THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE DEPARTMENT OF FINANCE MONEYBALL IN THE NFL: A FINANCIAL MANAGEMENT ANALYSIS OF THE IDEAL NFL SALARY CAP STRUCTURE JOHN PEREGRIM FALL 2019 A thesis submitted in partial fulfillment of the requirements for a baccalaureate degree in Finance with honors in Finance Reviewed and approved* by the following: Robert Novack Associate Professor of Supply Chain Management Thesis Supervisor Brian Davis Professor of Finance Honors Adviser * Signatures are on file in the Schreyer Honors College. i ABSTRACT The paper is meant to determine how the highly acclaimed Major League Baseball strategy Moneyball can be implemented into the National Football League. Overall, the goal is to build a regression model that results in an optimal salary cap allocation for various NFL player position groups based on each groups’ contribution to the team’s average win percentage. All data is sourced from historical National Football League statistics over the last ten seasons, from 2008 to 2017. Players were grouped into eight main position groups, and five key statistics were selected for each position group. Each team’s win percentage was regressed on the forty various player statistics to determine the contribution of each variable towards the team’s win percentage. Once the regression model returned the optimal salary cap allocations per position group, these values were compared to the weighted average salary cap allocation over all teams from 2011 to 2017. It was determined that quarterbacks, running backs, offensive linemen, cornerbacks and safeties, and kickers and punters are underpaid, wide receivers and tight ends and defensive linemen are overpaid, and linebackers are statistically unable to be valued using various player statistics. ii TABLE OF CONTENTS LIST OF FIGURES………………………………………………………………………………iv LIST OF TABLES...………………...…………………………………………………………….v ACKNOWLEDGEMENTS………………………………………………………………………vi Chapter 1 Introduction of Topic.………………………………………………………………….1 NFL: The Business………………………………………………………………………..1 Origins of Moneyball………………………………………………………..……..……...2 Moneyball in the NFL………………………………………………………………...…...3 Paul DePodesta’s Influence on Sports……………………………………………..……...4 Thesis Statement……………………………………………………………………...…...4 Chapter 2 Literature Review………...…………………………………………………………….5 Inconsistencies Between Moneyball in the MLB and in the NFL………………………...5 NFL Salary Cap Breakdown………….……………………………………..……..……...6 Unanswered Questions………...……………………………………………………...…...7 Chapter 3 Data Methodology………………..…………………………………………………….9 Data Collection….………………………………………………………………………...9 Position Groups……….……………………………………………..……..……...9 Quantitative Statistics………………………………………………………...….10 Data Exporting and Sorting……………………………………………………...12 Position Group Regression Model……………..…………………………………..…….14 Position Group Allocations………………………………………………………15 Data Methodology Summary…….…………………………………………………...….16 Chapter 4 Data Analysis…………….…………………………………………………………...17 Regression Model………….…………………………………………………………….17 Model Verification…………………………………………………..……..…….17 Regression Model 1.0…………...…………………………………..……..…….17 Regression Model 2.0…………...…………………………………..……..…….20 Regression Model 3.0…………...…………………………………..……..…….22 Regression Model 4.0…………...…………………………………..……..…….25 Regression Model 5.0…………...…………………………………..……..…….27 Statistical Analysis……………………….…………………………………………...….29 Salary Cap Comparison………………………………………………………...…….….33 iii Chapter 5 Conclusion..…………………………………………………………………………...39 Moneyball’s Potential Impact on NFL Franchises.…………..………………………….39 Topics to Further Explore………………………………………………………………..41 Appendix A Annual Statistics Per Team 2008-2017….…………………………………………44 Appendix B Annual Salary Cap Allocation Per Position Group Per Team 2011-2017…………85 BIBLIOGRAPHY...………...…………………………………………………………………..102 iv LIST OF FIGURES Figure 1: Regression Model 1.0 Output…..……………………………………………………...19 Figure 2: Regression Model 2.0 Output……..…………………………………………………...21 Figure 3: Regression Model 3.0 Output….………………………………………………………24 Figure 4: Regression Model 4.0 Output….………………………………………………………26 Figure 5: Regression Model 5.0 Output….………………………………………………………28 Figure 6: Python 3.7.2 OLS Regression Model 5.0 Output...……………………………………32 Figure 7: Various Salary Cap Breakdowns………………………………………………………33 Figure 8: NFL Salary Cap Simple Average.……..………………………………………………34 Figure 9.1: NFL Salary Cap Weighted Average……………………………………………..…..35 Figure 9.2: NFL Salary Cap Weighted Average………………………………………………....36 Figure 10: NFL Salary Cap Super Bowl Average……………………………………………….36 Figure 11: NFL Franchises Ranked by Total Cap Difference………….………………………..38 v LIST OF TABLES Table 1: Player Statistics Per Position Group..…..………………………………………………11 vi ACKNOWLEDGEMENTS I would like to thank my thesis supervisor, Dr. Novack, and my thesis advisor, Dr. Davis, for guiding my vision every step along the way and always being so encouraging about having productive meetings. I would also like to thank my good friend Trent Andraka, a computer engineering major at the Washington University in St. Louis, for teaching me how to use the coding language Python. Finally, and most importantly, I would like to thank my mom for her daily support through this thesis and all of my other Schreyer Honors College experiences. 1 Chapter 1 Introduction of Topic Many sports fans are already familiar with the Major League Baseball (MLB) salary cap management technique, coined the term, Moneyball. The purpose of this thesis is to extend the theory behind Moneyball and implement it into the National Football League. NFL: The Business It is no secret that the National Football League (NFL) is the most popular sports league in the world, both in regard to viewership and revenue. The average NFL game has an audience of 15.472 million viewers which is significantly larger than the second-place league, NASCAR, with an average of 3.332 million viewers (Maglio, 2018). NFL total revenue is currently at $13.68 billion with $1.32 billion coming from sponsorship revenue, proving that the National Football League is a moneymaking juggernaut (Fuller, 2017). It is also key to note that the average NFL franchise has a value of $2.57 billion (Fuller, 2017). The top five most valuable franchises are the Dallas Cowboys, New England Patriots, New York Giants, Los Angeles Rams, and Washington Redskins with five, six, four, one, and three Super Bowl victories respectively (Rovell, 2018). In a sports league, winning is indicative of success, both with viewers, stadium attendance, and overall revenue, and this franchise valuation helps prove that general theory. These various measures prove that the NFL is thriving financially, thanks to a large demographic, however both fans and franchise owners heavily value wins. With league personnel and team owners constantly searching for a formula for success, this thesis is meant to solve this conundrum by playing on the Kairos of the time. 2 Origins of Moneyball In the MLB, salary caps are not consistent amongst teams, proving that older and more revenue driven teams, like the New York Yankees, have a larger budget to acquire players than a newer and less established team, like the Oakland Athletics. This visually unfair playing field gave rise to the highly acclaimed technique called Moneyball. In 2002, the Oakland Athletics began using sabermetric statistics to find undervalued players in an attempt to find talent, while also maximizing their salary cap efficiency. For example, while most MLB teams looked at basic statistics like batting average and field percentage, the Athletics began analyzing statistics like slugging percentage and on-base percentage (Piellucci, 2017). The goal was not just to use statistics to value players but predominantly to find statistics more attributable to wins than were currently being used. At the time, front office assistant Paul DePodesta spearheaded the idea and was involved in the implementation process. DePodesta worked hand in hand with the current Athletics’ General Manager Billy Beane, analyzing statistics, activating player trades, and managing the tight salary cap allocation. By acquiring players with a greater bang-for-your-buck, the Oakland Athletics were able to go from one of the worst team in baseball into a squad that won the American League West division with over one- hundred years, thanks to their iconic twenty-game win streak. Theoretically, the Oakland Athletics were able to catapult themselves into relevancy by having a front office that was able to outsmart their counterparts by using numbers and statistics to value their players more accurately. 3 Moneyball in the NFL NFL coaches have recently begun to rely on statistics more heavily within the last decade, however numbers predominantly play into coaching decisions. The most discussed aspect of using quantitative data in football is in regards to probabilities and whether statistics encourage or discourage certain decisions. For example, coaches have become more inclined to go for it on fourth down, prior to the game being on the line, typically under a certain yardage. This is not because coaches are getting less risk-averse but because going for the first down is statistically favorable. However, when it comes to paying players, general managers are not convinced that statistics are the way to value their talent. Several high name NFL personnel have expressed their skepticism in Moneyball tactics due to the dissimilarities between MLB and NFL statistics. While positions in baseball