Wilson Rick L
Department of Management Science and Information Systems, Spears School of Business, Oklahoma State University, Stillwater, OK, United States.
Front Artif Intell. 2020 Aug 26;3:61. doi: 10.3389/frai.2020.00061. eCollection 2020.
The use of AI and machine learning in sports is increasingly prevalent, including their use for in-game strategy and tactics. This paper reports on the use of machine learning techniques, applying it to analysis of U.S. Division I-A College Football overtime games. The present overtime rules for tie games in Division I-A college football was adopted in 1996. Previous research (Rosen and Wilson, 2007) found little to suggest that the predominantly used strategy of going on defense first was advantageous. Over the past decade, even with significant transformation of new offensive and defensive strategies, college football coaches still opt for the same conventional wisdom strategy. In revisiting this analysis of overtime games using both logistic regression and inductive learning/decision tree analysis, the study validates there remains no advantage to the defense first strategy in overtime. The study found evidence that point spread (as an indicator of team strength) and red zone offense performance of both teams were useful to predict game results. Additionally, by altering the decision-making "frame," specific scenarios are illustrated where a coach can use these machine learning discovered relationships to influence end-of-regulation game decisions that may increase their likelihood of winning whether in regulation time or in overtime.
人工智能和机器学习在体育领域的应用越来越普遍，包括用于比赛中的战略和战术。本文报告了机器学习技术的应用情况，将其应用于美国第一级别大学橄榄球加时赛的分析。第一级别大学橄榄球平局比赛目前的加时规则于1996年采用。先前的研究（罗森和威尔逊，2007年）几乎没有发现先进行防守的主要使用策略具有优势。在过去十年中，即使新的进攻和防守策略发生了重大转变，大学橄榄球教练仍然选择相同的传统智慧策略。在使用逻辑回归和归纳学习/决策树分析重新审视对加时赛的这一分析时，该研究证实，在加时赛中先防守的策略仍然没有优势。该研究发现，让分盘（作为球队实力的指标）和两队的红区进攻表现有助于预测比赛结果。此外，通过改变决策“框架”，具体说明了教练可以利用这些机器学习发现的关系来影响常规赛结束时的比赛决策之处，这可能会增加他们在常规赛或加时赛中获胜的可能性。
