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A Bayesian approach for determining player abilities in football

Gavin A. Whitaker, Ricardo Silva, Daniel Edwards and Ioannis Kosmidis

Journal of the Royal Statistical Society Series C, 2021, vol. 70, issue 1, 174-201

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, Journal of Applied Statistics, 37(2), 253–264) 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.

Date: 2021
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Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

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