Bayesian method of predicting in-game win probability across sports.
| dc.contributor.advisor | Harvill, Jane L. | |
| dc.creator | Maddox, Jason, 1995- | |
| dc.date.accessioned | 2023-09-26T13:51:45Z | |
| dc.date.available | 2023-09-26T13:51:45Z | |
| dc.date.created | 2022-12 | |
| dc.date.issued | December 2022 | |
| dc.date.submitted | December 2022 | |
| dc.date.updated | 2023-09-26T13:51:45Z | |
| dc.description.abstract | In this dissertation, we create different in-game win probability models for several sports using a Bayesian methodology. In the first chapter, we create a college basketball model using score differential and time and compare the model to other models found in literature. In the second chapter, we extend the model from the first chapter into the NBA. In doing so, we also make adjustments to aid in the performance of the model. In the third chapter, we create a college football win probability model, accounting for many more factors than the score differential and time. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | ||
| dc.identifier.uri | https://hdl.handle.net/2104/12391 | |
| dc.language.iso | English | |
| dc.rights.accessrights | Worldwide access | |
| dc.title | Bayesian method of predicting in-game win probability across sports. | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.department | Baylor University. Dept. of Statistical Science. | |
| thesis.degree.grantor | Baylor University | |
| thesis.degree.name | Ph.D. | |
| thesis.degree.program | Statistics |
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