Description
The NFL is one of largest and most influential industries in the world. In America there are few companies that have a stronger hold on the American culture and create such a phenomena from year to year. In this project aimed to develop a strategy that helps an NFL team be as successful as possible by defining which positions are most important to a team's success. Data from fifteen years of NFL games was collected and information on every player in the league was analyzed. First there needed to be a benchmark which describes a team as being average and then every player in the NFL must be compared to that average. Based on properties of linear regression using ordinary least squares this project aims to define such a model that shows each position's importance. Finally, once such a model had been established then the focus turned to the NFL draft in which the goal was to find a strategy of where each position needs to be drafted so that it is most likely to give the best payoff based on the results of the regression in part one.
Details
Title
- Player Optimization in the National Football League: Creating a Winning Franchise
Contributors
- Balzer, Kevin Ryan (Author)
- Goegan, Brian (Thesis director)
- Dassanayake, Maduranga (Committee member)
- Barrett, The Honors College (Contributor)
- Economics Program in CLAS (Contributor)
- School of Mathematical and Statistical Sciences (Contributor)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2015-05
Resource Type
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