Estimating the Impact of Travel, Rest, and Playing at Home in the National Football League

Friday, October 30, 2020 - 1:25pm - 2:25pm
Tom Bliss (National Football League (NFL))

Estimating schedule difficulty in the National Football League is tricky given the limited number of games and the number of factors that impact game outcomes, including time-varying team strengths, the home advantage, and changes in rest, travel, and time zones. From the league’s perspective, understanding each of these factors can give us a better understanding of scheduling equity and competitive balance. We extend the Bayesian state-space model of Lopez, Matthews, and Baumer (2018) to estimate varying levels of rest and travel advantages using betting market data. The model accounts for team strength that varies by week and season. We estimate that a team coming off a bye is worth about three-quarters of a point, while a shorter rest advantage is worth about half of that. In addition, we find that the benefit of playing at home has dropped approximately a point in the last decade and explore if and how the game looks different on the field as a result.

Thompson Bliss is a Data Scientist for the National Football League. He completed his master’s degree in Data Science at Columbia University in the City of New York in December 2019. At Columbia, he worked as a graduate assistant for a Sports Analytics course taught by Professor Mark Broadie. He received a Bachelor of Science in Physics and Astronomy at University of Wisconsin - Madison in 2018.