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.

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.