Document Type

Article

Publication Date

2018

Publication Title

Annals of Applied Statistics

Volume

12

Issue

4

Pages

2483-2516

Publisher Name

Institute of Mathematical Statistics

Abstract

Statistical applications in sports have long centered on how to best separate signal (e.g. team talent) from random noise. However, most of this work has concentrated on a single sport, and the development of meaningful cross-sport comparisons has been impeded by the difficulty of translating luck from one sport to another. In this manuscript, we develop Bayesian state-space models using betting market data that can be uniformly applied across sporting organizations to better understand the role of randomness in game outcomes. These models can be used to extract estimates of team strength, the between-season, within-season, and game-to-game variability of team strengths, as well each team’s home advantage. We implement our approach across a decade of play in each of the National Football League (NFL), National Hockey League (NHL), National Basketball Association (NBA), and Major League Baseball (MLB), finding that the NBA demonstrates both the largest dispersion in talent and the largest home advantage, while the NHL and MLB stand out for their relative randomness in game outcomes. We conclude by proposing new metrics for judging competitiveness across sports leagues, both within the regular season and using traditional postseason tournament formats. Although we focus on sports, we discuss a number of other situations in which our generalizable models might be usefully applied.

Comments

Author Posting © Institute of Mathematical Statistics, 2018. This is the author's version of the work. It is posted here by permission of the Institute of Mathematical Statistics for personal use, not for redistribution. The article was published in Annals of Applied Statistics, Volume 12, Issue 4, 2018, http://dx.doi.org/10.1214/18-AOAS1165

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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