一种迭代马尔可夫评级方法

IF 1.1 Q3 SOCIAL SCIENCES, MATHEMATICAL METHODS

Journal of Quantitative Analysis in Sports Pub Date : 2020-10-05 DOI:10.1515/jqas-2019-0070

Stephen Devlin, T. Treloar, Molly Creagar, S. Cassels

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引用次数: 0

摘要

摘要本文介绍了一种简单自然的马尔可夫评级方法。我们证明了这种迭代马尔可夫方法收敛于通常的全局马尔可夫评级，并且与众所周知的Elo评级有着密切的关系。结合最近关于全局马尔可夫方法与布拉德利-特里(BT)模型中评级向量的最大似然估计之间关系的结果，我们连接并探索了真实和模拟数据上的全局和迭代马尔可夫，Elo和布拉德利-特里评级。

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An iterative Markov rating method

Abstract We introduce a simple and natural iterative version of the well-known and widely studied Markov rating method. We show that this iterative Markov method converges to the usual global Markov rating, and shares a close relationship with the well-known Elo rating. Together with recent results on the relationship between the global Markov method and the maximum likelihood estimate of the rating vector in the Bradley–Terry (BT) model, we connect and explore the global and iterative Markov, Elo, and Bradley–Terry ratings on real and simulated data.

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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.