A reinforcement learning based approach to play calling in football

Biro Preston and Walker Stephen G. ()
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Biro Preston: Department of Statistics and Data Science, University of Texas at Austin, Austin, USA
Walker Stephen G.: Department of Statistics and Data Science, University of Texas at Austin, Austin, USA

Journal of Quantitative Analysis in Sports, 2022, vol. 18, issue 2, 97-112

Abstract: With the vast amount of data collected on football and the growth of computing power, many games involving decision choices can be optimized. The underlying rule is the maximization of an expected utility of outcomes and the law of large numbers. The data available allows one to compute with high accuracy the probabilities of outcomes of actions, and the well defined points system in the game allows for a specification of the terminal utilities. With some well established decision theory we can optimize choices for each single play level. A full exposition of the theory and analysis is presented in the paper.

Keywords: expected points; football; Markov decision process; optimal decisions; reinforcement learning (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/jqas-2021-0029

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