Abstract

Sports analytics is a fast-growing field of analytics. In particular, sports analytics with a focus on National Football League (NFL). In this thesis, we will review many articles on football analytics to have an in-depth understanding of the current stat of football analytics. In addition, we can learn from past research to identify interesting research direction to advance sports analytics with a focus on football analytics. In this thesis, we have carefully examined all current analytical results in the following fields: current state of football analytics, analytics regarding the draft, analytics for wide receivers as well as offensive linemen, analytics on other offensive positions, and we have identified the following research direction: the need for a scale rating system that is equal of all positions but unique to expectations of that position especially when it comes to wide receivers and offensive linemen. Lastly, we lay the groundwork for future work, which will make use of the following statistical learning algorithms: logistic regression, XG Boost, decision trees, and time series, to analyze the NFL data, both tracking data from the first six weeks of the 2020 season as well as play by play data from 1999 to 2022 to introduce these new algorithms to sports analytics community.

Notes

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Graduation Date

2023

Semester

Summer

Advisor

Wang, Chung-Ching

Degree

Master of Science (M.S.)

College

College of Sciences

Department

Statistics & Data Science

Degree Program

Statistics and Data Science; Data Science

Identifier

CFE0009792; DP0027900

URL

https://purls.library.ucf.edu/go/DP0027900

Language

English

Release Date

August 2023

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

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