A Reinforcement Learning Based Approach to Play Calling in Football

TL;DR

This paper proposes a reinforcement learning approach to optimize play calling in football by leveraging extensive data and decision theory to maximize expected utility at each play.

Contribution

It introduces a novel reinforcement learning framework tailored for football play calling, integrating data-driven probability estimates with decision-theoretic optimization.

Findings

Effective play calling strategies derived from the model.

Improved decision-making accuracy in football scenarios.

Potential for real-time application in game strategy.

Abstract

With the vast amount of data collected on football and the growth of computing abilities, 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 us to compute with high accuracy the probabilities of outcomes of decisions and the well defined points system in the game allows us to have the necessary terminal utilities. With some well established theory we can then optimize choices at a single play level.