Matching Items (3)
Filtering by
- All Subjects: NFL
- Creators: Goegan, Brian
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.
ContributorsBalzer, 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)
Created2015-05
Description
This paper intends to analyze the National Football League (NFL) and the role stadiums play within it. The NFL, being the nation's largest professional sports league, has experienced a large amount of volatility over the past couple of decades. Teams have relocated a significant number of times and stadium projects have grown in size, cost, and frequency. Because of these observations, we chose to focus in on this particular sports league in order to answer our many questions surrounding the role of a professional sports stadium in the economics of a city. We seek to understand the economics these sports stadiums impact on the league and the cities they reside in. To do this, we compiled data of NFL franchise wins, average ticket prices, stadiums, and franchise values, while researching the stadium building process and referencing the opinions of leading sports economists across the nation. Next, we discussed the process of building a stadium, which entails the core steps of design, construction, cost, and funding. We discuss tax-exempt municipal bonds, and explain what an impact economic analysis is and how teams use them to get cities to support their projects. Moreover, we discuss the threats of relocation and how the NFL can exert pressure on stadium project decisions. Finally, we talk about the future of the NFL, with a new trend of empty stadiums and make predictions for upcoming relocation destinations. Based on these findings, we draw conclusions on the economics of sports stadiums and offer our opinion on the current state of the NFL.
ContributorsGuillen, Sergio (Co-author) / Willms, Jacob (Co-author) / Goegan, Brian (Thesis director) / Eaton, John (Committee member) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Beginning with the publication of Moneyball by Michael Lewis in 2003, the use of sabermetrics \u2014 the application of statistical analysis to baseball records - has exploded in major league front offices. Executives Billy Beane, Paul DePoedesta, and Theo Epstein are notable figures that have been successful in incorporating sabermetrics to their team's philosophy, resulting in playoff appearances and championship success. The competitive market of baseball, once dominated by the collusion of owners, now promotes innovative thought to analytically develop competitive advantages. The tiered economic payrolls of Major League Baseball (MLB) has created an environment in which large-market teams are capable of "buying" championships through the acquisition of the best available talent in free agency, and small-market teams are pushed to "build" championships through the drafting and systematic farming of high-school and college level players. The use of sabermetrics promotes both models of success \u2014 buying and building \u2014 by unbiasedly determining a player's productivity. The objective of this paper is to develop a regression-based predictive model that can be used by Majors League Baseball teams to forecast the MLB career average offensive performance of college baseball players from specific conferences. The development of this model required multiple tasks: I. Data was obtained from The Baseball Cube, a baseball records database providing both College and MLB data. II. Modifications to the data were applied to adjust for year-to-year formatting, a missing variable for seasons played, the presence of missing values, and to correct league identifiers. III. Evaluation of multiple offensive productivity models capable of handling the obtained dataset and regression forecasting technique. IV. SAS software was used to create the regression models and analyze the residuals for any irregularities or normality violations. The results of this paper find that there is a relationship between Division 1 collegiate baseball conferences and average career offensive productivity in Major Leagues Baseball, with the SEC having the most accurate reflection of performance.
ContributorsBadger, Mathew Bernard (Author) / Goegan, Brian (Thesis director) / Eaton, John (Committee member) / Department of Economics (Contributor) / Department of Marketing (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05