# Modeling goal chances in soccer: a Bayesian inference approach

## TL;DR

This paper introduces a Bayesian inference method to analyze soccer match data, modeling chances created and player contributions to better understand team and player attacking behaviors.

## Contribution

It presents an interpretable Bayesian framework combining Poisson and Gaussian mixture models to quantify team abilities and visualize player impact areas.

## Key findings

- Identified key areas on the pitch influencing chance creation.
- Quantified team abilities to generate scoring chances.
- Visualized differences in player attacking styles.

## Abstract

We consider the task of determining the number of chances a soccer team creates, along with the composite nature of each chance-the players involved and the locations on the pitch of the assist and the chance. We propose an interpretable Bayesian inference approach and implement a Poisson model to capture chance occurrences, from which we infer team abilities. We then use a Gaussian mixture model to capture the areas on the pitch a player makes an assist/takes a chance. This approach allows the visualization of differences between players in the way they approach attacking play (making assists/taking chances). We apply the resulting scheme to the 2016/2017 English Premier League, capturing team abilities to create chances, before highlighting key areas where players have most impact.