AnshChoudhary/xGModel
This repository contains code to predict the Expected Goals (xG) from shots in football using various machine learning models.
This project helps football analysts and scouts quantify the likelihood of a shot becoming a goal during a match. By inputting details like shot location, angle, distance, and play type, it outputs an 'Expected Goals' (xG) value, providing a data-driven measure of shot quality. It's designed for anyone involved in football analytics, coaching, or sports journalism who wants to evaluate player performance or team strategies.
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Use this if you need to calculate the probability of any given shot in a football match resulting in a goal, based on historical data and various shot characteristics.
Not ideal if you're looking for real-time, in-game predictions or if your primary interest is in player tracking or tactical analysis beyond shot quality.
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Last pushed
Jun 21, 2024
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