Bayesian estimation of in-game home team win probability for college basketball
Date
2022
Authors
Maddox, Jason T.
Sides, Ryan
Harvill, Jane L.
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Two new Bayesian methods for estimating and predicting in-game home team win probabilities in Division I NCAA men’s college basketball are proposed. The first method has a prior that adjusts as a function of lead differential and time elapsed. The second is an adjusted version of the first, where the adjustment is a linear combination of the Bayesian estimator with a time-weighted pregame win probability. The proposed methods are compared to existing methods, showing the new methods are competitive with or outperform existing methods for both estimation and prediction. The utility is illustrated via an application to the 2012/2013 through the 2019/2020 NCAA Division I Men’s Basketball seasons.
Description
Article originally published by Journal of Quantitative Analysis in Sports, 18(3), 201–213. English. Published 2022. https://doi.org/10.1515/jqas-2021-0086
Keywords
Bayesian estimation, Dynamic prior, In-game probability, Maximum likelihood, Pregame probability, Probability estimation
Citation
This is the published version of an article that is available at https://doi.org/10.1515/jqas-2021-0086. Recommended citation: Maddox, J. T., Sides, R., & Harvill, J. L. (2022). Bayesian estimation of in-game home team win probability for college basketball. Journal of Quantitative Analysis in Sports, 18(3), 201–213. This item has been deposited in accordance with publisher copyright and licensing terms and with the author’s permission.