衡量体育竞技平衡

IF 1.1 Q3 SOCIAL SCIENCES, MATHEMATICAL METHODS
Matthew Doria, B. Nalebuff
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引用次数: 3

摘要

为了比较不同体育联盟的竞争平衡,我们需要考虑不同赛季长度对观察到的平衡测量的影响。我们开发了与赛季长度不变的竞争平衡的第一个衡量标准。最常用的测量方法,ASD/ISD或Noll-Scully比率,是有偏差的。它人为地夸大了长赛季联盟(如MLB)与短赛季联盟(如NFL)之间的不平衡。我们提供了一个导致无偏方差估计的一般竞争模型。结果是联盟之间的新秩序:NFL从拥有最多的平衡变成了最少的平衡,而MLB成为了最平衡的运动。我们的模型还提供了关于游戏层面竞争平衡的见解。我们将注意力从团队层面转移到游戏层面,因为这与代表性比赛的可预测性更直接相关。最后,我们衡量赛季级别的竞争平衡。我们这样做是通过观察从赛季开始看到的最终排名的可预测性。在这方面,NBA的结果最容易预测,因此整个赛季的竞争平衡最低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measuring competitive balance in sports
Abstract In order to make comparisons of competitive balance across sports leagues, we need to take into account how different season lengths influence observed measures of balance. We develop the first measures of competitive balance that are invariant to season length. The most commonly used measure, the ASD/ISD or Noll-Scully ratio, is biased. It artificially inflates the imbalance for leagues with long seasons (e.g., MLB) compared to those with short seasons (e.g., NFL). We provide a general model of competition that leads to unbiased variance estimates. The result is a new ordering across leagues: the NFL goes from having the most balance to being tied for the least, while MLB becomes the sport with the most balance. Our model also provides insight into competitive balance at the game level. We shift attention from team-level to game-level measures as these are more directly related to the predictability of a representative contest. Finally, we measure competitive balance at the season level. We do so by looking at the predictability of the final rankings as seen from the start of the season. Here the NBA stands out for having the most predictable results and hence the lowest full-season competitive balance.
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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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