Abstract Bayesian methods are becoming increasingly popular in sports analytics. Identified advantages of the Bayesian approach include the ability to model complex problems, obtain probabilistic estimates and predictions that account for uncertainty, combine information sources and update learning as new data become available. The volume and variety of data produced in sports activities over recent years and the availability of software packages for Bayesian computation have contributed significantly to this growth. This comprehensive survey reviews and characterizes the latest advances in Bayesian statistics in sports, including methods and applications. We found that a large proportion of these articles focus on modeling/predicting the outcome of sports games and on the development of statistics that provides a better picture of athletes’ performance. We provide a description of some of the advances in basketball, football and baseball. We also summarise the sources of data used for the analysis and the most commonly used software for Bayesian computation. We found a similar number of publications between 2013 and 2018 as compared to those published in the three previous decades, which is an indication of the growing adoption rate of Bayesian methods in sports.

中文翻译：

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贝叶斯统计与运动相结合：全面回顾

摘要贝叶斯方法在体育分析中正变得越来越流行。贝叶斯方法的明显优势包括能够对复杂的问题进行建模，获得解释不确定性的概率估计和预测，结合信息源以及在获得新数据时更新学习的能力。近年来在体育活动中产生的数据量和种类繁多，以及用于贝叶斯计算的软件包的可用性为这种增长做出了重要贡献。这项全面的调查回顾并描述了贝叶斯运动统计领域的最新进展，包括方法和应用。我们发现，这些文章中有很大一部分着重于对体育比赛的结果进行建模/预测，并着眼于统计数据的发展，以更好地反映运动员的表现。我们描述了篮球，足球和棒球方面的一些进步。我们还总结了用于分析的数据源以及用于贝叶斯计算的最常用软件。我们发现2013年至2018年之间的出版物数量与前三个十年的出版物数量相似，这表明贝叶斯方法在体育运动中的采用率正在上升。