Joseph Molis

Date

5-7-2023

Document Type

Thesis

Abstract

Throughout baseball’s rich and long history, fans have been one of the most integral parts of the game. However, in recent years, baseball has seen a decrease in fans, allegedly due to the pace of play, or the length of games. Baseball games can take up to four hours to complete, and in today’s fast-moving society where all information is at one’s fingertips, it is believed that baseball’s slower pace turns people away from the game. However, how true is that? The primary goal of this project is to build models to accurately forecast fan attendance for every Major League Baseball (MLB) team. With teams across the country with varying levels of success, can fan attendance be accurately predicted using time series analysis using fan attendance data from the 1988 to 2019 season. This range allows us to analyze 32 years of data for total fan attendance per season for all thirty teams. The 2020 and 2021 seasons were not used as fans were not allowed in stadiums during these years due to COVID-19. After creating time series models using the data, forecasts were made for the 2022 and 2023 seasons to determine the accuracy of the models.

Department

Mathematics

Thesis Comittee

Dr. Wanchunzi Yu, Thesis Advisor
Dr. Kevin Rion, Committee Member
Dr. Irina Seceleanu, Committee Member

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