Bayesian estimation of in-game home team win probability for National Basketball Association games
Date
2022
Authors
Maddox, Jason T.
Sides, Ryan
Harvill, Jane L.
Journal Title
Journal ISSN
Volume Title
Publisher
Cornell Tech
Abstract
Maddox, et al. (2022) establish a new win probability estimation for college basketball and compared the results with previous methods of Stern (1994), Desphande and Jensen (2016) and Benz (2019). This paper proposes modifications to the approach of Maddox, et al. (2022) for the NBA game and investigates the performance of the model. Enhancements to the model are developed, and the resulting adjusted model is compared with existing methods and to the ESPN counterpart. To illustrate utility, all methods are applied to the November 23, 2019 game between the Chicago Bulls and Charlotte Hornets.
Description
Article originally published in arXiv preprint arXiv:2207.05114. Published online 2022. https://doi.org/10.48550/arXiv.2207.05114
Keywords
In-game probability, Pregame probability, Probability estimation, Maximum likelihood, Bayesian estimation, Dynamic prior
Citation
This is a pre-print version of an article that is available at https://doi.org/10.48550/arXiv.2207.05114. Recommended citation: Maddox, J. T., Sides, R., & Harvill, J. L. (2022). Bayesian estimation of in-game home team win probability for National Basketball Association games. arXiv preprint arXiv:2207.05114. This item has been deposited in accordance with publisher copyright and licensing terms and with the author’s permission.