THE EFFECTS OF USING PERFORMANCE ENHANCING DRUGS ON MAJOR LEAGUE 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

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THE EFFECTS OF USING PERFORMANCE ENHANCING DRUGS ON 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 (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)

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ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED AID ON THIS THESIS

Signature

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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

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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 ( 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, 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 ) 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, , 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.

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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. , Roger Clemmons, Mark

McGuire and 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 MVP, admitted to his use of PEDs

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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.

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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 ’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. , who is widely perceived to be the founding father of modern day baseball , 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 ) 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 '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

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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 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

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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. This model also outperformed the "Strawman" method of predicting this year’s homerun totals by mirroring last year’s homerun totals.

Many authors, including Gregory Krohn from Bucknell University and Ray Fair from Yale University, focused on modeling the relationship between age and batting performance. Krohn developed and estimated a model of the typical Major League

Baseball player's lifetime batting average profile. He examines the relationship between batting average and age of an individual player. The model suggests that there is a peak age for performance. This peak age occurs when the effect of additional experience is exactly offset by a loss of physical ability. The model reads as, batting average is a function of age plus a variable "U". He describes "U" as a random disturbance with an unconditional mean of zero. Krohn quantifies the variable "U" as "U" equals talent plus an error term. Krohn concluded that the peak of a baseball player's career was estimated to be at age 28, with a standard error of around 2 years. The expression for how batting averages change with age is given by:

BA(t) - BA (t-1) = 19.1 - 0.687 AGE(t)

This indicates that, on average, once a player reaches his peak age, he will not experience a sharp decline in batting average until about 6 years after the peak. In years 1-5 after the peak age, batters should expect only a decline of 2 percentage points per year.

Ray Fair similarly estimated age effects in Major League Baseball by using a nonlinear fixed-effects regression. This sample consisted of all players who have played

10 or more full seasons, at least 100 games played, in the major leagues from 1921 to

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2004. The data was taken from baseball.com which provides yearly data on every player who ever played Major League Baseball since 1871. Although Fair modeled age effects for pitchers and batters, I will mainly focus on batter performance which is measured using the on-base percentage (OBP) and on-base percentage plus slugging percentage

(OPS) metrics. Fair estimates the rate of improvement up to the peak-performance age, the peak-performance age itself, and the rate of decline after the peak-performance age.

Similar to Krohn's batting average estimates, Fair estimates the peak-performance age for

OPS is 27.6 years and peak-performance age for OBP is 28.3. By age 37 the percentage rate of decline for OPS is 1.21% and for OBP is 0.73% yearly with standard errors of

0.23 years and 0.26 years respectively.

Salary

Different authors have speculated as to how salaries should be determined for professional sports players. A popular theory among economists surrounds the idea that a player's salary should mirror his/her marginal revenue product. Gerald Scully, as well as

Charles Santo and Gerard Mildner, are proponents of this theory. Gerald Scully states in his 1974 paper, "Pay and Performance in Major League Baseball", that a player's marginal revenue product should be the most important determinant of the player's salary.

He defines marginal revenue product among baseball players as "the ability or performance that he contributes to the team and the effect of that performance on gate receipts". The rationale is that player's abilities contribute directly to the team's wins and losses. Wins raise gate receipts as well as broadcast revenues. Determining the true marginal revenue product for individual players is difficult but Scully was able to find the

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effects of player performance on team winning and the effect of team winning on team revenue. He estimated that raising a team win-loss record one point increases team revenue by $10,330. He then estimated that a one point increase in team slugging percentage produces .92 wins. So he concluded that the marginal revenue product for hitters to be 0.92 X $10,330, or $9,504 per point of team slugging percentage.

Santo and Mildner, authors of "Sport and Public Policy" state that an athlete’s value can be thought of as the number of wins that he generates for his team multiplied by the value of each victory, similar to Scully's estimations. In a perfectly competitive labor market, salaries would be equal to marginal revenue products; however, the market for athletes in professional sports is not perfectly competitive. Teams, the demanders of labor, organize to form a single league and players, the suppliers of labor, organize to form a single labor union. Every few years these parties meet and agree on a collective bargaining agreement. Santo and Mildner posit that the bargaining process is a battle in which players try to pull wages up to levels at or above their MRPs, and owners try to push wages below MRP.

Anthony Krautmann (2016) took a different approach in determining players’ salaries. His approach included a variable accounting for performance consistency. His study examines how the role of an owner's attitude toward risk affects his salary bids for free agents in Major League Baseball. Krautmann demonstrates that owners will pay a premium for consistency on the field, and that salaries are negatively correlated to the degree of variability in the player’s performance. Because Krautmann's hypothesis is surrounding risk aversion, his empirical model looks different than standard salary equations for MLB players.

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Krautmann’s empirical wage equation is Real Salary reads below.

Real salary is a function of performance, variance, a vector of player-specific attributes (ex: position, age), a TREND variable used to account for changes in players' salaries across a 4 year period, and population of the team's city. Krautmann's only two measures for performance are expected at-bats and OPS. Krautmann imputes the impact on salary of increasing variability by one standard deviation above the mean. His results imply that an in 2009 whose best OPS year and worst OPS year (within a five year period) have a difference of one standard deviation above the mean can expect a

$316,000 lower salary, equal to a 7% decrease from the average outfielder's salary.

Krautmann, Gustafson, and Hadley's (2003) article discusses the structural stability of salary equations of Major League Baseball pitchers. Their data collection included splitting pitchers into three groups: starters, long relievers, and stoppers.

Their basic earnings model follows the standard semi-logarithmic function form and holds real salary as the independent variable. The model estimates real salary as a function of total team revenue, a dummy variable for LHP and a performance variable

(which they list as "perform"). Perform is a vector of -performance variables that include innings pitched per season (IP), wins per season (WINS),

(ERA), per inning (K/IP) and saves per season (SAVES). Total team revenue is included in the salary model because Krautmann believed it to be an important proxy of the impact of a team's financial success and the possible resulting impact on the team's willingness to compete for high-priced free agents.

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Their sample included 180 pitchers who were all potential free agents for at least one season between 1990 and 1994 and who signed contracts between those years. The selection of the years for the sample was quite important because '90-'94 is far enough away from the owner collusion of the 1980s, but comes before the labor strike during the

1994 season. They conclude that the structure of salary rewards differs significantly between the three groups of pitchers. Krautmann ran a chow test to see if there a structural difference exists between the wage equations at the .05 level. The F-stat (1.67) was greater than the F-critical (1.52) which indicates salary structure for these three different types of pitchers are significantly different and therefore should be modeled differently.

Stone and Pantuosco estimate MLB players’ salaries for three separate periods of time, but I am most interested in their most recent data which spans from 1999 to 2005.

Their motivation was to find determining factors for the sharp salary increase in MLB players. Stone and Pantuosco express their salary equations logarithmically to take into account the over-inflated contracts possessed by the game's biggest superstars as these players have sufficient bargaining power to obtain salaries higher than their stats may indicate. Their model estimates salary as a function of a hitter's career slugging percentage, stolen-bases-per-game ratio, percentage of the player's total at-bats against total team at-bats, as well as years of experience in the major leagues and the population of the team's city.

The authors choose slugging percentage and stolen bases per game as their performance metrics for this model. Slugging percentage distinguishes between singles and extra-base-hits (XBH). They believe slugging percentage is a better indicator of

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player value than standard batting average. Stolen bases per game is truly the only indicator of speed in major league baseball players. Increased speed leads to more stolen bases as well as increased defensive efficiency, as faster players cover more of the field on defense than slower players.

Years of experience was also included in their model because player experience and durability has been shown to enhance salaries. First, players accumulate experience while playing in the major leagues and carry that experience with them as they begin every season. Second, there is a seniority effect in the major leagues. For example, a minimum one-year contract is less for a first-year player than it is for a veteran. Finally, after a poor season, owners can only cut the player's salary down to a certain amount agreed upon by the Players Union. Therefore, most earnings rise rather than fall over the course of a player's career.

Very briefly, their results suggest that the slugging percentage elasticity has increased nine-fold from 1961 to 2005. This result is particularly interesting because

PEDs are proven to increase strength and muscle mass in athletes. In baseball, these effects can manifest themselves in an increase in power which is directly measured by slugging average. If owners tend to pay a premium for increased slugging percentage, then the average player will wish to increase that metric at all costs, which includes the usage of illegal PEDs.

Performance Enhancing Drugs

Pantuosco's (2011) article examines the payouts of being unethical in the MLB, with a focus on performance enhancing drugs (PEDS). Using career data compiled in the

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2005 season, Pantuosco used regression analysis to estimate the effect of steroids on player salaries. Pantuosco states that while he found short-term player salary increases, the long term earnings, reputation, and physical health measures could take more time to surface as this topic endures more research.

His model is almost identical to the model contained in Pantuosco & Stone which was mentioned earlier, but adds a PED dummy variable. The model included performance measures (Slugging Average, OPS, and Stolen Bases) and a number of player-specific attributes such as player at-bats as a percentage of total team at-bats, years of service in the MLB, and population of home city. National League and PED usage were used as dummy variables. The model used the logarithms of all of these variables, except for the dummy variables, to account for the non-linear relationship between average players and superstars. His results show that owners are still more likely to compensate players based on their slugging as opposed to their ability to get on base. The coefficient of the lnSA (slugging average) is 3.44, therefore, if a player's slugging average were to increase from the survey average of 0.429 to 0.529, the move to stardom would result in an over $2 million increase in annual salary.

In each of the equations, the coefficient of the PED variable is positive and significantly different from zero. Players who have used steroids have earned higher salaries than players with comparable performance statistics who have not used steroids.

Steroid use was found to increase the average player's slugging average from .428 to .460 which led to an approximately $700,000 increase in the average steroid-using-player's salary, a 29% increase on average.

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Tobin's (2007) article does its part to show the effects of taking performance enhancing drugs (PEDs) has on hitting production, which in this case is home runs. Tobin argues that steroids can have such an effect on home run production because home runs fall on the tail end of a statistical distribution, and are especially sensitive to small changes in physical ability. He argues that PEDs increase home run production but do not increase batting average because putting the ball in play is largely a matter of skill and is less likely to be affected by the strength of the player. If a ball is being put in play at the same rate by a specific player who then takes PEDs, Tobin estimates that their homerun total and stats associated with power, slugging percentage (SLG), on-base plus slugging percentage (OPS) and runs batted in (RBI), will also go up.

Tobin focuses on the metric of home runs as a percentage of batted balls in play because it is a reliable way to measure raw power of hitting the baseball by stripping away strikeouts, walks, sacrifice bunt, and HBP. At his peak, regularly surpassed the 10% ratio of home runs per batted ball but never reached 15%. To show comparison of old-school sluggers versus "steroid era" sluggers, Mark McGuire registered seven seasons at or above the 15% mark during his career (1986-2001). Tobin does not, however, attribute this increase in home runs/ball put in play as credible evidence of cheating. Recent changes in the MLB such as altered ballpark dimensions, entry of African-American athletes and lowering of pitcher’s mound are other factors that have led to an increase in power statistics.

The University of Pennsylvania, Wharton School of Business (2008) wrote an article regarding "Baseball, Steroids, and Business Ethics". In the article they discuss public perception of cheaters in the MLB, or players who have used PEDs. The author

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states that consumer responses to ethical lapses depend on whether the wrongdoing strikes at the core of company's mission. They cite two examples of an accounting firm and clothing company. Once the accounting firm was found to be dishonest in their practices, the value of their company was lost. The clothing company was accused of unfair wages and improper employee firings. But since their clothes still looked fashionable, the value lost from customers was still mightily less because at the end of the day, they are known for their clothes. So in terms of MLB, the organization itself isn't being dishonest; they are paying everyone fairly and there are no labor disputes. But the players within the organization are being dishonest and are trying to keep up with the best players who may or may not be taking steroids. Rather than the league enduring the fallout of stars testing positive for PEDs, the authors explain that each team is an entity within itself and should be handled at the team level.

Cisyk and Courty's (2015) article explores how home fans have tended to react to doping scandals or steroid usage regarding their own players. According to Preston and

Szymanski (2003), the only rationale against doping that withstands economic scrutiny is that the use of PEDs devalues sport contests and decreases public interest. This study as well as many others were motivation for Cisyk and Courty. They chose to investigate

PED perception on the MLB because of the new regulations and tests the league had implemented, the fact that teams play almost every single day (162 games in 180 days), and lastly MLB games are not usually sold out on a nightly basis, so changes in attendance can be accurately quantified. They take data from 29 PED violations announced in 2005-2013. Their data collection on game outcomes came from Baseball-

Reference.com. Cisyk and Courty demonstrate that PED violations have a short-term

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impact on home-team attendance which was statistically significant. The announcement of a PED suspension initially decreases the demand by about 8%. This effect, however, decreases quickly to the point where, after 12 days, the effect disappears.

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Theoretical Framework

There are a few main aspects of economic theory that surround this topic, the first and foremost being labor market theory. The eligible pool of baseball players are the suppliers of labor and the Major League Baseball teams and their owners are the demanders of labor. Team’s goals are to maximize wins as well as profits, as baseball at its core is a business. Firm’s (team’s) profits are calculated as revenues minus costs.

MLB team’s revenue streams include, ticket sales, other venue related revenues, revenue sharing, licensing agreements and sponsorships, and television and radio broadcasting rights (Fontinelle, 2015). The latter of which has become extremely lucrative in the recent years. Teams’ main costs are salaries for players and non-player personnel, as well as operating and overhead costs including travel and venue costs.

Team’s revenues are closely correlated with the pedigree of the team. For example, in 2016 the five teams that produced the most revenue were (first to fifth) the

New York Yankees, Dodgers, , , and the . These five franchises have been historically successful in producing quality baseball teams that regularly compete for championships. These are also teams that regularly sell every seat to their 81 home games and are in large metropolitan markets where television/radio deals are more lucrative than deals for smaller markets

(Scully 1989; Sommers and Quinton 1982).

The marginal revenue productivity theory states that a profit maximizing firm will hire workers up to the point where the marginal revenue product is equal to the wage rate.

Unfortunately the market of professional baseball is not perfectly competitive but rather operates as a monopoly. Teams organize into a single league and therefore players

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combine and form players unions. For these reasons, player salaries are determined by factors other than their marginal revenue product. First is the past production by the player. Basic theory states that players who have consistently performed at an above average, or even all-star level, will receive a larger contract than players who are either inconsistent, or consistently produce average statistics. The second factor that affects player demand is the number of teams offering the player contracts. There is a limited number of “all-star” or “elite” level players and therefore they are highly sought after commodities when available. If a situation arises when multiple teams wish to sign a specific player, the demand of that player’s contract goes up and teams are therefore willing to pay even above market price to ensure the signing of the player. Though every team desires great players, not every team has the same team needs, therefore it is unlikely that all 30 teams will bid for one player.

The demand for labor is derived from the demand for the ultimate goods and services that labor is used to produce. Since customers are willing to pay more for higher quality athletic competition, the demand for players’ services depend on their marginal contribution to product quality. Professional sport is one of the few empirical cases where the marginal product of a player can be directly assessed. Sports are almost unique in affording the opportunity to measure specific work performance on a narrow set of jobs.

Empirical studies inevitably find that better performing professional athletes earn more money.

Economic theory states that salary is a function of production. Players take performance enhancing drugs because they believe that they will increase strength and endure quicker recovery times. Increased strength in a player has been shown to increase

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his power statistics, such as home runs and slugging percentage. Home runs, runs batted in, and slugging percentage are very important offensive statistics for which teams will pay a premium. Often times, elite players with a history of injuries will not receive as large of a contract when compared to their healthier counterparts. Injuries are a given risk surrounding any professional sports contract. Teams would rather invest more money into a player who has proven that he will consistently be on the field.

Though some teams can offer larger contracts than others, a player’s choice of which team to sign with does not always equate to the team that offers the highest salary.

Each player has a different utility function than the next. There have been many instances where players forego larger contracts in order to either play for a higher quality team (in terms of wins and playoff success), to play for a hometown team, to play for a specific , or something entirely separate of which reasons are unknown. It is almost impossible to model a utility function for a specific player because there are many more factors that go into a free agency decision than just money.

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Data

The data set is compiled of single regular season data of 103 Major League

Baseball players spanning different years ranging from the 1990 season to the 2016 season. The data I collected on each individual player, including statistics and salaries, was accessed from baseballreference.com. The city population data, a variable in the salary equation, was accessed from the U.S. Census Bureau for the relevant period. The data set includes all 36 Major League Baseball players who were suspended due to their use of illegal performance enhancing drugs (PEDs) after the MLB started testing for

PEDs in 2003. The data set also includes 17 Major League Baseball players who were among those mentioned as PED abusers in George Mitchell's federal investigation report presented to Major League Baseball in December, 2007 (The ). The data set also includes 's 1996 season, Mark McGwire's 1998 season, and Barry

Bonds' 2004 season because baseball historians have alleged that these superb hitting seasons have proved to be aided by the use of PEDs.

Not all players mentioned in the Mitchell Report were used in this data set because of the controversial nature of the Report's findings. For example, the Report included those players for whom there is direct evidence of them purchasing illegal PEDs but those who also denied ever using PEDs. The 17 players included from the Report either willingly admitted to PED use or the evidence for them using PEDs was overwhelming. The data set also includes a control group of 47 Major League Baseball players who had no history of PED use, and therefore I assume did not use PEDs. These players, as well as the year I chose to record as their statistics, were randomly selected.

Control group players and their years were selected from 2005-2015, with 4-6 different

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players being selected every year. In the data set, players who have used PEDs were given a PED dummy variable of 1, while all those who did not take PEDs were given a

PED dummy variable of 0.

Regression Equations and Variables

In this paper, the link between PEDs, performance, and salary is explored. The objective is to estimate the monetary benefits of using PEDs as a Major League Baseball player. In order to observe these effects, I have devised three tiers of linear regressions.

The first two tiers estimate performance while the third tier estimates salary. All three tiers are listed below:

Tier 1:

Tier 2:

Tier 3:

1a. Tier 1 Dependent Variables

Slugging percentage (SLG) and on-base percentage (OBP), are the two dependent variables for the two Tier 1 performance equations. SLG is calculated by dividing the number of total bases by the total at bats of a player. A single constitutes as one total base, a double constitutes as two total bases, a triple constitutes as three total bases, and a home run constitutes as four total bases. The number of at-bats differ from the number of

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plate appearances because a batter will not be credited with an at-bat if his plate appearance results in a base on balls (BB), is by the pitch (HBP), or hits a sacrifice bunt or fly (SF). OBP is calculated by dividing the amount Times on Base by plate appearances. Times on Base includes hits, BB, and HBP.

1b. Tier 1 Independent Variables

Independent performance variables included in the Tier 1 equations are age, draft round, and the PED dummy variable. The "draft" variable denotes which round within the MLB draft each player was selected. For players who were not drafted, I assigned them a draft round of 12. The rationale for using 12 as my default draft round for undrafted players is that an overwhelming amount of them were international, where players are often signed rather than entering the draft. The MLB draft is up to 40 rounds long, and players drafted in the back half of the draft usually do not consistently make rosters. A draft round of 12 for international players indicates that their talent level is high enough to initially make an MLB roster. As discussed above, players who have used

PEDs were given a PED dummy variable of 1, while all those who did not take PEDs were given a PED dummy variable of 0.

2a. Tier 2 Dependent Variable

Wins above replacement (WAR) is the single dependent variable in my Tier 2 equation. The WAR statistic is an attempt by the sabermetric baseball community to summarize a player’s total contributions to their team in one statistic. WAR is also effective because it is an all-inclusive statistic that bundles hitting, fielding, and base running into one metric making it the best way to compare players across teams, leagues,

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years, and even eras. WAR will be explained in more detail in the Regression Analysis section later to come.

2b. Tier 2 Independent Variables

Independent variables in my Tier 2 equation include games played, at-bats (AB),

SLG, OBP, and stolen bases. While SLG and OBP are dependent variables in the Tier 1 performance regressions, they are also independent variables in the Tier 2 regression.

3a: Tier 3 Dependent Variable

One-year player salary is the dependent variable in the Tier 3 salary regression.

Each observation contains one player from a specific year. This includes either the statistics from a player’s year in which they were suspended or suspected of using PEDs in that year, or simply a randomly selected year for the control group of players who I assume did not take PEDs. Years of observations spanned from the 1990 season to the

2016 season, and therefore all salaries were converted to 2017 dollars.

3b: Tier 3 Independent Variables

The independent variables that were collected for the salary equation include position, years of experience (up to and including year of collected statistics), and population of home city. Position players were used for this study while pitchers, though many of whom used PEDs, were not included. A dummy variable for infielders and was created to differentiate the two groupings. Infielders, including

(C), first-basemen (1B), second-basemen (2B), third-basemen (3B), and short-stops (SS) were attached with a "0" for the position dummy variable. Outfielders, including left fielders (LF), center fielders (CF), and right fielders (RF), were attached with a "1" for the position dummy variable.

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As discussed above in the salary section of the literature review, there are two reasons why years of experience and salary are positively correlated. Players gain experience while playing in the major leagues and bring that experience with them as they begin every season. This theory assumes players will learn from prior mistakes and use this knowledge to better their performance in the future. Also Major League Baseball salaries, much like in other sports leagues, boasts a seniority effect. For example, a minimum one-year contract is less for a first year player than it is for a veteran player.

Research indicates that team revenues such as ticket sales, concessions, and most importantly television and radio deals, are impacted by the size of their metropolitan market. Higher team revenue leads to greater value of the product. Sommers and Quinton

(1982) determined that the marginal revenue product of a win is greater in larger markets than in smaller markets. Scully (1989), using 1984 data, found that team revenue is directly related to market size. The Northwestern Business Review states that “the larger the fan base, the greater the demand for tickets. Ticket supply is relatively constant; a stadium can only hold so many people. As a result, prices rise. Larger ticket sales and higher ticket prices together increase a team’s revenue, allowing team owners to reinvest more money into their organization while still turning a profit.”

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Descriptive Statistics

Table 1 contains descriptive statistics for the whole data set containing all 103

observations. Table 2 contains descriptive statistics for the control group of players,

players who have not taken PEDs. Table 3 contains descriptive statistics for players who

have used PEDs.

Table 1 Descriptive Statistics: Whole Data Set Average Standard Observations Value Deviation Min Max WAR 103 1.802 2.337 -1.5 9.2 Salaries 103 4142455 5594276 190181 28000000 Years of Experience 103 7.049 4.645 1 20 Home Population 103 1502219 2097839 288073 8422000 Position 103 0.466 0.501 0 1 Age 103 29.631 4.516 21 40 Draft 103 8.757 7.296 1 38 Games 103 98.320 45.113 4 161 At-Bats 103 331.243 186.706 10 662 Slugging Percentage 103 0.426 0.095 0.231 0.752 On-Base Percentage 103 0.332 0.053 0.2 0.529 Stolen Bases 103 7.864 12.011 0 58

Table 2 Descriptive Statistics if PED=0 Average Standard Observations Value Deviation Min Max WAR 47 1.791489 1.928 -1.5 7.4 Salaries 47 2808298 3433953 387502 14300000 Years of Experience 47 6.043 3.426 2 14 Home Population 47 1123569 1379966 288073 8392000 Position 47 0.447 0.503 0 1 Age 47 29.043 3.989 22 39 Draft 47 9.447 8.198 1 37 Games 47 111.234 38.991 27 161 At-Bats 47 370.404 173.758 59 620 Slugging Percentage 47 0.405 0.069 0.265 0.579 On-Base Percentage 47 0.322 0.034 0.238 0.385 Stolen Bases 47 8.000 11.264 0 57

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Table 3 Descriptive Statistics if PED=1 Average Standard Observations Value Deviation Min Max WAR 56 1.811 2.649 -1.1 9.2 Salaries 56 5262175 6736441 190181 28800000 Years of Experience 56 7.893 5.349 1 20 Home Population 56 1820015 2518665 303625 8422000 Position 56 0.482 0.504 0 1 Age 56 30.125 4.895 21 40 Draft 56 8.179 6.461 1 38 Games 56 87.482 47.336 4 155 At-Bats 56 298.375 192.318 10 622 Slugging Percentage 56 0.443 0.109 0.231 0.752 On-Base Percentage 56 0.341 0.064 0.2 0.529 Stolen Bases 56 7.75 12.704 0 58

The PEDs group has an average salary of $5,262,175 which is higher than the

average salaries than the non-PEDs group ($2,808,298) by around $2.5 million. The

standard deviations of the salary variable for both the PEDs and non-PEDs group are

greater than the average values of salaries. PED users and perennial all-stars such as Alex

Rodriguez, Manny Ramirez, and Barry Bonds, all had yearly contracts worth well over

$20 million which skews the PED salary data upwards.

To remove these outliers and see a more realistic breakdown of the salaries for PED

users, I generated a dummy variable called "normalsalary" which gave every player with

a salary under $20 million a "1" and the players with salaries over $20 million a "0".

Then an interaction term, titled "normalsalaryped", was created. This interaction term is

the product of the PED and normalsalary dummy variables. This interaction term was

created in order to narrow my target group down to PED users with less than a $20

million salary.

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Table 4 details the difference in average salary between the two groups while ignoring “mega-contract” outliers. When using the interaction term, four less observations were recorded but the mean salary of the PED group dropped from $5.2 million to $3.8 million. When ignoring the outliers (Rodriguez, Ramirez, Bonds), the average salary for the PEDs group is approximately $1 million more than the non-PEDs group.

Table 4 Salary Descriptive Statistics Using Normal-Salary-PED Interaction Term Standard Observations Average Value Deviation Min Max Non-PEDs 47 2808298 3433953 387502 14300000 Normal Salary PED 52 3818313 4251264 190181 14900000

It is important to observe that, on average, players who used PEDs played in significantly less games, and therefore took less at-bats in their recorded seasons than players who did not use PEDs. Major League Baseball's regular season consists of 162 games, and each player is granted at least three at-bats per game, given that he is not substituted for during the game. On average, PED users played in 24 less games per year and took 72 less at-bats than their non-PED using counterparts. A reason for this discrepancy is that many PED using players were handed suspensions during the middle of their regular season which were enforced immediately, limiting the number of games the player could play that season. For example in 2013, ten players tested positive for

PEDs and were suspended in July and August, which are months right in the middle of the regular season.

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The PEDs group has a slightly higher overall average WAR when compared to the non-PEDs group. PEDs group boasts a 1.81 mean WAR, and the non-PEDs group put up a 1.79 mean WAR. The variation in mean WAR values is higher for the PEDs group as shown by the 2.65 standard deviation compared to 1.92 for the non-PEDs group. The maximum WAR was 9.2, provided by Barry Bonds, a member of the PEDs group, in

2003. Other notorious PED users including Mark McGwire, Ken Canimiti, , and inflated the mean WAR of the PEDs group with their abnormally incredible statistical seasons while other PED users had short, ineffective careers.

Average values of SLG and OBP were higher in the PEDs group, but with again higher standard deviations than the non-PEDs group.

Average age is almost identical for the two groups, with the average age of the

PEDs group being 30.125 and the average age of the non-PEDs group being 29.042. The difference in years of experience, however, is more easily noticed between the two groups. The average PED user used illegal substances approximately in their 8th year

(7.893). The average years of experience for the non-PEDs group was 6.042 years. In

2011 the average age for MLB rookies was approximately 24 years old (Fangraphs). The figure below was attained from baseball-reference.com by way of the Boston Globe.

From data compiled from 1984-2014, this figure details the percentage of starting pitchers and position players who account for a WAR of 2.0 or better, broken down by age. For position players, there is a significant rise in 2.0+ WAR seasons from age 22 to

26, remains high and begins to steadily decline once players hit 29 years old.

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The mean age for PED users in the data set is 29.6 years old which, for position players, is the age where this figure states performance begins to decline.

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Regression Analysis Tier 1 Studies have shown that MLB player's power driven statistics, such as home runs and slugging percentage are the statistics most affected while using PEDs. The rationale is that many of these PEDs which players take increase the muscle mass of the player which can therefore only affect the power of a certain player, not reflexes, vision, or bat speed. Other studies, including that of Dr. Gary Wadler, chairman of the World Anti-

Doping Agency's Prohibited List and Methods subcommittee, state that with increased muscle mass, a hitter can generate greater bat speed, producing more force on the ball and driving it farther. This theory assumes that increased muscle mass will increase bat speed and therefore will increase a player's power statistics while also increasing their contact statistics, which includes batting average and on-base-percentage.

To test these two schools of thought, linear regressions of slugging percentage

(SLG) and on-base percentage (OBP), were estimated to see effects of PEDs on these statistical categories. The independent variables in this regression were age of the player, round they were drafted in, and the PED dummy variable.

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Table 5 Tier 1 Performance Regression Results Slugging Percentage On-Base Percentage Age 0.00248 0.00242* (1.14) (2.12)

Draft -0.00300** -0.00196*** (-2.86) (-3.41)

PED 0.0317* 0.0139 (1.85) (1.48)

Constant 0.362*** 0.270*** (5.5) (8.12)

Observations 103 103

R2 0.1156 0.1624

Note: t statistics in parenthesis, * p<0.05, ** p<0.01, *** p<0.001

Table 5 displays the results of the two tier 1 performance regressions that estimated PEDs effect on SLG and OBP. The coefficient to focus on when examining these regressions is the PED dummy variable, which in Column 1 is .0317. This coefficient can be read as a player using PEDs is estimated to see a .0317 increase in their

SLG over the course of the year, which is a 7.5% increase when comparing to the average

SLG for all 103 observations. The PED dummy variable has a t statistic of 1.85 and a p value of 0.068 in the SLG regression, so there is a 90% level of confidence that PEDs are statistically significant in this model. The coefficient for the PED dummy variable in column 2 is .0139 which can be read as a player using PEDs is estimated to see a .0139 increase in their OBP over the course of the year, which is a 4.2% increase when

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compared to the average OBP of the data set. The PED dummy variable has a t statistic of

1.48 and a p value of .141 in the OBP regression, which makes this variable statistically insignificant. Though this coefficient is not statistically significant, I have chosen to keep

OBP in my Tier 2 performance model. The data collection process was controversial and the data set did not include many players who were assumed to use PEDs, but the evidence was not 100%. Had the PED indictment process been more black and white, the data collection would have been more accurate and more PED using players with high performing offensive seasons would have been included.

Tier 2

Rather than simply including a few baseball statistics in the performance model which measure power, reliability, and speed like most other similar studies, I have chosen to use wins above replacement (WAR) as the singular statistical measure for player performance. Fangraphs defines WAR as their hallmark statistic that attempts to estimate a player’s total value relative to a free available player, such as a minor league free agent.

Rather than just including batting attributes, WAR is an aggregation of batting runs, base running runs, fielding runs, replacement runs, and a positional and league adjustment.

The overall equation for WAR is:

WAR is the performance measure that will be use in the performance model because the statistics used are more robust. For example, the batting runs category uses the measure of weighted on base average (wOBA) instead of traditional baseball statistics such as batting average (BA) or on base percentage (OBP) because wOBA weights

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similar outcomes in different manors. For example, a base-on-balls, a hit-by-pitch, and a single will all get a batter to first base, but wOBA weights each outcome differently because a single, for example, gives runners on base the possibly to advance multiple bases, while a walk and a hit by pitch does not provide that possibility. OBP would weight these three actions the same but in reality they have different real-game consequences.

The numerator of WAR is all of the runs provided by the player. The batting metrics are runs which the player adds to his team via his hitting. The base running runs are the runs which the player adds (or subtracts) to the team via base running ability. And finally fielding runs are the runs which the player saves (or gives away from) their team via their fielding ability. The denominator for WAR is runs per win. Dividing the runs a player contributes to his team by the average runs per win produces a singular number which is used to depict the amount of wins a player will personally contribute to a team.

WAR is an individual statistic and is not affected by the performance of others. WAR at its basic form provides the number of wins that can be directly attributed to a single player’s performance when compared to the average MLB replacement player.

This Tier 2 performance regression estimates wins above replacement (WAR) as a function of the player's age, the round they were drafted, games played and at-bats (AB) taken during the season, as well as their slugging percentage (SLG), on-base percentage

(OBP), and stolen bases (SB). The reason why this is denoted as a Tier 2 regression is the same reason why PEDs is not an independent variable in this performance regression.

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The effect of PEDs is carried by the coefficients of the "SLG" and "OBP" variables in this regression.

Table 6 Tier 2 Performance Regression Results WAR Age -0.0814** (-2.66)

Draft 0.0262 (1.11)

Games 0.0133 (1.36)

At-Bats 0.00165 (0.66)

Slugging Percentage 9.981*** (4.7)

On-Base Percentage 10.28** (2.65)

Stolen Bases 0.0369** (2.92)

Constant -5.825*** (-4.2)

Number of Observations 103

R2 0.6905

Note: t statistics in parenthesis, * p<0.05, ** p<0.01, *** p<0.001

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Table 6 shows the results of the Tier 2 performance model. Coefficients to focus on are the SLG and OBP metrics. This regression shows that a one point increase in SLG is associated with a 9.98 increase in a player's WAR. To put in perspective, the mean

SLG for players in the data set is 0.426 with the maximum being 0.752 produced by a

PED using Mark McGwire in 1998. No player can expect a full one point jump in slugging percentage therefore it is more appropriate to state that a 0.1 slugging percentage increase will increase the player's WAR by 0.998 points. SLG has a t statistic of 4.7 and p value of 0.00, which indicates that there is a 99% confidence level that SLG is not equal to zero in this regression. A similar interpretation can be made with OBP.

This regression states that a 0.1 point increase in on-base percentage is associated with a

1.028 point increase a player's WAR. OBP has a t statistic of 2.65 and a p value of 0.009, which indicates that there is a 95% confidence level that OBP is significant in this regression.

Tier 3 The Tier 3 regression estimates one-year player salaries as a function of performance (WAR), position, years of experience, and population of the player’s team’s city. The effect of PEDs is carried by the WAR variable in this regression.

Table 7 details the results of the tier 3 salary model. The coefficient of note is the

WAR coefficient. Based on my data set, this regression estimates that a one full point increase in WAR is associated with a $326,177.90 increase one year player salaries.

WAR has a t statistic of 2.16 and p value of 0.033 which indicates a 95% level of

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confidence that this variable is significant in this model. In this salary model, position, as well as home population are statistically insignificant in determining player salary. Every year of experience adds $848,956 to a player’s salary and with a t statistic of 6.29, we are

99% confident that this variable is not equal to zero.

Table 7 Tier 3 Salary Regression Results Salaries

WAR 326177.9* 2.16

Position 544559.4 0.78

Years of Experience 848958.6*** 6.29

Home Population 0.430 1.74

Constant -3329056.0*** -3.83

Number of Observations 103

R2 0.6016

Note: t statistics below coefficients, * p<0.05, ** p<0.01, *** p<0.001

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Results

From the Tier 1 performance regressions, PEDs are shown to be associated with a

0.0317 point increase in slugging percentage which increases average SLG from 0.426 to

0.458. Similarly, PEDs are shown to be associated with a 0.0139 point increase in on-

base percentage which increases average OBP from 0.332 to 0.346. WAR is estimated by

multiplying each independent variable’s average value by the corresponding regression

coefficient and summing the totals. Table 8 estimates WAR using all original regression

coefficients and average values. Table 9 estimates WAR using original regression

coefficients but with the new PED affected SLG and OPS average values. With all of the

other average values remaining constant (age, draft, games, at-bats, stolen bases) and

multiplying by the same regression coefficients, PEDs are shown to increase average

WAR from 1.5697 to 2.0283.

Table 8 Estimating WAR Using Regression Analysis Non-PEDs Average PEDs Average Regression Non-PEDs PEDs Value Value Coefficient Total Total Age 29.63 29.63 -0.081 -2.4 -2.4 Draft 8.757 8.757 0.026 0.228 0.23 Games 98.32 98.32 0.013 1.278 1.28 At-Bats 331.24 331.24 0.001 0.331 0.33 SLG 0.426 0.458 9.981 4.252 4.57 OBP 0.332 0.346 10.28 3.418 3.56 SB 7.864 7.864 0.036 0.283 0.28 Constant 1 1 -5.82 -5.82 -5.82 WAR 1.5697 2.0283

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As seen in table 8, PEDs are associated with a 0.459 point increase in WAR.

Salaries are estimated the same way as WAR, by multiplying average values by the corresponding regression coefficients and summing the totals. Table 9 estimates salaries using the non-PED-influenced WAR average value (1.5697) while Table 10 estimates salaries using the PED-influenced WAR average value (2.0283).With all other average values remaining constant (YrsExp, Position, Homepop) and multiplying by the same regression coefficients, we can see that a 0.459 increase in WAR is associated with an increase in average salary by $149,810.15 (2017 dollars).

Table 9 Estimating Salaries Using Regression Analysis Regression Average Value Total Coefficient WAR 1.5697 326177.9 511986.11 YrsExp 7.049 848958.6 5983921.8 Position 0.466 544559.4 253775.25 Homepop 1502219 0.4300639 646050.29 Constant 1 -3329056 -3329056 Salaries 4,066,677.43

Table 10 Estimating Salaries Using New WAR Value Regression Average Value Total Coefficient WAR 2.0289 326177.9 661796.26 YrsExp 7.049 848958.6 5983921.8 Position 0.466 544559.4 253775.25 Homepop 1502219 0.4300639 646050.29 Constant 1 -3329056 -3329056 Salaries 4,216,487.58

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Discussion For my data set, PED use was found to increase the players SLG from 0.426 to

0.458 and OPS from 0.332 to 0.346, which led to an approximately 0.5 point increase in average WAR and led to an approximately $150,000 increase in average salary (2017 dollars). While in almost every context a $150,000 salary boost is a hefty increase, in the life of an MLB player however, the increase is quite miniscule. In 2017, Major League

Baseball set their league minimum contract at $535,000 with the average player salary reaching $4,470,000. In the grand scheme of MLB player’s contracts, a $149,810.15 increase would only increase the average player salary by 3.35%.

In 2014, The Major League Players Association agreed to increase the first-time penalties to 80 games, up from 50 games in the wake of the 2013 Biogenesis scandal.

Now if a player is caught a second time, he will miss 162 games and lose his entire pay.

Players will receive a lifetime ban for a third offense. MLB players will also be required to submit to two urine samples during the season, an increase from 1,400 to 3,200 overall. There will also be 400 random blood collections used to detect human in addition to the 1,200 mandatory tests during (USA Today).

MLB players are not paid while they are suspended which begs the question, is it worth it for MLB players to take PEDs? The advantages of taking PEDs include the statistical boost and monetary boost which, based on my data set, came out to a 0.46 increase in WAR and just under $150,000 per year in salary. Some PED using players in the data set experienced higher statistical increases than others. For example, Todd

Hundley of the . As part of the Mitchell Investigation, convicted steroid dealer, Kirk Radomski, said he sold anabolic steroids to Hundley early in 1996.

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Radomski said he told Hundley that if he used steroids he would hit 40 home runs. Prior to 1998 Hundley’s career high for home runs in a season was 16. That year Hundley hit

41.

The disadvantages of taking PEDs are risk of suspension, lost wages, and diminished reputation. The new MLB drug prevention policy has increased the consequences which players will face if they test positive for PEDs. A true “three strikes and you’re out” policy combined with an increase in testing, in theory, should dissuade any player from taking PEDs.

The average salary for PED using players in my data set, using the normal salary interaction term, is approximately $3.8 million. With the current drug prevention penalty system in place, the average PED user would experience a loss of half their total wages, or $1.9 million, after a first time offense. A second PED offense would result in a full season's salary, $3.8 million. Finally a third offense, resulting in a life-time ban, would deny the player from ever again being employed by Major League Baseball. At a certain age in a player's career, however, a PED suspension could be more consequential than one half of a year's salary. In my data set, the average age for PED users is approximately

30 years old with a standard deviation of 5 years. J.C. Bradbury (2010) states that the peak age of a Major League Baseball player is 29-30 years old. Witnauer, Rogers, and

Onge (2007) further state that unlike normal work careers in which workers' participation and performance ascend gradually and decline slowly, baseball careers are characterized by rapid ascent followed by rapid decline. It is then reasonable to assume that by the time a non-PED using player reaches age 33, his production is expected to decline while his salary would only increase, as my regression states that every year of experience adds

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$848,956 to a player's salary. Adding the negative stigma of using PEDs to lower expected production and a higher expected salary would incentivize teams to select a younger, cheaper option when assembling their roster.

For certain players, a diminished reputation is a worse consequence than lost wages. Barry Bonds (14-time all-star, 7-time MVP, 8-time Gold Glove winner, MLB best

762 career HR) had accumulated the most distinguishable accolades of any position player to ever play Major League Baseball. His 162.4 career WAR surpasses the average

Hall of Fame Left Fielder’s career WAR (65.1) by 97.3 units of WAR. Yet Bonds has not been elected to the Baseball Hall of Fame and has been eligible for the past 5 years.

Similarly 7-time Cy Young winner Roger Clemens, who also became Hall of Fame eligible since 2013, has not received enough votes to be inducted into the Hall. Links to

PED involvement is the main reason why the Baseball Writers Association (BBWA) have not voted in Bonds, Clemens, and many other players whose career statistics seem to be Hall of Fame worthy.

Other players who struggle to make it out of the minor leagues and onto a major league roster face serious consequences if they test positive for PEDs. Players such as

Jamal Strong, Ryan Jorgensen among others never received a second opportunity to make an MLB roster after serving their PED suspensions. As a replaceable player, teams are likely to look for other players who are not associated with PEDs.

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Conclusion This paper aims to estimate the monetary benefits of MLB players using performance enhancing drugs. Using single season data from 47 PED users and a control group of 56 non-PED users, PED use was found to increase SLG, OPS and overall WAR.

The 0.459 point increase in WAR positively affected salary, raising average salaries by

$149,810.15. The steroid era loosely spanned the late 80s all the way to the late 2000s and acts as one of the most controversial and murky times in American professional sports in the last 50 years. For that reason, many players who in all likelihood used PEDs during their playing career were not used in this study. PED testing began in 2003, well after the climax of the steroid era which was the mid-to-late 90s, which means most PED users in the 90s were not caught. This study would improve if there was more concrete information on PED users. As was previously mentioned in my Data Collection section, my PEDs group only consisted of players who were suspended by the league, players who willing fully admitted to their PED use and players whose evidence of them using

PEDs was overwhelming. Had more PED users been included in this study, I believe the magnitude of the statistical increases for SLG, OPS, WAR and salaries would have been greater.

In the eye of the common baseball fan, the steroid era brought a spark to the game because of all the excitement that increased offensive production brought to ballparks. In

1998 the chase to surpass the single season home run record revitalized an obsession with the long ball. More fans than ever were attending games, and teams’ revenues soared.

Eventually the integrity of the game was called into question and the league responded by implementing punishments for using PEDs. Now these penalties are stricter than ever and

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with a stern three strikes and you’re out rule in effect, players have very serious options to weigh. My results suggest that using PEDs will increase players’ salaries by approximately $150,000, or 3.35% of the average player’s salary. In my estimation, the costs surely outweigh the benefits of taking performance enhancing drugs in today’s

MLB. These penalties should dissuade current and future players from partaking in the type of behavior that scared the reputations of some of the best players in the league during the “steroid era.”

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