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.
Recommended Citation
Lopez, Michael J.; Matthews, Gregory J.; and Baumer, Benjamin S.. How Often Does the Best Team Win? A Unified Approach to Understanding Randomness in North American Sport. Annals of Applied Statistics, 12, 4: 2483-2516, 2018. Retrieved from Loyola eCommons, Mathematics and Statistics: Faculty Publications and Other Works, http://dx.doi.org/10.1214/18-AOAS1165
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Copyright Statement
© Institute of Mathematical Statistics, 2018.
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