xT Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## xT - Expected Threat tutorial\n",
    "\n",
    "Expected threat is how we can quantify a players contributions independent of the outcome\n",
    "#### Articles to read:\n",
    "    - https://karun.in/blog/expected-threat.html#visualizing-xt\n",
    "    - https://soccermatics.medium.com/explaining-expected-threat-cbc775d97935a\n",
    "    - https://theathletic.com/2751525/2021/08/06/introducing-expected-threat-or-xt-the-new-metric-on-the-block/\n",
    "    - https://sharmaabhishekk.github.io/projects/xt-derivation-julia (if you are interested in the Julia language)\n",
    "    - https://www.youtube.com/watch?v=RwVLUH4BFfA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# import data\n",
    "df = pd.read_csv(\"/Users/mckayjohns/Desktop/Soccer/europaFinal.csv\")\n",
    "\n",
    "# preprocess the dataframe so we only have the data we want\n",
    "# We want to get it down to be only successful passes made by Bruno Penandes\n",
    "# we can use pandas .loc method to achieve this with a bunch of & statements\n",
    "\n",
    "df = df.loc[(df['teamId']=='Manchester United') & (df['playerId']==18) & (df['outcome']=='Successful') & (df['type']=='Pass')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>minute</th>\n",
       "      <th>second</th>\n",
       "      <th>teamId</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>period</th>\n",
       "      <th>type</th>\n",
       "      <th>outcome</th>\n",
       "      <th>playerId</th>\n",
       "      <th>endX</th>\n",
       "      <th>endY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>49.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>59.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>6</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>65.4</td>\n",
       "      <td>96.8</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>63.3</td>\n",
       "      <td>87.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>6</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>69.8</td>\n",
       "      <td>30.3</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>74.4</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>84.3</td>\n",
       "      <td>23.3</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>12</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>39.0</td>\n",
       "      <td>64.8</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.8</td>\n",
       "      <td>72.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     minute  second             teamId     x     y     period  type  \\\n",
       "2         0     0.0  Manchester United  49.9  50.0  FirstHalf  Pass   \n",
       "119       6    11.0  Manchester United  65.4  96.8  FirstHalf  Pass   \n",
       "130       6    55.0  Manchester United  69.8  30.3  FirstHalf  Pass   \n",
       "133       7     1.0  Manchester United  84.3  23.3  FirstHalf  Pass   \n",
       "185      12    20.0  Manchester United  39.0  64.8  FirstHalf  Pass   \n",
       "\n",
       "        outcome  playerId  endX  endY  \n",
       "2    Successful      18.0  31.5  59.6  \n",
       "119  Successful      18.0  63.3  87.8  \n",
       "130  Successful      18.0  74.4  18.8  \n",
       "133  Successful      18.0  86.0  20.0  \n",
       "185  Successful      18.0  32.8  72.9  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import xT Grid, turn it into an array, and then get how many rows and columns it has\n",
    "xT = pd.read_csv(\"/Users/mckayjohns/Desktop/Soccer/xT_grid.csv\", header=None)\n",
    "xT = np.array(xT)\n",
    "xT_rows, xT_cols = xT.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00638303, 0.00779616, 0.00844854, 0.00977659, 0.01126267,\n",
       "        0.01248344, 0.01473596, 0.0174506 , 0.02122129, 0.02756312,\n",
       "        0.03485072, 0.0379259 ],\n",
       "       [0.00750072, 0.00878589, 0.00942382, 0.0105949 , 0.01214719,\n",
       "        0.0138454 , 0.01611813, 0.01870347, 0.02401521, 0.02953272,\n",
       "        0.04066992, 0.04647721],\n",
       "       [0.0088799 , 0.00977745, 0.01001304, 0.01110462, 0.01269174,\n",
       "        0.01429128, 0.01685596, 0.01935132, 0.0241224 , 0.02855202,\n",
       "        0.05491138, 0.06442595],\n",
       "       [0.00941056, 0.01082722, 0.01016549, 0.01132376, 0.01262646,\n",
       "        0.01484598, 0.01689528, 0.0199707 , 0.02385149, 0.03511326,\n",
       "        0.10805102, 0.25745362],\n",
       "       [0.00941056, 0.01082722, 0.01016549, 0.01132376, 0.01262646,\n",
       "        0.01484598, 0.01689528, 0.0199707 , 0.02385149, 0.03511326,\n",
       "        0.10805102, 0.25745362],\n",
       "       [0.0088799 , 0.00977745, 0.01001304, 0.01110462, 0.01269174,\n",
       "        0.01429128, 0.01685596, 0.01935132, 0.0241224 , 0.02855202,\n",
       "        0.05491138, 0.06442595],\n",
       "       [0.00750072, 0.00878589, 0.00942382, 0.0105949 , 0.01214719,\n",
       "        0.0138454 , 0.01611813, 0.01870347, 0.02401521, 0.02953272,\n",
       "        0.04066992, 0.04647721],\n",
       "       [0.00638303, 0.00779616, 0.00844854, 0.00977659, 0.01126267,\n",
       "        0.01248344, 0.01473596, 0.0174506 , 0.02122129, 0.02756312,\n",
       "        0.03485072, 0.0379259 ]])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xT # this is how our pitch is broken up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 12\n"
     ]
    }
   ],
   "source": [
    "print(xT_rows,xT_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bin Data for xT\n",
    "\n",
    "# We use a pandas method called .cut --> https://pandas.pydata.org/docs/reference/api/pandas.cut.html\n",
    "# We say labels=False because we want the integer locator of the bin\n",
    "df['x1_bin'] = pd.cut(df['x'], bins=xT_cols, labels=False)\n",
    "df['y1_bin'] = pd.cut(df['y'], bins=xT_rows, labels=False)\n",
    "df['x2_bin'] = pd.cut(df['endX'], bins=xT_cols, labels=False)\n",
    "df['y2_bin'] = pd.cut(df['endY'], bins=xT_rows, labels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>minute</th>\n",
       "      <th>second</th>\n",
       "      <th>teamId</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>period</th>\n",
       "      <th>type</th>\n",
       "      <th>outcome</th>\n",
       "      <th>playerId</th>\n",
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       "      <th>endY</th>\n",
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       "      <th>x2_bin</th>\n",
       "      <th>y2_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>49.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>59.6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>6</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>65.4</td>\n",
       "      <td>96.8</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>63.3</td>\n",
       "      <td>87.8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>6</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>69.8</td>\n",
       "      <td>30.3</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>74.4</td>\n",
       "      <td>18.8</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>84.3</td>\n",
       "      <td>23.3</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>12</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>39.0</td>\n",
       "      <td>64.8</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.8</td>\n",
       "      <td>72.9</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     minute  second             teamId     x     y     period  type  \\\n",
       "2         0     0.0  Manchester United  49.9  50.0  FirstHalf  Pass   \n",
       "119       6    11.0  Manchester United  65.4  96.8  FirstHalf  Pass   \n",
       "130       6    55.0  Manchester United  69.8  30.3  FirstHalf  Pass   \n",
       "133       7     1.0  Manchester United  84.3  23.3  FirstHalf  Pass   \n",
       "185      12    20.0  Manchester United  39.0  64.8  FirstHalf  Pass   \n",
       "\n",
       "        outcome  playerId  endX  endY  x1_bin  y1_bin  x2_bin  y2_bin  \n",
       "2    Successful      18.0  31.5  59.6       5       4       3       4  \n",
       "119  Successful      18.0  63.3  87.8       7       7       7       7  \n",
       "130  Successful      18.0  74.4  18.8       8       2       9       1  \n",
       "133  Successful      18.0  86.0  20.0      10       1      10       1  \n",
       "185  Successful      18.0  32.8  72.9       3       5       3       5  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return Bin Values for xT\n",
    "# We use a lambda function to assign the values from the bins to the x and y values with numpy array slicing\n",
    "# it is going through each row and assigning the values based on where those numbers are in the numpy array\n",
    "# kind of an advanced topic, so be sure to familiarize yourself with slicing and indexing\n",
    "# its basically doing this: xT[4][5] where 4 is the fourth list and 5 is the fifth item in that list\n",
    "\n",
    "df['start_zone_value'] = df[['x1_bin', 'y1_bin']].apply(lambda x: xT[x[1]][x[0]], axis=1)\n",
    "df['end_zone_value'] = df[['x2_bin', 'y2_bin']].apply(lambda x: xT[x[1]][x[0]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
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       "      <th>teamId</th>\n",
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       "      <th>end_zone_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>49.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>59.6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.014846</td>\n",
       "      <td>0.011324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>6</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>65.4</td>\n",
       "      <td>96.8</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>63.3</td>\n",
       "      <td>87.8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0.017451</td>\n",
       "      <td>0.017451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>6</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>69.8</td>\n",
       "      <td>30.3</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>74.4</td>\n",
       "      <td>18.8</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024122</td>\n",
       "      <td>0.029533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>84.3</td>\n",
       "      <td>23.3</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.040670</td>\n",
       "      <td>0.040670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>12</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>39.0</td>\n",
       "      <td>64.8</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.8</td>\n",
       "      <td>72.9</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.011105</td>\n",
       "      <td>0.011105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     minute  second             teamId     x     y     period  type  \\\n",
       "2         0     0.0  Manchester United  49.9  50.0  FirstHalf  Pass   \n",
       "119       6    11.0  Manchester United  65.4  96.8  FirstHalf  Pass   \n",
       "130       6    55.0  Manchester United  69.8  30.3  FirstHalf  Pass   \n",
       "133       7     1.0  Manchester United  84.3  23.3  FirstHalf  Pass   \n",
       "185      12    20.0  Manchester United  39.0  64.8  FirstHalf  Pass   \n",
       "\n",
       "        outcome  playerId  endX  endY  x1_bin  y1_bin  x2_bin  y2_bin  \\\n",
       "2    Successful      18.0  31.5  59.6       5       4       3       4   \n",
       "119  Successful      18.0  63.3  87.8       7       7       7       7   \n",
       "130  Successful      18.0  74.4  18.8       8       2       9       1   \n",
       "133  Successful      18.0  86.0  20.0      10       1      10       1   \n",
       "185  Successful      18.0  32.8  72.9       3       5       3       5   \n",
       "\n",
       "     start_zone_value  end_zone_value  \n",
       "2            0.014846        0.011324  \n",
       "119          0.017451        0.017451  \n",
       "130          0.024122        0.029533  \n",
       "133          0.040670        0.040670  \n",
       "185          0.011105        0.011105  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we find the difference between the start and end to get the xT value\n",
    "df['xT'] = df['end_zone_value'] - df['start_zone_value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>minute</th>\n",
       "      <th>second</th>\n",
       "      <th>teamId</th>\n",
       "      <th>x</th>\n",
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       "      <th>end_zone_value</th>\n",
       "      <th>xT</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>49.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>59.6</td>\n",
       "      <td>5</td>\n",
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       "      <td>0.011324</td>\n",
       "      <td>-0.003522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>6</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>65.4</td>\n",
       "      <td>96.8</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>63.3</td>\n",
       "      <td>87.8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0.017451</td>\n",
       "      <td>0.017451</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>6</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>69.8</td>\n",
       "      <td>30.3</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>74.4</td>\n",
       "      <td>18.8</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024122</td>\n",
       "      <td>0.029533</td>\n",
       "      <td>0.005410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>84.3</td>\n",
       "      <td>23.3</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.040670</td>\n",
       "      <td>0.040670</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>12</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>39.0</td>\n",
       "      <td>64.8</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.8</td>\n",
       "      <td>72.9</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
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       "      <td>0.011105</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "     minute  second             teamId     x     y     period  type  \\\n",
       "2         0     0.0  Manchester United  49.9  50.0  FirstHalf  Pass   \n",
       "119       6    11.0  Manchester United  65.4  96.8  FirstHalf  Pass   \n",
       "130       6    55.0  Manchester United  69.8  30.3  FirstHalf  Pass   \n",
       "133       7     1.0  Manchester United  84.3  23.3  FirstHalf  Pass   \n",
       "185      12    20.0  Manchester United  39.0  64.8  FirstHalf  Pass   \n",
       "\n",
       "        outcome  playerId  endX  endY  x1_bin  y1_bin  x2_bin  y2_bin  \\\n",
       "2    Successful      18.0  31.5  59.6       5       4       3       4   \n",
       "119  Successful      18.0  63.3  87.8       7       7       7       7   \n",
       "130  Successful      18.0  74.4  18.8       8       2       9       1   \n",
       "133  Successful      18.0  86.0  20.0      10       1      10       1   \n",
       "185  Successful      18.0  32.8  72.9       3       5       3       5   \n",
       "\n",
       "     start_zone_value  end_zone_value        xT  \n",
       "2            0.014846        0.011324 -0.003522  \n",
       "119          0.017451        0.017451  0.000000  \n",
       "130          0.024122        0.029533  0.005410  \n",
       "133          0.040670        0.040670  0.000000  \n",
       "185          0.011105        0.011105  0.000000  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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