How often does the best team win? A unified approach to understanding randomness in North American Sport
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. More generally, we use our framework to compare cumulative models fit across all weeks to sequential ones fit on all weeks prior. 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 a new metric for judging competitiveness across sports leagues. Although we focus on sports, we discuss a number of other situations in which our generalizable models might be usefully applied.
Speaker Bio: Benjamin S. Baumer is an assistant professor in the Statistical & Data Sciences program at Smith College. He has been a practicing data scientist since 2004, when he became the first full-time statistical analyst for the New York Mets. Ben is a co-author of The Sabermetric Revolution and Modern Data Science with R, and won the 2016 Contemporary Baseball Analysis Award from the Society for American Baseball Research