Bayesian estimation of in-game home team win probability for National Basketball Association games

dc.contributor.authorMaddox, Jason T.
dc.contributor.authorSides, Ryan
dc.contributor.authorHarvill, Jane L.
dc.date.accessioned2023-03-20T19:22:13Z
dc.date.available2023-03-20T19:22:13Z
dc.date.issued2022
dc.description.abstractMaddox, 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_US
dc.identifier.citationThis 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.en_US
dc.identifier.urihttps://hdl.handle.net/11274/14699
dc.identifier.urihttps://doi.org/10.48550/arXiv.2207.05114
dc.language.isoen_USen_US
dc.publisherCornell Techen_US
dc.rights.licenseCC BY 4.0
dc.subjectIn-game probabilityen_US
dc.subjectPregame probabilityen_US
dc.subjectProbability estimationen_US
dc.subjectMaximum likelihooden_US
dc.subjectBayesian estimationen_US
dc.subjectDynamic prioren_US
dc.titleBayesian estimation of in-game home team win probability for National Basketball Association gamesen_US
dc.typePre-Printen_US

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