AHL expected goals linear model in tensorflow
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Updated
Jan 12, 2021 - Jupyter Notebook
AHL expected goals linear model in tensorflow
An expected goals (xG) model for the AHL. Also applicable to the CHL.
An NHL expected goals (xG) model built with light gradient boosting.
This repository consists the supplemental materials of the paper "Decomposition of Expected Goal Models: Aggregated SHAP Values for Analyzing Scoring Potential of Player/Team".
Building xG and xGOT based on Statsbomb World Cup 2022 Open Data
A open source project to develop live xG prediction models from photo and video input using Computer Vision.
Classification model for expected goals (xG) in women's club soccer, predicting the likelihood that a shot will score using data extracted from StatsBomb.
FBref player stats scraper
We will analyze in our code the expected goal locations of football players based on which areas of the field, at what times, with which feet, using which parts of their bodies, and from which angles and distances they are most likely to score.
Some projects related to the application of Machine Learning for Sports Analytics
Analyze the relationship between expected goals and odds of winning a game in the English Premier League
Using wyscout data to create a xG model
Fun with Rocket League tracking data
📊⚽ A collection of football analytics projects, data, and analysis by Edd Webster (@eddwebster), including a curated list of publicly available resources published by the football analytics community.
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