A reinforcement learning based approach to play calling in football

IF 1.1 Q3 SOCIAL SCIENCES, MATHEMATICAL METHODS

Journal of Quantitative Analysis in Sports Pub Date : 2021-03-11 DOI:10.1515/jqas-2021-0029

Preston Biro, S. Walker

引用次数: 2

Abstract

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.

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一种基于强化学习的足球比赛呼叫方法

随着大量足球数据的收集和计算能力的提高，许多涉及决策选择的比赛都可以进行优化。基本规则是预期效用的最大化和大数法则。可用的数据让玩家能够准确地计算出行动结果的概率，而游戏中定义良好的积分系统也让玩家能够明确终端效用。有了一些完善的决策理论，我们就可以优化每个游戏关卡的选择。本文对其理论和分析进行了全面的阐述。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Quantitative Analysis in Sports SOCIAL SCIENCES, MATHEMATICAL METHODS-

CiteScore

2.00

自引率

12.50%

发文量

期刊介绍： The Journal of Quantitative Analysis in Sports (JQAS), an official journal of the American Statistical Association, publishes timely, high-quality peer-reviewed research on the quantitative aspects of professional and amateur sports, including collegiate and Olympic competition. The scope of application reflects the increasing demand for novel methods to analyze and understand data in the growing field of sports analytics. Articles come from a wide variety of sports and diverse perspectives, and address topics such as game outcome models, measurement and evaluation of player performance, tournament structure, analysis of rules and adjudication, within-game strategy, analysis of sporting technologies, and player and team ranking methods. JQAS seeks to publish manuscripts that demonstrate original ways of approaching problems, develop cutting edge methods, and apply innovative thinking to solve difficult challenges in sports contexts. JQAS brings together researchers from various disciplines, including statistics, operations research, machine learning, scientific computing, econometrics, and sports management.