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I the EFFECTS of USING PERFORMANCE ENHANCING

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I the EFFECTS of USING PERFORMANCE ENHANCING THE EFFECTS OF USING PERFORMANCE ENHANCING DRUGS ON MAJOR LEAGUE BASEBALL PLAYERS’ SALARIES A THESIS Presented to The Faculty of the Department of Economics and Business The Colorado College In Partial Fulfillment of the Requirements for the Degree Bachelor of Arts By William A. Swift May 2018 i THE EFFECTS OF USING PERFORMANCE ENHANCING DRUGS ON MAJOR LEAGUE BASEBALL PLAYERS’ SALARIES William A. Swift May 2018 Economics Abstract This paper estimates performance enhancing drugs' (PEDs) effect on Major League Baseball player's salaries. Our data set included single season data from 47 PED offenders and a control group of 56 non-PED users. Our performance and salary data was collected from baseballreference.com. We use ordinary least squares regressions to estimate PEDs effect on slugging percentage (SLG), on-base percentage (OBP), wins above replacement (WAR) and one year salaries. We find that a player using PEDs is estimated to see a 0.0317 increase in their SLG, a 0.0139 increase in their OBP, a 0.459 increase in WAR, and finally a $149,810.15 increase in yearly salary. KEYWORDS: (Performance Enhancing Drugs, Major League Baseball, Salary) JEL CODES: (Z2, E24) ii ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED AID ON THIS THESIS Signature iii TABLE OF CONTENTS ABSTRACT ii 1 INTRODUCTION 1 2 LITERATURE REVIEW 4 2.1 Performance................................................................................................... 4 2.2 Salary............................................................................................................. 7 2.3 Performance Enhancing Drugs……............................................................... 11 3 THEORETICAL FRAMEWORK 16 4 DATA 19 5 REGRESSION EQUATIONS AND VARIABLES 20 6 DESCRIPTIVE STATISTICS 24 7 REGRESSION ANALYSIS 29 8 RESULTS 36 9 DISCUSSION 38 10 CONCLUSION 41 11 REFERENCES 43 iv Introduction Recently the business of sports has become so economically valuable to the point that the sale of the two most recent Major League Baseball teams (Miami Marlins and Los Angeles Dodgers) reached approximately $1.3 Billion and $2 Billion respectively. To put it in perspective, in 1996, the Pittsburg Pirates sold for $92 Million. Today sports franchises are viewed as a worthy investment because of the steady increasing revenue pipeline which these franchises are automatically granted. MLB.com reported that a new television contract, started in 2014, will pay a combined $12.4 billion annually to the MLB in fees to broadcast MLB games, which is more than a 100% increase from the previous deal. The television deals have done their part in raising the overall face value of sports franchises, but they have subsequently armed teams with more money to spend on players. In 2012, Albert Pujols signed a 10-year, $240 million contract with the Los Angeles Angels which will pay him an average of $24 million per year until he is 41 years old. More recently, Giancarlo Stanton signed a 13-year, $325 million contract with the Miami Marlins (since traded to the New York Yankees) and will be paid an average of $25 million per year until 2028. With most contracts in Major League Baseball being guaranteed, teams need to be confident in the fact that the player's skills and production will be somewhat constant over the length of the contract and will not drop dramatically. With one of the MLB's brightest young stars, Bryce Harper, headed for free agency after the 2018 season, it is predicted that we may see the largest contract (magnitude and length) in the history of professional sports. 1 Major League Baseball franchises' willingness to spend on players is growing larger which makes the reward for a high-performing player more valuable. Free agents are judged on their career body of work when being evaluated by teams but often times their evaluations are short-sided and teams ask the "what have you done for me lately?" question, basically emphasizing performance during "contract years" or the year preceding a players free agency. Heather O'Neil (2013) concludes that players increase their effort to boost their performance in their contract year so as to garner another desired contract. One of the most historically infamous ways MLB players have attempted to increase their performance in a given year is by taking banned performance enhancing drugs (PEDs). A few of baseball’s best players are being held out of the Baseball Hall of Fame because of their involvement in PEDs. Barry Bonds, Roger Clemmons, Mark McGuire and Alex Rodriguez are just a few examples of generational-type baseball players whose success can be partly attributed on PEDs, as well as their God given talent. While these players may never be voted into the Baseball Hall of Fame, they certainly did reap the monetary benefits of taking PEDs. For example, Alex Rodriguez signed two separate contracts of 10-years in length and over $250 million in 2001 and 2007 (Rodriguez opted out of the first contract after 7 years). Rodriguez admitted to taking PEDs from 2010-2012 but many baseball historians believe his use of PEDs stretched back further to before his second mega-contract. There have been over 80 players who have been caught using illegal PEDs over the past 12 years but it is difficult to believe the league has come across every offender. Ken Caminiti, winner of the 1996 National League MVP, admitted to his use of PEDs 2 during his playing career and estimated that half of the players in the majors were using PEDs. It is my goal to calculate the overall salary benefits of Major League Baseball players using PEDs. Using a pool of players who have been suspended and/or accused of using PEDs and another control group of similar players who haven't used PEDs, I will estimate PEDs effect on overall player performance. From there I will plug their performance into a salary model which will estimate the monetary boost of using PEDs in the sport of Major League Baseball. 3 Literature Review Performance Many authors have estimated performance for athletes. Modeling and predictions of hitting performance is an area of very active research particularly in the baseball community. Very commonly used methods include Nate Silver’s PECOTA and Tom Tango's Marcel Forecasting System. PECOTA (Player Empirical Comparison and Optimization Test Algorithm) projects performance by fitting a given player's past performance to the performance of "comparable" Major League players by way of Bill James' similarity scores. Bill James, who is widely perceived to be the founding father of modern day baseball sabermetrics, created this metric of similarity scores originally to compare non-Hall of Fame baseball players to past Hall of Fame baseball players to see first if a given player was on track for the Hall of Fame and second, to see which players were wrongfully omitted from the Hall of Fame. The PECOTA forecasting system took this idea and added their own distinct differences. PECOTA compares a player to a database of 20,000 major league players and 15,000 minor league players. It uses four man categories of attributes in determining player's comparability. Production metrics, usage metrics, physical attributes, and fielding position (for hitters) and handedness (for pitchers) are the categories. PECOTA uses nearest neighbor analysis to match a single player to a wide group of players who are most similar to him. From there, PECOTA forecasts the single player's performance based on the average of his comparative group. The other widely accepted metric of performance forecasting is Tom Tango's Marcel the Monkey Forecasting System, or the “Marcels” for short. The Marcels takes a weighted average of the performance of a player from the three previous years, adding 4 most weight to the most recent season. Then, the Marcels regress to the mean of all non- pitchers. For example, whereas PECOTA regresses to their comparative group and other metrics regress towards position groups. The Marcels is a very broad and simple predictive metric that has performed well over the years. Tom Tango is quoted as saying, "Actually, it is the most basic forecasting system you can have, that uses as little intelligence as possible. So, that's the allusion to the monkey." Jensen, McShane, and Wyner developed a similar predictive model for hitting performance among Major League Baseball players, specifically homeruns. Before revealing their model the authors begin with three questions to account for single season anomalies. First, how should past consistency be balanced with advancing age when projecting future hitting performance? Second, in young players, how many seasons of above-average performance need to be observed before we consider a player to be a truly exceptional hitter? Third, what is the effect of a single sub-par year in an otherwise consistent career? Jensen et. al's data came from the publicly-available Lahman Baseball Database. They reference PECOTA as well as the Marcels as motivation and a benchmark for their model. Their outcome of interest for a given player in a given year is home run total. A player's home run total, which is modeled as a binomial variable, is a function of his home run rate and number of opportunities i.e. at-bats. Jensen assumes at- bats are fixed and known but homerun rate is not. They model homerun rate as a function of home ballpark, position, and age of player. They also derived a parameter for "elite" player status. Jensen used the 2006 season as external validation for their method and compared the actual 2006 home run totals to their predicted 2006 home run totals. They used RMSE, interval coverage, and interval width as the three comparison metrics. They 5 conclude that their full model gives proper coverage and a substantially lower RMSE than the version of their model without positional information or the elite/non-elite distinction model.
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