基于大型数据库的精英运动表现贝叶斯模型

IF 1.1 Q3 SOCIAL SCIENCES, MATHEMATICAL METHODS
J. Griffin, Laurentiu C. Hinoveanu, J. Hopker
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引用次数: 1

摘要

大型运动表现数据库的可用性为了解与年龄相关的表现进展提供了机会,并将个人表现与世界最佳表现进行比较。我们建立了一个灵活的个人性能进展贝叶斯模型,同时允许混杂因素,如大气条件,并可以使用马尔可夫链蒙特卡罗进行拟合。我们展示了如何使用该模型来理解个人和群体的绩效进展和峰值绩效的年龄。我们将该模型应用于女子和男子100米短跑和举重项目。在这两个学科中,我们发现与年龄相关的表现是扭曲的,女性和男性的平均总体表现轨迹大不相同,女性和男性的最佳表现年龄也有很大不同。我们还发现,在个人表现轨迹和最佳表现年龄方面存在很大的差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian modelling of elite sporting performance with large databases
Abstract The availability of large databases of athletic performances offers the opportunity to understand age-related performance progression and to benchmark individual performance against the World’s best. We build a flexible Bayesian model of individual performance progression whilst allowing for confounders, such as atmospheric conditions, and can be fitted using Markov chain Monte Carlo. We show how the model can be used to understand performance progression and the age of peak performance in both individuals and the population. We apply the model to both women and men in 100 m sprinting and weightlifting. In both disciplines, we find that age-related performance is skewed, that the average population performance trajectories of women and men are quite different, and that age of peak performance is substantially different between women and men. We also find that there is substantial variability in individual performance trajectories and the age of peak performance.
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来源期刊
Journal of Quantitative Analysis in Sports
Journal of Quantitative Analysis in Sports SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
2.00
自引率
12.50%
发文量
15
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.
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