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arXiv · 1710.00001

A Bayesian inference approach for determining player abilities in football

Gavin A. Whitaker·Ricardo Silva·Daniel Edwards·Ioannis Kosmidis
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Abstract

We consider the task of determining a football player's ability for a given event type, for example, scoring a goal. We propose an interpretable Bayesian model which is fit using variational inference methods. We implement a Poisson model to capture occurrences of event types, from which we infer player abilities. Our approach also allows the visualisation of differences between players, for a specific ability, through the marginal posterior variational densities. We then use these inferred player abilities to extend the Bayesian hierarchical model of Baio and Blangiardo (2010) which captures a team's scoring rate (the rate at which they score goals). We apply the resulting scheme to the English Premier League, capturing player abilities over the 2013/2014 season, before using output from the hierarchical model to predict whether over or under 2.5 goals will be scored in a given game in the 2014/2015 season. This validates our model as a way of providing insights into team formation and the individual success of sports teams.

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Gavin A. Whitaker, Ricardo Silva, Daniel Edwards, Ioannis Kosmidis. 2017-09-25. A Bayesian inference approach for determining player abilities in football. https://arxiv.org/abs/1710.00001

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