概括多人游戏和比赛的Elo评级系统:为什么耐力比速度更重要

IF 1.1 Q3 SOCIAL SCIENCES, MATHEMATICAL METHODS

Journal of Quantitative Analysis in Sports Pub Date : 2023-06-30 DOI:10.1515/jqas-2023-0004

B. Powell

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引用次数: 0

摘要

摘要针对两个或两个以上参赛选手的比赛，引入了Elo评分系统的非标准泛化。新系统可以理解为一种对Plackett-Luce模型参数的在线估计算法，可用于对未来比赛结果进行概率预测。该系统的显著特点是，它将比赛视为一系列淘汰类型的比赛，这些淘汰类型的比赛依次识别出最差的竞争者，而不是一系列选择类型的比赛，这些比赛依次识别出最好的竞争者。讨论了这一重要的建模选择的意义，并探讨了其后果。最后，用一级方程式赛车的数据证明了广义Elo系统的预测能力。

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Generalizing the Elo rating system for multiplayer games and races: why endurance is better than speed

Abstract We introduce a non-standard generalization of the Elo rating system for competitions involving two or more participants. The new system can be understood as an online estimation algorithm for the parameters of a Plackett–Luce model which can be used to make probabilistic forecasts for the results of future competitions. The system’s distinguishing feature is the way it treats competitions as sequences of elimination-type rounds that sequentially identify the worst competitors rather than sequences of selection-type rounds that identify the best. The significance of this important modelling choice is discussed and its consequences are explored. Finally, our generalized Elo system’s predictive power is demonstrated using data from Formula One racing.

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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.