Mathematics
Permanent URI for this collectionhttps://hdl.handle.net/11274/15373
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Browsing Mathematics by Author "Maddox, Jason T."
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Item Bayesian estimation of in-game home team win probability for college basketball(2022) Maddox, Jason T.; Sides, Ryan; Harvill, Jane L.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.Item Bayesian estimation of in-game home team win probability for National Basketball Association games(Cornell Tech, 2022) Maddox, Jason T.; Sides, Ryan; Harvill, Jane L.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.