Keywords

NFL, Analytics, XGBoost, Prediction, Fourth Down

Abstract

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.

Completion Date

2024

Semester

Spring

Committee Chair

Wang, Chung-Ching (Morgan)

Degree

Doctor of Philosophy (Ph.D.)

College

College of Sciences

Department

Statistics and Data Science

Degree Program

Big Data Analytics

Format

application/pdf

Identifier

DP0028340

URL

https://purls.library.ucf.edu/go/DP0028340

Language

English

Rights

In copyright

Release Date

May 2025

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Campus-only Access)

Campus Location

Orlando (Main) Campus

Accessibility Status

Meets minimum standards for ETDs/HUTs

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Restricted to the UCF community until May 2025; it will then be open access.

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