Maddox, Jason T.Sides, RyanHarvill, Jane L.2023-03-202023-03-202022This 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.https://hdl.handle.net/11274/14699https://doi.org/10.48550/arXiv.2207.05114Maddox, 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.en-USIn-game probabilityPregame probabilityProbability estimationMaximum likelihoodBayesian estimationDynamic priorPre-PrintCC BY 4.0