Date of Graduation

5-2021

Document Type

Thesis

Degree Name

Bachelor of Science in Industrial Engineering

Degree Level

Undergraduate

Department

Industrial Engineering

Advisor/Mentor

Rossetti, Manuel D.

Committee Member/Reader

Parnell, Gregory S.

Abstract

The National Football League (NFL) is the most popular sports league in the world, with millions of viewers every game and billions of dollars generated every season. Statistics are an important part of an NFL team’s business operating model and contribute greatly towards their decision making. Every season, general managers try to sign players that give the team the highest probability of winning games throughout the year. There are many factors that go into this decision, including the amount of money the team has to spend and the value that available players can bring to a team. Teams must abide by a league-sanctioned salary cap to pay players that they believe will give their team the best probability of winning. There are many statistics currently used in the NFL to value players, but this research aims to use multiple objective decision analysis to combine aspects of a player into one value for a given position. The scope of this research will be focused on the wide receiver group specifically, but the methodology used can be adapted to any position group within an NFL team. This research will provide a new way of quantifying players’ value for the use of decision makers in the decision-making process of signing free agents to their team.

Keywords

Multiple Objective Decision Analysis; NFL; Sports Analytics; Antonio Brown; Wide Receiver; Decision Analysis; Service Learning

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