XGBoost, renowned for its efficacy in various statistical domains, offers enhanced precision and efficiency. Its versatility extends to both regression and categorization tasks, rendering it a valuable asset in predictive modeling. In this dissertation, I aim to harness the power of XGBoost to forecast and rank performances within the National Football League (NFL). Specifically, my research focuses on predicting the next play in NFL games based on pre-snap data, optimizing the draft ranking process by integrating data from the NFL combine, and collegiate statistics, creating a player rating system that can be compared across all positions, and evaluating strategic decisions for NFL teams when crossing the 50-yard line, including the feasibility of attempting a first down conversion versus opting for a field goal attempt.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2023-1222 |
| Date | 01 January 2024 |
| Creators | Schoborg, Christopher P |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Thesis and Dissertation 2023-2024 |
| Rights | In copyright |
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