Date Presented

Spring 5-2021

Document Type

Thesis

Degree Name

Bachelor of Science

Department

Mathematics

First Advisor

Leslie Hatfield, PhD

Second Advisor

Mark Ledbetter, PhD

Third Advisor

Beth Savage, PhD

Abstract

This thesis analyzes the correlation between a team’s statistics and the success of their performances, and develops a predictive model that can be used to forecast final season results for that team. Data from the 2017-2018 Premier League season is to be gathered and broken down within R to highlight what factors and variables are largely contributing to the success or downfall of a team. A multiple linear regression model and stepwise selection process is then used to include any factors that are significant in predicting in match results.

The predictions about the 17-18 season results based on the model proved to be satisfactory. The model saw an accuracy that was very near to perfect and allowed for a correct prediction of table standings. In addition, possible complications and issues found within the model allow for future consideration and are discussed within the thesis. The next step becomes applying the results to the following season as well as to break down data game-by-game to see if common variables appear among multiple seasons and individual games. A more in depth breakdown allows for a full analysis on the data to see if these variables are actually deciding factors for winning games.

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