckukpSAEeefxzHXHwSySnJ/PDRBF6+5Vm2bNoSs2ybTu3oc1c/dtm1JrN+ncFz1z7JvwuXA9Djxj4ccEIXKlWtxPrV6xn72qd88NQ72ftauXKcclVPDupxGCmVU1k2dzH3nnE7aWvS4nKdZV1K9cqccH8/mh3cjrQV6/jy/jf5473vYpbtdmMv9urVFYBf3hjHF/e+kb2tSac2dL+5Nzs3rkvayrWMf2o0P78+Nh6XIDEcf/6JnNz/VJJTk5kwZjzP3PxUnu/fdp3bc+Fd/alVvxbTf/mLJ655lOXh+7dK9SpcdM8AOnTpgDv88tXPDLn5KTas2wDAnW8MotHujalQsQJL5y/ljYeG8/1nk+J2nWVVPO5v9V2qc/7AfrQ9YA+SU1OYN30uL935AjN+nR7PSy2dlCMiRcnMjk50HUqyWjV34aK+vTj52CMSXRUpoD0O3pNjLz6Z+3vfwdWdL6ZWozqcfFWvmGWr7FSVy565jnceep1L9jyH2ZP/ZsCTV2dv//qtL7ix2+Vc3K4Pd596E51OOpi9j9w/e/spV/Wk+d67c9cpN3HxHmcx5KrH2Zy+udivUQLH3NWXjM0ZPLT3AEZdMZhj7j6XWi3q5yrXsfdh7H7E3gw56iaGHPk/Wh7ekb3P7AZAufJJ9BhyFT8N/5L79riAkZc8wRG3nkWd1o3ifTkC7HnwXpwy4FRu730LF3Y6jzqN6nLG1WfGLFt1p2rcMOQmXn9oGH3an8Hfk2dyzeDrs7f3vq5P8GW18wX0P6gfNWrVoNdVvbO3vzDwOc7b52zObNuTp298kisfu5qdau9U7NdYlsXr/qZUTmHmbzO49tirOLt9b8aO+JJbht5OSqWUuFynlC4KRIqJmU0C7jGzJDOzRNenJOretTPdDu5EjerVEl0VKaAup3bl67e+YOGM+aStWc/ox0fQ5bSuMcvuc9QBLJwxnx/GTGBz+mZGPfomjVo3pt5uwZfZJbMWsWlDenb5zMxM6jSpC0ClapU54rxjeenGp7NbUBZOn69AJE4qpCbT+uj9GPvQ22xOS2f+j9OZ/vnPtD+lS66yHU47iInPjWHtkhWsXbqSCc99SIfTDgYgtUZlUqpVYvI73wKwaPIsls9cSM0YAY0Uv0NP68bnb37G/OnzWL96PW89/gaHntYtZtkDjj6Q+dPn8d2H49mcvpk3HnmNJm2aUn+3BgDUaViHSZ9MZMO6DaStTWPSxxNo2PK/AHPun3PIzAh/3XUnqXx5atarWezXWJbF6/4unbeU0c+/x8plK8nMzOSz1z6hfIXy7Lqb3tfbLTMzfksJoUCkGJhZH2AXd9/L3TMAvTulVKjfsiHzps3Jfj5v2hxq1NqJyjWqxCw7f9rc7OebNqSzbO5S6rdomL3u2P4nM2TKMB6d9BzJlVKY8N43ADRs1ZiMjEz2OeZAHvvhee778gm69Tmq+C5MctilWV0yMzJYMXtJ9rql0+ZSq2WDXGVrtWjA0mnz/is3dR61WgYfeev/WcPv733Hnj0OwcoZDTo2p0b9msz/4a/ivwjJpWHLRsyZNjv7+Zypc9ip9k5UrVE1V9lGUWXTN6SzdO6S7C+jH73yIft025fK1StTuXplDjy6Ez+P+ynHMW5+6TbenD6S+99/mD8m/s7MyTOL6coE4n9/szRp05TyFcqzZM7iIr4iKQuUI1I8FgPVzKwr0B3Y08xOc/cNCa2VyHZKqZTChrX/5WhkPU6tksr6VetylE2ulMLaFWtyrNuwNo2UKqnZzz98ehQfPj2KRm2bsvcR+2Ufb6e6O1O5WmXqNt2Va7sMoG7Telw//HaWzF7ElG8nF9flSahipRTS1+b8uNq4ZgMVK+fuelGxcgobI/4m0temkRxxj6e89x3H3dePo27vA8CHN7/EmsUriqnmkp/Uyik5cqzS1q4HIKVKKmtXrc1RNqVSCmui3r/r164ntXJwb//+42/KVyzPK7+9BsDk8b/x8StjcpQfdO6dJJVPokOXPWnQvAHuXuTXJP+J9/2F4LP/ykev5s3HXidtrfL3tptyRGR7mNlYMzscmAmMBl4FrgIudvcN6qIlO5oDTzyIIVOGMWTKMK4ZejMb0zaSWqVS9vasoCIrQTVSetpGUiO+kGaV3xij7Lwps9m0cVN2vsnm9E0AvPf422xO38T8P+cy6f3xdDi0Y5Fdm+RtU9pGkqvmvHfJVVPZtH5j7rLrN+YIPJKrpJIe3uNddqvHKU9eyntXP83dzc/h6e430Oni42hx2J7FWn8JHHzSIbw27S1em/YWt748kA3rN5Ja9b/3b6XwvRzrPRn9Xs8qv2F9UPa6p25g0axF9G7dgzPb9GTp3CVc+VjuAUcytmTw87if6HDwXuzbfb+ivLwyL9H3t2JyRW5+8Tam//IX7wweUdSXJ2WEApEiYmZDgbrAN+4+B9gE7ArMAnYHcHdXMCI7kgnvfcNFbc/iorZn8VDfQSycPp+GrZtkb2/Uugmrlq/M1RoCQU5Ho4iyFVOTqd24LgtnzI95rqSkctRuXAcgu0tX5C+o+jU1fv6dtYRySUns3KRO9ro6rRuxfPqCXGWXz1hAndaN/yvXpjHLpy8EoPbuDfl39hL+/vp3cOffWYuZ8eUv7Na1Q/FfhPD1u1/Ru3UPerfuwV3nDGT+9Hk0bd00e3uTNk1ZuWxlrl/LAeZNn0eTNk2ynyenJlO3cT3mT5+Xve+nwz8ifUM6G9M28vGwj+h46N551iWpfBJ1G9UruouThN7f8hXLc+PzN/PP4n94+sbBxXeRZY1yRGRbmFlVoB0w2N3TzexSoBZwJvAb8H9mdgIoGIm0ZUsG6embyMjIJCMzk/T0TWzZUvYm89mRjH9nHAf3PIxdmzegUrVKnHDZaXw7YlzMsj99Mon6LRuyz1EHUCG5AiddcTrz/5zL4r8XYmZ07d2dStUqA9CsQ3O6nX00U8f/DsCyeUv5a9JUTrj0VMpXLE+93epzwPFd+PWL2H2UpWht3pDOtI9/oOvVp1EhNZmG+7Rk9+57ZyedR5o88lsO6Hc0VevsRJXaNTig3zH8NuJrAJb8MYddmtSlSac2AOzUqDYtu+3FsoicEomfcSO/pFvP7jRo0ZBK1Spz+mU9GDvii5hlJ308gUYtG3PA0Z2okFyBHlf2Ys602Sz8OwhGZ/42g8N7HUHF5IpUTK7IEb2PYm6YP1Z/twZ07Lo3FZMrklQ+iUNO7kqb/doyZdIfcbrSsile9zepfBLXP/M/Nm3cxONXP6IfiWS7mP6Atp+ZJQOPAnsD84E9ge7uPsvMOgNXAE2Au919dLiPeT4v/uZ/ZpX6GzP4hWE8/eLwHOv6n3cml5x/VoJqFB8X7HNdoquwXY48/3iOvfgkKqZU5MePJzL05iHZ49Tf8+mjvD94ZHbSeZvO7elz5wXUrF+Tv3+dwfPXPsk/C5ZjZlw99GaadWhO+QrlWbV0Jd+MGJtjHpGd6uzMefcPoOU+rVjz7xo+fGYU4177LCHXXFi7+Y4/jGVK9cqc8MCFNDtoDzasXMcX9wXziDTad3d6v3w997Y5P7vs4f87I8c8Ip//3+vZ29ocuz8HX3Ey1evXJH3tBn5/dzxf3Pcm7MD/9vzia7ZeqIQ64YJgnomKKclM+Og7nrlpcPb797HPBzPyybf4+t2vAGjfpQP97ryYWg1qMeOX6Tx+zaMsX7AMgNoN63DBHRfSau9WYMaMX6fz/G1DWDxnMQ2aN+Cyh66kYYuGZGZksmjOYkY++RaTPpmYsOsuK+Jxf9vuvwd3v/1/pG9IJzPil/W7zhnItO+nxv+it8Goee+XyB+EN44fHrcPxpTOZ5aI10CByHYws67uPi583JogL6QJcKm7D4ko1wm4EmhMEIy8v7Vjl4VApKza0QMR2brSEIhI3nbkQERESnAg8s2r8QtEDupTIl4Ddc3aRmb2GHCGmSWFq1oCNYBvgbPCpHUA3P074BGCfJHHzSz3YPwiIiIiImWIhu/ddk8Bs9w9w8yaAu8DTYEOwPXAnWHvqy8A3H2CmVUkGFFrQqIqLSIiIiIlTzD1XNmiFpFCMrOzzWwXd//L3Teb2ZnAO8Ap7r7O3ccDTwLLgLvM7LCsfd39K3e/NQxekvI4hYiIiIhIqacWkUIIg467gDZm9n/uvhr4E1gFXGBm5dz9LXf/LBwYawBwh5mluHuOmYC8LIa9IiIiIhJbCRpWN17UIlI4bwDDgW7AzWHLyE8Eo2IlAReaWQ8Ad/8MGAw4cGKC6isiIiIiUiKpRaSAzCzZ3dOBm8JuVZ2B68zsXnefbGbXAg8SBCPu7m+7++dmthrQ5AciIiIikjdXi4jEEM75kR4+PgdIBloQdL263sx2dvffgGuBTIJuWucAuPsP7p5pZnqtRURERERC+nJcAFkTD5rZ7QQTF/4A9AM+Jeh2dYOZ7RQRjNQGOkYdo+yFuSIiIiJSMJmZ8VtKCHXNKgALMs9rAqcAN7n78HD9B8BDQG9gs5k9GHbTOp1gzhAREREREYlBgUgBuLub2VqCblfVAcysvLtvAa4ys/2AvkAtM7ve3WeGZcqpJUREREREtqoMfmVU16yC2wwsBY4wswruviUi7+MPgiF8NwJrsnZQECIiIiIiEptaRAogTFbPMLNLgYnAUDMbAKSF84XUIJhf5K2w9UQtISIiIiJScCUodyNeFIgUQBhcmLvPDPM/3gK+JZg9vVq49FYQIiIiIiJSMApECihr5Cx3H2tmrYErgapAOkECe4aZJWnGdBEREREptDL4O7YCkUIKg41/gFui1mclr4uIiIiIyFYoWT1CQSYdzGrxCGdXx0IKQkRERERECq5Mt4iYWU8gCZju7j9m5XaEgYXns59FdMGqmDXruoiIiIjINimDyepltkXEzFoCrwPnA3eZ2fNm1tbMUiODkHAyw8j9soMUM7sKGGZmFeJZdxERERGRomRmR5nZX2Y208xujLG9kZmNNbNfzGyymR2zvecss4GIu08H3gcWEuR7tACeAN4zs33NrGZYLivoKBcVhFwI3A6McvfNibgGERERESklMjPjt0QJUw4GA0cDbYAzzKxNVLFbCKaq2AvoBTy1vZdcJgORrPwO4DXA3P0n4EjgUmARwdC8r5rZRWaWCsHkhBFByEXAA8B57v5a3C9ARERERKTo7AfMdPdZ7r4JeAM4MaqME0xZAVCd4DvzdimTgUhEfsckoJuZXeHuG4E/gXbAz8DvwEPAb2Z2bta+ZtYPuJ8gCHknvjUXERERkVLJM+O35FYfmB/xfEG4LtJA4CwzWwCMAS7b3ksuk4FIONBVkrvPAe4EuppZM4IAZB1wlLtfD3QEhgOvhPtdBAwB+rr7yIRUXkREREQk/s4Ahrp7A+AYgt5D2xVLlIlRs8zsMOCA8Okn7v6TmWWFg78C/YAfCVpIznP31eEM6dOBO8JjGLAKON3dR8Wz/iIiIiJSyiV21KyFQMOI5w3CdZHOB44CcPcJZpYC1ASWbetJS32LSNiV6g2gO0FizdNm1jJipvSJwNdABeAid18crs/x1+CBN9USIiIiIiKlzA9ACzNramYVCb4zj44qMw/oBmBmrYEUYPn2nLRUByJhEPIU0B84DLga2Jnc1/0yQX7IgeF+hoiIiIhIvCQwRyScmPtS4BNgGsHoWFPM7E4zOyEsdg3Qz8x+I5gCo29+8+4VRKntmmVmvQjyOc6JaMX43My2ADeHOSHvA0OBKcBa4ELgze19UUVEREREdiTuPoYgCT1y3W0Rj6cCnYvynKU2ECHICVkPpJhZirtvNLN3gVRgMUFS+j1AXXe/0syeAK7c2qzqIiIiIiJFrgzOrF7qApFwNKyMMLioANwYrj8eaAx0dffZ4bplwI1m9gjwGfCuu3uYqF72/hpEREREROKk1AUiEXOE4O6XmNmzwCPAauAYd58dEWgsAqYC6919HQT5IQpCRERERCSuyuDXz1ITiJjZXsDuBHN/TAZmuft37n6hmaUBpwCdzWyBu/8bjnt8PDAD+DfrOOqWJSIiIiJS/EpFIBLOfH4zQcJ5ReAqYKGZveTud4TdtCoB14bl3yBIUm8O7KHuWCIiIiKSUGUwR2SHH77XzHoDg4FbgaPdvS3BnCGTgNvN7B4Ad7+QYEiyqwhmUG8BtHP3LWZWXkGIiIiIiEj87NAtImZWGxgA3Ojur2etd/dxZrYQ2AAMMLOf3H2ku/c3sxeANsDB7r45DEK2JOYKRERERETKph06EAF2ApoQzAYJZCebu7vPMLPHgCMIxjweCeDu52eVURAiIiIiIiWCumbtcCoTzJSekrUiK9k8DDZ+IZiYpYuZJYVT1hMGIaYgREREREQkMXa4QCQc7SrLaiAJOCzcZlkbshLQgUrAzHBukU2R2+NUZRERERGR/LnHbykhdrhAJCup3MxquPvfwNPA/8zs5IjgI0sqUIdgON8cgYqIiIiIiCTODheIAJjZacB0M6sOPAaMBUaY2cVAIws0B94AdgEeBLWCiIiIiEgJlZkZv6WE2CGS1bPm+MhKMgf+IpgR/QJ3f8jMbgCuAJ4ClgGZwAJgI7BvOERvUuSs6yIiIiIikjg7RCASMcdHA2A+QRAyHjjVzF5395/NrB/wAtCWIHn9N+Ard8/Q6FgiIiIiUqKVoJaKeNlhumaFrR5/m1lfIBkYCFQBHgZw903u/rW7P+3uj7j7l2EQkqQgRERERESkZNkhWkRClQnqexuwJzAROBcYamZ93X1orJ3UHUtERERESjwvey0iJT4QycoPAe4gSDzfiSAPpG/4fBrQ3cw+dvclCauoiIiIiIgUWInrmhU5/K6Z3Qw8aGb7hy0b3xIkoI8BziHIF+kOnAHsn4DqioiIiIhsP42alXgR84ScCmwBugF7mNk44D6C7ljnu/tlwClm1gfYF/gwMTUWEREREZHCKnEtIgBmVgd4G/gUOBr4CBgAvAOMBM4xsx4A7v6qu18eDtFb4gIrEREREZGt0szqiRFjxvP1wAyghbsvAp4A9iIYlvdMwIF+ZlY/cieNjiUiIiIismMoEYFI1oznZlYzfL4OmAx0CZ9vcffl7n4k8CLwK8EQvosSUmERERERkaKkHJHEMbMbCbpcrQd+B1oBGWbWGFjh7msB3H2omY0A0tzdI0bVEhERERGRHUSJCUSAr4E/gK5ARYJ5Q3oAdYE2ZvYrQZetx919LOQY2rfUuWCf6xJdBSkmz//4QKKrIMXsuL0uSXQVpBg1TKqc6CpIMapYMjqLiJQJJSYQcffvwocfAJjZR8BzwINA7XBpTxCwZO1TKoMQERERESljSlCXqXgpMYEIBEnrYXerJOAfIAP4MXqiQjNL0ozpIiIiIiI7rhLV/piVtB4GGT8TBCIHxiinIERERERESg/PjN9SQpSoQCRKOYKRseoluiIiIiIiIlK0SlTXrEjuvtnMrgfeTHRdRERERESKk2eWnIkG46Ukt4jg7sM1Y7qIiIiISOmzQ3zB14zpIiIiIlKqlcFRs0p0i4iIiIiIiJROO0SLiIiIiIhIqVaCRrOKF7WIiIiIiIhI3KlFREREREQk0TRqloiIiIiISPFTi4iIiIiISKJp1CwREREREZHipxYREREREZFEU4uIiIiIiIhI8VMgIiIiIiIicaeuWSIiIiIiieYavldERERERKTYqUVERERERCTRlKwuIiIiIiJS/NQiIiIiIiKSaJnKERERERERESl2ahEREREREUk0V46IiIiIiIhIsVOLiIiIiIhIoilHREREREREpPipRUREREREJMFc84iIiIiIiIgUP7WIiIiIiIgkmnJEREREREREip9aREREREREEk3ziIiIiIiIiBQ/BSIiIiIiIhJ36polIiIiIpJoSlYXEREREREpfmoRERERERFJNE1oKCIiIiIiUvzUIiIiIiIikmjKERERERERESl+ahEREREREUk0TWgoIiIiIiJS/NQiIiIiIiKSaMoRERERERERKX5qERERERERSTDXPCIiIiIiIiLFT4GIiIiIiEiiZXr8lhjM7Cgz+8vMZprZjXmU6WFmU81sipm9tr2XrK5ZIiIiIiJlmJklAYOB7sAC4AczG+3uUyPKtAD+B3R295VmVnt7z6tAREREREQk0RI7atZ+wEx3nwVgZm8AJwJTI8r0Awa7+0oAd1+2vSdVICJF6sjzj+OYi08iOSWZHz6awMu3PMuWTVtilm3TqR197urHLrvWZNavM3ju2if5d+FyAHrc2IcDTuhCpaqVWL96PWNf+5QPnnone18rV45TrurJQT0OI6VyKsvmLubeM24nbU1aXK5TCua1EaN5d8znzJg1m2MO78qgW65JdJWkkE6+4CR69D+d5NQUvh3zLU/c9CSbN23OVa58hfLc+MQNtGjfgroN63Dd6dczeeLv2ds7HNieM6/sTfM9mrN29TrO6dQ3jlchWbqffxxHXXwiFVOS+emjiQzL5zO6Vad2nHnXBey8a01m/zqDF699khUL/wFgn2MP5PDzjqNhmybM+W0mD/S6Pee+B+7B6TefTe3GdVm3ci0fPT2Kr1//vNivryypVL0yZ93fn9YHtWfdirW8d/9r/Dh6fMyyJ914Jp16HgbAd29+ybv3Ds/e1qBNY866rz91m9dnycyFDLvhaRZMnQtAywPbcszlp9KwbTPS1qzj1i6XZu+30667cOtnj+Q4T0rlFEbe/QpfPP9BUV+uFL/6wPyI5wuA/aPKtAQws/FAEjDQ3T/enpMqR0SKzB4H78mxF5/M/b3v4OrOF1OrUR1OvqpXzLJVdqrKZc9cxzsPvc4le57D7Ml/M+DJq7O3f/3WF9zY7XIubteHu0+9iU4nHczeR/73fjjlqp4033t37jrlJi7e4yyGXPU4m9NzfzmSxKpVcxcu6tuLk489ItFVkW2w9yEd6TmgBzee8T/OPvAc6jaqS5+rz8qz/JQfpnD/FQ/w79IVubZt3LCRT978lOcGvVCcVZZ8tD24A0dffBIP9b6DGzr3p1ajOpx4Vc+YZavsVJUBz1zLuw+9wRV79mXO5L+5KOIzev2qdXz+4gd89PSoXPsmlU9iwJDr+Oq1z7is3dkMufRhetxyDg1aNy62ayuLet51ARmbt3DjPv0YeuXjnHF3P+q1aJCrXJfeh9Oh+77cc/R1DDrqWtp125uDzuwOQFKFJC569nq+f/cbru1wLhNHfsVFz15PUoUkANLTNvLdW2N55/9ezXXclYv+5eq2Z2cvg468hsyMTH75eFLxXnhp5pnxW7ZNeaAF0BU4A3jOzGpszyUrEJEi0+XUrnz91hcsnDGftDXrGf34CLqc1jVm2X2OOoCFM+bzw5gJbE7fzKhH36RR68bU260+AEtmLWLThvTs8pmZmdRpUheAStUqc8R5x/LSjU9nt6AsnD5fgUgJ1L1rZ7od3Ika1asluiqyDbqfdjifvPkJc6fPY93qdbz22Ot0P/3wmGW3bN7CqBfeZcoPU8iMMQTlX79O54t3vmTJvMXFXW3JQ6dTu/LNW1+yaMYC0tas5/3HR9Apj8/ojkftz6IZC/hpzAS2pG9m9KNv0bB1Y+rutisA08b/zo8fTmD10pW59q1cowqVqlVm4jtfATBn8t8snrmQXWN8SZZtUzE1mb2O2p/3H3qT9LR0/v7xLyZ//iP7nXJwrrIHnHoInz//PquWrGD10pV88dz7HHDaIQC0PKAtSeWT+PKFD9myaQvjhn6EmbF7pz0AmPvb33w/6hv+mbf1Hjj7n3oIM7+fyooFy4v2YiVeFgINI543CNdFWgCMdvfN7j4bmE4QmGwzBSJSZOq3bMi8aXOyn8+bNocatXaico0qMcvOnzY3+/mmDeksm7uU+i3+ew8c2/9khkwZxqOTniO5UgoT3vsGgIatGpORkck+xxzIYz88z31fPkG3PkcV34WJlFGNWzZm1tTZ2c9nTZ3FzrV3pmqNqgmslWyrXVs2ZEHEZ/SCaXOonsdndHTZTRvSWT53Kbu2aJirbLQ1/6xm0nvf0Pn0w7By5WjWsSW71K/FjB/+LIrLEKB2s3pkZmSwbPZ/gf3CaXNj3p96LRqyMOLf2wXT5lIvLFevZUMW/jk3R/mFf/63vTD2P+VgJo78qtD7SYnxA9DCzJqaWUWgFzA6qsy7BK0hmFlNgq5as7bnpMoRKQZmVs5929u9dlQplVLYsPa/HI2sx6lVUlm/al2OssmVUli7Yk2OdRvWppFSJTX7+YdPj+LDp0fRqG1T9j5iv+zj7VR3ZypXq0zdprtybZcB1G1aj+uH386S2YuY8u3k4ro8kTInpXIq69euz36e9bhSlVTWrlqbqGrJNkqulEJajM/olDw+o9dt5TM6P9+P/pZz7u1Pr9vPBWDYLc+xcvG/21N9iZBcKYUN6zbkWLdhbRrJVVJyl62c+9/mrPuYHPXvdvT2gtpt31ZUrVmDX8ZMLNR+EiWByeruvsXMLgU+Icj/eNHdp5jZncCP7j463HaEmU0FMoDr3H273tgKRIpYZBBiZg2AVHefEbHd3D2hwyIUlQNPPIi+91wEwPQfprExbSOpVSplb8/6IIv+sISg32lq1AddSpVUNsYoO2/KbNodvCcnX9WL1+8eyub0TQC89/jbbE7fxPw/5zLp/fF0OLSjAhGR7XDoSYdyxb2XAfDH93+wcf0GKkW8p7Mep8V4n0rJs/+JB9HnngsBmPHDn7k+d7M+o2N97qanbcz1ZTSvz+hodXfblQufuIqnLn6Aqd9MpnbTelz+wv9YtXQFv4/9eXsuSUJ5/Ruavm5j7rLrc97LyPsY6z6nVqlUoPsc6YBTD+HXjyeRnpa+9cJSYrn7GGBM1LrbIh47cHW4FAl1zSpiEUHI/wFjgV/NbJyZXWNmFd3dzcwSW8uiMeG9b7io7Vlc1PYsHuo7iIXT59OwdZPs7Y1aN2HV8pW5fmmDIKejUUTZiqnJ1G5cl4Uz5ucqC5CUVI7ajesAZHfpioznSklsJ5JQY98dy0mtTuGkVqdwy9m3MXf6XJq1aZa9vVmbZqxYtkKtITuISe99w6Vt+3Bp2z481ncQi6I+oxu2bsLqPD6jo8tWTE2mVuO6LMrjMzpS/ZaNWDp7MVO+/g13Z+msRUwe+xPtuu5VFJclwLJZiymXlEStMHcSoEHrxjHvz+IZ82kQcS8btG7C4rDc4unzqd8q5yACu7ZqlL29ICokV6DjMQcyccS4wl2E5OKZHrelpFAgUkTMrFzE4wuAc4BbCcZgXgCcDvxfVjCSmFoWr/HvjOPgnoexa/MGVKpWiRMuO41v8/hg+umTSdRv2ZB9jjqACskVOOmK05n/51wW/70QM6Nr7+5UqlYZgGYdmtPt7KOZOj4YCnTZvKX8NWkqJ1x6KuUrlqfebvU54Pgu/PrFT/G6VCmgLVsySE/fREZGJhmZmaSnb2LLloxEV0sK6PORX3BkzyNo1KIRlatVpvflvfjs7byHYK1QsQIVkisAUD7iMYCZUSG5Aknly2MWfHkpX0GN8vH03Ttf0aXnYdRr3oDUapU49rJT+S6Pz+ifP/meXVs2pONR+1M+uQLHX3E6C/6cy5K/FwHBEOrlkytQrnwSZkb55AoklQ9GWpo3ZTa1m9Sl1YFBwnOtRnXocNjeLIjKRZBtt2lDOr9+Monjru5JxdRkmu29O+2778v373ydq+ykd76m2wXHUr3OTlSvvRPd+h3HxBFBLsf0iVPIzMjk0HOPpnzF8hxy9pEA/PXdHwD/3dsKSUDk4/90OHI/0tasY/qEKcV70VIqWSn9TpwwZnY4sBewxt2HhOsqA9cAJwF3ufuorXXROqfJqTvkjTny/OM59uKTqJhSkR8/nsjQm4dkj1F/z6eP8v7gkdlJ5206t6fPnRdQs35N/v51Bs9f+yT/LFiOmXH10Jtp1qE55SuUZ9XSlXwzYmyOeUR2qrMz590/gJb7tGLNv2v48JlRjHvts4Rcc2E9/+MDia5C3Ax+YRhPvzg8x7r+553JJefnPQRsaXDcXpckugpF5pR+J9Oj/+lUTElm/Eff8vj//ptH5NnPn+H1J99k7LtjAXj5u6HUbVgnx/5nH3gOSxcso/0B7Xjg7ftzbPttwmSu73FDfC6kCDVMqpzoKmyz7ucfx9EXn0SFlIr8/PFEXr35v3lE7vj0EcYMfodJ4Wd0687t6H3nBexSvyazf53Ji9c+yb/hiEidTuvKeQ9emuPY40eM5aVrBwPBPCPHX346u9SvRdraNCa99w3v3Dd8h2i9rriD/EZbqXpl+jwwgFZd2rF+5TrevW84P44ez277tuKSoTdxdduzs8uefOOZdOrVDYDv3viCUZHziLRtwln3XkzdFg1YMnMBw254hgVT5gDQ4oA2XPXGwBznnT5xCo/2uiP7+aWv3MScX//mg4ffLL6LLWJPzXmrRPZMWXv5cXF7g1R9/IMS8RooEClCZtaE/0YPuMvdb4/YZsAk4A93P29rx9pRAxHZurIUiJRVpSkQkdx25EBEtm5HCURk2ykQKTmBiN5t2yE618Pd5wCHAkuAg8ysXlaZsPXjK6COmVWIPpaIiIiIlGGZmfFbSggFItsoHB3Lw8fZ4+W5+1dAH2A/4EGgqZmVN7NU4GBgqbtr5j0RERERKdOUKbgNwvyOrNGxbgT2N7NawDvAR+7+hZmdDIwE9gf+AjYBFYCLI46h7lciIiIiktB5RBJFLSKFFNUS8j/gRuA3gu5YPYChZtbR3T8jGDGrMtACeATY1903mVkFBSEiIiIiUpapRaSQIlpCmgHtgJ7u/km47ghgAPCQmZ3t7mPN7AzgQ+Bc4Acz26SuWSIiIiKSg1pEpCDM7GxgOnAAsCZrvbt/CrwE1AOahOvGAccTtI4MA3JOYSoiIiIiUgYpENkG7v4K8AlBsLGvmSVHbHsPSAG6Rqz7EuhNkC9SLZ51FREREZGSz93jtpQUCkS2InLG9EjufizwJXAT0N3MksLy1YF1wPKo8h8DLd19UfHWWERERESk5FOOSD7CxPSsnJADgBrAPGCuu69398PN7CuCLldvmtlU4DDAgOejj+fuaXGrvIiIiIjsOJQjIlmihui9B3gbeAKYANxiZu0B3P2QcF0/grlDJrp7W3ffYmYK9EREREREYlAgkoeoIXrPAfq4ewuCZPRLgAFm1iEsezTwObAv8EPEMbbEu94iIiIiIjsCBSL5MLOmQCfgSncfZ2YnAn2B94CewOVmtieAux8BLAKeM7NjsnJGRERERES2KtPjt5QQCkTyt5KgBeSjMEfkSeBWd+8Trj8JuN7MWgG4e1dgFXA/kBzjeCIiIiIigpLV8+Xuq8zsU3dfZ2anA+OBZ8PNG4AZQCbBnCJZ++xlZo2VmC4iIiIiBeUlqKUiXhSIbIW7rwsf1iFoQUoF0oHWwN3Ah+7u4TC/5u4Z7j43MbUVEREREdkxKBApuEnAvcDbZlaH4LX7OAxCskfYEhEREREpNLWIlC2R84TkU8Y88ISZbQFaAL8C/wuH6E1y94x41FdEREREpLQos4FI1DwhnQnm/8gVUGR1u3L3THd/OuoY5TVEr4iIiIhstzLYt6ZMjpoVBhZZ84Q8AHwI7GpmFqt8RMBiUesVhIiIiIiIbIMy2SISEVjUAww40d3n57dPVhet8HEbYF5EIruIiIiIyDYri6NmlckWEQAzOwuYCxwNLNlK2cgg5DJgHFC7uOsoIiIiIlJalckWkdA84DPgUMLJB2PlfEQFIRcBA4EB7j4rvtUVERERkVKrDLaIlIlAJI/Rsb4hmJRwF+BDM9vf3RdFjoIVIwi5HzjP3UfGs/4iIiIiIqVNqQ9EIoMQMzsWqMV/c4D8EAYYg4GvzezgMBgp7+5bIoKQCwnmEFEQIiIiIiJFT6NmlT4RQch9wBDgNOAG4B0z6+XuvwHXAAuBsWbWMLJ7Vhi8PAOcryBERERERKRolPpABMDM+gJnEYyOdRzwf8BewHoAd59EEIxkAg9F7uvuHwIHu/s78ayziIiIiJQdnulxW0qKUt81K9QKeM/dfzKznsAjwOXu/r6ZVQaqufuPZnYq8FfWThFdtL5NUL1FREREREqlUt0iYmZJ4cPdgXlm1hF4HrjB3Z82s3LA2cDJYWL6VHfPyNpPExaKiIiIiBSPUtUiEj06VtboV8AHwFPAIOBMd38jXF8JOBn4JSsxPWo/EREREZHiVwaT1UtNIBI1OtbBwM7AHGAW8CrBfCHdgZVhd6wGwKMEw/fenIAqi4iIiIiUWaUmEIkIQh4iSExPAv4BlgGnEwQbmcAYYBGwAlgFHODuWyLnDxERERERiaeSlEQeLzt8IBI16eBRwBHAqQQtIfsDlxFMXtjF3c82s6eBXYGlwHfunhlrRnURERERESk+O3wgEhGE9AQ6A19GjHI1yszmAY8B95vZRe4+IXL/sEuXghARERERSZwymCNSKkbNMrMKwLXApUC7yG3u/hPwKdARqBC9b2Ryu4iIiIiIxMcOH4iEXbM2A4cA7wJtzayvmaVGFPuVIAjZKf41FBERERHJn2fGbykpSkXXrDDRPM3MzgLeA64EqpvZG0AycDmwEFiQuJqKiIiIiEiWHb5FBIJ5P7KCEeAkYAnwIDCRYBb1jcCxYdBSKq5ZREREREqRzDguJUSp+VIeEYysB04BPiQYwvdD4FR3TzezisoJERERERFJvB2ia1bkEL35iWwZMbPeBEHIAIJJDD9y943FXlkRERERkUIqiz+Vl/gWkXB43awhemttrXxUN63jgH+BhwnmFxERERERkRKgRAciYRCSNWP6tQRzgexRgF2zJilcD5wIjAemFGNVRURERES2XRnMESnRXbMigpAHgHMIRr9an98+Ed24tpjZDUBtdz+r2CsrIiIiIiIFVqIDEcieMb0ncKS7/xKuSwV2c/c/wucWjohlEd24LgT+RzDJoYiIiIhIiaUckZKpITDb3X8xs1Zmdh3BBIXjzexxyJ5LJDKX5CLgAeA8dx+WqIqLiIiIiEhsJapFJI/RsZYAdc1sJNAG+BF4jmBywtfMbKi7/xzRjetC4H6CIOSdOFZfREREREQKqMQEIlGJ6SlAsruvBt4HdgK6EQQYX7r7XDNrA3wPrIs4xkXAYOB0dx8V72sQEREREdkWZbFrVokIRKKCkJuA/YH9zWw48Im7P2FmT7v7FjMrZ2ZVgfuANGBmuF9FoBLQU0GIiIiIiEjJViICkYggZBBwIXAFMJwg2bybmf3p7vPMrDJwMsEIWrsA+7t7ZhjIbDKzJ919c4IuQ0RERERkm5TFFpESk6xuZm0JJiA8xd1fAxYDrYBHwyCkHGBAPeBnYD933xzOF5IJoCBERERERGTHUCJaRCKUd/dvzOw04CXgKncfamaVCIKUj4HH3T0dIJxBfUsC61tsdvOURFdBislxe12S6CpIMfvgl8GJroIUo9RdD0p0FaQYpZSvmOgqSDF7KtEVyItbomsQdwlpETGzWK90VWAnM7uMYFSsG939mXBbB4K5RFpmBSEA7p5R7JUVEREREZEiF/cWETOr6O6bwscp7r4RwN0nmtk44DHgVncfHJapBNwMOEGXLBERERGRUqUs5ojELRAxs07u/l1EEHIVcLiZLQM+cPeRwECgFnC9maUDVYDOBHkhe0UkppfBWyUiIiIiUnrEpWuWmd0GPB3mfmBm1wC3AX8CewPXmdkN7j6doAvWS8AZwAHANGDP6MR0EREREZHSwjMtbktJEa8WkXeBTkD/cAjeFsCp7v6lmdUkCEp6hMnn9wBXmtnO7r4i6wBhEFIqE9NFRERERMqaYm8RCbtSTQauAjKAswlaOuYAuPs/wD3AeOBkM7slXB8ZhJiCEBEREREprTwzfktJUayBSGQ+h7tPA64ENgMtgSOyyrn7EmAQ8C1wgZmdE3kcd/firKeIiIiIiMRXsXXNigxCzKwnMNvdvzezS4HBQC8z+9fd3wZw96Vm9gAwFxhWXPUSERERESlpXPOIFI2wK1VWEHIf8BDQxcx2cveZBC0jm4CLzez0rP3cfZG7P+ruGWaWVBx1ExERERGRxCuWFpGsrlRmdjlwHnAUMNXdN4QtJdPM7ArgEeBCM6vk7i9HHUOTFYqIiIhImVCScjfipdhyRMLZ0/cDnnH3n4CsGdENsnNGrgB2JhjCV0REREREyojiHL63GnAgsBAgnIzQwm5XyUATd//LzE4F5hVjPUREREREpIQpkhYRM9vDzOqEj+83sz3dfTXwKdDRzFpBjtGvdgduNbPm7j4na8b0oqiLiIiIiMiOpixOaLjdX/7NrD0wDjjHzJ4GriWYLwTgS6A5QR5Im7B8beAuoD4wK+s4mjFdRERERKTs2OauWWZ2sLt/7e6Tzex+4H9ACtDd3X8HcPe3w8DjMuBEM1sJJBHkieyb1RKiIEREREREyrKyOGveNgUiZvY4UNHMJrj7ZuDPcNMaYB8z+9Pds3JDBpvZbwTdsZoD04FX3X2LmZXXjOkiIiIiImXPtraIPEkwQeFmM2sCfAjUBa4haP1INrPn3X0RgLt/SzBrejYzS1IQIiIiIiJCicrdiJdC54iEI19ND4OQs4B3gJPcPd3d7wGeBy4E+prZruE+I82sbeRxNE+IiIiIiEjJYGZHmdlfZjbTzG7Mp9ypZuZmts/2nrNQLSIx8jn+AtYSJKObu49w94Fm5gTByL5mVhNoQdAlS0REREREoiSyRcTMkoDBQHdgAfCDmY1296lR5aoSzAM4qSjOW+AWkcggxMxahV2yfgbOIAhoLjaz0wHc/Q7gfoI5RKYBDcIWlOKct0RERERERApvP2Cmu89y903AG8CJMcrdBdwHbCyKkxYoMAhbO7KCkPuAU4EaBBHTQ8C5wEsEwYiHLSNPmVmFMJkdJaaLiIiIiMSW4FGz6gPzI54vAPaPLGBmHYGG7v6hmV1XFCfdaiAS1RLSA+gD9AMqA22Bl4ErgbOAV4F+Zpbs7sOzghAABSEiIiIiIjuecOLxh4G+RXncrQYiEUHIYQT9xh529w/DdRWBucBzBPkiFwKjgH2A4UVZURERERGR0irBo2YtBBpGPG8QrstSFdgDGGdmEIyWO9rMTnD3H7f1pAXKETGzugTBRk8gNWt9RB+yt4He7j4L6EEwjK+IiIiIiJR8PwAtzKxp2NDQCxidtdHdV7t7TXdv4u5NgInAdgUhUMBAxN2XACcDy4DTwj5iWdvSgH+ABmEuyV/hjOlJ21MxEREREZGywt3ituQ+t28BLgU+IRho6i13n2Jmd5rZCcV1zQUexcrdJ5vZKQR5IFeb2cPu/nM4jFd74G/3/9JsNE+IiIiIiMiOwd3HAGOi1t2WR9muRXHOQg2nGwYj5wCvAZ+Y2ffABoLE9Ysge4StxOb9i4iIiIjsQHLM1FdGFHpmdXf/lSAPZC1QHfjc3fd2901mVlFBiIiIiIiIbE2hAxEAd/8DOAWoAOxnZi3D9ZuKsG4iIiIiIlJKbVMgAtktIxcTDOU1yMxaF1WlRERERETKkky3uC0lxTYHIgDu/gtwCVALWFUUFRIRERERkdKvUMnqsbj7D2Z2lLtvLIoKiYiIiIiUNbGG1S3ttqtFJIuCEBERERERKYztbhEREREREZHt45lqERERERERESl2ahEREREREUmwsjgTn1pEREREREQk7tQiIiIiIiKSYMoRERERERERiQO1iIiIiIiIJFhJmvE8XtQiIiIiIiIicacWERERERGRBNPM6iIiIiIiInGgFhERERERkQTTPCIiIiIiIiJxoEBERERERETiTl2zREREREQSTMP3ioiIiIiIxIECESkWKdUr02PIldw47QUuH/8Ye5zYKc+y3W7sxbW/PsO1vz5Dtxt75djWpFMb+n14Nzf88TyXffMIHc84tLirLoVw8gUn8fpPw3ln6kiufvAqKlSsELNc+QrlueWZm3n5u6F8Mv8j2h/QLsf2Dge25/437+WdKSN4+buhcai5FIXXRoymx3mXs1fX47n57ocSXR3ZBldc3o8F835hxT9/8tyzD1GxYsWY5Vq3bsHECWNYvnQKy5dO4ZOP3qB16xbZ2z8Y/SqrVkzPXtLWzeaXnz+P12VIHi659Dz+nv09i5ZM5qln7svz/rZq1Zyvv32P+Qt/Zf7CX3n/g1dp1ap5juP8PuUrFi2ZzIy/J3LvfbeQlJQUr8soM9wtbktJoUBEisUxd/UlY3MGD+09gFFXDOaYu8+lVov6ucp17H0Yux+xN0OOuokhR/6Plod3ZO8zuwFQrnwSPYZcxU/Dv+S+PS5g5CVPcMStZ1GndaN4X47EsPchHek5oAc3nvE/zj7wHOo2qkufq8/Ks/yUH6Zw/xUP8O/SFbm2bdywkU/e/JTnBr1QnFWWIlar5i5c1LcXJx97RKKrItvgiO6HcP11l3DEUT1p1nx/mjVtxMDbr4lZdtGipfTsdSG16rSlTr12vP/Bpwwf9lT29uNO6EONnVtmLxMm/MjIkR/E61Ikhm6HH8w11/TnuGPOpE2rLjRt0oibb7kyZtnFi5dyVu8BNKy/J40bdmTMh18w9OUnsreP+fBzunQ6jl3rtme/fY6kXbvW9B/QNz4XIqWaAhEpchVSk2l99H6MfehtNqelM//H6Uz//Gfan9IlV9kOpx3ExOfGsHbJCtYuXcmE5z6kw2kHA5BaozIp1Sox+Z1vAVg0eRbLZy6kZoyARuKv+2mH88mbnzB3+jzWrV7Ha4+9TvfTD49ZdsvmLYx64V2m/DCFzMzMXNv/+nU6X7zzJUvmLS7uaksR6t61M90O7kSN6tUSXRXZBn36nM5LQ99g6tTprFq1mkH3PMbZfXrELLt69Rrmzl0AgJmRkZFB892axizbuHEDunTZn1eHjSi2usvWnXnmKbz8yptMmzaDVavWcN+9T3BWn9Nill29ei3z5i0EwvubmUGz3Rpnb589ex6rV6/N3p6ZmcluEdulaLjHbykpFIhIkdulWV0yMzJYMXtJ9rql0+ZSq2WDXGVrtWjA0mnz/is3dR61WgaBxvp/1vD7e9+xZ49DsHJGg47NqVG/JvN/+Kv4L0K2qnHLxsyaOjv7+ayps9i59s5UrVE1gbUSkYJq02Z3fps8Nfv5b5OnULdubXbeeac89/ln2VTWr53FY4/ezb33PRGzTJ+zTuPbbydlBy6SGK1bt+SPydOyn//++zTq1KnFzjvXyHOfBYt+49+Vf/LgQwN58IGncmw7vccJLFoymXkLfqFdu9a88MLrxVV1KUM0apYUuYqVUkhfuyHHuo1rNlCxckruspVT2Lg2Lft5+to0kqukZj+f8t53HHdfP466vQ8AH978EmsW5+7aI/GXUjmV9WvXZz/PelypSiprV61NVLVEpICqVK7EmtVrsp9n/eJdtWplVqxYGXOfmrXbUKlSKmf36cG8ebEDjbPOPI17/u/xoq+wFErlKpVYvea/z+Ks+1ulSmVWrFgVc58Gu3agUqVUzjzr1OwWkixvvzWat98azW67NaH3maewbNnyYqt7WVUWR81SIFIMzKycu+fqf2Jm5l6SGsSKx6a0jSRXTc2xLrlqKpvWb8xddv3GHIFHcpVU0tcFQcwuu9XjlCcv5e2LHuXvb/5gl6Z16fXitaxbtpIZX/5arNcguR160qFcce9lAPzx/R9sXL+BSlUqZW/Pepy2bkPM/UUksc4442SeHnwfAN9+O4l169OoWu2/Fsxq4eO1ET8wxJKWtoEhz77CkkW/s0f7Q1i+/N/sbZ077UvdurUZ+Y7yQ+KtR88TefyJQQB8990PrF+XRrWqkfe3CgDr1m39/j7/3HDmzPuJfTp2z3F/Af7+ew7Tpk7n0UfvovcZ/Yv4KqSsUdesIhYGG5nh47PMbKCZ9TWz+u7uZlbqX/N/Zy2hXFISOzepk72uTutGLJ+e+9ez5TMWUKf1f/1M67RpzPLpwa8wtXdvyL+zl/D317+DO//OWsyML39ht64div8iJJex747lpFancFKrU7jl7NuYO30uzdo0y97erE0zVixbodYQkRLq9ddHZSeTH3dCH6ZO/YsO7dtkb+/Qvg1LlizLszUkUrly5ahUKYX69evmWN+nz+mMevcj1q9Py2NPKS5vvfkedWvvQd3ae3DKSecybdp02rVvnb29XbvWLF26PM/WkEjB/U2l3q51Ym5PKl+epk2VI1LUNGqWbJfIFg8zuw94GDgRuBF4xcxau3tmaQ9GNm9IZ9rHP9D16tOokJpMw31asnv3vbOTziNNHvktB/Q7mqp1dqJK7Roc0O8YfhvxNQBL/pjDLk3q0qRT8A/lTo1q07LbXiyLyCmRxPl85Bcc2fMIGrVoROVqlel9eS8+ezvv4TorVKxAheRgeN/yEY8hSH6skFyBpPLlMYMKyRUoX0ENtiXdli0ZpKdvIiMjk4zMTNLTN7FlS0aiqyUFNGzYCM7t24vWrVtQvXo1bvrfFbzy6lsxyx7e7SD23LMt5cqVo2rVKjz4wO2sXLmaadNmZpdJSUnh9NOO55VXYh9D4uu1197h7HN60KpVc6pXr8r1N1zKsFdjDyBw6GFdaN+hTfb9vfe+W1i1ajV//Rnc33P69qRWrV2AYKjfa6/tz7hx4+N2LVJ6leovxPEUdsfKCkLaAk2AI4GOBIHIFuDlshKMjLnlJcqnVOSan5/ilMcvYcwtL7F8xkIa7bs7N079b4jWn4Z/wYzPf+HiT++l/2f3MfPLX/lp+BcArJy3jNHXPctRA8/mhinPc85btzLtox/4+Y1xCboqifTjuJ94+5kR3P/mvbw68RWWLVzGqw8Py97+7OfPcOhJ/8378vy45/hg5mhq1avJ/w0fxAczR1OnQW0A2u2/Bx/MHM2gV++iToM6fDBzNPcMHxT3a5LCGfLy6+x92Im8MOwtPvjkS/Y+7ESGvKwE1h3FJ5+O48GHnubzT99m9t/fM3feAgbe8d98ML/9+iVnnHEyANVrVGfYq0+x4p8/mf7nd+zWrAnHHn8W6enp2eVPPPFIVq1aw1h9QS0RPv/sax55eAhjPnqdaX+NZ978hQy6+9Hs7T/8+Ak9ep4IQI3q1Rg69HEWLZnM73+Mo2mzRpx0Yl/S0zcBcMCBezPp+49ZunwKI0e9xCefjGPg7Q8k4rJKtUy3uC0lhZWBlIViZWaHu/vnEc/PAPoDa4HT3H1DuP5o4EqgBnCOu/+ZX87InY3P1I0ppcZnKtm+tPvgl8GJroIUo9RdD0p0FaQYpZSPPemflB7r0maXnG/iESbtekrcvvvtv+idEvEalOpf5Yubmd0IXGGBrNeyOVALaAdkf5q5+0fAI8C/wEdm1rgsJK6LiIiIyNZ5HJeSQoHI9hkJnBwGFG0A3P0u4HEgDXjazLIz+dz9Y+BZYBSgAdZFREREpMxSILId3H2Gu28xs2OBr8ysT7j+aWAIQZ7I/WZWJ2Kfd939anfPMLOkhFRcREREREqUspgjokCkaCwG3gNuMrMzAdz9EWAEQVete82sXvRO7q7hZURERESkTFIgUkixRrty95+Bx4DxwMCIYORh4C3gIOD8eNZTRERERKQk00D9hRA5Y7qZnQpUAyoAr7r7b2b2EGAEwQjuPtzdHzWzxQStIyIiIiIiuZSkiQbjRYFIAUXNmP4I0JegS1YN4AYzu9DdvzCzh4FM4BYzq+Tuz7n7m+F+SeqOJSIiIiKirlkFFjFZYQtgX6Ar0AVoDcwAXjWzvd19CjAYmAJ0C/ex8BgKQkREREQkl8w4LiWFApECyAokzOx84HlgNTAdWOXuq939KGAmQQCCu/8K3AD0Dp+XpCGbRUREREQSToFIPsysqZlVcXc3syrAbkBDoLG7b3D3TDOrFBYfCDQys7YA7v53uF2vsYiIiIjky7G4LSWFviTnwcxOJ2j9GGhmKe6+jmBkrOeAJmb2BIC7p4W7ZACbwyVbVl6JiIiIiIj8R8nqMZjZecBDBK0ck9x9I4C7LzWz5wkCuIvMLAW4h2D0rBsIZkufmZBKi4iIiMgOK7MMduRXIBLFzI4E7gf6ufuIqG3l3H25mT0drroF6Am8A/wDnJrVHUstISIiIiIieVMgEgqH53WC0bDejgxCzOwAgkkJDzWzd4GRwOPh5l5AmrsPCMumZLWgiIiIiIgURGYJyt2IFwUioYiRrRoBdbLWm9mdBMP0tgQWAs8ADd39VjN7kWACwz5mNsjdb1YQIiIiIiKydQpEcpsInGtm7wN1gZoEwUd/d//LzB4HLjOzJ919sZk9S5CofrWZbXb3gQmruYiIiIjskErSaFbxokAkt+eB6kBzYBVwO7DY3TeE2+cBU4F0AHdfZmYvAZuAd+NdWRERERGRHZECkQhhkvkG4O48tlckmC19qruvylrv7kvM7BElqIuIiIjItiiLXyI1j0iE6EAiazJCM0s2s1bAKGBX4OJwveW1r4iIiIiI5E0tIvkIh+KtRjBM754Eien7uPsWM0ty94yEVlBERERESgXliEgs6cBsYBrwirtnmFl5d9+S4HqJiIiIiOywylQgEjFXSGHKp5vZkKyuV2FLiIIQEREREZHtUGYCkcjZzs2sClDZ3ZfG2p7F3T1qv2R3T49rxUVERESk1CuLycZlIlk9bNnICiZuAd4DppjZy2bWB2Inm0ftdwbQz8zKTPAmIiIiIlJcykQgktUdK5wl/TKCuUK6A/sBV5pZi+h9IrtxmdlFwCvADHXLEhEREZGilhnHpaQoE4GIBXYDjgfOdvfXgUpAY2Cwu88ws6SI8uWigpD7gF7u/kkCqi8iIiIiUuqU2kAkaw4QyG4RyQSS3f0TMzsZ+Bi42t1fNLNKwKlmVj+qO9ZFwP3A+e4+MgGXISIiIiJlgGNxW0qKUhuIRAQTQ8zsBmALUNvMHgBeBK5392fC4i2BC4BmES0h/YCHgPMUhIiIiIiIFK1Sl3gdOdGgme0PHEPQojHfzIYA1wBD3f3psEwqcDdBoDI+XLczcDLQx91HJeAyRERERKQMySw5DRVxU+oCkYgg5DyCZPRX3P3TcPNbQEOgt5mtI2gR6gDUBjqGM6knufsKM+vl7msScAkiIiIiIqVeqQlEzOxQoCtQGZgD7AX0BIZnlXH338xsIPAD0Dcs9zPwP3ffEjljuoIQEREREYmXzBKUuxEvpSIQMbMLgEHAn0AzgmDkE+ANgtaP1919HIC7zwKeCGdL3xRxDM2YLiIiIiISJzt8snoYhAwGLgG6AccCHwH7AF8DXwKDzeygsHy5cEStHEFHVpcuEREREZF48zguJcUOHYiYWVfgWeD/3H0EkOHuk4HPgSrh//8PmAY8ZWZd3D0za0lQtUVEREREyrwdOhABFgLfAvuYWdesoXcJghAIphCZCDxKEIy8Y2bt419NEREREZG8lcWZ1XfoHJFwRvTzgCeBW8xsMVCfYBLCPu6+OCz3rZklAzOAKQmrsIiIiIiIADt4IALg7jPN7DLgceBNoA3Q191HmFlSUMQz3f0L4AvIOdeIiIiIiEiiZVrZGzVrR++aBQQtI8BlwL8ErR6zwvUZxMjJURAiIiIiIpJYpSIQgaBlBLgImA/cZWZdwvUlaXAAEREREZESx8yOMrO/zGymmd0YY/vVZjbVzCab2Rdm1nh7z1lqAhHIDkYuAzYRzBXSIcFVEhERERHZqkQO3xumMwwGjiZIczjDzNpEFfsF2Mfd2wMjCHKyt0upCkQgu5vWtcBY4PcEV0dEREREpKTbD5jp7rPCCb/fAE6MLODuY909LXw6EWiwvSfd4ZPVY3H3acDVoMR0ERERESn5Ejysbn2C9IYsC4D98yl/PsEE4tulVAYikRSEiIiIiIgUDTM7C9gHOGR7j1XqAxERERERkZIuM7Gj9y4EGkY8bxCuy8HMDgduBg5x9/TtPWmpyxEREREREZFC+QFoYWZNzawi0AsYHVnAzPYChgAnuPuyojipWkRERERERBIsk8Q1ibj7FjO7FPgESAJedPcpZnYn8KO7jwYeAKoAb1sw+eI8dz9he86rQEREREREpIxz9zHAmKh1t0U8Pryoz6lAREREREQkwcriDNzKERERERERkbhTi0gJ9YuvSXQVpJg0TKqc6CpIMUvd9aBEV0GK0YZF3yS6ClKMfM0/ia6ClFEJHjUrIdQiIiIiIiIicacWERERERGRBEvwzOoJoRYRERERERGJO7WIiIiIiIgkmEbNEhERERERiQO1iIiIiIiIJJhGzRIREREREYkDBSIiIiIiIhJ36polIiIiIpJgGr5XREREREQkDtQiIiIiIiKSYGoRERERERERiQO1iIiIiIiIJJhr+F4REREREZHipxYREREREZEEU46IiIiIiIhIHKhFREREREQkwdQiIiIiIiIiEgdqERERERERSTBPdAUSQC0iIiIiIiISd2oRERERERFJsEzNIyIiIiIiIlL81CIiIiIiIpJgGjVLREREREQkDhSIiIiIiIhI3KlrloiIiIhIgqlrloiIiIiISByoRUREREREJME0oaGIiIiIiEgcqEVERERERCTBNKGhiIiIiIhIHKhFREREREQkwTRqloiIiIiISByoRUREREREJME0apaIiIiIiEgcqEVERERERCTBMstgm4haREREREREJO7UIiIiIiIikmAaNUtERERERCQO1CIiIiIiIpJgZS9DRC0iIiIiIiKSAApEREREREQk7tQ1S0REREQkwZSsLiIiIiIiEgdqERERERERSbBMS3QN4k8tIiIiIiIiEndqERERERERSbDMMjiAr1pEREREREQk7tQiIiIiIiKSYGWvPUSBiBSx488/kZP7n0pyajITxoznmZufYsumLTHLtuvcngvv6k+t+rWY/stfPHHNoyxfuByAKtWrcNE9A+jQpQPu8MtXPzPk5qfYsG4DAHe+MYhGuzemQsUKLJ2/lDceGs73n02K23WWVd3PP46jLj6RiinJ/PTRRIbd8mye97dVp3acedcF7LxrTWb/OoMXr32SFQv/AWCfYw/k8POOo2GbJsz5bSYP9Lo9574H7sHpN59N7cZ1WbdyLR89PYqvX/+82K9Pcrvi8n5cd+0AKlVKZeQ7H3LJpf9j06ZNucq1bt2Cl158jN2aNQbg559/58qrb2XatBkAfDD6Vbp02T+7fMWKFfhr+t/s1fHw+FyIFNprI0bz7pjPmTFrNscc3pVBt1yT6CpJAa1eu47bHnmeCT//To3qVbmibw+OPbRTrnJr1q3nvmeG8e2PvwHQ87jDGXDWKdnbjzznKv5dtZpy5YIONHu2bsGz99wQn4uQMkFds4qRmd1hZg0TXY942fPgvThlwKnc3vsWLux0HnUa1eWMq8+MWbbqTtW4YchNvP7QMPq0P4O/J8/kmsHXZ2/vfV2fIBjpfAH9D+pHjVo16HVV7+ztLwx8jvP2OZsz2/bk6Ruf5MrHrman2jsV+zWWZW0P7sDRF5/EQ73v4IbO/anVqA4nXtUzZtkqO1VlwDPX8u5Db3DFnn2ZM/lvLnry6uzt61et4/MXP+Cjp0fl2jepfBIDhlzHV699xmXtzmbIpQ/T45ZzaNC6cbFdm8R2RPdDuP66SzjiqJ40a74/zZo2YuDtsb+MLlq0lJ69LqRWnbbUqdeO9z/4lOHDnsreftwJfaixc8vsZcKEHxk58oN4XYpsg1o1d+Givr04+dgjEl0VKaRBg1+mQoXyjHt9MPde15+7nxzKzLkLcpW7/9nhbEhP5+Ohj/DaY3fw/hfjGfXp1znKPDHwar4f9Tzfj3peQUgxy4zjUlIoECkmZvYR0BtYErGuVA/Mduhp3fj8zc+YP30e61ev563H3+DQ07rFLHvA0Qcyf/o8vvtwPJvTN/PGI6/RpE1T6u/WAIA6Desw6ZOJbFi3gbS1aUz6eAINWzbK3n/un3PIzAjfSu4klS9PzXo1i/0ay7JOp3blm7e+ZNGMBaStWc/7j4+g02ldY5bteNT+LJqxgJ/GTGBL+mZGP/oWDVs3pu5uuwIwbfzv/PjhBFYvXZlr38o1qlCpWmUmvvMVAHMm/83imQvZtUWDYrs2ia1Pn9N5aegbTJ06nVWrVjPonsc4u0+PmGVXr17D3PCLjpmRkZFB892axizbuHEDunTZn1eHjSi2usv26961M90O7kSN6tUSXRUphLSNG/ls/A9c2udUKqWm0HGP3el6QEfe/2J8rrJfTfqF8047jtSUZOrXqcUpRx7Cu59+lYBaS1mlQKQYmNnewO7AYe6+2cwOA3D3Ut39r2HLRsyZNjv7+Zypc9ip9k5UrVE1V9lGUWXTN6SzdO6S7GDjo1c+ZJ9u+1K5emUqV6/MgUd34udxP+U4xs0v3cab00dy//sP88fE35k5eWYxXZkA7NqyIQumzcl+vmDaHKrX2onKNapsteymDeksn7uUXVtsvYFwzT+rmfTeN3Q+/TCsXDmadWzJLvVrMeOHP4viMqQQ2rTZnd8mT81+/tvkKdStW5udd8679fGfZVNZv3YWjz16N/fe90TMMn3OOo1vv52UHbiISNGZu2AJ5ZOSaNKgXva63Zs25O883m8ekZng7syIKnfj/U9zcM8BXHjTffw1a27xVFqAYNSseC0lhXJEipiZ7QxkvVNvNbNVwLlmtoe7L01czYpfauUU0takZT9PW7segJQqqaxdtTZH2ZRKKaxZsSbHuvVr15NaORWAv//4m/IVy/PKb68BMHn8b3z8ypgc5QedeydJ5ZPo0GVPGjRvQCmP8xIuuVIKaWv/u78bwscpVVJZv2pdrrLrou7vhrVppFRJLdC5vh/9Lefc259et58LwLBbnmPl4n+3p/qyDapUrsSa1f/dx9Wrg/dx1aqVWbEid2sWQM3abahUKZWz+/Rg3rzYX3zOOvM07vm/x4u+wiJC2sZ0KlfK+VlbpXIl1m/YmKts573b88JbHzDomgv5d9UaRn36NRs3/pcDdu/1/WndvAnuzvD3PuGiWx5g9LP3Ua1K5WK/Dikb1CJShMzsF+BKYBVwHdADuBTo4u5LzaxUBX4Hn3QIr017i9emvcWtLw9kw/qNpFatlL29UpXg8cYwwTzSxrSNpFaplGNdpSqV2LA+KHvdUzewaNYierfuwZlterJ07hKufCx33/SMLRn8PO4nOhy8F/t2368oL6/M2//Eg3hyyqs8OeVVrhh6M+lpG0mNCCSygopY9zc9bWOuoCOlSmrMstHq7rYrFz5xFS9c8wQXt+jFbUdcxVEXnUi7Qztu5xXJ1pxxxsmsWjGdVSum88HoV1m3Po2q1f5r0awWPl4b/siQl7S0DQx59hVeevExatXaJce2zp32pW7d2ox8R/khIsWhUkoy69NyftauT9tA5dSUXGX/178PyRUrcOwF13H5HY9wdNcDqFNz5+zte7VtSUpyRVJTkrmg5wlUrVyJn//4q9ivoazyOC4lhQKRImJmg4Ak4C533wLUBgxYAVwB4O5bzCwpcbUsWl+/+xW9W/egd+se3HXOQOZPn0fT1v/1CW/Spikrl63M1RoCMG/6PJq0aZL9PDk1mbqN6zF/+rzsfT8d/hHpG9LZmLaRj4d9RMdD986zLknlk6jbqF6e26XwJr33DZe27cOlbfvwWN9BLJo+n4atm2Rvb9i6CauXr8zVGgLkKlsxNZlajeuyaMb8rZ63fstGLJ29mClf/4a7s3TWIiaP/Yl2XfcqisuSfLz++qjsZPLjTujD1Kl/0aF9m+ztHdq3YcmSZXm2hkQqV64clSqlUL9+3Rzr+/Q5nVHvfsT69Wl57Cki26Nxg7psychg7sLsFFX+mj2P3RrnzrOrXrUK990wgHGvPcm7Q+7F3Wm3e7M8j21mJepLrOz4FIgUnXrA4jAn5C6gC9AZuA040syeA3D3jNIUjEQaN/JLuvXsToMWDalUrTKnX9aDsSO+iFl20scTaNSyMQcc3YkKyRXocWUv5kybzcK/g64cM3+bweG9jqBickUqJlfkiN5HMTfMOai/WwM6dt2biskVSSqfxCEnd6XNfm2ZMumPOF1p2fTdO1/Rpedh1GvegNRqlTj2slP5bsS4mGV//uR7dm3ZkI5H7U/55Aocf8XpLPhzLkv+XgSAlStH+eQKlCufhJlRPrkCSeWDt8W8KbOp3aQurQ7cA4BajerQ4bC9WfCn+ibH27BhIzi3by9at25B9erVuOl/V/DKq2/FLHt4t4PYc8+2lCtXjqpVq/DgA7ezcuVqpk37L3crJSWF0087nldeiX0MKVm2bMkgPX0TGRmZZGRmkp6+iS1bMhJdLdmKSikpHN5pHwa/OpK0jRv5Zcp0xk74meO7dc5Vdv6ipaxas5aMjEy++eE3Rnw0lgvPOBGAxcv+4Zcp09m8eQvpmzbx0ogPWbV6LXu1aRHvSyozyuKoWaZ+9UXDzI4HniQYJaslsJ+7zzCzGgSjZ10NjHX3fmH5JHfP8xP95EbH75A35oQLgnlEKqYkM+Gj73jmpsHZ80w89vlgRj75Fl+/G4zI0b5LB/rdeTG1GtRixi/TefyaR1m+YBkAtRvW4YI7LqTV3q3AjBm/Tuf524aweM5iGjRvwGUPXUnDFg3JzMhk0ZzFjHzyLSZ9MjFh110Yu5RLTnQVtln384/j6ItPokJKRX7+eCKv3vzfPCJ3fPoIYwa/w6T3vgGgded29L7zAnapX5PZv87kxWuf5N8FwTwxnU7rynkPXprj2ONHjOWlawcDwTwjx19+OrvUrxWMmvbeN7xz3/AdJg9o6KIJia5Ckbnyigu57toBpKam8M6oMQy45MbseUR++/VL7r3vCV5/fRSnnnocdwy8jgb167Fhw0Z++OFXbr71//j992nZx+rZ80Tuufsmdmuxf16n2yFsWPRNoqsQF4NfGMbTLw7Psa7/eWdyyflnJahG8eFr/kl0Fbbb6rXruPWR55j48x9Ur1aVK88N5hH56Y+/6H/rA3w/6nkAPv56EvcPGcba9Wk0rl+Xq87rSee92wMwc+4Crr/3KRYsXkrFihVp1awRV53Xk7Yt824x2VFUbLZfiRzF9NomZ8TtH7kH57xeIl4DBSLbwcyuB55y93Xh86+Ag4Dh7t4nolx14EzgKuBLd79oa8feUQMR2bodORCRgilNgYjkVlYCkbKqNAQikj8FIiUnEFHXrG1kZmcD7YEN4fPdwk0PA53N7JGssu6+GhgebuttZpoRSERERESyafheKYzXgNfDnI+jgHHufoiZVSXonnWpmbm7Xw1BMGJmb4TbRieu2iIiIiIiiacWkUIys3vM7Fh33xImpncBBgNPmlkNd18LvAo8AZxiZg9l7evuK919VGlOWBcRERGRwtPwvZIvM2sHHAz8z8y6hqsnAS8BrYH7w2BkKTAMeBw4ycxeiD5WfonqIiIiIiKlnQKRQnD33wmG410B3G1m3d19M3Af8CHQgZzByHDgFaCGmem1FhEREZGYyuLwvfpyXEBmVgHA3b8kCDBWEQQjB4XByAPAewTByH0RwcjjwGnunqlgREREREQkoC/GBWBB1vnm8PGtwKlAfWBf4EEz6xYRjLwLtAOeNbMqYV6Ih8coSUGoiIiIiJQQHsf/SgqNmlUAHk62YmaXAtcDJwAzCPJFLgDuMrNMdx9rZg8C1YCaQFr0MURERERERIFIgZhZ1qQvnYG33X1s+Pw1M1sF3Avca2bXuPu3ZnYLkBm2hJRTS4iIiIiI5KcsfllU16wC8BCwBqhjZskR28YAbwEdgZfMbH93z1B3LBERERHZUZjZUWb2l5nNNLMbY2xPNrM3w+2TzKzJ9p5TgUjh/A4cABwUlXi+APiSYBjfH7NWqjuWiIiIiBREImdWD+e3GwwcDbQBzjCzNlHFzgdWuntz4BGCUWO3iwKRQnD3J4GxBLOqn2hmzcysGnAy8A3wf5qsUERERER2MPsBM919lrtvAt4ATowqcyLwcvh4BNAtIn1hmyhHpIDMLCnscnWamQ0HngSSgNUE3fpOjeiOpckKRURERKTAEtyNpj4wP+L5AmD/vMq4+xYzWw3sAvyzrSdVIFJAYUuHhekiZ5rZYUAtIAUYltUSoiBERERERGTrFIgUQtjikdUy8mXkNjMr7+5bElU3EREREdlxxcrdiKOFQMOI5w3CdbHKLDCz8kB14N/tOWmZzxHJa7bzvPI8slo8ovdTECIiIiIiO6gfgBZm1tTMKgK9gNFRZUYD54SPTwO+3N6Bmcp0i0jkHB9mdhKQCqx19w/y62oVOSyvme0GLHT3jfGsu4iIiIhIUQhzPi4FPiHIgX7R3aeY2Z3Aj+4+GngBeNXMZgIrCIKV7VJmA5GoYOIhgggvDcgws1Pc/bxYwUhWnkj4+HLgCuAQgqQeEREREZFCS/TEc+HceGOi1t0W8XgjcHpRnrPMdc3K6lIVEUw0BfYBDgW6AncAR5jZW2G57OF4o4KQi4CBwM3uriBERERERKQQylSLiJnVcPdVEc/7EcwBMhuYFjZLLQE2AI+Y2Zvu3jMMRiq4++Zwv4uA+4Hz3H1k/K9EREREREoTT/QAvglQZlpEzOwZ4JXwcblwIsIWQDugRVayubunESTjXAkcaGZfhOuzgpB+BDNJKggREREREdlGZalF5Glgavg41d3XmNlgYB1wrZkNcvebAdx9g5m9D1QCTs5Kajez04AhBJMXjkrERYiIiIhI6ZPoHJFEKDMtIu7+m7tvNrO+wDwzq+vuc4EXgYeB08ORAbLKbwBed/cTs5LagY+BIxSEiIiIiIhsn7LUIpJlNjATGGtmh7r7AjN7Mdx2hplluvtAAHdPz9opnLBwHfB53GssIiIiIqWackRKmTwmK/yGYMjdlcDXYcvIPIKWkeHAVWZ2QfROmrBQRERERKTolNoWkajJCg8luNYN7v4tMNHMrgAeJwhGDnL3eWb2KsF8IC8nrOIiIiIiUuYoR6QUiQhCHgTeAZ4DxpnZ62a2t7v/QNAysjxcv6u7z3b3FyPnDhERERERkaJX6gIRM7OIx3sApwBHA10IZkDfGxhoZq3c/XvgOoLX4eHI/SNnUxcRERERKU6Z7nFbSopS1zUrYubza4FmwBh3nxjOir4gHIL3E6A/QYvIBOBUYFrk/iIiIiIiUnxKTYtIZGK6me0MtAcuIghGwtVW0d0nA7cBvcJEdXf3P9QdS0REREQSxeO4lBSlIhCJSkw/C2gLPAo8CRxlZie7e6a7bwp3SQOWARsjj6PuWCIiIiIi8bHDd80Ku1xlBSH3AucCNwNvAo8A1YC3zexs4CtgM3A2sBRYnZBKi4iIiIhEyCxRbRXxscMHIhE5ITcQBCHHAJPdfTOw1szuAdYDrwIrgDeASsDh7u6RrSkiIiIiIhIfpaVrVjKwH/B/7v4TsKuZnWBmY4ALgF+B/yMIQH5w94PcPT3MGVEQIiIiIiISZzt8i0goCWgFrA9HxTqbIOhIJxi2tyZBzkg9YLCZLXX3Twi6aYmIiIiIJJSXwa5ZpaJFxN3TCIbi7UqQoP4LMNDdjwU+B2q6++/AIGA48JGZdddQvSIiIiIiiVFaWkRw98/NbG8gxd3nQ/aQvvsBC8Iys8zsIYKWkvkJq6yIiIiISISymCtQagIRAHdfDmBmVYHOwCVAA+D4iDIzzOzaMJldREREREQSoFQFIhGaAwMIgsuO7r7FzMq7+xYABSEiIiIiUpJo+N5Swt1/MbOrgNnunhkZhIiIiIiISOKVykAEwN3/huxZ1xWEiIiIiEiJpVGzSiHNEyIiIiIiUvKU2hYREREREZEdRVn85bzUt4iIiIiIiEjJoxYREREREZEEK4vzbKtFRERERERE4k4tIiIiIiIiCVYW5xFRi4iIiIiIiMSdWkRERERERBKsLI6apUBEJM4qqiGy1EspXzHRVZBi5Gv+SXQVpBhZtZqJroJImaFvRCIiIiIiEndqERERERERSTBXsrqIiIiIiEjxU4uIiIiIiEiCafheERERERGROFCLiIiIiIhIgrmrRURERERERKTYqUVERERERCTByuKEhmoRERERERGRuFOLiIiIiIhIgmkeERERERERkThQi4iIiIiISIJpHhEREREREZE4UIuIiIiIiEiCaR4RERERERGROFCLiIiIiIhIgilHREREREREJA4UiIiIiIiISNypa5aIiIiISIJpQkMREREREZE4UIuIiIiIiEiCZWr4XhERERERkeKnFhERERERkQQre+0hahEREREREZEEUIuIiIiIiEiCaUJDERERERGROFCLiIiIiIhIgqlFREREREREJA7UIiIiIiIikmCueURERERERESKn1pEREREREQSTDkiIiIiIiIicaAWERERERGRBHO1iIiIiIiIiBQ/BSIiIiIiIhJ36polIiIiIpJgGr5XREREREQkDtQiIiIiIiKSYBq+V0REREREJGRmO5vZZ2Y2I/z/TjHK7GlmE8xsiplNNrOeBTm2AhERERERkQRz97gthXQj8IW7twC+CJ9HSwPOdve2wFHAo2ZWY2sHViAiIiIiIiJ5ORF4OXz8MnBSdAF3n+7uM8LHi4BlQK2tHVg5IiIiIiIiCVaCc0TquPvi8PESoE5+hc1sP6Ai8PfWDqxARERERESkDDOzz4G6MTbdHPnE3d3M8oyYzKwe8Cpwjrtnbu28CkRERERERBLME9gi4u6H57XNzJaaWT13XxwGGsvyKFcN+BC42d0nFuS8yhEREREREZG8jAbOCR+fA7wXXcDMKgKjgFfcfURBD6xAREREREQkwTLd47YU0r1AdzObARwePsfM9jGz58MyPYCDgb5m9mu47Lm1A6trloiIiIiIxOTu/wLdYqz/EbggfDwMGFbYYysQERERERFJsETmiCSKumaJiIiIiEjcqUUkDszMfBumsdwRHX/+iZzc/1SSU5OZMGY8z9z8FFs2bYlZtl3n9lx4V39q1a/F9F/+4olrHmX5wuUAVKlehYvuGUCHLh1wh1+++pkhNz/FhnUbqL5Ldc4f2I+2B+xBcmoK86bP5aU7X2DGr9PjeamlXqXqlTnr/v60Pqg961as5b37X+PH0eNjlj3pxjPp1PMwAL5780vevXd49rYGbRpz1n39qdu8PktmLmTYDU+zYOpcAFoe2JZjLj+Vhm2bkbZmHbd2uTR7v5123YVbP3skx3lSKqcw8u5X+OL5D4r6ciUPl1x6HldfczGpqSm8++5HXHn5rWzatClXuVatmvPs8w/RtGljAH795Xeuu/YO/vxzZvZxLu5/DrvsshPr16cxcsQH3HzT/5GRkRHX65HA6rXruO2R55nw8+/UqF6VK/r24NhDO+Uqt2bdeu57Zhjf/vgbAD2PO5wBZ52Svf3Ic67i31WrKVcu+F1zz9YtePaeG+JzEbJdXhsxmnfHfM6MWbM55vCuDLrlmkRXqczbhtyNHZ5aROKgrAQhex68F6cMOJXbe9/ChZ3Oo06jupxx9Zkxy1bdqRo3DLmJ1x8aRp/2Z/D35JlcM/j67O29r+sTBCOdL6D/Qf2oUasGva7qDQRfRmf+NoNrj72Ks9v3ZuyIL7ll6O2kVEqJy3WWFT3vuoCMzVu4cZ9+DL3ycc64ux/1WjTIVa5L78Pp0H1f7jn6OgYddS3tuu3NQWd2ByCpQhIXPXs937/7Ddd2OJeJI7/iomevJ6lCEgDpaRv57q2xvPN/r+Y67spF/3J127Ozl0FHXkNmRia/fDypeC9csnU7/GCuuaY/xx1zJm1adaFpk0bcfMuVMcsuXryUs3oPoGH9PWncsCNjPvyCoS8/kb19zIef06XTcexatz377XMk7dq1pv+AvvG5EMll0OCXqVChPONeH8y91/Xn7ieHMnPuglzl7n92OBvS0/l46CO89tgdvP/FeEZ9+nWOMk8MvJrvRz3P96P+v727jrO6zv44/jrEDGkHmBggGKhYGKvYsbu2YGCu6NrY7Rprty52B2u3a6+1doGFggEIEvpTERkYYs7vj/O545fLgKAz9965837uYx7O/cbd7/C98T3fz+ecc5OCkEZk0UUW5uD9dmenP29V7EORJkyBSAMysyFmdlSxj6NQNt11c56/9zm+GTqSSRMmcd9V97DprrPkNgHQc9v1+WboSF5/8jWmVU/jnssH0mnl5VhyhbjQXXzpxXnrmTeZ/MtkqiZW8dbTb7B0l2UAGDdyHI/d9Cg/jv+Rmpoanhv4DC1atmCJFZYs2N9a7ipaV7LmNuvx+KX3Ul1VzZfvfs6Hz7/LujtvPMu2PXfZhOdvepyfxv7AhHE/8sKNj9Nz100A6NJzFZq3aM5/b36S6VOn89JtT2FmrLTBqgCMGPwlbz/8Kt+PrLMk+UzW22UTvnj7U34Y9V39/rEyW3vttTO333EvQ4YM46effubCC66m79671rnthAkTGTlyNABmxoyaGSy/wrK167/+eiQTJkysXV9TU8MKmfVSOFVTpvDca+9w+N670KZ1K3qsuhK9evbg8RdmHfF8+a0POGDXv9C6VSVLLr4oO2+9CY88+3IRjlrq25a9NmTzjTdggfnnK/ahSBOmQKSBmNm1wGTgqmIfS6Es3WUZhg/5uvbx8E+Hs+BiC9J+gfazbLtM3rbVk6sZN2JsbbDx1B1Psvbm69B2/ra0nb8t62+7Ae+/9F6d/7+dVl6OFi1bMHb4mHr+i5quxZbvSM2MGYz/+td/09FDRrBE56Vn2bZj56UZPWRE7eNRQ0bQMW3XscvSjP5sxEzbj/7s1/XzYr2dN+bNB3UBVEjdunXh4w+H1D7+6KMhLL74oiy00AKz3WfUt4P5vx8/45JLz+SSi6+Zad1uvbfn27EfMnLUB6y2WjduvvnfDXXoMgcjRo2lRfPmdFqqY+2ylZZbmi/rGBGBmRNo3Z1heduddNG1bNznUA465UI+/2pE/u4iMpe8gP8rFQpEGk4H4C13dzM73My2KfYBNbTWbVtR9XNV7eOqiZMAaNWu9SzbtmrTiqqJVTMtmzRxEq3bxrZffvwlLSpacMfggdwxeCAzamp4+o7/zPr/2a41/a84hnuv/Pcszye/X2WbVkz+ZfJMyyZPrKKy3azT3yrbtmJy5t9+8sSq2nNe2Wbmdfnr59YK63Sl/SIL8MF/5qpRq9STtu3aMOHnibWPcyMa7dq1ne0+Sy2xOkt06M6xx/yDwYM/mWnd/fc9xhIdurP6apty880DGT9eo1vFUDWlmrZtZn4PtmvbhkmTp8yy7YZrdefm+55gUtVkRn47joeffYUpU37NEbrghEN45rbLeeb2y1l39W4cfNrF/PzLpAb/G0SkPCgQqWdmlus8+Qmwmpk9C5wNfFy8o2oYG++4CQOH3MfAIfdx+u1nMnnSFFq3b1O7vk27+H1K3gUtwJSqKbRu12amZW3atWHypNj2+GtO5NuvvmXPbr3Za+U+jBsxlv5XzpxIV1FZwam3nMHQDz7noQFz3cRT5kJ11RRa5wULrdq1pvqXWS9UqidNmSmwaNWude05r66aMkvQ0bpdmzpfE3PSc5dNGPT0W1RXVc/TfjJvevfZgbHjP2bs+I956JFbmfRLFfO1/3VEc7752gHwy29caFZVTeamG+/mhhsvZdFFF55l/ZdfDmfIp0O54opz6vcPkLnSplUlk6pmfg9OqppM29az3mg4+ZC9qaxoyZ8PPJ4jz7qcbXv1ZPFFFqpdv+YqXWhVWUHrVpUc2Gd72rdtw/sff97gf4NIOSrhhoYNRoFIPTKzl4F/pIfnAosAvYAB7j4qbVM2/+avPPIye3brzZ7denPOvmfyzdCRLNdtudr1nVZejh/H/8jEnybOsu/IoSPptHKn2seVrSvpsGxHvhk6snbfZ+9+iurJ1UypmsLTdz1Fj03Xqt2+RUULTrrpVL4f8z3XnjSg4f7IJmr8V2No1rw5i3bqULtsqW7L8u2wb2bZdsywb1iqW6fMdp0Yk7YbM/Qbluw6cx7AEl2XqV0/N1pWtqTHduvz5gMvzdsfIfPsvnsfpcNiq9JhsVXZecf9GTJkKKt171a7frXVujFu3Hf88MNPv/lczZo1o02b1nRcYvE61zdv0aK2wpYU1rJLdWD6jBmMGD22dtnnX49khWVnLUYxf/t2XHjiobw08F88cv0FuDurrbT8bJ/bzEpo0oeIlLqyuSguNjPbGegErJIW9QFaAS8CG5vZwQDuXlNOwUjWSw/+l837bMlSnZemzXxt2e2I3rz4wAt1bvvW02+wTJdl6bntBrSsbEnv/rszfMjXjP4y5h5/MXgYW+y+FRWVFVRUVrDVntswYshwAJq3aM4J153M1ClTueqYy2kiRckKaurkagY98xZ/OaYPFa0rWX6tlei+5Tq8/dArs2z71kOvsPmBf2b+xRdk/sUWZPN+f+HNByKXY+ibn1Azo4ZN99+WFhUt2GSfrQH4/PUYIDQzWlS2TFW0sr//avWt16Xq518Y+sbM03yk4Q0c+BD77Nubrl1XZP7523PCiYdz1511jz5uutlGdF99ZZo1a0b79u244MLT+OmnCXyeyvfuu1+f2tGRrl1X5LjjDuGll+ouBy0Nq02rVmyxwdoMuPNBqqZM4YNPhvLiG+/z1803nGXbb74dx08/T2TGjBpefWcwDzz1IgftsQMAY8Z/zwefDGXatOlUT53KrQ88yU8TJrLmyp0L/SfJ7zB9+gyqq6cyY0YNM2pqqK6eyvTpKqddTE0xR8R0EVc/zGwj4GbgJGA9YHHg+LT6GmAJ4A53vyFt38zda2b3fDst89dGeWK2PzD6iFS0quSNp17nulMG1PYRufL5ATz4r/t45ZG4SO2+0er0O/vvLLrUogz7YChXHXsF342K6kmLLb04B551EF3X6gpmDBs0lJvOuJ4xw8ewynqr8s/7z6d6cjU1Nb/+E56z75kMefvTwv/R86hjs3nLjyiWNvO3Ze+LD6XrRqsx6cdfeOTCu3n3sddYYZ2uHHbbKRyzyj612+500l5ssHtUSHv9nhd4ONtHZJVO9L3g73TovBRjvxjFXSdex6hPhgPQuefKHH3PmTP9/w598xOu2P2s2seH33EKwwd9yROX3dtwf2w9u2P8O8U+hHpz+BF/45hj/k6r1pU8+ujTHHXEabV9RN559xkuvvga7rv3UXbaaTtOP+MYlliyA1MmT+Hd9wbzjzMu5pOPPwPg2usvYuutNqVtuzZ8//0PPPzQfzjn7Euprp61J0mp++HjxvNanJ0JE3/h9Mtv5M33P2b++drTf//oI/Lex59zyOkX8/bDNwHw9CtvcdH1dzFxUhXLLtmBow/ow4ZrdQfgixGjOOGCaxg1ZhwVFRV0XX4Zjj6gD6t0mf2ISWNg8y1S7EMoiAE338W1t9w907JDDtiLw/7Wt0hHVDgtF1nein0Mdem86FoFu/Yb9t17JfFvoEDkDzKzlu4+zcw6AhcCGxIjIyu7++dpm05p3ZLA7e5+Y1o+20aHjTUQkd/WWAIR+f3KKRCRWZVDICKz11QCkaasVAORFRbpUbBrvy+/f78k/g3KcopQoZjZHcAWKaDI1TldCvgAWDW3nbsPB04ERgF9zax/Wq5gQ0RERESaJAUiv5OZNQcmAs9nAoq3gT2B4cCRZlY7vpkJRqYCK5lZSUSiIiIiIlJ8TTFHpEWxD6AxMrMl3X00cFh6/HciKLnB3aea2VDgHKCfmeHudwG4+wgz2xsYn/qLzHZqloiIiIhIOVMgMo/M7Bqg0szOc/cvUwWsvsBCgJvZw+7+kZmdBvwTODDFG3cDuPvY9DxzTFYXERERkaajKV4WamrWvBsF/Ak4xMy6pGCiF/AFMfVqZzNr7e4fA6cCPwCnmNnW2SdRECIiIiIiTZlGROZSbhqVu59nZhOAI9Lym9z9s9RH5FHghLT8IXf/xMzOBnYHni/awYuIiIhISaspodyNQlEgMvcMal8hTwIbEQFGczMb4O5fmNkORDByPFBjZo+6+yBgEESCu7urW5CIiIiINHmamjWXclOpzOxK4BEiKBkOHA4clqZpTQd2AEYClxHBSvY5FISIiIiIyCzcvWA/pUIjIvPAzLYD9ga2AAa7+wwzOwHoH6vtX2lkZBfgPOCF4h2tiIiIiEjpUiAybyqBCcB3pGla7n6RmVUAZwHTzOwOd/+ImJ6l6VgiIiIi8puaYo6IpmbNGwMWBCrcvcbMWqfl1wPfAwcBm2R3UBAiIiIiIjIrBSLzwN0fAj4EHjWzSnefnFa1Ax4jKmZdW6zjExERERFpLDQ1ay5luqAfDdwMfGpmJwEziJGQ6e5+fdpW07FEREREZK6VUhJ5oSgQmUv+66vjfWA3Ihn9MqAa+Ab4K9QGLApCRERERETmQIHIPEoByTBgNzNbFpgGjE05Iy1SCV8RERERkblWoxGRpikz7Sq7rFmud0gd2+fWjcztl5YpCBERERERmQsKRPh12pWZ7UVUxLo1jXDMEqCk7etaZ4U6XhEREREpL94Ey/cqEEnMbEHgMGCGmU1293vc3esaGckGIWa2N/Cyu48swmGLiIiIiDRKTbZ8r5nNNILh7j8C+xENCw82sz3S8prstnlBSD/gdmC1Qh23iIiIiJQfdy/YT6losoFIJpjomFk2lCjPOwU4yMz65LY1s2Z5QcjBwCXALu7+ZMH/ABERERGRRqzJBiIAZnYocJuZrZdb5u7DgP5Ek8KTzWy3tLwmLwi5CDjA3R8u+IGLiIiISFmpwQv2UyqaVCBiZvl/72dAN+BYM1s3t9DdPwfOAJYDTjWzrTPPcRRwPhGEPNjwRy0iIiIiUn6aTCCSup3XpN+7mlknd/8vsAWwFnBidmQEaAk8m36eS/stDewPHKYgRERERETqi3JEypCZHW5mPXLdzs3sIuAJ4F0zGwT0ILqirw6cYGa9zawDcCDwrrufkElYHwts7e7/LsofIyIiIiJSJsq6fK+ZLQecBDxrZucT07D6Av2AtsAqwEDgcGBr4GbgaqAa+B64LD1PLkl9GjCuwH+GiIiIiJQ5dVYvM+7+tZn9hQgw+hMjQJflqlyZWQUwArgR2A7YCVgZWBh40t1nmFkLdUwXEREREalfZR2IALj7oNTv4wZgeeCKzLqpZnYPsBWwh7s/A7yWW5/yShSEiIiIiEiDKqXcjUIp+xwRAHd/n0gy/xnYxcx6ZNZVEdOwlqpjvxkFO0gRERERkSakSQQiAO7+EbB9enhMLhgxs/ZAd+CbYh2biIiIiEhTU/ZTs7Lc/UMz25dIUH/azN4GqojE9YNgpsR0EREREZGCKKVGg4XSZEZEctx9ENAbmAB0Ah4H1nH3aSkxvem9CkRERERECqxJjYjkuPvHZrYn0SvkLnd3M2umxHQRERERKYameC+8SQYiAO7+jpm9mwlCaop9TCIiIiIiTUWTDUQAUhBiCkJEREREpJiaYkPDJpcjkk85ISIiIiIihdekR0REREREREqBq2qWiIiIiIhIw9OIiIiIiIhIkSlHREREREREpAA0IiIiIiIiUmRNsX6SRkRERERERKTgNCIiIiIiIlJkqpolIiIiIiJSABoREREREREpMuWIiIiIiIiIFIACERERERERKThNzRIRERERKTJNzRIRERERESkAjYiIiIiIiBRZ0xsPAWuKw0AiIiIiIlJcmpolIiIiIiIFp0BEREREREQKToGIiIiIiIgUnAIREREREZlrZmbFPgYpDwpEpKyZ2bbFPgYRkabMzM4ys6WLfRxSf1yVjqSeKBCRsmVmbwHnmVlz3b0RESk8M3sK2BMYm1mmz+NGysyGmNlRxT4OKR8KRKQsmdnewMLuvqa7zwCWLPYxiYg0JWa2FrASsJm7TzOzzUB30xsrM7sWmAxcVexjkfKhhoZSrsYA85lZL2BLYA0z29XdJxf1qKRozKyZu9cU+zhEmgIzWwgYkR6ebmY/Afub2aruPq54RyZ/QAfgLXd3Mzsc+MLdny72QUnjpkBEyoqZvQicC3wBPAbcCSwMrOTuk83MdDeu6ckGIWa2FNDa3Ydl1ut1UWCzCwx1Lho/M/sAeBw4GzgeuBmoANZ093Fm1sLdpxfzGGXumdm+7n478AnQy8yeBdYGuhf3yKQcaGqWlA0zu424Y/Oquw8HpgJLAF8R0wNId3I0P7mJyQQh5wMvAoPM7CUzO9bMKvS6KKwUbOTOSV8zO9PM9jOzJdO50HdTI2Vm5wLNgXNSsLEYYMAPwFEA7j7dzJoX7yhlbpnZy8A/0sNzgUWAXsAAdx+VttH7VX43vXikLJhZe2A14sOxOg0bLwrsBQwGzjez7UHBSFOS/YI0swOBfYHTgR2AUcBuxGujQnfhCyM74mFmFwKXEefjJOAOM+vm7jW6uGm0OgJjUk7IOcBGwIbAGcDWZnYjgLvPUDBS2sxsZ6ATsEpa1AdoRdzM2djMDoa40aP3q/xempol5WIq8Dawj5ltCqwBbOnuX5nZN8SduNPMDHd/LBeM6OKzvGXuum8BLAic5e73pGVvAMcCOwJ/Bh7Wa6Jh5U2RW4W4yNkaGEQEI4cAt6epIEOU19MoPQz8K1Ut7AKs6+7DzGwUcRF7jJnd6O79csFIKigipWc8MAXYxszWAxYnpmQBXAPsnT4yb8gFI3q/yrxSBCuNWkpGx92riUoeCwLbAxe5+1dp3WvAFcBw4FQz+2targvOJsDMOgHPAhcSU/UAcPdJwDnAdECviQaUAsFsYLgHcC3QDvjMwyPE+/RH4DYz65oubjR6WeLM7AQzawfg7o8Tn7XrAE/kcrHc/SfgbmIErJeZXZ+WKwgpMWbWMv36JfAWcAmR63OBu3/v7t8DJwCjiZt//aB2ZETvV5knCkSk0TKzK4E9MsP7XYAFgP8BfXMXPwDu/jpwOZEvcpWZbVTgw5UCyf8iTPlCmxJ9DP5kZh1z26TA42Vg8cyXr9QjMzsJOMpC7jtnRWLq5GpEEjMA7v4U8T79P+ApM1tWwWFpM7N9iKTlyenxCmnVZcCGZnZ5blt3n8CvwcieZnZigQ9XfoOZ3QFskUaHx6TFSwEfAKvmtkufqycSU1z7mln/tFzvV5knpteMNFZmthLwVZqLvBxRKrINsDpxt2ZR4HR3fyGzzybAFsCZuhNXfvKm/rRy9ymZdZsDj6af04GRQEvgJeATdz+g8Edc/sysM/B1SlBe1d0/TssPIaZMvg8c4+7Zhnc7AhsDx+t9WtrMrAVxLTHNzLYBXnL3KSlv72DgcOAhdz8ms8+CRMLzYzq/pSPd1LsK6O/u09Kyw4ly+HsQ36k3uvtdmX2WBW4iKlUeqkBE5pUCEWl00h24J939/9LjvYDjgHPd/YG0bEvgMKJiy2nu/t86nkdzk8tIXhL0ScB6xBfnQ8BTKedgS+BBYu7z50Ru0bJAT3efqhyRhmNmfwbuIC5y7kzLjiYKBnxBBB2z9JfQ+7Q0mdl5wGvu/mR6vBFwO5HIfJy7/2RmiwN9gSOAB9392DqeR+e3BKSKdaMzj/8OTATuT5+NqxFTWRdk1mCkAzA+NzVLn6EyLzQ1SxqVFHScAxxvZvOnxZ8BPwEHmllvAHd/DhgAjAPOMrPt8p9LX37lI42E5IKQk4kKTIOJ6Vi9iZyDHul1sQPQFuhMTANaJ33RttQXaIMaQ4xGnZLex7j75cADxFStC8ysY/5Oep+WnnRRujFwci5Pj8gluBXoBlxkZgukwPIu4i77jmZ2c/5z6fwWn5ldA5ydm1aXplD2BU4FdjWz1u7+EXAakcN1YO49DODuYzPJ6voMlXmiQEQam3uIOcabE4nnC7v7e8QUj+bAQXUEI05cfEqZykzHWp7IO+jj7me6+65E2dAxwKVmtrS7v0hMM1gS2B+oSHdlpxXp8MtOXaU83f194ErgNeDMTDByGXAf8Cfgb4U8Tvl90kXpGURvkH+a2Zbp/XMh8CQxPTYbjNxNjIYtUNdrQ4puFPH+O8TMuqTP017ESOWJwM4pGPmYCE5+IG4obJ19ElXMkt9DU7Ok0TCzylQdK9d/YEMiMf2CNA1gdaK6hwHXu/v9adt1gPf0IVne0pS9W4jcj73c/Y3Muh2Ii6R+7v5qWrYZcTf+RWBfd/+l8EddfvLydHYB5iNyce5098lm1o2YSrkxkat1d9q2D/CA7pCXtjRymMsf6APsTUyBPM7dX01FH44nbv4MAk5Mn88LAj+5R8NKfR4XX9501sOIKXRPADe5+2cp/+dRIln9IiLXZ7KZrQHsDpyq96v8UQpEpFHI+8DcF1iTuKvdGvgXcIm7/5CCkYuJUZCB7n575jn05VfmzOxJYFugPxGMVmfWDQdudvdzMsu2IRIt13X3bwt7tOUn7316ObAfMRq1AFFV6SB3f8Gih0h/otndZe5+Y+Y5lDNQovLO7+nE6GNnYgTkHeCUdH5bEsHmX4m77QfkAn3lEJSOvJsGnYDziZGR+4nmwF9kgpEliZs5j7p7VeY59H6VP0RDpNIoZL78/kH0GngH6Ef0h9gBONHMFnT3wcQX4GJAj7znUBBSJmY3vcPd/wz8FzgF2DJVgSHlE/0CfJe3/dNAFwUh9SPzPu1M9JHoRQQb3YBhwJ1mtpa7f0JMm/yEmGZZW3ZZFzWlK3N+DycqE15LBBt9gUnAOWa2aRoxuYQojT0BqMp/Dim+TBByJfAIcQNvOFHp7LA0TWs68R07kii7vFHec+j9Kn+IRkSkUUgXKYsAzwPXufu1aXkz4FJgV6JiyyVpGsCKRGlfBR9lJu8uXk/ibvtIYIRHk0LM7GXiLu29wKfAZkRC9Orpi1XqWe5Ot5n9DdiHCPx2Baoz5+sVoMLde6bHKxClffU+bQRywSIwEJjsmZLXqSDIBcTI17Hu/r90I6BG07FKVzpvdxFl7Qd7dLs/gRixvA/4VxoZaQmcB5yk4EPqk0ZEpFFId9EmAjXA/BD16929xt2PJi5E9wMuNLP53f2LXBWPoh201Lt0sZu7qD2PmEJwNfAGcJqZdQdw903Ssn7AusCb7r6KRy+LFsU5+vJkZsuZWbt0sdkOWAFYGljW3Sen92GbtPmZwDJpahbu/qXep42HJ8DPRBPQysy6/xAXrj2AW81sPXefkV4XpiCkZFUSo1bfESMiuPtFwDVEzsjBZraau09z9+NToNJ89k8nMm/04S+NyTSiHO9WKWFyeuYC5mOihO8U4ksS0HSscpOZGnIysC+wt7t3JsqGHgYcmvKEcPdtiRG0dYipfLnn0IhIPTGz3YgcmzMtGkj+QlTGuhHoZGZXA2TmlM8g3sczVSjT+7TR+QjoCfwpL4gcRUyNvBV4N7dQ07FKmhG9QSrSTYHWafn1wPfAQcAm2R00IiL1SYGINArpjtoMYu5qd6IvxPxA8/RFuADRX6R/bhpA8Y5WGpKZLQdsQJzrl1JFrP2IhMo+wJGpqgvuvhXwLXCjmW2nO3n1x8wOAG4AHiOqXU0B8CjXehOR+LqDmd2YRk1WJ0qBjiLKgkoj5e7/IqrNDSTO8fJmNh+wE/AqcL7unDcO7v4Q8CHwaKpMOTmtake8t3O5QCINQjki0mhk5qBvSkwBGEt0yJ4v/aycvvw0F7mMmdkCRM7Hs8CqxPSsi9z9ajO7jBgpeQY4290/S/t8QJSQXTdb8UV+H4v+AXcDf3f3B/LWNUt3VhcCDiGaoE0jOtw3Aw72KAGq92kjlK2SZGZ3EwUJmhPTe2qA1dJotapjlbjMd+pawM1Ae6IZ7AxiJGS6u/8lbavqWNIgFIhIo2RmixDJdO2BaqJs5HR9WDYNKSfhFzO7lCgrua+7V5vZuUQVpi+AfbIXuma2rLuPKNIhl4XMhcv5wALufkhmXU+i9OemRAWeB4GpwJFEz4FX3f3QtG2r3AiKND55ZXw3I/qItALuyo2E6HO48UhFCFYkktF7Et+p3wBbufs0BZXSkBSISKMzuy+5lLyu+f9NiJndBVQQ/Sl+MrOHiKaGT2am6JkuiupXuhO+uLtvkR6fTZT17AKMJvJyznX3082sI9ExfW9iCtepRTpsqUf6HC5PZrYsMYI5No1s6nxKg1IgIiVlXqZr5L4IM/0H9GJuYszsCKJk6OvA4kALoLumhjSs9O++PxF0dCBKa18HPOLun5vZVUQJ35XcfZyZLUYEI8cQjdLOLM6Ry+zM7rP3t0Y3NMWuNNX1+Tenc5WZUpkd7dK5lQanMpZSVGbWh5hfPNTd382UZp3jRWQmeR2i2kf17LaVxmVuvvxyr4+UFzKd6O48CDhZU/QK4iaijPaKRLW6fwBjMomuI4n+LdUA7j7ezG4lpmo9UuiDlTmzmXvz7Ai0Bia6+xNzmmqVLctr0RNmtKbclYZMMLEX8R15a36gkbd9XessfzuR+qYRESkaM+sCfEZUX5lK3F29nGhEODmz3Uwfjnl3bI4mKijt6dHNVxqxvHO7IdH/o86AYg53cDWVoAH9VqBoZhVEBbPR7n7gvOwrhZf3nruUKPZQRSQsv+ipaWF+MJK335HAUcAm7j6q0H+D1M3MFgSeJM7lAHe/Jy2f5X2Ydz73Bl5295GFPmZpelTiVIrG3YcCjxMByGnEXe2riTKC66SE9OydnWZ5H5YHEXdiH1YQ0vilL8fcub2Y+AJdIjf1Ll929CxvuYKQBlTHBUyz9N9KM+sKPAwsAfw9LbfZ7SvFkztvmffccsDaRLGBXsBZRM+m+9J2teV48z6HDyYaVZ6qIKS46vgs/JEobT6BaEy4R1pek90273z2A24HVivUcUvTpkBEisJ+rS8/kBiZew/YmugT8i3wP+BOMzvYUoMljy7q2S+/i4ED3H1gwf8AqXeZwKIjMSVgB3f/Zi6m6OVeEytbdPaWAkoXNfMRfXyuIooHrJ2ZIqdh9xJjZgv4zBXl+gEDgK+BIe7+FVEi/VhgIzO7F2qDkZZ5n8MXAf1yd9uleDLnpWNm2VDgaKLZ70FpOjTuUcyjjqDyEmAXd3+y4H+ANEkKRKQoMkP8bwGbm9lRaW7xZ8SdmPeJ7r2XAoPNbP/cvulL8yIiCHmosEcuDcnM+gIjgG2JPjFz2jb7BXoE8BKwWEMfo9SpmriI/TewjUfJzxbK0yk9ZnYdcEf6vVkKIjsTn7udcyOKHv12HiPKpK9vZi+k5dPSvv2AC4nP4QcL/XdI3czsUKLh73q5Ze4+jDiP7YCTzWy3tDz/5l7ue/Xhgh+4NFkKRKQoLDR39+HA2UAvM1ueCEB+IS5mTgB6EI3Tcl+cBwPXA/vpy68sjQSeA5YDKiFyPvI3ms3UkCPSnVz5A2Y3FW5O23sUi7g+JcTmkps1Ra40XQvskn5v7e4/E6MhNwKrWfTiASDl6j0OnAH8kpmGtyvxOby/PoeLK3dOMj4DugHHmtm6uYXu/jlxHpcDTrVoSpp7jqOA81FQKUWgZHUpCIumVz3Tw2fc/b3cxaRFI7QBxAfkW8SH4Zj8hLp0gdSb6PaqD8tGbnYJk8Q89auJRoXrufu3NnM35/wgJHcXT6+JP8hmrp7UDmjr7uPqWj+H/SpdVexKnpntR4w4r+LuY81sKeBAYE/gHnc/I7PtTOc0vTZ6uvvzBT5sycj7XOwKTHH34RaFYJ4iKgle5O5vpW22J/r5fA2clKZVLk0Emxe6+7+L8XdI06ZARBpcGsI/F/gEWJiYq9o3zV3NbXM58SW4iip1lL+8C9c/E52ZWwBPu/soM1udCE47ABunYGSmalipWMGFwIEKQv64vADvNCJpeXWiaMDz7n7nXOy3B/Eev04jIqXNzDYhgvj5gE1TMLIMcACwB/Bvr6PfS/77UArPzA4HXnf399Pji4CdgQWAUURvpQ+JqXWDgXuBV4AbgNfc/cK0nxGfuwtlbziIFJKmZkmDSkHINcAhwGZEQ7OFmPW1dzsxpLx+2k/1y8tYJgi5kJjisStwIvCQme3u7oOJRNnRwItmtnReEPJnooHe3xSE1I9MMHE2cATRK2RLYF2gv5l1zt+njtGpO4BhulAtLXVM3wF4lSi5+yPwipl1SDeBbiGmwx5tZgfm76RzW1wW1c1OAg43s85plKMvcS4PJUpnDyQqn21N3Bi4GngbWAq4LD1PrhfTNAUhUkwaEZEGY2a7Ex+I+2bvpprZZ8A7wPLEkPBtwP8BzxDXQ5sX/mil0NLUkHOB7dNUvQOIoGRnd388bbM2cCfwkbv3ztt/I3f/X4EPu2yl4H954AFi2sYzFr1cngMOd/db8qaCZEe1DiZGpxQYlpi887QpcQd8cu69Y2brENXOFgb+5O7j0sXupsDtKjhQesxsDeBm4E3ipt6X7n5JWldBBCY3AtsRAcjKxPl9MuVwaVRLSoZGRKQh9QQmAa3MrBWAmT1CdO0dQwwdn0dc9Ewj7tq00GhIk9EVeDQFIX2IZpZHuvvjZtbWzDq6+7tEYu0euZ1yyesKQv647J3yNLJRA1SmIGQn4GngmBSEtAF2MbMl093UbBByEQpCSlLmPF0CPERcoL5kZv82s7Xc/R3ibvp3afkS7v61u9/imd4hUjrcfRDQD1gP6AO0yaybCtwD3A/s4e4/uvtr7v6YCklIKVIgIvUu98Xl7v2JqRonAXub2WNEQnovdz/B3Q8heg8cYmbLEndee6UEdr02y1TmwmYlYKSZ9SCmAZ3o7temc78PsFO64P00e0GkL9H6k7lIvd7MTgSmA4tZNJS8BTjB3a9Lm3ch8riWz0zH6kckPKtYQInJ3tAxs1WJHIJtgY2ATYC1gDPNrKu7vw0cT1wTXJbdXyMipSnlh+wP/EzcIOiRWVcFfE9MxcrfT+dTSoou9qTeZT/o3P0w4AXibvdawD7u/nUm0PgW+BSY5O6/pCCk9m6rNH75QWXm9fEE0b35HaIhWu6Ctw2wE7Bs7oI3bz/5g7J3uS36DWwHfODu3xDT444A7nX3a9M2rYF/EoHKa2nZQsR52ltBSOnJBIvHEbkD/3H3N4HR7v4akZe1NpG/B/AGMfq4V3Z/KV3u/hGwfXp4TC4YMbP2QHfgm2Idm8jcUo6I1BszW5O4y92DmHb1lbu/ntZdQdyRu4C4wPm/dIH6GFAF9NEXX/nJm5++MVGoYDjwFVE97RYiIXof4H/EHbwriMaE62n0o2GlvJx1gf9z91PTstWJQgE7ElV2mhHVsxYDeng0K2yeRqnm8+hDISUi7z23EPF+2osom75d+txt4e5T04jWP4HV3X1s5jmaK/BvPFLOyEBgESInpApYgSixPC1bVEKk1MzSKEzk97DofH4qMBGoAI4GRpvZre5+lrv3T3PMj0vb30Mkqa8IrJqbjqWRkPKSuSC6lEigbE5MGRgP7Ea8ZmqA/xCjYz8APxFfoNN1QVS/UrJyL6AtERCuScwxvzu3jbsPNrMziZGq/dJ27wMnp3NSm+iqIKS05AUhfYERRCDyI3CEme3k0TV7atqlingvTsk+j95zjYu7DzKz3sDDQCeicMTdHn1ClJguJU0jIvKHmdmexBz/vwEvetSj70UM+e8GXODup6RtrwW2AFoCk4Hu6Y6NPizLSF5Z122Ai4nXw1dEguURxOjHRu4+3szWB5YAxhH18fUFWs9SKdZziTLZyxPByDNEQYk+wF/d/aW8fSpS8mvusQLDEpX3nruAyB84leghsTDwD6KZ3T7Ay8A0oiJdc2BL3TFv/FIFtAOBv+vmnjQWCkTkDzGzxYhKLPe5+1V56zoTX4Q7kqmoY2Y3E+UEN1YQUt5SNawNiSnnR2WWrwVcCXwBHOx5nbj1BVq/UhAygJii8wjx/juRmJZ1DpEbsCJxAfNqXjUtnYdGJBUdOIbI+/nQoyJh7vP4KOKGwA9EZaU1gC3cvVrvufKQC0h1PqWxULK6/FELEkPB7+QWZKqtDCMuNquIi1HS8r8BGygIKW9m1pKYinc4sFp2nbu/BzxL5BO1zN9XX6D1J41O3gCc7+4PADPc/UPgeaBd+u/5wBDgGov+LDW5nyIdtvwOZlZJBJfnp/fYEma2vZn9h7hTPog4122Ad9z9TykIqdC5Lg8q+CKNjQIR+aPaEgnIrXILMtMDzN0/IOb/b2RmzS2aLWU/LBWElKF0bqcRZUIfAVYxs/1S9aWcQUQQsmDhj7BJGU0UAljbzHplpuC0S//1VE3pCiIYecjMuhf+MKUeNCf68/Qws12J3kxHAk6U7V2fmKp1DzDAzLZO+00rwrFKA9E0O2lMlKwu8yxvyHcC8eW3GfBidp5ybniYuPv2RZpbni3tqw/LMpXOfXN3r0pJs48C/YH5U6GCSuICaTQwqnhHWv7cfViqjvUv4DQzGwMsSTQh3Nvdx6Tt/pfuqA8DPinaAcvvlt5vRxHV6LYiSjE/l87tWcAa7v6RmZ1LBB9PmdnW7v5cEQ9bRJow5YjI72ZmC7j7T6k07+HAbu7+cF7llrZEid7n3P0ClRFsWjJlXtsCDwKbE4HH+0QwsovmpxdGyhG4CuhI5Ijs5+4DU08Rz//3V2J642VmiwKtPPrC5Hr5PAmMcvd+aVln4nP7Wnf/rGgHKyJNmgIR+V3SsP81QGdiatYNxKjIYcDTRNnIFYhGhksDa2saVtOUCUbaELXuewBnEuUlc/PTp87xSaRemNmKxF3yDkQBiTfTct0gKEOpsd2GxOdyJ2DN7OewmbXMJbOLiBSDckRkruSq6OQS0YHPiY7oB7r710QFnruI4ORNYsrNQGB+YJ1cT4iCH7g0mMxrYY5SENLc3auAPYEviU7P25lZKwUhhePuXwAHEx2XzzGzjdJyBSHlaUXivVZDNKOcbma1U7IVhIhIsWlEROaJmS3t7t+koOJsYFNgV3f/NiWi9wRWIZLXBwMvpwtRVccqI3nT7xZ19+/mYp/sNK2HgC7AUe7+WAMfruRJ03KuIHq37Ofug4t7RNJQzGwF4Gv15hGRUqRAROZaqk9/DnAQcB+R7Pge8Km77z6H/TTXvIzkBSHHEYHnpe7+8W/sZ0DzdFe2FdEE8x/u/mWDH7TMwsy6Af2A45SfU/6UhyUipUhVs2RetCVeM2cQjbDeJLr33mZm+7n7bXXtpCCkvGSCkIuBfYnqV5PmtE8mB2F6CmgXc/e+DX6wMlvuPoRofKebBU2AghARKUUKROQ3Ze6knQUsTPR9GA/slx4PAbY0s6fdfWzRDlQKJnVM7wNsnXrFkHqErJAbGcl0+K1NhDazg4CTiWo9UiIUhIiISDEoWV1mkUtMT7+fClxiZuuli5X/AVOIJoX7EkmvWwJ7AOsV4XClOJYm5p1/YGZdzex4okHha2Z2FfzaRyYThBwMXAwc4O53FevARUREpDQoR0Rmy8x2Iaqu7AmMA14CLgSeAj539yPSdnsD6wDHKBGy/NRV2jU1KTwd+JjoSfEuUZxgFFEtbW13fz+z/UH8GoQ8WKhjFxERkdKlQETqZGaLA2OAtYggpA9wLJGc/h/iovJAd78vbz9VZSkjeYnprYBKd59gZvMD+xANCh8F/uvuI8xsZaKr8z7uPjTtdzAwgNTwsih/iIiIiJQcBSICzHrX28zaEUHH6e5+X6o9vyDRK6Q1sDrwNlH6c3QxjlkaVl4Qcgox9W494G7gGXd/Nhd4pul8bYnRkLbAFqlcaAXRTG2kRkJEREQkS4GIzMTMFnH379Pv9wNj3P3IvG32I6plGbCJmqGVNzM7lyjZfBQwnUg2N2B7dx+Z+oLsROQMLQys5+7TcoGMujeLiIhIXVQ1S2qZ2UnAvmY2CfgI6ArMMLNlgR/cfSKAu99mZg8AVZmEZJWGLENmtgrwF2Bnd3/VzP5EvC4OSUFIMyIo6Qi8D5ya696cm6KnIERERETqokBEsl4hko97ARXEFJveQAdgZTMbRPSLuMrdXwQ1yWoiWqQgZFfgVuDoFIy2IYKUp4nXRDXU9qRQnpCIiIjMkQIRqeXur6dfnwAws6eAG4FLgMXST3ciYMntoyCkTNRVHQtoDyxoZkcAZwMnuft1ad3qRBGDr9z93dwO6kkhIiIic0OBiMwk04SuOfA9MAN4N79RoToxlxczq3D3qen3Vu4+BcDd3zSzl4AricIFA9I2bYBTASemZImIiIjMEwUiMpPcHXF3n2Fm7xOByPrAw3nbKQgpA2a2gbu/nglCjga2MLPxwBOp0tWZwKLACWZWDbQDNiTyQtZMCemaoiciIiLzRJ3VZU6aAZXEBaeUGTM7A7g25X5gZscCZwCfEf1jjjezE1M/kD5EfsgeQE9gCLBGqo7VQkGIiIiIzCuV75U5MrO9gHuVfFx+zKw7cBHQEriDGPm6z93/a2aLEEHJhsCD7n5e2mchd/8h8xxqYCkiIiK/iwIRmSu64CwvmR4f3Yj8j+ZED5Cd3f2rtE0H4BQiQHnU3f+Z9xx1JbeLiIiIzBVNzZK5oiCkfGTzOdx9CNAfmAZ0AbbKbZcKFJwL/A840Mz2zT6PghARERH5IzQiItKEZIMQM+sDfO3ub5vZisAAIidogLvfn9lnCaKfzNUqUiAiIiL1RYGISBORnUplZhcCewGXAbe6+49507SuywYjmedQ2WYRERGpFwpERJoYMzsSOB3YBvjU3Sfn5YxcTgQjd7n77cU8VhERESlfyhERaULMzIB1iRGP94Dq3CqozRk5CliIKOErIiIi0iDU0FCkaZmPqII1GiCNglhqYFkJdHL3z81sF2BkMQ9UREREyptGRETKlJmtamaLp98vMrM13H0C8CzQw8y6wkzVr1YCTjezFd19eK5jenGOXkRERMqdLjJEylBqVvgSsK+ZXQscB+SSzP8LrAgcZGYrp+0XA84BlgS+yj2POqaLiIhIQ1GyukgZMbON3f2V9PsJwMlAK+Av7v5CZrvDgCOIruo/EsnpBqzj7tOyZX5FREREGoJyRETKhJldBVSY2RvuPg34LK36GVjbzD5z91xuyAAzG0xMx1oRGArc6e7TzayFGliKiIhIQ9OIiEiZMLMuRIPCaWbWCfiGuNlwLHAocANwk7t/O4fnUJ8QERERKQjliIiUgVT5amgKQvoCDwE7unu1u58H3AQcBOyXOqVjZg+a2SrZ51EQIiIiIoWiqVkijVwd+RyfAxOJZHRz9wfc/UwzcyIYWcfMFgE6E1OyRERERApOU7NEGrFsEJLK8U4hpmQtDtwJOHC9u9+ftjkUWBmoAA5VToiIiIgUiwIRkUYqjXZ4+v1CYBdgAWAUcCnwMnBr2vxad38gbdsyJbOjIERERESKRVOzRBqhvJGQ3sDeQD+gLbAKcDvQH+hLjIz0M7NKd787F4QAKAgRERGRYlEgItIIZYKQzYAtgcvc/cm0rAIYAdxI5IscBDwMrA3cXZQDFhEREcmjqVkijZSZdQBeAxYFLnb3czLr2gC3ANXuvq+ZrQQMU5NCERERKRUq3yvSSLn7WGAnYDywq5n1yKyrAr4Hlkq5JJ+7e42ZNS/S4YqIiIjMRIGISCPm7h8CO6eHx+SCETNrD3QHRnpm2FN9QkRERKRUaGqWSBkwszWAgcQ0rbeBycBywPruPjVbYUtERESkFGhERKQMuPsgoDfRyHB+4Hl3XysFIRUKQkRERKTUKBARKRPu/jExTaslsK6ZdUnLpxb1wERERETqoKlZImXGzNYEridK+J7h7kOKfEgiIiIis9CIiEiZcfcPgMOIfJGfins0IiIiInXTiIhImTKzVu4+pdjHISIiIlIXBSIiIiIiIlJwmpolIiIiIiIFp0BEREREREQKToGIiIiIiIgUnAIREREREREpOAUiIiIiIiJScP8P27NJk4VlmskAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate correlations - All Pitchers\n",
    "all_spin_xwoba_corr = apps_spin_xwoba_bu[\n",
    "  ['xwOBA_diff', 'pitches_per_app', 'Pre_Spin', 'spin_diff', 'bu_diff']\n",
    "  ].corr()\n",
    "\n",
    "# Plot the correlations - All Pitchers\n",
    "fig, ax = plt.subplots(figsize=(12, 10))\n",
    "att_heatmap = sns.heatmap(all_spin_xwoba_corr, annot=True,\n",
    "                          annot_kws={\"fontsize\":12})\n",
    "plt.title(\"xwOBA Correlations - All Pitchers\",\n",
    "          fontsize=24, fontweight=750, pad=15)\n",
    "plt.yticks(rotation=45, fontsize=14)\n",
    "plt.xticks(rotation=45, fontsize=14)\n",
    "\n",
    "plt.savefig('images/all_pitcher_corr.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't see any strong correlations of note among all pitchers. Is there a correlation amongst starters? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 110 pitchers that meet our criteria.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_name</th>\n",
       "      <th>appearances_june_7_later</th>\n",
       "      <th>appearances_june_7_before</th>\n",
       "      <th>total_apps</th>\n",
       "      <th>pitches_thrown</th>\n",
       "      <th>pitches_per_app</th>\n",
       "      <th>Pre_Spin</th>\n",
       "      <th>Post_Spin</th>\n",
       "      <th>spin_diff</th>\n",
       "      <th>Pre_xwOBA</th>\n",
       "      <th>Post_xwOBA</th>\n",
       "      <th>xwOBA_diff</th>\n",
       "      <th>Pre_b_units</th>\n",
       "      <th>Post_b_units</th>\n",
       "      <th>bu_diff</th>\n",
       "      <th>pct_spin_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Alcantara, Sandy</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>1925</td>\n",
       "      <td>91.7</td>\n",
       "      <td>2286.956007</td>\n",
       "      <td>2225.974428</td>\n",
       "      <td>-60.981579</td>\n",
       "      <td>0.30994</td>\n",
       "      <td>0.335116</td>\n",
       "      <td>0.025176</td>\n",
       "      <td>24.443001</td>\n",
       "      <td>23.584321</td>\n",
       "      <td>-0.858681</td>\n",
       "      <td>-0.026665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Alzolay, Adbert</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>1454</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2441.722557</td>\n",
       "      <td>2322.7056</td>\n",
       "      <td>-119.016957</td>\n",
       "      <td>0.38456</td>\n",
       "      <td>0.438252</td>\n",
       "      <td>0.053692</td>\n",
       "      <td>27.367482</td>\n",
       "      <td>26.022544</td>\n",
       "      <td>-1.344939</td>\n",
       "      <td>-0.048743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Anderson, Ian</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>1647</td>\n",
       "      <td>91.5</td>\n",
       "      <td>1893.712062</td>\n",
       "      <td>1866.922456</td>\n",
       "      <td>-26.789607</td>\n",
       "      <td>0.377605</td>\n",
       "      <td>0.346729</td>\n",
       "      <td>-0.030876</td>\n",
       "      <td>21.14057</td>\n",
       "      <td>20.782563</td>\n",
       "      <td>-0.358007</td>\n",
       "      <td>-0.014147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Anderson, Tyler</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>1579</td>\n",
       "      <td>87.7</td>\n",
       "      <td>2338.880855</td>\n",
       "      <td>2169.008375</td>\n",
       "      <td>-169.87248</td>\n",
       "      <td>0.38673</td>\n",
       "      <td>0.350449</td>\n",
       "      <td>-0.036281</td>\n",
       "      <td>27.245865</td>\n",
       "      <td>25.039594</td>\n",
       "      <td>-2.206271</td>\n",
       "      <td>-0.07263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Arrieta, Jake</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>1327</td>\n",
       "      <td>78.1</td>\n",
       "      <td>2261.345094</td>\n",
       "      <td>2113.661905</td>\n",
       "      <td>-147.683189</td>\n",
       "      <td>0.420189</td>\n",
       "      <td>0.440085</td>\n",
       "      <td>0.019895</td>\n",
       "      <td>25.885944</td>\n",
       "      <td>23.762797</td>\n",
       "      <td>-2.123147</td>\n",
       "      <td>-0.065308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>Wheeler, Zack</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>2117</td>\n",
       "      <td>100.8</td>\n",
       "      <td>2343.539474</td>\n",
       "      <td>2356.008879</td>\n",
       "      <td>12.469405</td>\n",
       "      <td>0.328299</td>\n",
       "      <td>0.296471</td>\n",
       "      <td>-0.031829</td>\n",
       "      <td>25.027936</td>\n",
       "      <td>25.361436</td>\n",
       "      <td>0.3335</td>\n",
       "      <td>0.005321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>Wood, Alex</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>1494</td>\n",
       "      <td>87.9</td>\n",
       "      <td>2078.672302</td>\n",
       "      <td>1959.417931</td>\n",
       "      <td>-119.254371</td>\n",
       "      <td>0.369228</td>\n",
       "      <td>0.358115</td>\n",
       "      <td>-0.011113</td>\n",
       "      <td>23.639601</td>\n",
       "      <td>22.294317</td>\n",
       "      <td>-1.345284</td>\n",
       "      <td>-0.05737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>Woodruff, Brandon</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>1899</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2378.997333</td>\n",
       "      <td>2300.074935</td>\n",
       "      <td>-78.922398</td>\n",
       "      <td>0.321418</td>\n",
       "      <td>0.325767</td>\n",
       "      <td>0.00435</td>\n",
       "      <td>25.871776</td>\n",
       "      <td>25.121799</td>\n",
       "      <td>-0.749977</td>\n",
       "      <td>-0.033175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>Yarbrough, Ryan</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>1722</td>\n",
       "      <td>86.1</td>\n",
       "      <td>2147.625621</td>\n",
       "      <td>2017.573427</td>\n",
       "      <td>-130.052194</td>\n",
       "      <td>0.365902</td>\n",
       "      <td>0.349237</td>\n",
       "      <td>-0.016665</td>\n",
       "      <td>27.005376</td>\n",
       "      <td>25.4365</td>\n",
       "      <td>-1.568876</td>\n",
       "      <td>-0.060556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>deGrom, Jacob</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>1226</td>\n",
       "      <td>81.7</td>\n",
       "      <td>2388.131752</td>\n",
       "      <td>2439.905782</td>\n",
       "      <td>51.774029</td>\n",
       "      <td>0.30225</td>\n",
       "      <td>0.339645</td>\n",
       "      <td>0.037395</td>\n",
       "      <td>24.795363</td>\n",
       "      <td>25.702881</td>\n",
       "      <td>0.907517</td>\n",
       "      <td>0.02168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           player_name  appearances_june_7_later  appearances_june_7_before  \\\n",
       "6     Alcantara, Sandy                         8                         13   \n",
       "13     Alzolay, Adbert                         8                         10   \n",
       "16       Anderson, Ian                         7                         11   \n",
       "18     Anderson, Tyler                         7                         11   \n",
       "21       Arrieta, Jake                         6                         11   \n",
       "..                 ...                       ...                        ...   \n",
       "492      Wheeler, Zack                         9                         12   \n",
       "504         Wood, Alex                         8                          9   \n",
       "506  Woodruff, Brandon                         8                         12   \n",
       "510    Yarbrough, Ryan                         8                         12   \n",
       "518      deGrom, Jacob                         6                          9   \n",
       "\n",
       "     total_apps  pitches_thrown  pitches_per_app     Pre_Spin    Post_Spin  \\\n",
       "6            21            1925             91.7  2286.956007  2225.974428   \n",
       "13           18            1454             80.8  2441.722557    2322.7056   \n",
       "16           18            1647             91.5  1893.712062  1866.922456   \n",
       "18           18            1579             87.7  2338.880855  2169.008375   \n",
       "21           17            1327             78.1  2261.345094  2113.661905   \n",
       "..          ...             ...              ...          ...          ...   \n",
       "492          21            2117            100.8  2343.539474  2356.008879   \n",
       "504          17            1494             87.9  2078.672302  1959.417931   \n",
       "506          20            1899             95.0  2378.997333  2300.074935   \n",
       "510          20            1722             86.1  2147.625621  2017.573427   \n",
       "518          15            1226             81.7  2388.131752  2439.905782   \n",
       "\n",
       "      spin_diff  Pre_xwOBA  Post_xwOBA  xwOBA_diff  Pre_b_units  Post_b_units  \\\n",
       "6    -60.981579    0.30994    0.335116    0.025176    24.443001     23.584321   \n",
       "13  -119.016957    0.38456    0.438252    0.053692    27.367482     26.022544   \n",
       "16   -26.789607   0.377605    0.346729   -0.030876     21.14057     20.782563   \n",
       "18   -169.87248    0.38673    0.350449   -0.036281    27.245865     25.039594   \n",
       "21  -147.683189   0.420189    0.440085    0.019895    25.885944     23.762797   \n",
       "..          ...        ...         ...         ...          ...           ...   \n",
       "492   12.469405   0.328299    0.296471   -0.031829    25.027936     25.361436   \n",
       "504 -119.254371   0.369228    0.358115   -0.011113    23.639601     22.294317   \n",
       "506  -78.922398   0.321418    0.325767     0.00435    25.871776     25.121799   \n",
       "510 -130.052194   0.365902    0.349237   -0.016665    27.005376       25.4365   \n",
       "518   51.774029    0.30225    0.339645    0.037395    24.795363     25.702881   \n",
       "\n",
       "      bu_diff  pct_spin_change  \n",
       "6   -0.858681        -0.026665  \n",
       "13  -1.344939        -0.048743  \n",
       "16  -0.358007        -0.014147  \n",
       "18  -2.206271         -0.07263  \n",
       "21  -2.123147        -0.065308  \n",
       "..        ...              ...  \n",
       "492    0.3335         0.005321  \n",
       "504 -1.345284         -0.05737  \n",
       "506 -0.749977        -0.033175  \n",
       "510 -1.568876        -0.060556  \n",
       "518  0.907517          0.02168  \n",
       "\n",
       "[110 rows x 16 columns]"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create dataframe of just starting pitchers\n",
    "healthy_starters = apps_spin_xwoba_bu[\n",
    "    (apps_spin_xwoba_bu['appearances_june_7_later'] >= 5)\n",
    "    &\n",
    "    (apps_spin_xwoba_bu['appearances_june_7_before'] >= 5)\n",
    "    &\n",
    "    (apps_spin_xwoba_bu['pitches_per_app'] >= 75)\n",
    "                                ]\n",
    "print('There are',\n",
    "      len(healthy_starters.index),\n",
    "      'pitchers that meet our criteria.')\n",
    "healthy_starters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate correlations - Starters\n",
    "starters_spin_xwoba_corr = healthy_starters[\n",
    "  ['xwOBA_diff', 'pitches_per_app', 'Pre_Spin', 'spin_diff', 'bu_diff']\n",
    "  ].corr()\n",
    "\n",
    "# Plot the correlations - Starters\n",
    "fig, ax = plt.subplots(figsize=(12, 10))\n",
    "att_heatmap = sns.heatmap(starters_spin_xwoba_corr, annot=True,\n",
    "                          annot_kws={\"fontsize\":12})\n",
    "plt.title(\"xwOBA Correlations - Starters\", fontsize=24, fontweight=750, pad=15)\n",
    "plt.yticks(rotation=45, fontsize=14)\n",
    "plt.xticks(rotation=45, fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again there aren't any strong correlations, so we'll take a look at relievers (pitchers who have appeared in at least 5 games before and 5 games after Juen 7 and average less than 50 pitches an outing)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 241 pitchers that meet our criteria.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_name</th>\n",
       "      <th>appearances_june_7_later</th>\n",
       "      <th>appearances_june_7_before</th>\n",
       "      <th>total_apps</th>\n",
       "      <th>pitches_thrown</th>\n",
       "      <th>pitches_per_app</th>\n",
       "      <th>Pre_Spin</th>\n",
       "      <th>Post_Spin</th>\n",
       "      <th>spin_diff</th>\n",
       "      <th>Pre_xwOBA</th>\n",
       "      <th>Post_xwOBA</th>\n",
       "      <th>xwOBA_diff</th>\n",
       "      <th>Pre_b_units</th>\n",
       "      <th>Post_b_units</th>\n",
       "      <th>bu_diff</th>\n",
       "      <th>pct_spin_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abreu, Bryan</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>652</td>\n",
       "      <td>21.7</td>\n",
       "      <td>2439.801762</td>\n",
       "      <td>2415.792929</td>\n",
       "      <td>-24.008833</td>\n",
       "      <td>0.349086</td>\n",
       "      <td>0.364821</td>\n",
       "      <td>0.015736</td>\n",
       "      <td>26.809219</td>\n",
       "      <td>26.586252</td>\n",
       "      <td>-0.222967</td>\n",
       "      <td>-0.00984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adams, Austin</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>45</td>\n",
       "      <td>636</td>\n",
       "      <td>14.1</td>\n",
       "      <td>2864.226629</td>\n",
       "      <td>2781.925795</td>\n",
       "      <td>-82.300834</td>\n",
       "      <td>0.297703</td>\n",
       "      <td>0.301828</td>\n",
       "      <td>0.004125</td>\n",
       "      <td>32.598137</td>\n",
       "      <td>31.932238</td>\n",
       "      <td>-0.665899</td>\n",
       "      <td>-0.028734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Alcala, Jorge</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>41</td>\n",
       "      <td>604</td>\n",
       "      <td>14.7</td>\n",
       "      <td>2346.144737</td>\n",
       "      <td>2387.246667</td>\n",
       "      <td>41.10193</td>\n",
       "      <td>0.398377</td>\n",
       "      <td>0.418228</td>\n",
       "      <td>0.019851</td>\n",
       "      <td>25.225345</td>\n",
       "      <td>25.579069</td>\n",
       "      <td>0.353724</td>\n",
       "      <td>0.017519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alexander, Scott</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>259</td>\n",
       "      <td>14.4</td>\n",
       "      <td>2169.885057</td>\n",
       "      <td>2214.152941</td>\n",
       "      <td>44.267884</td>\n",
       "      <td>0.243658</td>\n",
       "      <td>0.408563</td>\n",
       "      <td>0.164905</td>\n",
       "      <td>24.058878</td>\n",
       "      <td>24.587266</td>\n",
       "      <td>0.528388</td>\n",
       "      <td>0.020401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Alexander, Tyler</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>778</td>\n",
       "      <td>26.8</td>\n",
       "      <td>2204.988439</td>\n",
       "      <td>2129.361111</td>\n",
       "      <td>-75.627328</td>\n",
       "      <td>0.398459</td>\n",
       "      <td>0.368374</td>\n",
       "      <td>-0.030086</td>\n",
       "      <td>25.22101</td>\n",
       "      <td>24.324016</td>\n",
       "      <td>-0.896994</td>\n",
       "      <td>-0.034298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        player_name  appearances_june_7_later  appearances_june_7_before  \\\n",
       "2      Abreu, Bryan                         9                         21   \n",
       "3     Adams, Austin                        21                         24   \n",
       "5     Alcala, Jorge                        18                         23   \n",
       "7  Alexander, Scott                         5                         13   \n",
       "8  Alexander, Tyler                        13                         16   \n",
       "\n",
       "   total_apps  pitches_thrown  pitches_per_app     Pre_Spin    Post_Spin  \\\n",
       "2          30             652             21.7  2439.801762  2415.792929   \n",
       "3          45             636             14.1  2864.226629  2781.925795   \n",
       "5          41             604             14.7  2346.144737  2387.246667   \n",
       "7          18             259             14.4  2169.885057  2214.152941   \n",
       "8          29             778             26.8  2204.988439  2129.361111   \n",
       "\n",
       "   spin_diff  Pre_xwOBA  Post_xwOBA  xwOBA_diff  Pre_b_units  Post_b_units  \\\n",
       "2 -24.008833   0.349086    0.364821    0.015736    26.809219     26.586252   \n",
       "3 -82.300834   0.297703    0.301828    0.004125    32.598137     31.932238   \n",
       "5   41.10193   0.398377    0.418228    0.019851    25.225345     25.579069   \n",
       "7  44.267884   0.243658    0.408563    0.164905    24.058878     24.587266   \n",
       "8 -75.627328   0.398459    0.368374   -0.030086     25.22101     24.324016   \n",
       "\n",
       "    bu_diff  pct_spin_change  \n",
       "2 -0.222967         -0.00984  \n",
       "3 -0.665899        -0.028734  \n",
       "5  0.353724         0.017519  \n",
       "7  0.528388         0.020401  \n",
       "8 -0.896994        -0.034298  "
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create dataframe of relievers\n",
    "healthy_relievers = apps_spin_xwoba_bu[\n",
    "    (apps_spin_xwoba_bu['appearances_june_7_later'] >= 5)\n",
    "    &\n",
    "    (apps_spin_xwoba_bu['appearances_june_7_before'] >= 5)\n",
    "    &\n",
    "    (apps_spin_xwoba_bu['pitches_per_app'] <= 50)\n",
    "                                ]\n",
    "print('There are',\n",
    "      len(healthy_relievers.index),\n",
    "      'pitchers that meet our criteria.')\n",
    "healthy_relievers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate correlations - Relievers\n",
    "relievers_spin_xwoba_corr = healthy_relievers[\n",
    " ['xwOBA_diff', 'pitches_per_app', 'Pre_Spin', 'spin_diff', 'bu_diff']\n",
    " ].corr()\n",
    "\n",
    "# Plot the correlations - Relievers\n",
    "fig, ax = plt.subplots(figsize=(12, 10))\n",
    "att_heatmap = sns.heatmap(relievers_spin_xwoba_corr, annot=True,\n",
    "                          annot_kws={\"fontsize\":12})\n",
    "plt.title(\"xwOBA Correlations - Relievers\",\n",
    "          fontsize=24, fontweight=750, pad=15)\n",
    "plt.yticks(rotation=45, fontsize=14)\n",
    "plt.xticks(rotation=45, fontsize=14)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again no strong correlations, so lastly I'll take a look to see if the top 50 pitchers by loss of b units who meet either our starter or reliever criteria have been impacted by their loss in spin. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "351"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pitchers who meet starter or reliever criteria\n",
    "starters_relievers = apps_spin_xwoba_bu[\n",
    "    ((apps_spin_xwoba_bu['appearances_june_7_later'] >= 5)\n",
    "     &\n",
    "     (apps_spin_xwoba_bu['appearances_june_7_before'] >= 5)\n",
    "     &\n",
    "     (apps_spin_xwoba_bu['pitches_per_app'] >= 75))\n",
    "    |\n",
    "    ((apps_spin_xwoba_bu['appearances_june_7_later'] >= 5)\n",
    "     &\n",
    "     (apps_spin_xwoba_bu['appearances_june_7_before'] >= 5)\n",
    "     &\n",
    "     (apps_spin_xwoba_bu['pitches_per_app'] <= 50))\n",
    "                                ]\n",
    "len(starters_relievers.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pitchers who lost the 50 most b units \n",
    "top_50_bu_loss = starters_relievers.sort_values('bu_diff')[0:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyIAAAKuCAYAAABdSKhJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAC4FElEQVR4nOzdd3gU5drH8e9NKAkdBAEpYqGKYkfFgqIe7A2l2LD3gg3bUY+9oceC2MWCoIJi74oFBdHXTu8d9VCkhECS+/1jZuNmsilAspvy+3DNRWbmmdlndnZn556nmbsjIiIiIiKSTNVSnQEREREREal6FIiIiIiIiEjSKRAREREREZGkUyAiIiIiIiJJp0BERERERESSToGIiIiIiIgknQIREUkKM7vFzDxuGpvi/AyL5GdYKvMjUhQzaxv5vLqZtU11vlLFzAZE3os5qc5TjK4tIiVXPdUZkMrDzNoAA4ADgXbAFkA28BfwM/Ax8IK7rypiH8OA0wtZnQ2sBuYDPwGvuvs7Jcxbi3C7tMiq19z9pJLso6TMrBFwKsH7sDPB+5ABrASmA+OA0e7+bWm+rgQ3J0DbuEVj3X1sSjJTBYQ3f1tvxi4OrCjnx8y2A84j+F5vD9QFVgH/A/4AfgV+BMa5+2+pymdpM7OPgYOBWcDvwFFxq3939y7FbH8g8Flk8b/c/aNSzWgFY2a3RBYNc/c5KchKot/due7eNhV5kapHgYhsNjOrCdwDXATUSJCkLsHN4THArWZ2jbs/swkvVR1oGE47Aqea2QfAce6+rpht+1EwCAE4yszqu/vfm5CffMysGnAtcB3BMUdtEU57AVea2QPufuXmvq7kMwA4ILJsbPKzIZWJmZ0JDAHSI6sahdP2wD7hsg+BXsnLXdkxs/r88316C/iW/IHIDma2o7v/WsRu+kXmFwOfll4uK6ybI/NjgTnJz4ZIaqlqlmwWM6tFUNJxOYmDkKjGwNNmdncpZaEXwc1/cU4pZHk60HtzM2FmNQh+qO8gcRCSyBab+7qyWa4CtombrkptdqQ8MrPuwFMUDEKSbQH5P6/bhMvK0r/457r+NsE1LvrQpn9hG4fXxRMii0e6e06p5bB80rVFpIRUIiKb61Fg/8iy34E7CapP1QR6AtcTBCExg8zsZ3cfUcz+FwL7hn/XInjqOISgqlNMP+CWwnZgZp2AXYp4jVOAZ4vJR3EeA46ILPsbeITgCekSoB6wE0HJ0FFISrn7XwTVBmXT7Uvi35HZkfmHgP8mSLektDNUBq4m/0O7mcBtwC8EVUUbA50JqmxFrwGlxt2zSf4T89h1aiXwlbtvMLPRwBlxafoSlAIn8i/yX/cBXirdLJY/uraIbAR311SOJ6ANwY+Ax02nRdJkANMiae4L190dWX5PZNtDIuuvjqzvH1n/Qdy6LkBuZP14ICPBcXQAlkXSzgdqRtINi6SZk2Bfj0fSrCvmPbwzkv5DYEPcfA7QajPOUbfI/p3gRmzbIrZpD5xRyLqOwMMENzorgPXAUoLqDJcDdQvZ7pZIHsaGywcA34T7cqBHIe/1MIKbyoHA/xHUf3egbeR1moWv9Q3Bj+164E/gc+ASIH1j8he3viZBPeUhBFVAZhN89jeEn50fCG5oOyfY99gE5yDRNCdumwLHX0i+awJnAu8QBMZZBEHmFOBpoFsR5zn6+j2AJsBgghvarPDcDge2K2Qf1QiC5XcIvjPrwmk+8B3wBMGNYdNUX6+KOO5bikjbGrgLmEjQ3iL2efoa+DfQpJDtBiQ6twTXtPfDfa0leCByCZC2icfyR+R19isibS1gz7LIK0H11uj7Gv1uzomsHwDUIagGNAnIJPguvQXsUsxxpxF8v52gFCO2/KAE+dinkH0Mj6SblCBNA4ISg8/C78L6MI/fEjzAarAx57+QtIcQBEAzCYLHTGAeMBo4EahWzHvRiCAg/YQgeM4Clofv6bPAEZH0wyJ5Gxa3riTXqdj1+LzIsvGF5K8x+X/TcoFtNuIzHs1voe9lEfuoC1xGUENiSXgeVxD8jj0CdCpi2/rAIOArgu/+eoLfn5nAF8B9BCVr0fuFCndt1JTg/Kc6A5pKcJLgtMhF4q/4L1b4JY1f/wtQK1x3WGTdV5F93xJZPyayfkhk/bVx6x5LcPHcqYjjGJgg/UmRNMVeEIGhkTRLinhNo+CP85EEP/7xy67ZjPPzaoLj2nsT9mPAfwgCo6J+oBaR4Ic/wbn8AnghwfY9CnmvXyb4oY2mbxv3GqcAa4rJ33QS/OgkyN/YyPrmxew3Nm0ALoxsO7aE286J2yZ6/MMS5LkjMLkE+32SyI9kuH003UAK3tjGpj+J3DwQ/NC+U8JjG5Dqa1URx31LIenOI7ipK+q4VgLHJNh2QPTcElTTLGw/7yQ6RyU4lvWR/Ry+CfvY7LyyaYHILQTfx0SvsZYEQVPcvvaNS3ty5DM5P7KvRxJsX5vgpj8+3fWRNIeGn/uizv8SoHtJ3tMEaeoDbxazfyd4qLJVIe/D8QRBR1Hb/xTZZlhk/bC4dSX5Lnu4j7oUfBCZ6Np6RiTNFxv5+Yzmt8B7Wcz2+xA8pCnqeHIJft8ssm0LgoCjJO9J27jtKuS1UVOCz0+qM6CphCcKRkW+WCPC5XsS9CYVW55FXDAQXojj12cS9wNH8PQifr9/RF7358j6vePWTY2s+7GYY9iCgiUoj0fSRC+ICwh+gNsSlCKcTsEb4eeKeM39I2n/R1DnORrc/bKJ5yWNgj9SX2/ivm4q4UXVCZ7Id45sf0skTXYh2/Yo5L0uLH3bMP2JCc5fYdMCoHkx+RsbWV/SQMQJgrVd47YdW8Lt5hTxWRuWID/F/bjGT88kOKfRNMW9f69Etj9pI15/QKqvU0Uc9y0J0kS/g0VNGwh62IrffkCCNMXt5+5NOJboTfcqgie8RwJblnAfm51XNi0QKe7zNqGIPN/DP9eFxpF190b2s5RIKQ5Bla1oXraOW9+d4oPQ2JToehd9T+dE1qcBH23EZ+xnoE5kH8dQ/IMhp4wCkTD9o5Hl9yY4V9Eb8rM28jMeze+cjdh2h/D8lPS4/h3ZPtEDzcKmtnHbVchro6aCkxqrVxznkb8+dV8zO46gWDi+N6gb3f2X2IwHvUH9GLc+HdgVwMzSCHpwitfUzNqH6+sTVL+KWQt8H66rThAYxPuhqANw9/9RsO5456K2AVqG28wmCHyGETxpi5lK0Y3Vo43UR7v7BmAMQRFuzI5mtlMxeUmkDUEvXvE+39idmNk2BNVQ4k0CjiboAvgKgh/tmHoE1beKkkbw438XQRuZrsC5FF4vP40gUB1E0CvZbuHrrjazugQ/GBaXfjRBNY2OBHXJf45b15Kg4f7GcIL2RXcQ3ADsRfAZ25WgatTcuLTVgIvj5vsSNAqdENnnQ+RvNLovJXcHsFUkf3cAuxNU9fgykv5MMytu/wa8QfAAYT/yfzcBjg17oYvZL7L+RYKqgO0Izk8/glLLOcW8brkSXlv+G1m8EOhD0I7qLIInwTHVgaHhNasw1Qm+05cRfNZPIP9nBuAKM2u2kdl9KzJfl+Cz9zaw1MzmmNmLZnZC2Di7JMoqr1FGUN1lf4LPTbTL3D3NbOtCto21Dxnn7ssi66LtPLYkaAsYL9pb1tfuPhfyehh8kqDaY8znwOFAJ4LugsfGratHEPxtjP4E39N4rxNUj9yLoIpvvJ0ISiwJ81iHoGpP/H3SBoLqzrFr08EE1+G1G5Gv2LUoqh+JG7g/Fkl3avj7G8tnffIfZybw2kbkZ3M9THB+YrKAKwl+t44kuKbH+3f4excTvcZdQ3Au2hOUtJxDUMVvZSRdpbw2VkmpjoQ0lXwiuEjHR/nRp2pfkKCuK3B/JN0V4fJd4pYtjfv7jHB9r8h2H8ftc0sKPnW4swTH8G1km0mR9cMS7LewaSWRp6SRfdWiYLuUA+PWj46sK/CkqQTHs2eCfJ2/Cfu5NbKPTKBZJM1lCV5rm7j1tyRY/58iXjPRe11Yu5XTI+m+oWAR+7aRNFlA7SLyN3Yj36PjI9tPTZBmbCTNLRtx/MPi1mUQ3CjGr38gwedrcSTN85E00fd3KnFPjgmCrGiaLnHro9UQE7ZHIbjhrFPYsSZ7SnBMt0TWn5kgTddImmMSpIn//g5IsP6iyD66FJemBMfSlGAMjZJck2ZSsmpEG51XNq1EZDlQL279Fgn2cWSC/G4Xt/6qQt6XXyL7if/+NKRgace5cesPiKybS8GqaLUJroPx6bYr4j2dE9n+y8j6Hyh4zXojmo+4dYlK7M4p5L1oUNJrSxHfkR5FfAbHRtIeFbcu2o5z+CZ8X6P5nVPC7aLXfAcujaRpluA8/idufXzV15VAjUJeq1b8OirotVFTwUklIhWIu79H8IQmJr63mr8JGrHnJtj0i8h898j/kP9pU6L1ifaTavWBz8zs6kLWH0HQyDBmMfmPYWQkfb/wSV0qRJ+kv+/uSyPLnivBdvHWk7inosIsJniqlEj06dPeQG786MEEN2HxagJ7bMTrY2YNzOxKM/vYzBaY2dq4/Y+OJN8q0T5KyR4EP3zxhsXPuHsWEO31rbgSkWc9f9el0xKkaRj394+RdW+b2XNmdrWZHRV7mu2BNcW8dj7hSN2FTSXtgnpTRd+nH93958iytwiqUha1XVS+z68HAwv+XyTNRn0m3f1PgifgLxJUUyrKtsCHsVLlYpR6XhMY6XEDyHpQKh19Txsm2C6+V7+3C9l3tFTkODOLdXF8AvlLO7LI/5Q+ej1pA2RFridrKNhlcolKNMMSg2hp//Me3pnGiV5T25hZ6/DvaG+Qcwg6pyjA3aNP60vb0Mh8fK9l0e6RXyjjvMRLdD6Gxc+Ev2PvF7Fd/DWuPvCLmT1iZheZWU8z2yLcT5YHtRkSbQeleG2U5FIgUvFcScEifAieWiVaDkHRfHyAsnf4/z5xy57jn+4GYwFI9CITfxMfLaqHoJSkONE0xXVxONfdzd2NIPBqR1BMG+/eQqpVRatlvRoJ1N4haEwZ04qg2H5j/JlgWcuN3AcE7RHizY4m8KCa3fLI4hZF7HO+u0fTF2WSB12EJrKpN/3R4yqUmXUh6InqfoIqDy3J301zVFneLCfKd4FzQsFi/6LOBwQNh+NlJkgT/4DhJYIRu2OaEjwJvpfgRn2Omc0Kf3xLWi0oZnYR02aPrVOMknzenaBno3hFvb/LPPHApNHr4kZXd3L3P9z9NIJrxFnA8yQOIiHopeqKYnZZZnmNiH7eoOBnLlH3y7FAZLq7Ty1k3y+T/3elPkGpPRSslvVe5FpU1teTxhQc16ok31/45zMWzeNvCQKZZHmd/FVqjzSzpmZWm/yDZy4i6HAkWaLno7DP9ZzIfPz3+A7yj03TkaDq46MEx/KnmY0Pq6LHK8troySRApGKpy2JL8aFPily9xXkr7/fIqyjGQs45rr7QoLqNgAdzKw5QbWjmEyC7vBi+8ym4A/xbkVlPHyyEa0bO6mobeK5e467zyB4GvRHZHX8EyLMrCH//CjGXBZ54raWgjezhQ18WJh5BF0UxjtwI/dRVhaVcfqSKCqQiHqejQhcKqh8AbwXM7Cbu68leHBwI8GPbqIboW0IfnyjdcklUGo3j+6+1N2fdfcB7t6B4IbqGoJqsvG6bepLbFYGC0r0wKjIz5yZNeCfEovCSkNw9wUULCXvH/529IgsL62xQzbmelJphCUB8aUxNQh+qw4jf5vJl4q7ppQ37v47QTupISQeoNMIvk+vm9nJcdvp2lhJKBCpQMKo/iUKVhkBOM3Mji9i8+gPRh+C4nCAcZH/DbiI/Be48WFVlHifRuZ3NrMdi8jDaeRv7AxB3/EbJbwoR58cbh+ZP4nE71NxToirXlCSvOQQ9DwWr7uZRasFFCfagLxAY8bwBqFRZPHiIvaZqJpeUYpKH83fGAqO8pxoGlWSFw4D410ji18hqB6xfbivASXZVylJ1KA/UQPTtpH5os7HJnH3Ne5+h7vvRPDUOdYQ8wny//ieGavGUAGU5PNu/HONiinq/W0cNtyNahuZj1Z53GTuvsTd7yNSHYX8185Ekp7XjdCL/KOpFyUaYBwBnE3+DlRWAO9G0kXP//eU7Hry3+IyH1pGweCwJN9f+OczFn0w0yX8TKbKE+QPIs8gtdWyoOB5LOnnOt/32N3nuPvF7t6aoBRwX4LPUbRzhWsi21XGa2OVo0CkYvkPQU8UMdFqC08U0ctKNBC5NO7vbyL/QxCIFLU9/NNYLN6TZlbgqVVYZzraK9QCghvajRLWX28XWRx9CrSxJRsx9Ql6qtoYgxMsG15EbzSY2fZmNiBu0deRJIclOJcDKCi6XVn5KjK/P5Ad/oAUmAi6ON3F3VcX2FNiiarcnO/uX7n7zHCfXUuwn+jNx6Y+Qf2e/L2UQeT9N7NaJOgZaBNfL6Gw+kXeTZ27r3b3/3P3ke5+PvlLOqtR8HtRqFiVx0KmYaV3FAlF36ddzCx6fo8maFxd1HZRp8bPmFlnCga4E0uUw3/28VxY57yo38s6kfloiW0ipZ7XUhK7/i2n+Pd7FPl7H0wneEId77UED7Gi15OuQP0irid/EXQdv6IkBxCW2I+PLD49QSBxRmR+nrvPD/+O9orXlqCThQLCh0QbK1oNtshrVVgCFR8Y7kj+KpQ/hKULyZTo8zEgfib8HTussO3CErQ8YTXIce7+DMEDxXgd4rYrs2ujJFeiuqFSDpnZPuR/GpBJUI/+DoLxHSAYsflp8jc0jPmKIGiIXYjjb/xiJSHfEzRwrknBJ+8FAhF3/9XMniF4chGzF/Cdmd1F0JisJkG3jjck2Ocgd1+fIK/xqptZ2/DvNIKnWtdTsIFl3kUnDACiVdV6kLhtDeH+zombP4VgkMIScfcJZvY0+d+HbYGfzexhgpHc/yCoBtaF4If+WIK2LsPC9MOA6/jnO5kOfGpm1xIEnAcRdBsZ71N3T1TvuSyMIhg4M3Zj2Bj40szuIfjcrCKoo7sT8K9wmkDQK01JJGordIeZPUHwGTqJoNew4kTb7BxjZm8RPN10YEVJbmbcfa2ZDSf/jcflZraWoL52I4JxX6JVyZ4qQR43Rh/gOjMbQ/Adnk7Qs0ysbniXSPqK0ihzFEFboPhrwrtmdgVBLzq7Aw9EtplKwZvDqPvDm5OxBN/BByPrN1Cw04Pi7E1wc7XQzEYTXC9nEHzmtyS4Gewf2aa4fJZVXjdL2Mg7dtP4fhFtxoCg3ZqZvc0/v0FQsCQ6UbWsLwjOc6dwvgbwsZndS3CTuozgs9GF4PfjqHBZtHOIojxF/kbxuwKjzOwhguDpDILrcHSbmNEE17z4do2Pmdm2BA/QVhC0YzuS4HdvY7oGh+BaFf87fI6ZLeSf9hJL3H1dZJuhkTzHt30ozdKQ+N/dRNa7+yJ3n2VmnxH8PsXcG36OPiV4f+4lf6cDG8hfgviombUjaNcxkaA9yRqC35PzIq8bf32rrNfGqsfLQdddmoqeCG5gZ5C/q7qrwnVNKThSc2FdDEa7W3SCi158d6LR7nWd4KKdXsg+0wl+VKLbFDfdU8j+hm3CvtaQf6Cs6yPrZxXz/kZHn18PbLGR56gGJR/lNTYNi+yjtAc0HFtMnqPv9bBi0kcHKCtuGrsx+SNoqF7U/pZElyXI46XF7OP2kh4/wU1CaQ9o2GNj0hA02izp688lQffdKbpmRfN2S4I0pT2g4TqKH3xuU7roLu5zmei72aK088qmdd87IMHxFJqG/N3q9i3h+3N0Eccwh0iXuXHb7UfJBzR0CnbPG31Po+vTKDhgb1HTLxQc0PA4SjaI608be20leKBR1D4PTrCNEbTNjKZdDzTZjO9rNL8lPl42f0DD6GDNRU0vVfRro6aCk6pmVQwPEPTrHvMd4dMzD7qWvDCaPnxqE5WoetUEz9+4bVyCNN95wSczhK+/jmAwpYcpvmtLCIr7z3b3QSVIWxIrgRM9f49hJ0fSRLsOjBpL/uoFNQietpSYB+1WjiaollDS6kjRbjRvCycvZrvFwGHuXuKG/qXB3UcSVCcp6fElanhYlHPJfx7iTQMuKcE+XqSUGt27+2KCUsfCeg2K9zRwQWm87iZaReHdd5dL7v4CwXtWXKno3wTf8c+LSbeEoEppYe/BBxSsHloSyzci7V/AMeFnpyhlldfNFStNzw7zUBLvU/BaFvOyh3eNUe7+Vfh6JanGBht5PQl/106g+HYuEFTj6uWRbl7d/Q2C34JEPUFtrsEU03FAVPheRgdihKBXsuJ6oCwTHlQH60Xx7eOc4Pft9k18qSlAYV31F6XCXRurGgUi5ZyZHUH+akPrCQady7uAufso8lclqgu8kKBOc6JA5JvIfKJApMjxQ9x9vbtfRtCo+BaCagmLCZ52rSF4GvEWwY3k1h7U/dxUWQQ/4p8TlHy092B8FQDMbFcKjtZeZCDi7pkUPMaNbmPi7rnufgdBA9vLCI55LsGNezbBj/V4gsCyu7tfGdne3f0mgiLlR4HfCC6i2QTF+GMJugVt7+6JzlOZc/eXCKqR3EDwnv1J8LQ6k+BY3w/X7eTuG/UeuvuXhL2jEFTDyCIYm+Q+gvEUEnWVHN3HcoLe4IYB8ynYZmSjuPtkgupmZwPvEXz21hN8rqcDzxLUXT/Hi69muCleIAhw7yeofjCTfz5Pywiqv90BdHT38jbOT7Hc/XGCutv3EAw4t4J/ju1b4GZge3cfsxH760HQOPp/BIHtL8DlBAP3RdsqlMQ+BFVvrgfeJKhStJLgJjKL4Fr3KcFNUscSBExlmdfNFQtEvvKSt8fYQOFVWYvsLcvdPyL43biMoGFy7PuVRVAa+SnBzeve7r6xVZ9w97/d/WiCqqIvE3Thmxm3/zEEgUZ3d0/4AMPdXyO45l1L8LvzB8F1ZSXBZ2EYwTVvY/M2jqBK03sE57+kN8rPUbAL5mQ3Us/H3b8hGAl9IEEHNH8SfI9XEYysPoRgoNabEgSmlxGUjj5NUM13PsF3YT3Bd+tjgnuHXSMBfqW+NlYlVsjDChERkXIr7OzhubhFc929bWpyU7SKkNewQ5FY6d8V7v5gKvMjiYUPGOcAsYEXlxFUBSyLByEiZU4lIiIiIlKS0dQlhcJevy7lnyAEgvYnCkKkwlKvWSIiIjKfoIv4NR4MHCvlhJldShCANCZ/T3OZFOxdTqRCUSAiIiJSxbl7ibssl6RrTP4Oa2Iuc/eFyc6MSGlS1SwRERGRiuF/BI3mD3X30h63SCTp1FhdRERERESSTiUiIiIiIiKSdApEREREREQk6RSIiIiIiIhI0ikQERERERGRpFMgIiIiIiIiSadARERERESkCjOzZ83sDzP7rZD1ZmYPm9kMM/vFzHYtjddVICIiIiIiUrUNA3oVsf4woF04nQsMLY0XVSAiIiIiIlKFufuXwLIikhwDvOCB8UBDM2uxua+rQERERERERIrSEpgfN78gXLZZqm/uDqRsbPhrloa8r6RO2+2KVGdBylj/rIxUZ0HK0PvpOanOgpShVZ6d6ixIGXtx7uuW6jwkksx7vxpNti0X74FKREREREREpCgLgdZx863CZZtFJSIiIiIiIqmWW65LW98CLjazkUA3YKW7L97cnSoQERERERGpwsxsBNADaGJmC4CbgRoA7v448B5wODADWAucURqvq0BERERERCTVPDd1L+3er5j1DlxU2q+rNiIiIiIiIpJ0CkRERERERCTpVDVLRERERCTVclNXNStVVCIiIiIiIiJJpxIREREREZEU8xQ2Vk8VlYiIiIiIiEjSqURERERERCTV1EZERERERESk7KlEREREREQk1dRGREREREREpOypREREREREJNVyc1Kdg6RTiYiIiIiIiCSdSkRERERERFJNbURERERERETKnkpERERERERSTeOIiIiIiIiIlD2ViIiIiIiIpJirjYiIiIiIiEjZUyAiIiIiIiJJp6pZIiIiIiKppsbqIiIiIiIiZU8lIiIiIiIiqabG6iIiIiIiImVPJSIiIiIiIqmWm5PqHCSdSkRERERERCTpVCIiIiIiIpJqaiMiIiIiIiJS9lQiIiIiIiKSahpHREREREREpOypREREREREJNXURkRERERERKTsqURERERERCTV1EZERERERESk7KlEREREREQkxdw1srqIiIiIiEiZUyAiIiIiIiJJp6pZIiIiIiKppu57RUREREREyp5KREREREREUk3d94qIiIiIiJQ9lYiIiIiIiKSa2oiIiIiIiIiUPZWIiIiIiIikWq4GNBQRERERESlzKhGRlHl51FuMee8Tps+azeEH9+COG69MdZakBA476yiOPv94ambU4rv3v+GZGx4ne312wrQ7dN+JM249lyYtmzLjp2k8fuXD/LXwz3xp6jSoywOfD2HRrIX8p/f1ADRptSWPjHuSdWsy89K99fgbvPHwq2V3YJJPjYZ16PrAeTTtsSPrl61iyh0jWfjGNwXSbXfhkbQ6aX9qt2rC+mWrmDPsY2Y+9k6BdFvs3Yl93riJaQ++wdR7dB6TrXaDOpxy7wV02m8nVi9bxZv3vsz3b41LmPbYa09mnz4HAfDNK58x5u7heev633ku7bp1puk2zXnpmqGMH/VF3rpuJxzAgQMOo2nb5qxbncn3b37Nm/eNIDen6tV7Ly96nXUkR5x/HLUyavHd+98y7IYnCr1ed+6+I6ffeg5btGzKzJ+m8eSVj/K/8Hrd97pT2evo/ahdrzZrVq7hs5c/4u0ho5N5KFWD2ohIaTKzw1Kdh/KsaZMtOG9AX4474tBUZ0VKaKf9d+aYC07gjv43cek+57Bl6+b0HtgvYdp6jepxxeODeG3wy5zT9RRm/TKDSx+9qkC6/tedxsIZCxLu46wdT+aMzv04o3M/BSFJtuNdZ5K7IZuPupzPjxcOYcd7zqJuh1YFE5rx0yWP8UGHsxnf727annkoWx2zd/4k1dPY4bbTWP7D9CTlXqL63HY2ORuyuXb3cxh2+cP0u/0cWrQreD737X8wXQ/ZgzsPu5o7el3Fjj13Y7+TD8lbv2DyXEb++2nm/za7wLY1M2ry2q3DuGbXs7j32Ovp0H1HDj73qDI9LincjvvvzJEXHM/d/W/h8n3OY8vWzTh+YN+Eaes2qsdlj1/D6MEjuKDracz+ZSYXP/rPw8Gxr3zKoIMu4dwup3Dr8dexz7H7sXuvbsk6FKnEFIiUETObANxpZmlmZqnOT3l0SI/u9Nx/Hxo2qJ/qrEgJ7d/7ID5/5RMWTJ/Pmr/X8Pojr3JA74MSpt2j194smD6fCe99w4asDYx+cCRbd27LVtu1zEvTbrcOtOrQhi9e+zRZhyAlkFa7Fi2O2JOp97xKztosln03laUf/kCr3vsWSDtzyNus/HUOnpPLmpmLWfLBDzTes0O+NNudfwR/fvErq2csStYhSJyaGbXYpVc33h78Cllrs5j5/VR++eR79jx+/wJp9zrhAD55+m1WLFnGyqXL+fSpt9mr9wF567988UOmfvMbG7I2FNj2q5c+ZubEKeRsyGHl0uV8N+YrttutY5kemxRu3949+OKVT1k4fT5r/17DmEdeY7/eByZMu0evvVg4fT7fvfctG7I28MaDr9Cm89a0CK/XS2YtIiszKy+95zrNtm6RlOOoUnJzkzeVEwpEyoCZnQps4e67uHsO0LK4bUQqglbtWjN38j9PQudNmk3DLRtRt2G9gmnbt2bupH/SZmVmsXTuElq1bwOAVavGGbeey7B/P4V74td75JuneHT805x33yXUa1TwNaRs1Nm2BZ6dw5pZS/KWrZw0l3qJSkQitujWkVVT/ynhymjVhNb9ejBtsKpxpMqW27YgNyeHP2Yvzlu2cPJctmrXukDaFu1as3Dy3Lz5BZPn0iJBupJo160zi6bP36RtZfO1ateGeZPn5M3PmzQnvF7XLZC2ZfvWzJv0T9qszCz+mLuUlu3/OfdHXnAcT00azsPfPU2t2rX45s2vyjL7UkUoECkbi4H6ZtbDzO4AnjCzjFRnSmRzpdfJIHPV2rz5teHf6XULfrzT62TkrY9Pn14nSNvrjCOY8eM0Zv82s8C2q5b9zQ1HXskl+5zDDUdeSUbdDC566IrSPBQpQvU6tdiwOjPfsuy/M6me4DzHa391b6hmzB85Nm9Zl9tPZ+q9QcmKpEat2ulkRs5n5qq11KqbXjBtnfR83/HMVWsTfr+Ls/eJB9Jmx2355Mm3Nz7DUipq1UnPdw3OLPJ6nZ7wep1R55+07wx9g3M6n8yNh1/JuNe/IHPVmjLKeRXmucmbygk1Vi9FZvY5cAcwA3gLeBHYAujg7plmZu6FPfsVKX+6H7s/Z995AQBTJk5i3ZpMMurWzlsf+3td5CYHYN2aTGrXq51vWUbd2qxbk0mjLRvR64wjuf6IxB0UZK1dx6xfgwBl5V8ree6mJ3n8+2Gk10ln3Zp1pXJsUrjsNVnUiNysVK+XQXaC8xzT9sxDaX3ifow75j/kho1hmx2yK9XrZrDozfFlml8pWtbadWREzmd63QyyVhf8LmWtWZfvRjW9bkbC73dRuh66B8dc04+HT7mNNctXbVqmZaPtc+z+nHHneQBMnTiZrDX5z3vs78TX63VkFLheZ5C5pmDaub/PZsf9d+b4K/ry8m3DSvEIpCpSIFJKzGwY0Bz4yt2zzGw9sBUwGegAzHd3VzAiFcm4MV8ybsyXefMXP3wFbTq3Zfy7QW87W3duy4o/lrN6RcGbjQXT5rN/XH3kWhm1aLZ1cxZMm8d2O7enYdNG3P/JIwDUTK9JzfSaDJ34HBd2OwuP1l8NvzJWTYW4ybBm1mKsehp1tmnOmtlB9az6O7TJV+UqXut+Pdj+4qP55thbWbd4Wd7yJvt1oUHXbTjkl6EA1KhXG8/NpX6n1kwcMLjsD0QA+GPWYqqlpdG0bXP+nBOcz1adtk5YbWrx9Pm06tSWuT/PDNO1ZfFGVK/qfEBX+t91Ho+deReLpqpaVjJ9M+ZLvom7Xl/w8OW06dyW794Nertrk3e9Xl1g24XT5rNv5Hq95dbNWTgt8TlMq57Glm2al/IRSHlqu5Es+lUvBWZWD9gRGBIGIRcDTYGTgZ+Bu8zsaIBYMJK63JYf2dk5ZGWtJycnl5zcXLKy1pOdXfUG86lIvhr9OQeedDAt27Widv06HHfJiXwx6rOEaSd+OJ7W7duw52F7U6NWDY6/rA/zJs9h0cyF/DT2By7d91yuPXwg1x4+kNceGMGc32dz7eED8dxcttu5HS223Qozo27Depx+yzn8/u2v+aqMSNnJWZvF4ve+o8M1J5JWuxaN9mhP83/tzoJRXxdI2/L47nS8rg/j+9zJ2nl/5Fs35Z5X+XyfK/iy57V82fNalnz0A/Ne+oyfLn88WYciwPrMLH76cAJHXtGHmhm12Ha3Dux0yB589/qXBdJOeP1Lep59BA2aNaLBlo3oec6R+broTauRRvVaNTCDtOrVw7+Dn7T2e+/AgP9eylMXDM4LZCR1vh79BQec1JOt2rWidv3aHHPJiXw16vOEab//cAKt2rdm98P2okatGhx72UnMnzyXxTMXYmYc2P9QatevA8C2Xbfn4NMOY9K4X5J5OFJJmR7Obz4zqwX8F9gNmA/sDBzi7rPMrDtwGdAWuN3d3wq3KbJkZMNfsyr9iRnyzEsMfXZ4vmUXnHkyF511SopylByn7Vax2zocfvbRHH3+8dRIr8l373/LMzcMzeuX/r6PH2bMkFF5pShduu/EgFvPpWmrpsz4cTpDr3qYvxb8UWCf+/c+iAP7Hpw3jsg+R+9Hn2tOof4WDchcvZZfv/qZl+96npV/rkjacW6O/lkVv0lYjYZ12PnB82hywI5sWLaayXeMYOEb39C4Wwe6vXwt7293BgA9v3uI9BaN86pjASwY9TW/DnqmwD53fuh8Mhctq/DjiLyfXvEemNRuUIdT77uQjvvuyJrlqxlzz3C+f2sc2+3RkYuGXc8VO5yWl/a4a09mn749Afhm5Ke8ETeOyOUjb6b9Xjvk2/eDfW9h+vhJXD7iJrbbo1O+HrVmTpzMkAF3lfHRla5VnnicjYqo19lHceT5x1EzvSYT3x/Pc3HjPt318X95e8jreaUoO3TfidNuPZsmrZoy88fpPHnVI/y14E/MjKuG3ci2O29P9RrVWb50OV+N+rxCjyPy4tzXy+UD4XXjhift3i+9+8nl4j1QILIZzKyHu48N/+5E0C6kLXCxuz8Rl24f4HJga4JgpNjWe1UhEKmqKnogIsWrDIGIFK4iBiJScpUpEJHEym0g8tWLyQtE9ju1XLwHqpq1iczsIaCfmaWFi9oDDYGvgVPM7OBYWnf/BngQmAU8bGYFO+MXEREREalC1Fh90z0GzHL3HDPbBngb2AboClwD3BrWvvoUwN2/NbOaBD1qfZuqTIuIiIhI+RMMPVe1qERkI5nZaWa2hbtPdfcNZnYy8DpwvLuvdvdxwKPAH8BtZpY37LS7f+Hu/w6Dl7RCXkJEREREpNJTichGCIOO24DOZnaXu68EpgArgLPNrJq7v+ruH4e9iFwI/MfM0t39vfh9eVUMe0VEREQkMXXfK8UYCQwHegI3hCUjPxD0ipUGnGtmJwG4+8fAEMCBY1KUXxERERGRckklIiVkZrXcPQu4PqxW1R242szudvdfzOwq4H6CYMTd/TV3/8TMVgI/pDLvIiIiIlLOuUpEJIFwzI+s8O/TgVpAO4KqV9eYWWN3/xm4CsglqKZ1OoC7T3T3XDPTey0iIiIiEtLNcQnEBh40s5sJBi6cCJwDfERQ7WqQmTWKC0a2BHaN7KPqhbkiIiIiUjK5ucmbyglVzSoBC1qeNwGOB6539+Hh8neAwUB/YIOZ3R9W0zqRYMwQERERERFJQIFICbi7m9kqgmpXDQDMrLq7ZwMDzWxPYADQ1MyucfcZYZpqKgkRERERkWJVwVtGVc0quQ3AUuBQM6vh7tlx7T5+I+jCdx3wd2wDBSEiIiIiIompRKQEwsbqOWZ2MTAeGGZmFwJrw/FCGhKML/JqWHqikhARERERKbly1HYjWRSIlEAYXJi7zwjbf7wKfE0wenr9cOqvIEREREREpGQUiJRQrOcsd//czDoBlwP1gCyCBuw5ZpamEdNFREREZKNVwefYCkQ2Uhhs/AXcGFkea7wuIiIiIiLFUGP1OCUZdDBW4hGOro6FFISIiIiIiJRclS4RMbM+QBowzd2/j7XtCAMLL2I7i6uCVTM26rqIiIiIyCapgo3Vq2yJiJm1B0YAZwG3mdnTZraDmWXEByHhYIbx2+UFKWY2EHjJzGokM+8iIiIiIqXJzHqZ2VQzm2Fm1yZY38bMPjezH83sFzM7fHNfs8oGIu4+DXgbWEjQ3qMd8AjwppntYWZNwnSxoKNaJAg5F7gZeMPdN6TiGERERESkksjNTd4UETY5GAIcBnQG+plZ50iyGwmGqtgF6As8trmHXCUDkVj7DuBlwNz9B+BfwMXAIoKueV80s/PMLAOCwQnjgpDzgPuAM9395aQfgIiIiIhI6dkTmOHus9x9PTASOCaSxgmGrABoQHDPvFmqZCAS175jAtDTzC5z93XAFGBH4P+AX4HBwM9mdkZsWzM7B7iXIAh5Pbk5FxEREZFKyXOTNxXUEpgfN78gXBbvFuAUM1sAvAdcsrmHXCUDkbCjqzR3nwPcCvQws20JApDVQC93vwbYFRgOvBBudx7wBDDA3UenJPMiIiIiIsnXDxjm7q2AwwlqD21WLFEles0ys4OAvcLZD939BzOLhYM/AecA3xOUkJzp7ivDEdKnAf8J92HACuBEd38jmfkXERERkUoutb1mLQRax823CpfFOwvoBeDu35pZOtAE+GNTX7TSl4iEValGAocQNKwZambt40ZKHw98CdQAznP3xeHyfJ8GD7yikhARERERqWQmAu3MbBszq0lwz/xWJM08oCeAmXUC0oE/N+dFK3UgEgYhjwEXAAcBVwCNKXjczxO0D9k73M4QEREREUmWFLYRCQfmvhj4EJhM0DvW72Z2q5kdHSa7EjjHzH4mGAJjQFHj7pVEpa2aZWZ9CdpznB5XivGJmWUDN4RtQt4GhgG/A6uAc4FXNvdNFRERERGpSNz9PYJG6PHLbor7exLQvTRfs9IGIgRtQtYA6WaW7u7rzGwMkAEsJmiUfifQ3N0vN7NHgMuLG1VdRERERKTUVcGR1StdIBL2hpUTBhc1gGvD5UcBWwM93H12uOwP4FozexD4GBjj7h42VK96nwYRERERkSSpdIFI3BghuPtFZvYk8CCwEjjc3WfHBRqLgEnAGndfDUH7EAUhIiIiIpJUVfD2s9IEIma2C9CBYOyPX4BZ7v6Nu59rZmuB44HuZrbA3f8X9nt8FDAd+F9sP6qWJSIiIiJS9ipFIBKOfH4DQYPzmsBAYKGZPefu/wmradUGrgrTjyRopL490EXVsUREREQkpapgG5EK332vmfUHhgD/Bg5z9x0IxgyZANxsZncCuPu5BF2SDSQYQb0dsKO7Z5tZdQUhIiIiIiLJU6FLRMxsS+BC4Fp3HxFb7u5jzWwhkAlcaGY/uPtod7/AzJ4BOgP7u/uGMAjJTs0RiIiIiIhUTRU6EAEaAW0JRoME8hqbu7tPN7OHgEMJ+jweDeDuZ8XSKAgRERERkXJBVbMqnDoEI6WnxxbEGpuHwcaPBAOz7GtmaeGQ9YRBiCkIERERERFJjQoXiIS9XcWsBNKAg8J1FlsRa4AO1AZmhGOLrI9fn6Qsi4iIiIgUzT15UzlR4QKRWKNyM2vo7jOBocB1ZnZcXPARkwE0I+jON1+gIiIiIiIiqVPhAhEAM+sNTDOzBsBDwOfAKDM7H2hjge2BkcAWwP2gUhARERERKadyc5M3lRMVorF6bIyPWCNzYCrBiOhnu/tgMxsEXAY8BvwB5AILgHXAHmEXvWnxo66LiIiIiEjqVIhAJG6Mj1bAfIIgZBxwgpmNcPf/M7NzgGeAHQgar/8MfOHuOeodS0RERETKtXJUUpEsFaZqVljqMdPMBgC1gFuAusADAO6+3t2/dPeh7v6gu38WBiFpCkJERERERMqXClEiEqpDkN+bgJ2B8cAZwDAzG+DuwxJtpOpYIiIiIlLuedUrESn3gUisfQjwH4KG540I2oEMCOcnA4eY2QfuviRlGRURERERkRIrd1Wz4rvfNbMbgPvNrFtYsvE1QQP094DTCdqLHAL0A7qlILsiIiIiIptPvWalXtw4IScA2UBPoIuZjQXuIaiOdZa7XwIcb2anAnsA76YmxyIiIiIisrHKXYkIgJk1A14DPgIOA94HLgReB0YDp5vZSQDu/qK7Xxp20VvuAisRERERkWJpZPXUSDDi+RpgOtDO3RcBjwC7EHTLezLgwDlm1jJ+I/WOJSIiIiJSMZSLQCQ24rmZNQnnVwO/APuG89nu/qe7/wt4FviJoAvfRSnJsIiIiIhIaVIbkdQxs2sJqlytAX4FOgI5ZrY1sMzdVwG4+zAzGwWsdXeP61VLREREREQqiHITiABfAr8BPYCaBOOGnAQ0Bzqb2U8EVbYedvfPIV/XvpXOabtdkeosSBl54YcHUp0FKWP9dxuY6ixIGWpCrVRnQcpQRzJSnQWRKqPcBCLu/k345zsAZvY+8BRwP7BlOO1EELDEtqmUQYiIiIiIVDHlqMpUspSbQASCRuthdas04C8gB/g+OlChmaVpxHQRERERkYqrXDRWj4k1Wg+DjP8jCET2TpBOQYiIiIiIVB6em7ypnChXgUhENYKesVqkOiMiIiIiIlK6ylXVrHjuvsHMrgFeSXVeRERERETKkueWn4EGk6U8l4jg7sM1YrqIiIiISOVTIW7wNWK6iIiIiFRqVbDXrHJdIiIiIiIiIpVThSgRERERERGp1MpRb1bJohIRERERERFJOpWIiIiIiIikmnrNEhERERERKXsqERERERERSTX1miUiIiIiIlL2VCIiIiIiIpJqKhEREREREREpewpEREREREQk6VQ1S0REREQk1Vzd94qIiIiIiJQ5lYiIiIiIiKSaGquLiIiIiIiUPZWIiIiIiIikWq7aiIiIiIiIiJQ5lYiIiIiIiKSaq42IiIiIiIhImVOJiIiIiIhIqqmNiIiIiIiISNlTiYiIiIiISIq5xhEREREREREpeyoRERERERFJNbURERERERERKXsqERERERERSTWNIyIiIiIiIlL2FIiIiIiIiEjSqWqWiIiIiEiqqbG6iIiIiIhI2VOJiIiIiIhIqmlAQxERERERkbKnEhERERERkVRTGxEREREREZGypxIREREREZFU04CGIiIiIiIiZU8lIiIiIiIiqaY2IiIiIiIiImVPJSIiIiIiIinmGkdERERERESk7KlEREREREQk1dRGREREREREpOwpEBERERERSbVcT96UgJn1MrOpZjbDzK4tJM1JZjbJzH43s5c395BVNUtK1WFnHcXR5x9PzYxafPf+Nzxzw+Nkr89OmHaH7jtxxq3n0qRlU2b8NI3Hr3yYvxb+mS9NnQZ1eeDzISyatZD/9L4egCattuSRcU+ybk1mXrq3Hn+DNx5+tewOTDbJy6PeYsx7nzB91mwOP7gHd9x4ZaqzJMU44qyjOTb8Do9//xueumFood/hLt134uxbz6NJy6ZM/2kaQ658KO87fNH9l7LvMfuTveGfbU/v0p/csDFmzfSanHbjGex9xL5Ur57GnMlzuPmk68v+AKuQ2g3qcOq9F9Bpv51YvWwVb977MhPfGpcw7bHXnkz3PgcBMO6Vzxhz9/C8da06b82p91xA8+1bsmTGQl4cNJQFk+YCcPGw69huj055aavXqM7SWYu4vddVecsOPOMwDjrzCOptUZ9li/7i8XPu44/Zi8vikKu09AZ1OPLec9hm/x3JXLaaz+99hd/f/CZh2gOv7cvOfXsA8NPIsXx+98i8de167kKPQX1o2Kopf0yZx7uDnuav6Qvz1h9w1Yl0PXF/atROZ+nvc/jg38PyrZeKyczSgCHAIcACYKKZveXuk+LStAOuA7q7+3Iz23JzX1eBiJSanfbfmWMuOIHb+/2b5UuXccWT19F7YD9G3vNigbT1GtXjiscH8eSgIfzfpxM58cr+XProVdx03KB86fpfdxoLZyzAqlmBfZy148nk5lS9HiYqkqZNtuC8AX0ZN+EHsrLWpzo7Uoyu++/CsRecwH/63cjypcu4+snr6DOwP8PveaFA2nqN6nH149cydNCj/PDpRPpeeTIDH72aG467Ji/Nm0+8wcj7hxfYFuC8uy8iLS2NgT0vYvWK1bTtvE2ZHVdV1fe2s8nekM2g3c+hVee2XPTsdSyYPJfF0xfkS7dv/4PZ+ZA9uOOwq3F3Lnvp3/xv/h98Nfxj0mqkcf6T1/DZc+/x5Ysfsl//Qzj/yWu4+cBLydmQw6MD7sq3r4Ejb2bqN7/lzXfvcxD7nHQQQ864iyUzFtKkTTPWrlydlOOvanrdNoCcDTn8d7cLadZ5a/o8dzVLJ80tECTs0v8gOhy6G0/3uh7c6Tf8OlbO/5P/G/4pjdo245iHLmLkgHtZ+OMM9j7vSE58+goeP+hqPCeXTkd0o+tJB/DCCf9h5cK/OOCqkzjmvxfwzBE3puioK5nUjqy+JzDD3WcBmNlI4BhgUlyac4Ah7r4cwN3/2NwXVdUsKTX79z6Iz1/5hAXT57Pm7zW8/sirHND7oIRp9+i1Nwumz2fCe9+wIWsDox8cydad27LVdi3z0rTbrQOtOrThi9c+TdYhSCk7pEd3eu6/Dw0b1E91VqQEevQ+kM9e+TjvOzzqkVfpUch3uFuvvZk/fT7jw+/wqw+OoG3kO1yYrbZrye4H78kT1w3h72V/k5uby6zfZpb24VRpNTNqsUuvbrw9+BWy1mYx8/up/PLJ93Q7fv8Cafc64QA+efptVixZxsqly/nkqbfZu/cBALTfawfSqqfx2TPvkr0+m8+HvY+Z0WGfLgX207hVU7bfoxPjX/8SADPj8Mt6M+q251kyI7gZ/mveUtauXFOGR1411cioRcfD9uSLwa+xYW0WC76fxvRP/o8dj9+3QNqdeu/HhKfeY9WSZaxaupwJT73LTr2Dz8V2B+zE/IlTWPD9NDwnl2+Gvk295o3Zeq+g1Kth66bMnziVFfP/xHOd3974mibbF/+dlwqhJTA/bn5BuCxee6C9mY0zs/Fm1mtzX1SBiJSaVu1aM3fy7Lz5eZNm03DLRtRtWK9g2vatmTvpn7RZmVksnbuEVu3bAGDVqnHGrecy7N9P4YV0IvHIN0/x6PinOe++S6jXqOBriMjGadWuDXMnz8mbn1vEd7h1+zYFvsNL5i6hdfgdBvjXqYfx3M8vcc87g+l22N55y7fv2p6/Fv7BSQP788yPLzL4w4fyrZfNt+W2LcjNyclXBWrB5Lm0aNe6QNqt2rVmweS5CdO1aN+ahVPm5ku/cEri/ex1/P7MmDiZZQuC6nkNWzSm8VZN2KpDa+745jFu++pRjhx4ImYFS7hl8zTetjm5OTksm70kb9nSyXNp2r5VgbRN2rVi6eR5efN/TJpHk/Zx95tx58cMDPL28/vb42m0dTMab9OcatXT2Kn3/sz84pfSPyApr6oD7YAeQD/gKTNruDk7VCBSBsysSr6v6XUyyFy1Nm9+bfh3et2MhGnXxqWNpU+vE6TtdcYRzPhxGrMTPCVdtexvbjjySi7Z5xxuOPJKMupmcNFDV5TmoYhUSel10lm76p+n1bHvaEbC73B6wu9wRvgdfm/YO1xywAWctetpjBz8Mhfdfxkddu8IwBYttqBNx7asXbWG8/Y8g2duepKLB19Gy+0L3jTJpkmvnU7m6sx8yzJXrSW9bnqBtLXqpOe7dgfpgvNYq3b+ddH18bodfwDfjhqbN9+o+RYAdNqvK7f3uooH+/2H3Y/qzj59EpeyyaarWTudrFX5z3fW35nUrFPwfNesk05W3Dldt2ottcLzOfvr32jTrSNt9upEtRppdL/4GNJqVqdGRi0AVv+xnPkTp3LB2MEMmvocnY7Yk49ve6kMj6yKSW1j9YVA/BOGVuGyeAuAt9x9g7vPBqYRBCabTG1ESpmZVXMPKvmZWSsgw92nx60398Ke8Vcs3Y/dn7PvvACAKRMnsW5NJhl1a+etj/29LvJjCLBuTSa169XOtyyjbm3Wrcmk0ZaN6HXGkVx/ROKGzVlr1zHr1yBAWfnXSp676Uke/34Y6XXSWbdmXakcm0hVsO+xB3Be+B2ePHES69asS/gdjt7QAkHaevlvRmvXzSAz7ERi9m+z8pb/+PkPfDXmC7r12pup309h/br1ZK/fwOhHXiU3J5dJE37nt29/pet+O7NwRv72C7Jp1q1dVyCATK+bwbrVBa+RWWvW5QssMupm5F23s9auKxB0pNetXeC6vt3uHajftCE/vjc+b9n6sF3Yx0+8Sebfa8n8ey1fvfwJXQ7chXEjVeW2NK1fu45ake9jrXoZrE/wm7h+zbq8wAOgVt0MssLz+b+Zi3n7yif4162nU3fLhvz2xjj+nL6QvxcvA2C/y45nq67b8nC3S1j95wp2PG5fThlxPU8cPIjsdWoHWMFNBNqZ2TYEAUhfoH8kzRiCkpDnzKwJQVWtWWwGBSKlLC4IuQvoDWxlZhOBt4FH3H19ZQlGxo35knFjvsybv/jhK2jTuS3j3w16Zdm6c1tW/LGc1StWFdh2wbT57N/7wLz5Whm1aLZ1cxZMm8d2O7enYdNG3P/JI0DQu07N9JoMnfgcF3Y7C8+NNOYK30qrViULokQ22ddjvuDrMV/kzV/28BW07bwN34bf4bZFfIfnT5uXrw1Y8B1uwfxp8wqkBcA9r0rO3ClzEqzf9OOQgv6YtZhqaWk0bducP+cE1XVaddqaxdPnF0i7aPp8WnVqy9yfgwc8LTu1zUu3eNp8Dj77yHzpW3ZswxcvfpBv2V4n9OCnDyaQtTYrb9nSmYvYkLUhf/Xaiv/TVy4tm7WEamlpNGrbjOVzlgKwZac2/DmtYGD/1/QFbNlpaxb9HNw/Nuu8NX9N++fB95T3vmPKe98BUKt+bXbu04PFv8zMSzvp7fGsWhIEJr+M+pJDbjqFpu1asvjX2cjm8RQOaOju2WZ2MfAhkAY86+6/m9mtwPfu/la47lAzmwTkAFe7+/8253V151ZK4qtjmdnZwOnAvwl6HFgAnAjcZWY1K0MQkshXoz/nwJMOpmW7VtSuX4fjLjmRL0Z9ljDtxA/H07p9G/Y8bG9q1KrB8Zf1Yd7kOSyauZCfxv7Apfuey7WHD+Tawwfy2gMjmPP7bK49fCCem8t2O7ejxbZbYWbUbViP0285h9+//bVA9QFJvezsHLKy1pOTk0tObi5ZWevJzs5JdbakEF+M/pyDTjqYVu1aU7t+HU645CTGFvId/u7D8bRp34Zu4Xe492V9mBt+hwH2Onwf0munY2bstN/O7HdcDyZ+HNzcTJ7wO38t+ovjLupNtbRqdNi9IzvsvSM/ffljsg610lufmcVPH07gqCv6UDOjFtvu1oGuh+zBhNe/LJB2wutf0vPsI2jQrBENtmzEweccybejggB12vjfyc3J5cAzDqN6zeoccNq/APL1jFWjVg12O2JvxsdVywLYsG49P7zzDYeedzS16qTTsHlj9u1/ML9++kPZHXgVtSEziykfTOSAK3pTI6MWrXZvT/tDduPX178ukPbX0V/T7ZzDqNesEXW3bEi3cw7nl1H/fC6ad2mLVTNqN67H4XedxbRP/o//zQzaGi3+eRadjuhGnSb1wYwux+1LtRppLJu7NGnHKmXH3d9z9/buvp273xEuuykMQvDAFe7e2d13dPeRRe+xeFZJ74lTxswOBnYB/nb3J8JldYArgWOB29z9jeJKRfptfWyFPDGHn300R59/PDXSa/Ld+9/yTNwYBPd9/DBjhozKK0Xp0n0nBtx6Lk1bNWXGj9MZetXD/LWgYE9w+/c+iAP7Hpw3jsg+R+9Hn2tOof4WDchcvZZfv/qZl+96npV/rkjacW6OF354INVZSJohz7zE0Gfzd996wZknc9FZp6QoR8nRf7eBqc7CJjvy7KM55vwTqJlekwnvf8uTNzyW9x1+4ONHeH3IqLxSlB27d+Ws8Ds8/cdpDLnqYf4Mv8O3vnYnW3dsC2b8MX8pbzw2mm/e/irvdVq1a80F915Mm45t+Wvhn4y47yW++3B8gfyUR02sVqqzUCK1G9Th1PsupNO+O7Jm+WrG3DOciW+NY/s9OnLRsOsZuMNpeWmPu/ZkuvftCcC4kZ/yRvw4Iju05dS7z6d5u1YsmbGAFwc9zoLf5+St3/3o7hx7TX9u3PeiAnlIr5vByXedS5cDd2Xt32sYN/JT3nt4dNkddClo5TVSnYVNkt6gDkfedy7b7NeFzOWr+fyeYByR1nt0oO/z13Bf57Py0h50Xb9844h8dteIvHWnjbqJLTu1ITc7h8nvTuCT24azITMo6UqrVYODbzyZjr12p0ZGOsvnLuHze19lVgVrsH7D3OHlsseEVZcembR7v3oPv1Mu3gMFIqXIzNryT12529z95rh1BkwAfnP3M4vbV0UNRKR4VSkQqaoqciAixasogYhsmooaiEjJKRApP4GIqmZtBov0Qejuc4ADgSXAfmbWIpYmLP34AmhmZrrKiYiIiMg/cnOTN5UTCkQ2Udg7lod/5/WP5+5fAKcSjFB5P7CNmVU3swxgf2Cpu29IRZ5FRERERMoL9Zq1CcL2HbHesa4FuplZU+B14H13/9TMjgNGA92AqcB6oAZwftw+VP1KRERERAob36NSU4nIRoqUhFwHXAv8TFAd6yRgmJnt6u4fE/SYVYdgsJcHgT3C7ntrKAgRERERkapMJSIbKa4kZFtgR6CPu38YLjsUuBAYbGanufvnZtYPeBc4A5hoZutVNUtERERE8lGJiJSEmZ1GMKz9XsDfseXu/hHwHNACaBsuGwscRVA68hKQf+hTEREREZEqSIHIJnD3FwhGl2wL7GH2T1+O7v4mkA70iFv2GdCfoL1I/WTmVURERETKP3dP2lReKBApRvyI6fHc/QjgM+B64BAzSwvTNwBWA39G0n8AtHf3RWWbYxERERGR8k9tRIoQNkyPtQnZC2gIzAPmuvsadz/YzL4gqHL1iplNAg4CDHg6uj93X5u0zIuIiIhIxaE2IhIT6aL3TuA14BHgW+BGM9sJwN0PCJedQzB2yHh338Hds81MgZ6IiIiISAIKRAoR6aL3dOBUd29H0Bj9IuBCM+sapj0M+ATYA5gYt4/sZOdbRERERKQiUCBSBDPbBtgHuNzdx5rZMcAA4E2gD3Cpme0M4O6HAouAp8zs8FibERERERGRYuV68qZyQoFI0ZYTlIC8H7YReRT4t7ufGi4/FrjGzDoCuHsPYAVwL1Arwf5ERERERAQ1Vi+Su68ws4/cfbWZnQiMA54MV2cC04FcgjFFYtvsYmZbq2G6iIiIiJSUl6OSimRRIFIMd18d/tmMoAQpA8gCOgG3A++6u4fd/Jq757j73NTkVkRERESkYlAgUnITgLuB18ysGcF790EYhOT1sCUiIiIistFUIlK1xI8TUkQa88AjZpYNtAN+Aq4Lu+hNc/ecZORXRERERKSyqLKBSGSckO4E438UCChi1a7cPdfdh0b2UV1d9IqIiIjIZquCdWuqZK9ZYWARGyfkPuBdYCszs0Tp4wIWiyxXECIiIiIisgmqZIlIXGDRAjDgGHefX9Q2sSpa4d+dgXlxDdlFRERERDZZVew1q0qWiACY2SnAXOAwYEkxaeODkEuAscCWZZ1HEREREZHKqkqWiITmAR8DBxIOPpiozUckCDkPuAW40N1nJTe7IiIiIlJpVcESkSoRiBTSO9ZXBIMSbgG8a2bd3H1RfC9YCYKQe4Ez3X10MvMvIiIiIlLZVPpAJD4IMbMjgKb8MwbIxDDAGAJ8aWb7h8FIdXfPjgtCziUYQ0RBiIiIiIiUPvWaVfnEBSH3AE8AvYFBwOtm1tfdfwauBBYCn5tZ6/jqWWHw8jhwloIQEREREZHSUekDEQAzGwCcQtA71pHAXcAuwBoAd59AEIzkAoPjt3X3d4H93f31ZOZZRERERKoOz/WkTeVFpa+aFeoIvOnuP5hZH+BB4FJ3f9vM6gD13f17MzsBmBrbKK6K1tcpyreIiIiISKVUqUtEzCwt/LMDMM/MdgWeBga5+1AzqwacBhwXNkyf5O45se00YKGIiIiISNmoVCUi0d6xYr1fAe8AjwF3ACe7+8hweW3gOODHWMP0yHYiIiIiImWvCjZWrzSBSKR3rP2BxsAcYBbwIsF4IYcAy8PqWK2A/xJ033tDCrIsIiIiIlJlVZpAJC4IGUzQMD0N+Av4AziRINjIBd4DFgHLgBXAXu6eHT9+iIiIiIhIMpWnRuTJUuEDkcigg72AQ4ETCEpCugGXEAxeuK+7n2ZmQ4GtgKXAN+6em2hEdRERERERKTsVPhCJC0L6AN2Bz+J6uXrDzOYBDwH3mtl57v5t/PZhlS4FISIiIiKSOlWwjUil6DXLzGoAVwEXAzvGr3P3H4CPgF2BGtFt4xu3i4iIiIhIclT4QCSsmrUBOAAYA+xgZgPMLCMu2U8EQUij5OdQRERERKRonpu8qbyoFFWzwobma83sFOBN4HKggZmNBGoBlwILgQWpy6mIiIiIiMRU+BIRCMb9iAUjwLHAEuB+YDzBKOrrgCPCoKVSHLOIiIiIVCK5SZzKiUpzUx4XjKwBjgfeJejC913gBHfPMrOaahMiIiIiIpJ6FaJqVnwXvUWJLxkxs/4EQciFBIMYvu/u68o8syIiIiIiG6kqPiov9yUiYfe6sS56mxaXPlJN60jgf8ADBOOLiIiIiIhIOVCuA5EwCImNmH4VwVggXUqwaWyQwjXAMcA44PcyzKqIiIiIyKargm1EynXVrLgg5D7gdILer9YUtU1cNa5sMxsEbOnup5R5ZkVEREREpMTKdSACeSOm9wH+5e4/hssygO3c/bdw3sIesSyuGte5wHUEgxyKiIiIiJRbaiNSPrUGZrv7j2bW0cyuJhigcJyZPQx5Y4nEtyU5D7gPONPdX0pVxkVEREREJLFyVSJSSO9YS4DmZjYa6Ax8DzxFMDjhy2Y2zN3/L64a17nAvQRByOtJzL6IiIiIiJRQuQlEIg3T04Fa7r4SeBtoBPQkCDA+c/e5ZtYZ+A5YHbeP84AhwInu/kayj0FEREREZFNUxapZ5SIQiQQh1wPdgG5mNhz40N0fMbOh7p5tZtXMrB5wD7AWmBFuVxOoDfRRECIiIiIiUr6Vi0AkLgi5AzgXuAwYTtDYvKeZTXH3eWZWBziOoAetLYBu7p4bBjLrzexRd9+QosMQEREREdkkVbFEpNw0VjezHQgGIDze3V8GFgMdgf+GQUg1wIAWwP8Be7r7hnC8kFwABSEiIiIiIhVDuSgRiVPd3b8ys97Ac8BAdx9mZrUJgpQPgIfdPQsgHEE9O4X5LTP9szJSnQUpI/13G5jqLEgZe/mHB1OdBSlDLbbtleosSBlasa7I4cqkErgh1RkojFuqc5B0KSkRMbNE73Q9oJGZXULQK9a17v54uK4rwVgi7WNBCIC755R5ZkVEREREpNQlvUTEzGq6+/rw73R3Xwfg7uPNbCzwEPBvdx8SpqlNELw6QZUsEREREZFKpSq2EUlaIGJm+7j7N3FByEDgYDP7A3jH3UcDtwBNgWvMLAuoC3QnaBeyS1zD9Cp4qkREREREKo+kVM0ys5uAoWHbD8zsSuAmYAqwG3C1mQ1y92kEVbCeA/oBewGTgZ2jDdNFRERERCoLz7WkTeVFskpExgD7ABeEXfC2A05w98/MrAlBUHJS2Pj8TuByM2vs7stiOwiDkErZMF1EREREpKop8xKRsCrVL8BAIAc4jaCkYw6Au/8F3AmMA44zsxvD5fFBiCkIEREREZHKynOTN5UXZRqIxLfncPfJwOXABqA9cGgsnbsvAe4AvgbONrPT4/fj7l6W+RQRERERkeQqs6pZ8UGImfUBZrv7d2Z2MTAE6Gtm/3P31wDcfamZ3QfMBV4qq3yJiIiIiJQ3rnFESkdYlSoWhNwDDAb2NbNG7j6DoGRkPXC+mZ0Y287dF7n7f909x8zSyiJvIiIiIiKSemVSIhKrSmVmlwJnAr2ASe6eGZaUTDazy4AHgXPNrLa7Px/ZhwYrFBEREZEqoTy13UiWMmsjEo6evifwuLv/AMRGRDfIazNyGdCYoAtfERERERGpIsqy+976wN7AQoBwMEILq13VAtq6+1QzOwGYV4b5EBERERGRcqZUSkTMrIuZNQv/vtfMdnb3lcBHwK5m1hHy9X7VAfi3mW3v7nNiI6aXRl5ERERERCqaqjig4Wbf/JvZTsBY4HQzGwpcRTBeCMBnwPYE7UA6h+m3BG4DWgKzYvvRiOkiIiIiIlXHJlfNMrP93f1Ld//FzO4FrgPSgUPc/VcAd38tDDwuAY4xs+VAGkE7kT1iJSEKQkRERESkKquKo+ZtUiBiZg8DNc3sW3ffAEwJV/0N7G5mU9w91jZkiJn9TFAda3tgGvCiu2ebWXWNmC4iIiIiUvVsaonIowQDFG4ws7bAu0Bz4EqC0o9aZva0uy8CcPevCUZNz2NmaQpCREREREQoV203kmWj24iEPV9NC4OQU4DXgWPdPcvd7wSeBs4FBpjZVuE2o81sh/j9aJwQEREREZGqa6NKRBK055gKrCJojG7uPsrdbzEzJwhG9jCzJkA7gipZIiIiIiISoRKRIsQHIWbWMayS9X9AP4KA5nwzOxHA3f8D3EswhshkoFVYglKW45aIiIiIiMgmMLNeZjbVzGaY2bVFpDvBzNzMdt/c1yxRYBCWdsSCkHuAE4CGwAJgMHAG8BxBMOJhychjZlYjbMyOGqaLiIiIiCSWyl6zzCwNGAIcQnB/P9HM3nL3SZF09YDLgAml8brFloiEJSEe/n0ScGqYgQuBN4HngWOBUwAHzjGzkwFiQUj4t4IQEREREZHyZ09ghrvPcvf1wEjgmATpbgPuAdaVxosWWyISVxJyEEGU9IC7vxsuqwnMBZ4iaC9yLvAGsDswvDQyKCIiIiJS2aW4jUhLYH7c/AKgW3wCM9sVaO3u75rZ1aXxoiWtmtWcINhoCtwXW+7u681sJHAo0N/dTw9LTaaXRuZERERERCS1zKwa8AAwoDT3W6LG6u6+BDgO+APoHUZEsXVrgb+AVmFbkqnhiOlppZlREREREZHKyt2SNiWwEGgdN98qXBZTD+gCjDWzOcBewFub22C9xL1mufsvwPHh7BWxYCRstLITMC/WliRMr3FCRERERETKv4lAOzPbJmx60Rd4K7bS3Ve6exN3b+vubYHxwNHu/v3mvOhGdafr7r+Y2enAy8CHZvYdkAnUAc6DvB62UtjuX0RERESkYsk3Ul+yX9s928wuBj4E0oBn3f13M7sV+N7d3yp6D5tmo8f1cPefwnYgbwENgLfd/XEIGq+HLe1FRERERKSCcPf3gPciy24qJG2P0njNElfNirz4bwTVtGoAe5pZ+3C5ghARERERESnWJgUiEJSMAOcTNFy5w8w6lVamRERERESqkly3pE3lxSYHIgDu/iNwEUG3vitKI0MiIiIiIlL5bXQbkSh3n2hmvdy9VEZYFBERERGpagrpVrdS26wSkRgFISIiIiIisjE2u0REREREREQ2j+eqRERERERERKTMqURERERERCTFquJw4CoRERERERGRpFOJiIiIiIhIiqmNiIiIiIiISBKoREREREREJMXK04jnyaISERERERERSTqViIiIiIiIpJhGVhcREREREUkClYiIiIiIiKSYxhERERERERFJAgUiIiIiIiKSdKqaJSIiIiKSYuq+V0REREREJAkUiEiZqNGwDrs/ewWHzXqOnt8/TMvj9kmYbrsLj+SAsfdy2Ixn6fndQ2x34ZEJ022xdyeOWjKCDoNOKstsSwkccdbRPDVxGM//NoIL7ruE6jULL1jt0n0n/vvpEF6a8io3j7ydJi2b5q276P5LGTF9FC9OGpk3Vav2zyWpZnpNzr79PJ758UWe//Vl/vPqnWV6XLLxXh71FiedeSm79DiKG24fnOrsyCY4/6IB/D59HLMX/B8PDbmTmjVrJEy32x5dGTXmOabP/Y4ps8bzzPMP0azZP9/n+g3q8ejj9zB55rdMnvkt11x3SbIOQYpw2aXnsGDejyz7awpPPTmYmjVrJkzXqVM7xn/7Hn8u/Z0/l/7Oh++PpFOndvnS7LJzFz7/dDQrlk1j4fyfuOTis5JxCFWKuyVtKi8UiEiZ2PGuM8ndkM1HXc7nxwuHsOM9Z1G3Q6uCCc346ZLH+KDD2YzvdzdtzzyUrY7ZO3+S6mnscNtpLP9hepJyL4Xpuv8uHHvBCfyn/7+5cJ+zada6GX0G9k+Ytl6jelz9+LWMHDycM7qezKxfZjDw0avzpXnziTc4tXPfvCk3Nzdv3Xl3X0TdBvUY2PMizuh6Cs/f+kyZHptsvKZNtuC8AX057ohDU50V2QQH9tyXSweey/FHnc7OXXrQtm1rBl1/WcK0DRs24IVhr7BLlwPZZYcerF69hoeH3pW3/va7ricjI51duxzIoQf25sS+x9Dv5OOTdSiSwKGHHMA1V1/Eob36sO323dh2mzbccvOVCdMuWrSUPn3PpWmzHWjWYkfefucjhr/0WN76LbZoxLvvDOfJp19iy+Zd6NCpOx9/8kWyDkUqMQUiUurSateixRF7MvWeV8lZm8Wy76ay9MMfaNV73wJpZw55m5W/zsFzclkzczFLPviBxnt2yJdmu/OP4M8vfmX1jEXJOgQpRI/eB/LZKx+zYPp81vy9hlGPvEqP3gclTNut197Mnz6f8e99w4asDbz64Ajadm7LVtu1LPZ1ttquJbsfvCdPXDeEv5f9TW5uLrN+m1nahyOb6ZAe3em5/z40bFA/1VmRTdC3/3EMf2EUU6fMYOWKv7n/3sfoe/JxCdN++vGXvDXmA1avWkNm5jqeefIlunXbNW/9vw47iEcfeprMzHXMn7eQ4S+Mov+pvZN1KJLAqaeeyHPDRjJp0jRWrFjJHXc+xGmnJq5VsHLl38yduwAAMyMnJ4ftt9smb/3Ay8/jo4+/YMSIN1i/fj2rV69hypQZSTmOqsQ9eVN5oUBESl2dbVvg2TmsmbUkb9nKSXOpl6hEJGKLbh1ZNXVB3nxGqya07teDaYNHl0leZeO0ateGuZPn5M3PnTSbhls2om7DegXStm7fhrmTZufNZ2VmsWTuElq3b5O37F+nHsZzP7/EPe8Mptth/5SEbd+1PX8t/IOTBvbnmR9fZPCHD+VbLyKbr0PHdvz+25S8+d9/nUKzZk1p1Lhhsdvu3X2PgjeiZvn+jFbtkeTq3LkDP/8yKW/+519+p3nzLWncuFGh2/z1xyTWrJrFQ/+9nbvveSRvebc9d2XZsuV89cWbLFrwM2PeGEbr1luVaf6lalAgIqWuep1abFidmW9Z9t+ZVK+bUeR27a/uDdWM+SPH5i3rcvvpTL03KFmR1Euvk87aVWvy5teuWgtARoJzG6Rdm2/Z2lVryagTpH1v2DtccsAFnLXraYwc/DIX3X8ZHXbvCMAWLbagTce2rF21hvP2PINnbnqSiwdfRsvtiw9mRaRk6tStzd9/r8qbj/1dt26dIrfrvEMHrhp0EbfceG/ess8++ZLLBp5L3bp12GbbNvQ/tTcZtYu+5kvZqlunNn+v/DtvfuXK4PzWq1f4+W2yZWcaN+nIpZfdyE8//Za3vGWrFpx26okMvOImttluT2bPnsfwFx8rdD+yaXLdkjaVFwpEyoCZJXxfzaz8nPkylL0mixqRG9Pq9TLIjgQn8dqeeSitT9yP7065l9z12QA0O2RXqtfNYNGb48s0v1K4fY89IK8h+fXP38S6NevIqFs7b33s78wE53bdmnVk1Mv/OahdN4PMNUHa2b/NYvWKVeTm5PLj5z/w1Zgv6NYrKPVYv2492es3MPqRV8nekM2kCb/z27e/0nW/ncvoSEUqv94nHcWcRT8yZ9GPjBz9NGtWr6Vevbp56+vVD/5evXpNYbtgm23b8Mrop7l+0B2M//b7vOXXXXM769atY8KPH/HiiKG8PuodFi1aUuh+pPT163ccK5ZNY8Wyabzz1ousXrOWevX/Ka2uH/69alXh5xdg7dpMnnjyBZ579iGaNt0CgMzMdYx58wO+/+FnsrKyuO32B9lnnz3y9imyqTSOSCkzM3P33PDvU4DtgTnAx+6+0MyqxdZXVmtmLcaqp1Fnm+asmR38ENXfoU2+KlfxWvfrwfYXH803x97KusXL8pY32a8LDbpuwyG/DAWgRr3aeG4u9Tu1ZuIA9dCTDF+P+YKvx/zTIPGyh6+gbedt+PbdcQC07dyWFX8sZ/WKVQW2nT9tHgfEtR+plVGLZlu3YP60eYlfzJ1YrD53ypwE6zf9OEQERr36NqNefTtv/olnBrNDl468+cb7AHTp0pGlS/9k+bIVCbdv1XorRr85jMH3DuG1kW/mW7di+UrOP/uqvPkbbrqCH3/4pfQPQgo1YsQbjBjxRt78iy88StedOjNqVHDOu+7UmSVL/mDZsuXF7qtatWrUrp1Oy5bN+fPP//Hrr5PxuIYFXp4aGVQi5ak3q2RRiUgpCoMQD/++B3gAOAa4FnjBzDq5e25hJSaVRc7aLBa/9x0drjmRtNq1aLRHe5r/a3cWjPq6QNqWx3en43V9GN/nTtbO+yPfuin3vMrn+1zBlz2v5cue17Lkox+Y99Jn/HT548k6FIn4YvTnHHTSwbRq15ra9etwwiUnMXbUZwnTfvfheNq0b0O3w/amRq0a9L6sD3Mnz2HRzIUA7HX4PqTXTsfM2Gm/ndnvuB5M/Pg7ACZP+J2/Fv3FcRf1plpaNTrs3pEd9t6Rn778MVmHKiWQnZ1DVtZ6cnJyycnNJStrPdnZOanOlpTQKyPGcPJpvWnfYTvqN6jHFVdfwMjhbyRM27xFM9545wWeefIlhj07ssD6ttu0plHjhlSrVo2eh+zPaWf0YfC9qrqTSi+9NIozBvSlU6d2NGhQn+uvu4wXXnw1YdqDe+7HzjvvQLVq1ahXry7333czy5evZPLkoB3Q88+/wrHH9KJr1x2oXr06N15/OV9/PSFf1T6RTVGpb4iTKSzpiAUhOwBtgX8BuxIEItnA81UlGPn12mdJS6/Bob89zm5DL+HXQc+weuoCGnfrwGEzn8tL1/Hak6jZqC77fXAHh818jsNmPseO9wR9k+esWUfWnyvzptx168lem8WGFUUXK0vZ+emLH3nzide5ecTtDP3maf5c8CevPPhy3voHPn6EfY89AIC/l/3N/effQ7+rTmHYL8Npt3N7/nvJ/XlpDz/jSJ6Y8CzDfn2ZU68fwOPXDmHS+KBOck52DvecfQe7Hrgbz/82gvPvvphHr/hvXhAj5cMTz49gt4OO4ZmXXuWdDz9jt4OO4YnnR6Q6W1JCn33yFY/+92nGvPsiP//+BfPnL+KeOx/KW//1hHfpfdJRAJx6+olss00brr7ukrzqXXMW/fNgoOvOXfjq27eZs+hHbrz5Ss4/+0qmqlellPrwo7HcP3gon3z0GrNnfsfceQu45T//1Cb4+afP6Ncv6CWtQcMGvPTiYyz7awrTpnzDdtu25YijTiErK2if+fnYcdz473t4a8zzLF74M9tt35ZTTrs4JcdVmVXFNiKm4rXNY2YHu/sncfP9gAuAVUBvd88Mlx8GXA40BE539ynxJShRbzfvpxNTSb1Qa23xiaRCe/mHB1OdBSlDLbbtleosSBlasU4Puyq77PULy8+deJwJWx2ftHu/boteLxfvQaV+Kl/WzOxa4DILxN7L7YGmwI5A3hCm7v4+8CDwP+B9M9u6sCBERERERKoWT+JUXigQ2TyjgePCgKIzgLvfBjwMrAWGmlnzWGJ3/wB4EngDSNxyW0RERESkClAgshncfbq7Z5vZEcAXZnZquHwo8ARBO5F7zaxZ3DZj3P0Kd88xs7SUZFxEREREypWq2EZEgUjpWAy8CVxvZicDuPuDwCiCqlp3m1mL6Eburu5lRERERKRKUiCykRL1duXu/wc8BIwDbokLRh4AXgX2A85KZj5FRERERMozDWi4EeIHIzSzE4D6QA3gRXf/2cwGA0YQjODuw939v2a2mKB0RERERESkgKo4oKECkRKKjJj+IDCAoEpWQ2CQmZ3r7p+a2QNALnCjmdV296fc/ZVwuzRVxxIRERERUdWsEosbrLAdsAfQA9gX6ARMB140s93c/XdgCPA70DPcxsJ9KAgRERERkQJykziVFwpESiAWSJjZWcDTwEpgGrDC3Ve6ey9gBkEAgrv/BAwC+ofz5anLZhERERGRlFMgUgQz28bM6rq7m1ldYDugNbC1u2e6e66Z1Q6T3wK0MbMdANx9Zrhe77GIiIiIFMmxpE3lhW6SC2FmJxKUftxiZunuvpqgZ6yngLZm9giAu68NN8kBNoRTnli7EhERERER+YcaqydgZmcCgwlKOSa4+zoAd19qZk8TBHDnmVk6cCdB71mDCEZLn5GSTIuIiIhIhZVbBSvyKxCJMLN/AfcC57j7qMi6au7+p5kNDRfdCPQBXgf+Ak6IVcdSSYiIiIiISOEUiITC7nmdoDes1+KDEDPbi2BQwgPNbAwwGng4XN0XWOvuF4Zp02MlKCIiIiIiJZFbjtpuJIsCkVBcz1ZtgGax5WZ2K0E3ve2BhcDjQGt3/7eZPUswgOGpZnaHu9+gIEREREREpHgKRAoaD5xhZm8DzYEmBMHHBe4+1cweBi4xs0fdfbGZPUnQUP0KM9vg7rekLOciIiIiUiGVp96skkWBSEFPAw2A7YEVwM3AYnfPDNfPAyYBWQDu/oeZPQesB8YkO7MiIiIiIhWRApE4YSPzTOD2QtbXJBgtfZK7r4gtd/clZvagGqiLiIiIyKaoijeRGkckTjSQiA1GaGa1zKwj8AawFXB+uNwK21ZERERERAqnEpEihF3x1ifopndngobpu7t7tpmluXtOSjMoIiIiIpWC2ohIIlnAbGAy8IK755hZdXfPTnG+REREREQqrCoViMSNFbIx6bPM7IlY1auwJERBiIiIiIjIZqgygUj8aOdmVheo4+5LE62PcXePbFfL3bOSmnERERERqfSqYmPjKtFYPSzZiAUTNwJvAr+b2fNmdiokbmwe2a4fcI6ZVZngTURERESkrFSJQCRWHSscJf0SgrFCDgH2BC43s3bRbeKrcZnZecALwHRVyxIRERGR0pabxKm8qBKBiAW2A44CTnP3EUBtYGtgiLtPN7O0uPTVIkHIPUBfd/8wBdkXEREREal0Km0gEhsDBPJKRHKBWu7+oZkdB3wAXOHuz5pZbeAEM2sZqY51HnAvcJa7j07BYYiIiIhIFeBY0qbyotIGInHBxBNmNgjIBrY0s/uAZ4Fr3P3xMHl74Gxg27iSkHOAwcCZCkJEREREREpXpWt4HT/QoJl1Aw4nKNGYb2ZPAFcCw9x9aJgmA7idIFAZFy5rDBwHnOrub6TgMERERESkCsktPwUVSVPpApG4IORMgsboL7j7R+HqV4HWQH8zW01QItQV2BLYNRxJPc3dl5lZX3f/OwWHICIiIiJS6VWaQMTMDgR6AHWAOcAuQB9geCyNu/9sZrcAE4EBYbr/A65z9+z4EdMVhIiIiIhIsuSWo7YbyVIpAhEzOxu4A5gCbEsQjHwIjCQo/Rjh7mMB3H0W8Eg4Wvr6uH1oxHQRERERkSSp8I3VwyBkCHAR0BM4Angf2B34EvgMGGJm+4Xpq4U9auULOmJVukREREREks2TOJUXFToQMbMewJPAXe4+Cshx91+AT4C64f93AZOBx8xsX3fPjU0pyraIiIiISJVXoQMRYCHwNbC7mfWIdb1LEIRAMITIeOC/BMHI62a2U/KzKSIiIiJSuKo4snqFbiMSjoh+JvAocKOZLQZaEgxCeKq7Lw7TfW1mtYDpwO8py7CIiIiIiAAVPBABcPcZZnYJ8DDwCtAZGODuo8wsLUjiue7+KfAp5B9rREREREQk1XKt6vWaVdGrZgFByQhwCfA/glKPWeHyHBK0yVEQIiIiIiKSWpUiEIGgZAQ4D5gP3GZm+4bLy1PnACIiIiIiQiUKRCAvGLkEWE8wVkjXFGdJRERERKRY6r63EgiraV0FfA78muLsiIiIiIhIApUuEAFw98nufoW754YN1kVEREREyq1Ud99rZr3MbKqZzTCzaxOsv8LMJpnZL2b2qZltvbnHXCkDkXhqmC4iIiIiUrjwwf0Q4DCCHmj7mVnnSLIfgd3dfSdgFMFwGZul0gciIiIiIiLlXa4lb0pgT2CGu89y9/XASOCY+ATu/rm7rw1nxwOtNveYFYiIiIiIiFRtLQl6no1ZEC4rzFnA+5v7ohV+QEMRERERkYoul4oxoKGZnQLsDhywuftSICIiIiIiUrUtBFrHzbcKl+VjZgcDNwAHuHvW5r6oAhERERERkRRL8fgeE4F2ZrYNQQDSF+gfn8DMdgGeAHq5+x+l8aJqIyIiIiIiUoW5ezZwMfAhMBl41d1/N7NbzezoMNl9QF3gNTP7ycze2tzXVYlIOfV+unodrqyaUCvVWZAy1mLbXqnOgpShxbM+SHUWpAzlTB6X6ixIFVVIb1ZJ4+7vAe9Flt0U9/fBpf2aKhEREREREZGkU4mIiIiIiEiKFTbieWWmEhEREREREUk6lYiIiIiIiKRYinvNSgmViIiIiIiISNKpREREREREJMVS3WtWKqhEREREREREkk6BiIiIiIiIJJ2qZomIiIiIpJi67xUREREREUkClYiIiIiIiKSYSkRERERERESSQCUiIiIiIiIp5uq+V0REREREpOypREREREREJMXURkRERERERCQJVCIiIiIiIpJiKhERERERERFJApWIiIiIiIikmKc6AymgEhEREREREUk6lYiIiIiIiKRYrsYRERERERERKXsqERERERERSTH1miUiIiIiIpIECkRERERERCTpVDVLRERERCTFVDVLREREREQkCVQiIiIiIiKSYhrQUEREREREJAlUIiIiIiIikmIa0FBERERERCQJVCIiIiIiIpJi6jVLREREREQkCVQiIiIiIiKSYuo1S0REREREJAlUIiIiIiIikmK5VbBMRCUiIiIiIiKSdCoRERERERFJMfWaJSIiIiIikgQqERERERERSbGq10JEJSIiIiIiIpICCkRERERERCTpVDVLRERERCTF1FhdREREREQkCVQiIiIiIiKSYrmW6hwkn0pEREREREQk6VQiIiIiIiKSYrlVsANflYiIiIiIiEjSqURERERERCTFql55iAIRKUW1G9ThlHsvoNN+O7F62SrevPdlvn9rXMK0x157Mvv0OQiAb175jDF3D89b1//Oc2nXrTNNt2nOS9cMZfyoL/LWdTvhAA4ccBhN2zZn3epMvn/za968bwS5OVWx07uyVbtBHU6NnM+JRZzP7uH5HBc5n606b82p91xA8+1bsmTGQl4cNJQFk+YCcPGw69huj055aavXqM7SWYu4vddVecsOPOMwDjrzCOptUZ9li/7i8XPu44/Zi8vikCWB8y8awCWXn0PtjAzeevMDrh54M+vXbyiQbrc9unLdDZfTdZcdyMnJZdxXE7j+mttZuvRPAOo3qMed99xIz0P2B+C5p1/m3rseSeqxyMZ5edRbjHnvE6bPms3hB/fgjhuvTHWWpIRWrs7k5uff4dvfZ9GobgaXnnAQh3frUiDd+g3Z3DPyIz77v6lk5+Sw8/atufHUw2jWqD4AZ937Ar/MWkhaWlCBZsuG9XjrjguTeixSuSkQKUNm9h/gaXefn+q8JEOf284mZ0M21+5+Dq06t+XCZ69j4eS5LJ6+IF+6ffsfTNdD9uDOw67G3bn0pX/zv/l/8NXwjwFYMHkuP7zzDcdee3KB16iZUZPXbh3GnJ+mU7dxfS54ehAHr1zNR0PfTMoxViV9bzub7A3ZDArP50XPXseCQs7nzofswR3h+bws7nym1Ujj/Cev4bPn3uPLFz9kv/6HcP6T13DzgZeSsyGHRwfclW9fA0fezNRvfsub797nIPY56SCGnHEXS2YspEmbZqxduTopxy9wYM99uXTguRx35GksWfIHLwwfwqDrL+O2W+4vkLZhwwa8MOwVPjv1K3Kyc7j7/pt4eOhd9Dn+bABuv+t6MjLS2bXLgTRpugWvv/088+ctZMTw15N9WFJCTZtswXkD+jJuwg9kZa1PdXZkI9z58vvUSEvj8wcGMmX+Ei55+BXat2rG9i2b5ks3/JPv+GXmAkbdcg51a6dz6wvvcvfLH/LgRSfmpbmufy+O33+XZB9ClVQVH6mqjUgZMbP3gf7AkrhllbZjtpoZtdilVzfeHvwKWWuzmPn9VH755Hv2PH7/Amn3OuEAPnn6bVYsWcbKpcv59Km32av3AXnrv3zxQ6Z+8xsbsgo+df3qpY+ZOXEKORtyWLl0Od+N+YrtdutYpsdWFRV2PruV4Hx+8tTb7B2ez/Z77UBa9TQ+e+Zdstdn8/mw9zEzOuxT8Mlc41ZN2X6PTox//UsAzIzDL+vNqNueZ8mMhQD8NW8pa1euKcMjl3h9+x/H8BdGMXXKDFau+Jv7732MvicflzDtpx9/yVtjPmD1qjVkZq7jmSdfolu3XfPW/+uwg3j0oafJzFzH/HkLGf7CKPqf2jtZhyKb4JAe3em5/z40bFA/1VmRjbA2az2f/DCFi449gNrpNdm1XRsO6NqOd779tUDahX+tYJ8dtmOLBnWpVaM6/9qjMzMX/ZmCXEtVpUCkDJjZbkAH4CB332BmBwG4e6Wt/rflti3IzcnJV2Vm4eS5bNWudYG0Ldq1ZuHkuXnzCybPpUWCdCXRrltnFk2vEgVOSZXofBZ2nrZq15oFhZzPFu1bs3DK3HzpF05JvJ+9jt+fGRMns2xB8CPYsEVjGm/VhK06tOaObx7jtq8e5ciBJ1KJ4/lyp0PHdvz+25S8+d9/nUKzZk1p1Lhhsdvu3X0PpkyZkX9h3Lkzg06d2pVWVkUkNHfpMqqnVaNt8y3ylnVo3SxhgHHcfjvz04z5/LFiFZlZG3hv/G/su+P2+dI8/PrnHHD5YE6/axgTp8wp6+xXabl40qbyQlWzSpmZNQZid17/NrMVwBlm1sXdl6YuZ2WrVu10Mldn5luWuWotteqmF0xbJ53MVWvzpUuvm7HRr7n3iQfSZsdteWnQ4xufYSlSeiHnM30jz2et2vnXRdfH63b8Abz/6Oi8+Ubhj2in/bpye6+ryKhfh0tfuIHli5cxbuSnm35wUmJ16tbm779X5c3H/q5btw7Ll60odLvOO3TgqkEXcWrff+qSf/bJl1w28FwuPn8QTbfcgv6n9iaj9sZ/70WkaJnr1lMnvVa+ZXUzarF2XVaBtG22bEyzxvU55KqHSKtmbN9yS67r3ytv/WW9e7LdVk2okZbGB9/9zqWPvMqrN59N6y0bl/lxSNWgEpFSZGY/ApcDK4CrgZOAi4F93X2pmVXawC9r7ToyIjeX6XUzyFq9rmDaNevy3Yim181gXeSmtzhdD92DY67px5ABd7Jm+ariN5CNsq6Q87muBOczI+58Zq1dVyDoSK9bu8D53m73DtRv2pAf3xuft2x9WCf94yfeJPPvtSxb8CdfvfwJXQ5UXeWy0vuko5iz6EfmLPqRkaOfZs3qtdSrVzdvfb36wd+rVxdePW6bbdvwyuinuX7QHYz/9vu85dddczvr1q1jwo8f8eKIobw+6h0WLVpS6H5EZNNkpNdkTSToWJ25ntqR4ATgzuEfsD47hy//eyXjhwyi564dufChEXnrd9q2JXXSa1GzRnWO7t6VnbdvxVe/zizzY6iqPIlTeaFApJSY2R1AGnCbu2cDWwIGLAMuA3D3bDNLS10uy84fsxZTLS2Npm2b5y1r1WnrhNWmFk+fT6tObePStWXxRlSv6nxAV/rfdR5Dz76HRVNVLassFHY+E52nRZHz2TLufC6eNp+WHbfOl75lxzYF9rPXCT346YMJZK3958dz6cxFbMjaQL4KjZW3dmO5MOrVt2m71S603WoX+p5wNlOnTGeHLv+0werSpSNLl/5ZaGlIq9ZbMfrNYQy+dwivjczfgcSK5Ss5/+yr2KFdd/btdgTVrBo//vBLWR6OSJW0dbPGZOfkMnfpsrxl0xYsZbutmhZIO3X+Uo7ZZyca1M2gZo3q9Ou5B7/NXsTySEl2jJlRiWuZSwooECk9LYDFYZuQ24B9ge7ATcC/zOwpAHfPqYzByPrMLH76cAJHXtGHmhm12Ha3Dux0yB58FzY8jjfh9S/pefYRNGjWiAZbNqLnOUfm66I3rUYa1WvVwAzSqlcP/w7qlrffewcG/PdSnrpgMHN/1lOZshI7n0fFnc+uh+zBhBKcz4PPOZJvw/M5bfzv5ObkcuAZh1G9ZnUOOO1fAPl6xqpRqwa7HbE340eNzbffDevW88M733DoeUdTq046DZs3Zt/+B/Prpz+U3YFLPq+MGMPJp/WmfYftqN+gHldcfQEjh7+RMG3zFs14450XeObJlxj27MgC69tu05pGjRtSrVo1eh6yP6ed0YfB9z5W1ocgmyE7O4esrPXk5OSSk5tLVtZ6srNzUp0tKUbtWjXpuWtHHntzLGuz1vPj9PmM/WkaR+69Y4G0O7Rtwdvf/sqqtevYkJ3Dq59/T9OG9WhUrzZ/r13HuN9mkrUhm+ycXN4d/ys/TJtH9y7bpeCoqobcJE7lhSmyLR1mdhTwKEEvWe2BPd19upk1JOg96wrgc3c/J0yf5u6FXtEvbHtShTsxtRvU4dT7LqTjvjuyZvlqxtwznO/fGsd2e3TkomHXc8UOp+WlPe7ak9mnb08Avhn5KW/EjTtx+cibab/XDvn2/WDfW5g+fhKXj7iJ7fbolK9HrZkTJzMk0g1seVZRTmzsfHaKO58T3xrH9uH5HBg5n93D8zkucj5b7dCWU+8+n+btWrFkxgJeHPQ4C36fk7d+96O7c+w1/blx34sK5CG9bgYn33UuXQ7clbV/r2HcyE957+HRBdKVN68t+ynVWSg1F1x0BpcMPIeM9HTefutDrrr8prxxRL6e8C7/Hfw4o159m6uvvZhB119aoNpW262CqnTHHHcYd9x9PfUb1GfmjDncevN9fP7p10k/ntKweNYHqc5CUgx55iWGPjs837ILzjyZi846JUU5So6cyYnHS6pIVq7O5OZhb/PtpNk0rJvBZeE4Iv83bR4XPjSC8UMGAbBi9VruGfEh306aTXZ2Dtu1bMpVJx3Cjtu2ZNmqNVz80EhmL/4fadWMti2acNExB7D3Dtum+Og2X/p+p5bLXk+uatsvabcI988ZUS7eAwUim8HMrgEec/fV4fwXwH7AcHc/NS5dA+BkYCDwmbufV9y+K2IgIiWjE1v5VaZARAqqKoFIVVUZAhEpmgKR8hOIqGrWJjKz04CdgMxwPlZW+QDQ3cwejKV195XA8HBdfzMblOTsioiIiEg5pu57ZWO8DIwI23z0Asa6+wFmVo+getbFZubufgUEwYiZjQzXvZW6bIuIiIiIpJ5KRDaSmd1pZke4e3bYMH1fYAjwqJk1dPdVwIvAI8DxZjY4tq27L3f3Nyprg3URERER2TTqvleKZGY7AvsD15lZj3DxBOA5oBNwbxiMLAVeAh4GjjWzZ6L7KqqhuoiIiIhIZadAZCO4+68E3fEuA243s0PcfQNwD/Au0JX8wchw4AWgoZnpvRYRERGRhKpi9726OS4hM6sB4O6fEQQYKwiCkf3CYOQ+4E2CYOSeuGDkYaC3u+cqGBERERERCejGuAQsaHW+Ifz738AJQEtgD+B+M+sZF4yMAXYEnjSzumG7EA/3UZ6CUBEREREpJzyJ/8oL9ZpVAh4OtmJmFwPXAEcD0wnai5wN3GZmue7+uZndD9QHmgBro/sQEREREREFIiViZrFBX7oDr7n75+H8y2a2ArgbuNvMrnT3r83sRiA3LAmpppIQERERESlKVbxZVNWsEvAQ8DfQzMxqxa17D3gV2BV4zsy6uXuOqmOJiIiIiBROJSIb51egN7CfmX0WF2QsAD4DvgC+jyVWdSwRERERKYnyNOJ5sqhEZCO4+6PA5wSjqh9jZtuaWX3gOOAr4C4NVigiIiIiUjwFIiUUCy7cvTfwMfAo8A0wEWgP3B1XHUuDFYqIiIhIiaV6ZHUz62VmU81shpldm2B9LTN7JVw/wczabu4xq2pWCYUlHRY2FznZzA4CmgLpwEuxkhAFISIiIiJSkYQP3IcAhxA0OZhoZm+5+6S4ZGcBy919ezPrSzCgd5/NeV0FIhshLPFICxujfxa/zsyqu3t2qvImIiIiIhVXituI7AnMcPdZAGY2EjgGiA9EjgFuCf8eBTwae0i/qS9a5atmFTbaeWHtPGIlHtHtFISIiIiISAXVEpgfN78gXJYwTXjfuxLYYnNetEqXiMSP8WFmxwIZwCp3f6eoqlbx3fKa2XbAQndfl8y8i4iIiIhUZFU2EIkEE4OB0wlGQs8xs+Pd/cxEwUh8EZSZXQpcBhxAEDmKiIiIiGy0FA88txBoHTffKlyWKM0CM6sONAD+tzkvWuWqZsWqVMUFE9sAuwMHAj2A/wCHmtmrYbq87ngjQch5BPXkbnB3BSEiIiIiUlFNBNqZ2TZmVhPoC7wVSfMWwYN7CMbV+2xzx8yrUiUiZtbQ3VfEzZ9DMAbIbGCyu2eb2RIgE3jQzF5x9z5hMFLD3TeE250H3Auc6e6jk38kIiIiIlKZeAobq4f3wBcDHwJpwLPu/ruZ3Qp87+5vAc8AL5rZDGAZQbCyWapMIGJmjwNbAUeHpSJ1gXbAjsC8WGNzd19rZm8RdLN8v5l96u4944KQcwi6K1MQIiIiIiKVgru/B7wXWXZT3N/rgBNL8zWrTCACDOWfLsgy3P1vMxsCrAauMrM73P0GAHfPNLO3gdrAcbFG7WbWG3gCOMHd30jFQYiIiIhI5ZPiNiIpUWXaiLj7z+6+wcwGAPPMrLm7zwWeBR4ATgyLn2LpM4ER7n5MrFE78AFwqIIQEREREZHNU5VKRGJmAzOAz83sQHdfYGbPhuv6mVmuu98C4O5ZsY3CAQtXA58kPcciIiIiUqmlso1IqlTqEpFCBiv8iqDL3eXAl2HJyDyCkpHhwEAzOzu6kQYsFBEREREpPZW2RCQyWOGBBMea6e5fA+PN7DLgYYJgZD93n2dmLxKMB/J8yjIuIiIiIlWO2ohUInFByP3A68BTwFgzG2Fmu7n7RIKSkT/D5Vu5+2x3fzZ+7BARERERESl9lS4QMTOL+7sLcDxwGLAvwQjouwG3mFlHd/8OuJrgfXggfvv40dRFRERERMpSrnvSpvKi0lXNihv5/CpgW+A9dx8fjoq+IOyC90PgAoISkW+BE4DJ8duLiIiIiEjZqTQlIvEN082sMbATcB5BMBIutpru/gtwE9A3bKju7v6bqmOJiIiISKp4EqfyolIEIpGG6acAOwD/BR4FepnZce6e6+7rw03WAn8A6+L3o+pYIiIiIiLJUeGrZoVVrmJByN3AGcANwCvAg0B94DUzOw34AtgAnAYsBVamJNMiIiIiInFyy1VZRXJU+EAkrk3IIIIg5HDgF3ffAKwyszuBNcCLwDJgJFAbONjdPb40RUREREREkqOyVM2qBewJ3OXuPwBbmdnRZvYecDbwE3AXQQAy0d33c/essM2IghARERERkSSr8CUioTSgI7Am7BXrNIKgI4ug294mBG1GWgBDzGypu39IUE1LRERERCSlvApWzaoUJSLuvpagK94eBA3UfwRucfcjgE+AJu7+K3AHMBx438wOUVe9IiIiIiKpUVlKRHD3T8xsNyDd3edDXpe+ewILwjSzzGwwQUnJ/JRlVkREREQkTlVsK1BpAhEAd/8TwMzqAd2Bi4BWwFFxaaab2VVhY3YREREREUmBShWIxNkeuJAguNzV3bPNrLq7ZwMoCBERERGR8kTd91YS7v6jmQ0EZrt7bnwQIiIiIiIiqVcpAxEAd58JeaOuKwgRERERkXJLvWZVQhonRERERESk/Km0JSIiIiIiIhVFVXxyXulLREREREREpPxRiYiIiIiISIpVxXG2VSIiIiIiIiJJpxIREREREZEUq4rjiKhEREREREREkk4lIiIiIiIiKVYVe81SIFJOrdIYjJVWRzJSnQUpYyvWrUl1FqQM5Uwel+osSBlK69Q91VkQqTJUNUtERERERJJOJSIiIiIiIinmaqwuIiIiIiJS9lQiIiIiIiKSYuq+V0REREREJAlUIiIiIiIikmLuKhERERERERH5//buO0yqKunj+LcYchKQKCCIgAISxIBrxIyurxHFAMKugquoYA6YXQOYddFVzIppVUyYFXMOKAoIgpIkqASJQ5h6/zinx0szICjTPdPz++zDs9M39N7e2+HWPaeqip1GREREREREsqwsNjTUiIiIiIiIiGScRkRERERERLJMfUREREREREQyQCMiIiIiIiJZpj4iIiIiIiIiGaARERERERGRLFMfERERERERkQzQiIiIiIiISJYpR0RERERERCQDFIiIiIiIiEjGaWqWiIiIiEiWqaGhiIiIiIhIBmhEREREREQkywpUvldERERERKT4aURERERERCTLyt54iEZEREREREQkCzQiIiIiIiKSZWpoKCIiIiIikgEaERERERERyTKNiIiIiIiIiGSARkRERERERLLM1UdERERERESk+GlEREREREQky5QjIiIiIiIikgEaERERERERyTLXiIiIiIiIiEjxUyAiIiIiIiIZp6lZIiIiIiJZpvK9IiIiIiIiGaARERERERGRLFP5XhERERERkQzQiIiIiIiISJYpR0RERERERCQDNCIiIiIiIpJlyhERERERERGJzKyOmb1mZhPjf9cuYptOZvahmX1rZl+bWY/1eW4FIiIiIiIiWeYZ/M8GOh94w91bAW/Ex+mWAMe7ezugG3CzmdX6oydWICIiIiIiImtzCPBA/PsB4ND0Ddx9grtPjH//BMwB6v3REytHREREREQkywpKbtWsBu4+M/49C2iwro3NbEegIjDpj55YgYiIiIiISBlmZq8DDYtYNSj5wN3dzNYaMZlZI+AhoLe7F/zR/64CERERERGRLPsTuRsb73/bfZ+1rTOz2WbWyN1nxkBjzlq2qwmMBAa5+0fr87+rHBEREREREVmb54De8e/ewLPpG5hZRWAE8KC7P7m+T6wRkQwwM/Oy2C4T6HbCQfz9X4dRqUolPnnpQ+4fdCcrl68sctu2u7Sn9xV92bRxPSaNnsBdZ/2HX2f8DMDRF/Rip4N3o2qNqixesJg3H3mV54c+lcmXUuZV3qQaBw3pyxa7t2fp3EWMGvI43z77QZHb7nn+0XQ6uisAox97i1HXPla4rtXe29L1vB7UalKPOeOnMvK8u/ll4ozC9XucfSQdj9ydClUrM/vbH3n54vtXWy+ZNeD0vpxz9ilUrVqFp54eSf9TL2D58uVrbNemTSvuu/cWtmzRDIAvvhjDwDMvZty4iYXbbNtpG2684XK23bY9ixcv4drBt3Hbf+7J2GuR3y1YtJRLH3iBD7+dTO3qVTj9iL04sMs2a2y3fMVKBj/2Km9+8R0rV62iU8umXNTrABrUrgnACUMe5OvJM8jLC/c169eqwXNXnZLR1yJ/ziNPPsczL77OxMk/cOA+XbnqorOyfUhlXgnOEbkWeMLMTgCmAEcBmNn2wL/c/cS4bHdgUzPrE/fr4+6j1/XEGhHJgLIahLTfvRMHnXw41x57GQN3Pon6TRtw+BlHF7lt9do1GPDfc3nqhkc5uePx/PD1JE79z+9fim89/gbn7XUa/bbpyRWHX8DOh+7G9t26ZOqlCNDtyj6sWrGKm7c7hWcGDKXbv/9B3VaN19hu22P3Yqv9tuPubhdy9/4X0GqfznQ+bm8AajdvwCG39OelC+/l+vZ9mfj6lxx595lYvIhp8/cudDxqDx7sfgU3duzH9C++55CbT87o65Tf7bfvHpx7Tn/269aDFi270GKLzbns0qIvVn76aTY9ju5HvQbtaNCoPc+/8CrDH769cP2mm9Zm5AvDuevuh6nfcBu2arMLr73+dqZeiqS5+pGXqJCXx6gbz+Dqvody1cMv8X288ZM0/PVP+HrSdJ68rC+v3zCQmtUqc+0jr6y2zQXHduOjoefx0dDzFISUIvXqbspJfY7msL/vl+1DkRLO3X91973dvZW77+Puc+Pyz2IQgrs/7O4V3L1T4t/oP3puBSLFyMzGmdmAbB9HtuzavStvP/4GMyZOY8lvi3nmtv+xW/c9i9x2h247MWPiND558UNW5K9gxE2Ps3nbZjTaMlzozpr8E/lL8wu39wKnQbNGGXkdAhWqVGLrA3bk7Rv+x4ol+Uz/bAITX/+C9ofvusa2HbrvxsfDXmThrLksnD2Pj4eNpEP33QHYco8OTPt0PNM/m4CvKuCDO56nRsM6NNupDQC1mtZj2qffMX/az3iB882I96jbcs1gRzKjV68jue/+xxg7dgLz5y/gqqtv4fheRxW57YIFvzFlynQAzIxVq1bRcsstCtefMfAkXn3tbR59dATLly9n0aLFjB//fUZeh6xuSf5yXv98PP0P3YOqlSvSudXm7NGxFS98OGaNbWf8Mp+d223JpptUp1KF8uy/Q1sm/bRmwCKlz75dd2Hv3Xem1iY1s30oUoYpECkmZnYHsBS4NdvHki1NWm3O1HE/Fj6eOvZHatWvTfVa1dfYtnHrpkwd+/u2+UvzmTNlNo1bNy1cdtDJhzFs7HBu/eRuKlWtxAfPvluchy8JdVo0pGDVKub+MKtw2exxU6jXuska29Zt1YTZ46YWPp4zdip1WyeCCbPV/jQofJ5vn/+I2s0aUGeLhpQrn0eH7rsz6e2vN/4LkvXStu1WfPX12MLHX339LQ0b1qdOnTWa6hb6Zc5YFi+czC03/5trB99WuLzLjp2ZO3ce7779LD9N/4pnRtxP06abFevxS9GmzJ5L+bxyNG+4aeGyrZo2KDLAOGy3Toz+fhpz5i9kaf4KXvzoG3Zt33K1bW59ehR7DLyB3tfcz6fjfyzuwxfJWSW4oWGxUY5I8WkIfBzLnJ0KfO/uL2f7oDKpUrXKLFm4pPDx0vh35epVWDR/0WrbVq5Wmd9+/W21ZUsWLqFKtSqFj1+4YwQv3DGCZu22YLv9dmTpwsXFePSSVLFqZfIXLl1tWf5vS6lYrfKa21arTH7ivC9buIRK1cN5/OG9b9jz/KPZfKc2TP98Ajuf/H/kVSxPhSqVAFg0Zx7TPv2Ok9+6gYKVq/ht5q88fMzVxfjKZF2qV6vKbwt+/1wuWLAQgBo1qjF37rwi96lbvy1Vq1bh+F5HMXXq9MLljZs0Ytttt6HbAccw5pvxXHvNIIY/dDu7dz20WF+DrGnpsuVUq1xptWXVq1RiybL8NbbdvH4dGtSpyb5n30JeOaNl4/pccGy3wvUDuu/NlpvVpUJeHi9/8i2n3/YET1x6Ik3r1yn21yEipZ9GRDYyM0tVFfgWaG9mrwJXAN9k76gyY+dDd2fY2OEMGzucsx+4iPzFy6hS/fdAIvX3skVL19h32eJlVKlRdbVlVapXYeniNbed8u0PLF+2nMPPLDrfRDa+5UuWUalGldWWVapRheWLl6257eJlhYEHQKXqVciP5/zXSTN5/qw72f+K3gz4dChVatfg54kz+G3mXAB2G3A4m3Vswa1dTuPa1n149+YR9Hz0QspXrliMr05SjjnmMObPncD8uRN44bmHWLR4CTVq1ihcXzP+vfAPbgIsWbKUO+96kPvuvYV69cJd96VLl/HMsy/z2edfkZ+fz5X/vomdd96h8Dklc6pUrsjitKBj0dLlVE0LTgCuHv4yy1eu4p2bz+Kjoeexd+etOeWWRwvXd2jRmGqVK1GxQnkO3qUjnVo24d0xf9jDTESKUOCesX8lhQKRjcjM3gYujQ+vAuoCXYGh7j49bpOz/59/8Mw79G17HH3bHsf1vf/N9IlT2bxt88L1m7dtzvw589YYDQGYMWEam7f5fdtKVSpRv1lDZkyYVuT/Vl75POpvXlTfHSkOcyfPolxeHrWb/95MtX6bzfl5wvQ1tv1l4nTqt2lW+LhB22b8MuH3qlfjX/yEYfudz02d/sU7Nz1FrSb1mPn1pMJtxz7/EQtnzcVXFfD1k+9QuWY16hWRFC8b36OPjqBWndbUqtOagw7uxdix39GxQ9vC9R07tGXWrDlrHQ1JKleuHFWrVqZx4/A5HTNmHMm6HWW0hkeJ0KxBHVauKmDK7LmFyyZMn82Wm9VbY9vvps3mkJ07sEn1KlSsUJ5j9t6Bb374iXmJUc8kM9O5FZH1lrMXxZlmZocDzYF2cVEPoDIwCtjdzE4CcPeCXA5Gkt576m32OGpvNmvVhKo1q3LIaUfy7pOjitz2s1c+pknrpmx/wE5UqFSBQwccxbRxU5g5aQZmxp7H7kfVmtUAaNGxJfscfwBj31fuQKasWJrP+Jc/ZY8zu1OhSiWabN+a1vtux5in31tj2zFPvUeXvgdQo0FtqtevRZe+B/L1k+8Urm+4TXOsnFG1Tg0OvOYEJrz+Bb9OmgnAzK8m0+bvXahWtyaYsc1hu1KuQh5zp8zO2GuV3z388JP8o8/RtGnTik02qcmFFwzgwYeeKHLbffbejU6d2lGuXDlq1KjO9dddyrx5Cxg3LiSkP/DA4xx6SDc6dmxH+fLluejCgbz33sf89tvCTL4kAapWqsjenbfm9mffYkn+cr6cOI23Rk/goL+1X2Pbds0b8fyHY1i4ZBkrVq7iiVGfUa9WDWrXqMpvS5bx/jeTyF+xkpWrChj50Rg+nzCVXbbZMguvSjbUypWryM9fzqpVBawqKCA/fzkrV67K9mGVaWUxR8R052LjMLNdgXuA84EuQAPgnLj6dmAzQpOXu+L25dy9YG3P16vZ4TlxYrqd+H8c9K/DqFi5Ip++9BH3DfpvYR+Ra167meeHPs0Hz4SL1Ha7dOD4K06kbpN6TPpyInedfRu/TP8ZM+Ps+y+iRaeWlK9Qnnmz5/Huk6NKbR+RranyxxuVQJU3qcZB1/Vji922Yem8RYwaHPqINN1hK45+4Fyua3tC4bZ7XXDMan1E3rzm96kcxz95CfXbbE7BylWMG/kxr185nBWxIlpepQrsc9FxbN1teypUqcy8KbMYNeQJJpeyhPVLZ76V7UPYaAYO6Mc5Z59ClSqVeXrEi5zS//zCPiJfjX6TawffxqOPjuCIIw7i8svOoUnjRixduoxPPx3NoIuvYcyYcYXPdVK/47nwgtOpWrUK73/wKaeediHTp/+UrZf2py1649psH8JftmDRUi69/3k+HPsDtapXYUDsI/LFhKmccsujfDT0PADmL1rC4Edf4cOxP7By5Sq2bFyPs4/al/YtGjN34WJOveUxfpj5K3nljOaN6tL/kD34W7sWWX51f01em12yfQgZMfSeh7nj3uGrLTv5n8fR/4SeWTqizKlQt4X98VaZ16redhm79pv48+cl4v8DBSJ/kZlVcPcVseX9YGAXwshIW3f/Lm7TPK5rDDzg7sPi8rU2OsyVQETWVFoDEVl/uRSIyJpyIRCRtSsrgUhZVlIDkS3rds7Ytd+kX74oEf8flIkpQsXFzB4E9okBxcy4uAnwJVDYotbdfwTOA6YDPc1sYFyuYENEREREyiQFIn+SmeUBC4HXEwHFJ8CxwI/A6WZWOL6ZCEaWA1uZWYmIREVEREQk+8pijoj6iPwJZtbY3WcA/ePjfxGCkrvcfbmZTQCuBPrGCiIPA7j7FDPrBcyJ/UXWOjVLRERERCSXKRDZQGZ2O1DJzK5290mxAlZPoA7gZjbC3ceY2UXAv4ETY7wxHMDdZ8XnWWeyuoiIiIiUHWXxslBTszbcdGA34GQzax2Dia7A94SpV4ebWRV3/wYYBMwFLjSz/ZNPoiBERERERMoyjYisp9Q0Kne/2swWAKfF5Xe7+/jYR+RZ4Ny4/Gl3/9bMrgCOBl7P2sGLiIiISIlWUIJyNzJFgcj6Myh8h4wEdiUEGHlmNtTdvzezQwjByDlAgZk96+6jgdEQEtzdXd2CRERERKTM09Ss9ZSaSmVmtwDPEIKSH4FTgf5xmtZK4BBgKnAjIVhJPoeCEBERERFZg7tn7F9JoRGRDWBmBwK9gH2Ar9x9lZmdCwwMq+0/cWTkCOBq4I3sHa2IiIiISMmlQGTDVAIWAD8Tp2m5+xAzqwhcDqwwswfdfQxhepamY4mIiIjIHyqLOSKamrVhDKgNVHT3AjOrEpffCfwC9AP2SO6gIEREREREZE0KRDaAuz8NfA08a2aV3H1pXFUdeI5QMeuObB2fiIiIiEhpoalZ6ynRBf0M4B5grJmdD6wijISsdPc747aajiUiIiIi660kJZFnigKR9eS/vzu+AI4kJKPfCOQD04D/g8KARUGIiIiIiMg6KBDZQDEgmQgcaWbNgBXArJgzUj6W8BURERERWW8FGhEpmxLTrpLLyqV6hxSxfWrd1NR+cZmCEBERERGR9aBAhN+nXZnZcYSKWPfFEY41ApS4fVHrLFPHKyIiIiK5xctg+V4FIpGZ1Qb6A6vMbKm7P+buXtTISDIIMbNewNvuPjULhy0iIiIiUiqV2fK9ZrbaCIa7zwP6EBoWnmRmx8TlBclt04KQvsADQPtMHbeIiIiI5B53z9i/kqLMBiKJYKJRYtkEQnneZUA/M+uR2tbMyqUFIScB1wNHuPvIjL8AEREREZFSrMwGIgBmdgpwv5l1SS1z94nAQEKTwgvM7Mi4vCAtCBkC/NPdR2T8wEVEREQkpxTgGftXUpSpQMTM0l/veKANcJaZ7Zha6O7fAZcAWwCDzGz/xHMMAK4hBCFPFf9Ri4iIiIjknjITiMRu5wXx763NrLm7vwnsA2wHnJccGQEqAK/Gf6/F/ZoC/wD6KwgRERERkY1FOSI5yMxONbPOqW7nZjYEeAH4zMxGA50JXdE7Auea2VFm1hA4EfjM3c9NJKzPAvZ390ez8mJERERERHJETpfvNbMtgPOBV83sGsI0rJ5AX6Aa0A54BDgV2B+4B7gNyAd+AW6Mz5NKUl8BzM7wyxARERGRHKfO6jnG3X8ws4MIAcZAwgjQjakqV2ZWEZgCDAMOBA4D2gKbAiPdfZWZlVfHdBERERGRjSunAxEAdx8d+33cBbQAbk6sW25mjwH7Ace4+yvA+6n1Ma9EQYiIiIiIFKuSlLuRKTmfIwLg7l8Qksx/A44ws86JdUsI07CaFLHfqowdpIiIiIhIGVImAhEAdx8DHBwfnpkKRsysBtABmJatYxMRERERKWtyfmpWkrt/bWa9CQnqL5vZJ8ASQuJ6P1gtMV1EREREJCNKUqPBTCkzIyIp7j4aOApYADQHngd2cPcVMTG97L0LREREREQyrEyNiKS4+zdmdiyhV8jD7u5mVk6J6SIiIiKSDWXxXniZDEQA3P1TM/ssEYQUZPuYRERERETKijIbiADEIMQUhIiIiIhINpXFhoZlLkcknXJCREREREQyr0yPiIiIiIiIlASuqlkiIiIiIiLFTyMiIiIiIiJZphwRERERERGRDNCIiIiIiIhIlpXF+kkaERERERERkYzTiIiIiIiISJapapaIiIiIiEgGaERERERERCTLlCMiIiIiIiKSAQpEREREREQk4zQ1S0REREQkyzQ1S0REREREJAM0IiIiIiIikmVlbzwErCwOA4mIiIiISHZpapaIiIiIiGScAhEREREREck4BSIiIiIiIpJxCkREREREZL2ZmWX7GCQ3KBCRnGZmB2T7GEREyjIzu9zMmmb7OGTjcVU6ko1EgYjkLDP7GLjazPJ090ZEJPPM7CXgWGBWYpm+j0spMxtnZgOyfRySOxSISE4ys17Apu6+rbuvAhpn+5hERMoSM9sO2ArYy91XmNleoLvppZWZ3QEsBW7N9rFI7lBDQ8lVM4GaZtYV2BfoZGbd3X1pVo9KssbMyrl7QbaPQ6QsMLM6wJT48GIzmw/8w8y2cffZ2Tsy+QsaAh+7u5vZqcD37v5ytg9KSjcFIpJTzGwUcBXwPfAc8BCwKbCVuy81M9PduLInGYSYWROgirtPTKzX+yLD1hYY6lyUfmb2JfA8cAVwDnAPUBHY1t1nm1l5d1+ZzWOU9Wdmvd39AeBboKuZvQpsD3TI7pFJLtDULMkZZnY/4Y7Nu+7+I7Ac2AyYTJgeQLyTo/nJZUwiCLkGGAWMNrO3zOwsM6uo90VmxWAjdU56mtllZtbHzBrHc6HfplLKzK4C8oArY7BRHzBgLjAAwN1Xmlle9o5S1peZvQ1cGh9eBdQFugJD3X163EafV/nT9OaRnGBmNYD2hC/H/DhsXA84DvgKuMbMDgYFI2VJ8gfSzE4EegMXA4cA04EjCe+NiroLnxnJEQ8zGwzcSDgf5wMPmlkbdy/QxU2p1QiYGXNCrgR2BXYBLgH2N7NhAO6+SsFIyWZmhwPNgXZxUQ+gMuFmzu5mdhKEGz36vMqfpalZkiuWA58Ax5vZnkAnYF93n2xm0wh34i4yM9z9uVQwoovP3Ja4674PUBu43N0fi8s+BM4CDgX+DozQe6J4pU2Ra0e4yNkfGE0IRk4GHohTQcYpr6dUGgH8J1YtbA3s6O4TzWw64SL2TDMb5u59U8FILCgiJc8cYBnQzcy6AA0IU7IAbgd6xa/Mu1LBiD6vsqEUwUqpFpPRcfd8QiWP2sDBwBB3nxzXvQ/cDPwIDDKz/4vLdcFZBphZc+BVYDBhqh4A7r4YuBJYCeg9UYxiIJgMDI8B7gCqA+M9eIbwOZ0H3G9mW8eLG41elnBmdq6ZVQdw9+cJ37U7AC+kcrHcfT4wnDAC1tXM7ozLFYSUMGZWIf45CfgYuJ6Q63Otu//i7r8A5wIzCDf/+kLhyIg+r7JBFIhIqWVmtwDHJIb3WwO1gPeAnqmLHwB3/wC4iZAvcquZ7Zrhw5UMSf8hjPlCexL6GOxmZo1S28TA422gQeLHVzYiMzsfGGBB6jenJWHqZHtCEjMA7v4S4XP6K/CSmTVTcFiymdnxhKTlpfHxlnHVjcAuZnZTalt3X8DvwcixZnZehg9X/oCZPQjsE0eHZ8bFTYAvgW1S28Xv1fMIU1x7mtnAuFyfV9kgpveMlFZmthUwOc5F3oJQKrIq0JFwt6YecLG7v5HYZw9gH+Ay3YnLPWlTfyq7+7LEur2BZ+O/i4GpQAXgLeBbd/9n5o8495lZK+CHmKC8jbt/E5efTJgy+QVwprsnG94dCuwOnKPPaclmZuUJ1xIrzKwb8Ja7L4t5eycBpwJPu/uZiX1qExKen9P5LTniTb1bgYHuviIuO5VQDv8Ywm/qMHd/OLFPM+BuQqXKUxSIyIZSICKlTrwDN9Ldf42PjwPOBq5y9yfjsn2B/oSKLRe5+5tFPI/mJueQtCTo84EuhB/Op4GXYs7BvsBThLnP3xFyi5oBO7n7cuWIFB8z+zvwIOEi56G47AxCwYDvCUHHGv0l9DktmczsauB9dx8ZH+8KPEBIZD7b3eebWQOgJ3Aa8JS7n1XE8+j8lgCxYt2MxON/AQuB/8XvxvaEqay1WTMYaQjMSU3N0neobAhNzZJSJQYdVwLnmNkmcfF4YD5wopkdBeDurwFDgdnA5WZ2YPpz6ccvd8SRkFQQcgGhAtNXhOlYRxFyDjrH98UhQDWgFWEa0A7xh7aCfkCL1UzCaNSF8XOMu98EPEmYqnWtmTVK30mf05InXpTuDlyQytMj5BLcB7QBhphZrRhYPky4y36omd2T/lw6v9lnZrcDV6Sm1cUplD2BQUB3M6vi7mOAiwg5XCemPsMA7j4rkayu71DZIApEpLR5jDDHeG9C4vmm7v45YYpHHtCviGDECRefkqMS07FaEPIOerj7Ze7enVA2dCZwg5k1dfdRhGkGjYF/ABXjXdkVWTr8nFNUKU93/wK4BXgfuCwRjNwIPAHsBpyQyeOUPydelF5C6A3ybzPbN35+BgMjCdNjk8HIcMJoWK2i3huSddMJn7+Tzax1/D7tShipPA84PAYj3xCCk7mEGwr7J59EFbPkz9DULCk1zKxSrI6V6j+wCyEx/do4DaAjobqHAXe6+//itjsAn+tLMrfFKXv3EnI/jnP3DxPrDiFcJPV193fjsr0Id+NHAb3dfVHmjzr3pOXpHAHUJOTiPOTuS82sDWEq5e6EXK3hcdsewJO6Q16yxZHDVP5AD6AXYQrk2e7+biz6cA7h5s9o4Lz4/VwbmO8eGlbq+zj70qaz9idMoXsBuNvdx8f8n2cJyepDCLk+S82sE3A0MEifV/mrFIhIqZD2hdkb2JZwV7sK8B/genefG4OR6wijII+4+wOJ59CPX44zs5HAAcBAQjCan1j3I3CPu1+ZWNaNkGi5o7v/lNmjzT1pn9ObgD6E0ahahKpK/dz9DQs9RAYSmt3d6O7DEs+hnIESKu38XkwYfWxFGAH5FLgwnt8KhGDz/wh32/+ZCvSVQ1BypN00aA5cQxgZ+R+hOfD3iWCkMeFmzrPuviTxHPq8yl+iIVIpFRI/fpcSeg18CvQl9Ic4BDjPzGq7+1eEH8D6QOe051AQkiPWNr3D3f8OvAlcCOwbq8AQ84kWAT+nbf8y0FpByMaR+Jy2IvSR6EoINtoAE4GHzGw7d/+WMG3yW8I0y8Kyy7qoKbkS5/dUQmXCOwjBRk9gMXClme0ZR0yuJ5TGXgAsSX8Oyb5EEHIL8AzhBt6PhEpn/eM0rZWE39iphLLLu6Y9hz6v8pdoRERKhXiRUhd4Hfivu98Rl5cDbgC6Eyq2XB+nAbQklPZV8JFj0u7i7US42z4VmOKhSSFm9jbhLu3jwFhgL0JCdMf4wyobWepOt5mdABxPCPy6A/mJ8/UOUNHdd4qPtySU9tXntBRIBYvAI8BST5S8jgVBriWMfJ3l7u/FGwEFmo5VcsXz9jChrP1XHrrdn0sYsXwC+E8cGakAXA2cr+BDNiaNiEipEO+iLQQKgE0g1K939wJ3P4NwIdoHGGxmm7j796kqHlk7aNno4sVu6qL2asIUgtuAD4GLzKwDgLvvEZf1BXYEPnL3dh56WZTPztHnJjPbwsyqx4vN6sCWQFOgmbsvjZ/DqnHzy4DN49Qs3H2SPqelh0fAb4QmoJUS614kXLh2Bu4zsy7uviq+L0xBSIlViTBq9TNhRAR3HwLcTsgZOcnM2rv7Cnc/JwYqeWt/OpENoy9/KU1WEMrx7hcTJlcmLmC+IZTwXUb4kQQ0HSvXJKaGXAD0Bnq5eytC2dD+wCkxTwh3P4AwgrYDYSpf6jk0IrKRmNmRhBybyyw0kFxEqIw1DGhuZrcBJOaUryJ8jlerUKbPaakzBtgJ2C0tiJxOmBp5H/BZaqGmY5VoRugNUjHeFKgSl98J/AL0A/ZI7qAREdmYFIhIqRDvqK0izF3tQOgLsQmQF38IaxH6iwxMTQPI3tFKcTKzLYCdCef6rVgRqw8hobIHcHqs6oK77wf8BAwzswN1J2/jMbN/AncBzxGqXS0D8FCu9W5C4ushZjYsjpp0JJQCnU4oCyqllLv/h1Bt7hHCOW5hZjWBw4B3gWt057x0cPenga+BZ2NlyqVxVXXCZzuVCyRSLJQjIqVGYg76noQpALMIHbJrxn9t44+f5iLnMDOrRcj5eBXYhjA9a4i732ZmNxJGSl4BrnD38XGfLwklZHdMVnyRP8dC/4DhwL/c/cm0deXindU6wMmEJmgrCB3uywEneSgBqs9pKZSskmRmwwkFCfII03sKgPZxtFrVsUq4xG/qdsA9QA1CM9hVhJGQle5+UNxW1bGkWCgQkVLJzOoSkulqAPmEspEr9WVZNsSchEVmdgOhrGRvd883s6sIVZi+B45PXuiaWTN3n5KlQ84JiQuXa4Ba7n5yYt1OhNKfexIq8DwFLAdOJ/QceNfdT4nbVk6NoEjpk1bGdy9CH5HKwMOpkRB9D5cesQhBS0Iy+k6E39RpwH7uvkJBpRQnBSJS6qztRy4mr2v+fxliZg8DFQn9Keab2dOEpoYjE1P0TBdFG1e8E97A3feJj68glPVsDcwg5OVc5e4Xm1kjQsf0XoQpXIOydNiyEel7ODeZWTPCCOasOLKp8ynFSoGIlCgbMl0j9UOY6D+gN3MZY2anEUqGfgA0AMoDHTQ1pHjF/9//QQg6GhJKa/8XeMbdvzOzWwklfLdy99lmVp8QjJxJaJR2WXaOXNZmbd+9fzS6oSl2JVNR33/rOleJKZXJ0S6dWyl2KmMpWWVmPQjziye4+2eJ0qzrvIhMJK9DqPaRv7ZtpXRZnx+/1Psj5oWsJHR3Hg1coCl6GXE3oYx2S0K1ukuBmYlE16mE/i35AO4+x8zuI0zVeibTByvrZqv35jkUqAIsdPcX1jXVKlmW10JPmBmaclcyJIKJ4wi/kfelBxpp2xe1ztK3E9nYNCIiWWNmrYHxhOorywl3V28iNCJcmthutS/HtDs2ZxAqKB3roZuvlGJp53YXQv+PIgOKddzB1VSCYvRHgaKZVSRUMJvh7iduyL6SeWmfuRsIxR6WEBKWR3lsWpgejKTtdzowANjD3adn+jVI0cysNjCScC6Huvtjcfkan8O089kLeNvdp2b6mKXsUYlTyRp3nwA8TwhALiLc1b6NUEZwh5iQnryzUy7ty7If4U7sCAUhpV/8cUyd2+sIP6CbpabepUuOnqUtVxBSjIq4gCkX/7uSmW0NjAA2A/4Vl9va9pXsSZ23xGduC2B7QrGBrsDlhJ5NT8TtCsvxpn0Pn0RoVDlIQUh2FfFdOI9Q2nwBoTHhMXF5QXLbtPPZF3gAaJ+p45ayTYGIZIX9Xl/+EcLI3OfA/oQ+IT8B7wEPmdlJFhsseeiinvzxuw74p7s/kvEXIBtdIrBoRJgScIi7T1uPKXqp90RbC529JYPiRU1NQh+fWwnFA7ZPTJHTsHsJY2a1fPWKcn2BocAPwDh3n0wokX4WsKuZPQ6FwUiFtO/hIUDf1N12yZ7EeWmUWDYBOIPQ7LdfnA6NeyjmUURQeT1whLuPzPgLkDJJgYhkRWKI/2NgbzMbEOcWjyfcifmC0L33BuArM/tHat/4ozmEEIQ8ndkjl+JkZj2BKcABhD4x69o2+QN6GvAWUL+4j1GKlE+4iH0U6Oah5Gd55emUPGb2X+DB+He5GES2InzvtkqNKHrot/McoUz638zsjbh8Rdy3LzCY8D38VKZfhxTNzE4hNPztklrm7hMJ57E6cIGZHRmXp9/cS/2ujsj4gUuZpUBEssKCPHf/EbgC6GpmLQgByCLCxcy5QGdC47TUD+dJwJ1AH/345aSpwGvAFkAlCDkf6RutZWrIafFOrvwFa5sKt67tPRSLuDMmxKaSmzVFrmS6Azgi/l3F3X8jjIYMA9pb6MUDQMzVex64BFiUmIbXnfA9/A99D2dX6pwkjAfaAGeZ2Y6phe7+HeE8bgEMstCUNPUcA4BrUFApWaBkdckIC02vdooPX3H3z1MXkxYaoQ0lfEF+TPgynJmeUBcvkI4idHvVl2Upt7aEScI89dsIjQq7uPtPtno35/QgJHUXT++Jv8hWr55UHajm7rOLWr+O/Sq5qtiVeGbWhzDi3M7dZ5lZE+BE4FjgMXe/JLHtauc0vjd2cvfXM3zYkpD2vbg1sMzdf7RQCOYlQiXBIe7+cdzmYEI/nx+A8+O0yqaEYHOwuz+ajdchZZsCESl2cQj/KuBbYFPCXNWece5qapubCD+C7VSpI/elXbj+ndCZuTzwsrtPN7OOhOC0IbB7DEZWq4YVixUMBk5UEPLXpQV4FxGSljsSiga87u4Prcd+xxA+4//ViEjJZmZ7EIL4msCeMRjZHPgncAzwqBfR7yX9cyiZZ2anAh+4+xfx8RDgcKAWMJ3QW+lrwtS6r4DHgXeAu4D33X1w3M8I37t1kjccRDJJU7OkWMUg5HbgZGAvQkOzOqz53nuAMKT8t7if6pfnsEQQMpgwxaM7cB7wtJkd7e5fERJlZwCjzKxpWhDyd0IDvRMUhGwciWDiCuA0Qq+QfYEdgYFm1ip9nyJGpx4EJupCtWQpYvoOwLuEkrvzgHfMrGG8CXQvYTrsGWZ2YvpOOrfZZaG62fnAqWbWKo5y9CScy1MIpbMfIVQ+259wY+A24BOgCXBjfJ5UL6YVCkIkmzQiIsXGzI4mfCH2Tt5NNbPxwKdAC8KQ8P3Ar8ArhOuhvTN/tJJpcWrIVcDBcarePwlByeHu/nzcZnvgIWCMux+Vtv+u7v5ehg87Z8XgvwXwJGHaxisWerm8Bpzq7vemTQVJjmqdRBidUmBYwqSdpz0Jd8CXpj47ZrYDodrZpsBu7j47XuzuCTygggMlj5l1Au4BPiLc1Jvk7tfHdRUJgckw4EBCANKWcH5HxhwujWpJiaERESlOOwGLgcpmVhnAzJ4hdO2dSRg6vppw0bOCcNemvEZDyoytgWdjENKD0MzydHd/3syqmVkjd/+MkFh7TGqnVPK6gpC/LnmnPI5sFACVYhByGPAycGYMQqoCR5hZ43g3NRmEDEFBSImUOE/XA08TLlDfMrNHzWw7d/+UcDf957h8M3f/wd3v9UTvECk53H000BfoAvQAqibWLQceA/4HHOPu89z9fXd/ToUkpCRSICIbXeqHy90HEqZqnA/0MrPnCAnpXd39XHc/mdB74GQza0a489o1JrDrvZmjEhc2WwFTzawzYRrQee5+Rzz3xwOHxQvesckLIv2IbjyJi9Q7zew8YCVQ30JDyXuBc939v3Hz1oQ8rhaJ6Vh9CQnPKhZQwiRv6JjZNoQcggOAXYE9gO2Ay8xsa3f/BDiHcE1wY3J/jYiUTDE/5B/Ab4QbBJ0T65YAvxCmYqXvp/MpJYou9mSjS37RuXt/4A3C3e7tgOPd/YdEoPETMBZY7O6LYhBSeLdVSr/0oDLx/niB0L35U0JDtNQFb1XgMKBZ6oI3bT/5i5J3uS30GzgQ+NLdpxGmx50GPO7ud8RtqgD/JgQq78dldQjnqZeCkJInESyeTcgdeNHdPwJmuPv7hLys7Qn5ewAfEkYfj0vuLyWXu48BDo4Pz0wFI2ZWA+gATMvWsYmsL+WIyEZjZtsS7nJ3Jky7muzuH8R1NxPuyF1LuMD5NV6gPgcsAXrohy/3pM1P351QqOBHYDKhetq9hITo44H3CHfwbiY0Juyi0Y/iFfNydgR+dfdBcVlHQqGAQwlVdsoRqmfVBzp7aFaYF0epanroQyElRNpnrg7h83QcoWz6gfF7t7y7L48jWv8GOrr7rMRz5CnwLz1izsgjQF1CTsgSYEtCieUVyaISIiXNGo3CRP4MC53PBwELgYrAGcAMM7vP3S9394FxjvnZcfvHCEnqLYFtUtOxNBKSWxIXRDcQEijzCFMG5gBHEt4zBcCLhNGxucB8wg/oSl0QbVwxWbkrUI0QEG5LmGM+PLWNu39lZpcRRqr6xO2+AC6I56Qw0VVBSMmSFoT0BKYQApF5wGlmdpiHrtnL4y5LCJ/FZcnn0WeudHH30WZ2FDACaE4oHDHcQ58QJaZLiaYREfnLzOxYwhz/E4BRHurRdyUM+R8JXOvuF8Zt7wD2ASoAS4EO8Y6NvixzSFpZ127AdYT3w2RCguVphNGPXd19jpn9DdgMmE2oj68f0I0slmK9ilAmuwUhGHmFUFCiB/B/7v5W2j4VY/Jr6rECwxIq7TN3LSF/YBChh8SmwKWEZnbHA28DKwgV6fKAfXXHvPSLFdBOBP6lm3tSWigQkb/EzOoTKrE84e63pq1rRfghPJRERR0zu4dQTnB3BSG5LVbD2oUw5XxAYvl2wC3A98BJntaJWz+gG1cMQoYSpug8Q/j8nUeYlnUlITegJeEC5t20alo6D6VILDpwJiHv52sPFQlT38cDCDcE5hIqK3UC9nH3fH3mckMqINX5lNJCyeryV9UmDAV/mlqQqLYykXCxuYRwMUpcfgKws4KQ3GZmFQhT8U4F2ifXufvnwKuEfKIK6fvqB3TjiaOTdwHXuPuTwCp3/xp4Hage//saYBxwu4X+LAWpf1k6bPkTzKwSIbi8Jn7GNjOzg83sRcKd8tGEc10V+NTdd4tBSEWd69yggi9S2igQkb+qGiEBuXJqQWJ6gLn7l4T5/7uaWZ6FZkvJL0sFITkontsVhDKhzwDtzKxPrL6UMpoQhNTO/BGWKTMIhQC2N7OuiSk41eN/e6ymdDMhGHnazDpk/jBlI8gj9OfpbGbdCb2ZTgecULb3b4SpWo8BQ81s/7jfiiwcqxQTTbOT0kTJ6rLB0oZ8FxB+/PYCRiXnKaeGhwl3376Pc8uTpX31ZZmj4rnPc/clMWn2WWAgsEksVFCJcIE0A5ievSPNfe4+MVbH+g9wkZnNBBoTmhD2cveZcbv34h31icC3WTtg+dPi520AoRrdfoRSzK/Fc3s50Mndx5jZVYTg4yUz29/dX8viYYtIGaYcEfnTzKyWu8+PpXlPBY509xFplVuqEUr0vubu16qMYNmSKPNaDXgK2JsQeHxBCEaO0Pz0zIg5ArcCjQg5In3c/ZHYU8TT//9XYnrpZWb1gMoe+sKkevmMBKa7e9+4rBXhe/sOdx+ftYMVkTJNgYj8KXHY/3agFWFq1l2EUZH+wMuEspFbEhoZNgW21zSssikRjFQl1LrvDFxGKC+Zmp++fJ1PIhuFmbUk3CVvSCgg8VFcrhsEOSg2ttuF8L3cHNg2+T1sZhVSyewiItmgHBFZL6kqOqlEdOA7Qkf0E939B0IFnocJwclHhCk3jwCbADukekJk/MCl2CTeC+sUg5A8d18CHAtMInR6PtDMKisIyRx3/x44idBx+Uoz2zUuVxCSm1oSPmsFhGaUK82scEq2ghARyTaNiMgGMbOm7j4tBhVXAHsC3d39p5iIvhPQjpC8/hXwdrwQVXWsHJI2/a6eu/+8Hvskp2k9DbQGBrj7c8V8uJImTsu5mdC7pY+7f5XdI5LiYmZbAj+oN4+IlEQKRGS9xfr0VwL9gCcIyY6fA2Pd/eh17Ke55jkkLQg5mxB43uDu3/zBfgbkxbuylQlNMC9190nFftCyBjNrA/QFzlZ+Tu5THpaIlESqmiUbohrhPXMJoRHWR4TuvfebWR93v7+onRSE5JZEEHId0JtQ/WrxuvZJ5CCsjAFtfXfvWewHK2vl7uMIje90s6AMUBAiIiWRAhH5Q4k7aZcDmxL6PswB+sTH44B9zexld5+VtQOVjIkd03sA+8deMcQeIVumRkYSHX4LE6HNrB9wAaFaj5QQCkJERCQblKwua0glpse/BwHXm1mXeLHyHrCM0KSwNyHpdV/gGKBLFg5XsqMpYd75l2a2tZmdQ2hQ+L6Z3Qq/95FJBCEnAdcB/3T3h7N14CIiIlIyKEdE1srMjiBUXTkWmA28BQwGXgK+c/fT4na9gB2AM5UImXuKKu0amxReDHxD6EnxGaE4wXRCtbTt3f2LxPb9+D0IeSpTxy4iIiIllwIRKZKZNQBmAtsRgpAewFmE5PQXCReVJ7r7E2n7qSpLDklLTK8MVHL3BWa2CXA8oUHhs8Cb7j7FzNoSujof7+4T4n4nAUOJDS+z8kJERESkxFEgIsCad73NrDoh6LjY3Z+ItedrE3qFVAE6Ap8QSn/OyMYxS/FKC0IuJEy96wIMB15x91dTgWeczleNMBpSDdgnlgutSGimNlUjISIiIpKkQERWY2Z13f2X+Pf/gJnufnraNn0I1bIM2EPN0HKbmV1FKNk8AFhJSDY34GB3nxr7ghxGyBnaFOji7itSgYy6N4uIiEhRVDVLCpnZ+UBvM1sMjAG2BlaZWTNgrrsvBHD3+83sSWBJIiFZpSFzkJm1Aw4CDnf3d81sN8L74uQYhJQjBCWNgC+AQanuzakpegpCREREpCgKRCTpHULycVegImGKzVFAQ6CtmY0m9Iu41d1HgZpklRHlYxDSHbgPOCMGo1UJQcrLhPdEPhT2pFCekIiIiKyTAhEp5O4fxD9fADCzl4BhwPVA/fivAyFgSe2jICRHFFUdC6gB1Daz04ArgPPd/b9xXUdCEYPJ7v5Zagf1pBAREZH1oUBEVpNoQpcH/AKsAj5Lb1SoTsy5xcwquvvy+Hdld18G4O4fmdlbwC2EwgVD4zZVgUGAE6ZkiYiIiGwQBSKymtQdcXdfZWZfEAKRvwEj0rZTEJIDzGxnd/8gEYScAexjZnOAF2Klq8uAesC5ZpYPVAd2IeSFbBsT0jVFT0RERDaIOqvLupQDKhEuOCXHmNklwB0x9wMzOwu4BBhP6B9zjpmdF/uB9CDkhxwD7ASMAzrF6ljlFYSIiIjIhlL5XlknMzsOeFzJx7nHzDoAQ4AKwIOEka8n3P1NM6tLCEp2AZ5y96vjPnXcfW7iOdTAUkRERP4UBSKyXnTBmVsSPT7aEPI/8gg9QA5398lxm4bAhYQA5Vl3/3facxSV3C4iIiKyXjQ1S9aLgpDckczncPdxwEBgBdAa2C+1XSxQcBXwHnCimfVOPo+CEBEREfkrNCIiUoYkgxAz6wH84O6fmFlLYCghJ2iou/8vsc9mhH4yt6lIgYiIiGwsCkREyojkVCozGwwcB9wI3Ofu89Kmaf03GYwknkNlm0VERGSjUCAiUsaY2enAxUA3YKy7L03LGbmJEIw87O4PZPNYRUREJHcpR0SkDDEzA3YkjHh8DuSnVkFhzsgAoA6hhK+IiIhIsVBDQ5GypSahCtYMgDgKYrGBZSWgubt/Z2ZHAFOzeaAiIiKS2zQiIpKjzGwbM2sQ/x5iZp3cfQHwKtDZzLaG1apfbQVcbGYt3f3HVMf07By9iIiI5DpdZIjkoNis8C2gt5ndAZwNpJLM3wRaAv3MrG3cvj5wJdAYmJx6HnVMFxERkeKiZHWRHGJmu7v7O/Hvc4ELgMrAQe7+RmK7/sBphK7q8wjJ6Qbs4O4rkmV+RURERIqDckREcoSZ3QpUNLMP3X0FMD6u+g3Y3szGu3sqN2SomX1FmI7VEpgAPOTuK82svBpYioiISHHTiIhIjjCz1oQGhSvMrDkwjXCz4SzgFOAu4G53/2kdz6E+ISIiIpIRyhERyQGx8tWEGIT0BJ4GDnX3fHe/Grgb6Af0iZ3SMbOnzKxd8nkUhIiIiEimaGqWSClXRD7Hd8BCQjK6ufuT7n6ZmTkhGNnBzOoCrQhTskREREQyTlOzREqxZBASy/EuI0zJagA8BDhwp7v/L25zCtAWqAicopwQERERyRYFIiKlVBzt8Pj3YOAIoBYwHbgBeBu4L25+h7s/GbetEJPZURAiIiIi2aKpWSKlUNpIyFFAL6AvUA1oBzwADAR6EkZG+ppZJXcfngpCABSEiIiISLYoEBEphRJByF7AvsCN7j4yLqsITAGGEfJF+gEjgO2B4Vk5YBEREZE0mpolUkqZWUPgfaAecJ27X5lYVxW4F8h3995mthUwUU0KRUREpKRQ+V6RUsrdZwGHAXOA7mbWObFuCfAL0CTmknzn7gVmlpelwxURERFZjQIRkVLM3b8GDo8Pz0wFI2ZWA+gATPXEsKf6hIiIiEhJoalZIjnAzDoBjxCmaX0CLAW2AP7m7suTFbZERERESgKNiIjkAHcfDRxFaGS4CfC6u28Xg5CKCkJERESkpFEgIpIj3P0bwjStCsCOZtY6Ll+e1QMTERERKYKmZonkGDPbFriTUML3Encfl+VDEhEREVmDRkREcoy7fwn0J+SLzM/u0YiIiIgUTSMiIjnKzCq7+7JsH4eIiIhIURSIiIiIiIhIxmlqloiIiIiIZJwCERERERERyTgFIiIiIiIiknEKREREREREJOMUiIiIiIiISMb9P2tRcuPZRszvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate correlations - Top b unit loss\n",
    "top_50_spin_xwoba_corr = top_50_bu_loss[\n",
    " ['xwOBA_diff', 'pitches_per_app', 'Pre_Spin', 'spin_diff', 'bu_diff']\n",
    " ].corr()\n",
    "\n",
    "# Plot the correlations - Top b unit loss\n",
    "fig, ax = plt.subplots(figsize=(12, 10))\n",
    "att_heatmap = sns.heatmap(top_50_spin_xwoba_corr, annot=True,\n",
    "                          annot_kws={\"fontsize\":12})\n",
    "plt.title(\"xwOBA Correlations - Top Spin/Velocity Loss\",\n",
    "          fontsize=24, fontweight=750, pad=15)\n",
    "plt.yticks(rotation=45, fontsize=14)\n",
    "plt.xticks(rotation=45, fontsize=14)\n",
    "\n",
    "plt.savefig('images/top_BU_loss_corr.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again we don't see any correlation between these two factors. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions\n",
    "\n",
    "Even though the enforcement of the sticky stuff ban didn't come into place until June 21, the threat of future enforcement appears to have been enough for pitchers to stop using it to augment the spin of their pitches. The first reports of a coming change appeared on June 5th (to my knowledge) and spin dropped noticably on the 7th. How much spin dropped varied by team and player, however at this point it does not appear as though the drop in spin has had an impact on the success of the pitchers. \n",
    "\n",
    "### Potential Further Analysis\n",
    "1. Look back on the same analysis later in the year when more time has passed and the post June 7 sample grows. \n",
    "2. Compare the same time frame and xwOBA from the last few years to this year to see if the improving weather might be helping hitters.\n",
    "3. Look at the strikeout rate for these pitchers over this time frame. Maybe their xwOBA remains reletively consistent because they are giving up some strikeouts that become weak contact outs. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}