Modelling Australian Rules Football as spatial systems with pairwise comparisons

Andreacchio Anton (), Bean Nigel and Mitchell Lewis
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Andreacchio Anton: School of Mathematical Sciences, The University of Adelaide, 5005, Adelaide, Australia
Bean Nigel: School of Mathematical Sciences, The University of Adelaide, 5005, Adelaide, Australia
Mitchell Lewis: School of Mathematical Sciences, The University of Adelaide, 5005, Adelaide, Australia

Journal of Quantitative Analysis in Sports, 2022, vol. 18, issue 4, 215-226

Abstract: Statistical analysis in competitive sport is an important tool for developing strategy and seeking competitive advantages. However, for complex team sports such as Australian Rules Football, major limitations occur when using possession event data for game analysis. First, focusing on counting possession events does not capture the impact of off-the-ball actions such as ground positioning of other players. Second, it is difficult to determine the extent that an event is due to either team’s relative proficiency or skill. Third, there is limited possession event data available from each match and modelling efforts often have low statistical power. Here we reinterpret event data into positional systems and utilise pairwise performance metrics to understand the relative team proficiency in each of these states. These metrics can then be used to construct transition probabilities between states for future games, and ultimately, absorbing probabilities of goal states. Our approach effectively predicts match outcomes using team ratings for forward, midfield and defensive systems and is sufficiently interpretable to support strategic decision-making by coaching departments in the Australian Football League (AFL).

Keywords: Australian Football League (AFL); Australian Rules Football; Elo; machine learning; Markov chain (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/jqas-2021-0035

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