# A Reinforcement Learning Based Approach to Play Calling in Football

arXiv: https://arxiv.org/abs/2103.06939 · HTML: https://mlsanity.com/paper/2103.06939/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 42 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [Provable Self-Play Algorithms for Competitive Reinforcement Learning](https://mlsanity.com/paper/2002.04017/) (2020)

## Abstract (arXiv)

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.