a test for salary discrimination

Salary Discrimination in Major League Baseball

University of California, Berkeley

The Labor Market of Major League Baseball

A Test For Salary Discrimination

Jorge Augusto Arias

Topics in Economic Research

Professor Joseph Farrell

GSI David J. Birke

May 11, 2016


Abstract

The sport of baseball has been a source of entertainment in America for more than 100

years. With the passage of time the labor market of baseball has experienced changes in the

overall competition between employers. A series of research initiatives have studied the impact

this has had on the salary level of players across different races. Given that baseball presently

produces approximately $9.5 billion dollars in total annual revenue, a contemporary test on

whether or not America’s pastime hosts salary discrimination on the basis of race was warranted.

This paper observed the performance of 100 players in 2015 to question whether non-White

players received lower wages in 2016 than White players who had equivalent levels of

productivity. A regression analysis suggested that salary discrimination on the basis of race was

not practiced at significant levels against the majority of the variables regarded as predictors of a

total player’s value. The analysis did however suggest that stolen bases is currently undervalued

by employers. Given that non-white players on average are faster and obtain more steals per

season, the current market valuation on this statistic can be deemed as a potential source of

unconscious bias.


There are scores of industries in America that are presently regarded as hosting employer

bias against the wages of employees. A socio-economic problem that is currently at the forefront

of American politics is racial discrimination. A present source of inequality in America is the

disparity in per capita income amongst different racial groups. For instance, studies show that

African Americans earn about 35 percent lower wages than Whites, and Latinos earn about 39

percent less. Conducting studies to prove the presence of unequal treatment is particularly

challenging given the difficulty of estimating the effects of confounding factors like the level of

productivity of workers, a main determinant of salary amount.

The sports field is not characterized by some of the dwelling challenges that exist in

industries such as finance, agriculture, and manufacturing. As Lawrence Khan, professor of

Labor Economics at the University of Cornell, states, “there is no research setting other than

sports where we know the face, name, and life history of every production worker and supervisor

in the industry” (Khan). Unlike other markets, where a worker’s success and productivity level is

not so easily measurable and is ultimately a personal judgement call, Major League Baseball

offers a labor force that compete in the same setting and tracks the productivity of every player

unbiasedly. Is salary discrimination on the basis of race presently practiced in the labor market of

Major League Baseball? This research will attempt to answer this question by studying a wealth

of objective data on player performance statistics considered to be accurate determinants of a

player’s total value. This question has been addressed by other researchers in the past. Given the

gradual rise in the industry’s revenue totals, which have led to a number of historic contracts, and

the differences in racial attitudes that exist between the present and past time periods, the setting

remains a unique and exciting point of study.


Methodology

Yi =β0 + β1(Race) + β2(Hits) + β3(Batting Average) + β4(Homeruns) + β5(Wins

Above Replacement) + β6 (Stolen Bases) + β6(RBI) +ε

The research process will attempt to collect information that will accurately estimate the

coefficient on the variables listed in the equation above. The analysis will focus on the markets

in the United States despite that all teams have operations in foreign nations where they hire

players to provide their services. Furthermore, Major League Baseball is the hierarchy of

professional baseball, consequently the research will only focus on the findings provided through

this professional level and dismiss all data available in minor league levels such the Triple A,

Double A, and Rookie leagues despite that these represent millions of dollars in annual US

economic activity. The dependent variable in the equation is Yi (measured in thousands), and

represents the expected 2016 salary level of a player given his respective performance statistics

in 2015. The performance levels of athletes will be reflected through the independent variables

Hits, Average, Homeruns, RBI, Wins Above Replacement, and Stolen Bases. The beta

coefficient of each independent variable will estimate the increase in the salary level that can be

expected from an additional unit increase in each one of the variables.

Race: Is a dummy variable that takes on the value 0 if the player is white and 1 if he is non-

white. The null hypothesis (H0) will assume the coefficient β1 is 0, while the economic theory is

that this coefficient is negative.

Wins Above Replacement: Regarded as a robust measure of a player’s total contribution to his

team’s success, is the additional amount of wins a team is estimated to have gained above the

scenario where he was substituted for a low cost player. The figure is calculated using the


formula (Batting Runs+ Base Running Runs +Fielding Runs + Positional Adjustment + League

Adjustment +Replacement Runs) / (Runs Per Win). Its coefficient is expected to be positive.

Batting Average: Is calculated by the formula number of hits/total at bats and reported with

three decimal places. A higher batting average is positively correlated with on base percentage

and number of runs, hence a positive coefficient is expected.

RBIs: Is the amount of runs that are attributed to a player’s total amount of at bats. Since runs

are positively correlated with wins, a positive beta coefficient is expected.

Home Runs: Every home run produces a run or more, depending on number of men on base, so

a positive beta coefficient is expected.

Stolen Bases: Represent the amount of extra bases a player obtained through his running

abilities. A positive beta coefficient is expected.

The methodology will be the processes taken by the Ordinary Least Squares. The

assumptions of OLS would be that there aren’t significant outliers, the expected value of ε given

the independent variables is 0, and Yi and the independent variables are identically and

independently distributed.

Data

The cross sectional data of interest will be the 2016 salary level of professional baseball

players and the performance statistics they produced in 2015. The main sources of data include

Spotrac, ESPN, Baseball Prospectus, and Mlb.com. These entities have gathered all the

information necessary for the statistical tests. The regression analysis will use 100 observational


points. Through a simple random sampling, the research will collect data from a pool of 30 teams

and approximately 1200 players. The data generating process involves using a random number

generator to randomly select a team then repeating this step to randomly select a specific player.

This process was implemented until 100 data points were obtained. In an attempt to minimize

any OLSE bias in the coeffcients that may arise from error in measurement variable, the

dependent and independent variables were double checked using more than one website to

ensure the accuracy in the data. The race of the players was obtained from Mlb.com. The skin

tone observable in pictures and the last name of a player were considered to determine a player’s

race.

Overview of MLB Labor Market

This research paper conducts an in depth study on the level of wage discrimination

practiced by executives in Major League Baseball. Major Leagues Baseball is the organization

that operates one of the four scholarly deemed major sports in America. The organization is

comprised of two leagues, the American League and National League, and each league has 15

teams. The organization has many professional levels, but this paper focuses on the 40 active

players each team has on their starting day roster. The sport was founded in 1876, but for a

significant period of time it did not permit the participation of African Americans. In 1947, a

player named Jackie Robinson broke the appalling color barrier practiced by the league, when he

became the first African American to start in a game. This paper recognizes that although the

league has taken scores of initiatives to make the sport seemingly unsegregated, there may still

be a bias towards white players that is reflected in their salaries.

Economic theories suggest that in extremely competitive labor markets a worker’s wage

should be equal to the marginal revenue product an employer receives from an employee’s


efforts. The historic fluctuations of salaries in the MLB markets raise the notion that perhaps the

sport of baseball is not characterized with the necessary amount of employer competition related

to this economic law. The National League was founded in 1876, and in 1901 the American

League was founded, leading to the creation of eight more employers in the league. Players

consequently had more leverage in the negotiation process of their salaries. The picture below

illustrates the rise in the wages of players following the influx of these new employers in 1901.

The sport is of major economic interest as it represents a significant amount of the total

annual economic activity in America. The sources for the industry’s revenue include ticket sales,

tv-rights deals, merchandise sales, among others. For example, according to The Licensing Letter

retail sales for MLB merchandise alone surpassed 3 billion dollars in 2015. Furthermore, close to

74 million fans were attendants at games last year alone, setting a league record. All of the

business activity from the league culminated in a reported 9.5 billion dollars in total revenue


collected in 2015; representing approximately 0.52% of the total nominal US GDP in 2015 or the

employment of over 183,000 Americans at the 2015 average US household income of $51,939.

Similar to many American markets, baseball was a workforce not characterized by equal

rights between races for a significant period of time. John Holway, former chairman of SABR's

Negro Leagues committee states, “segregated baseball lasted sixty years, from 1887 when Adrian

Cap Anson, the Babe Ruth of his day, tried to order a black opponent off the field, until 1947

when Jackie Robinson took his place in the infield at Ebbets Field in Brooklyn” (Holway). In an

effort to counteract the appalling inequality of the league that disallowed African Americans to

play in the mlb, in 1920 former player and club owner Rube Foster lead the initiative to create

the Negro National League. This organization presented scores of African Americans with the

opportunity to showcase their talents to the nation. The impressive abilities shown by players

over time created a strong demand amongst fans to see the teams compete against the prominent

white teams. Incentivized by the opportunity for profits, many racists ball club owners approved

of the games, 60% of which were won by the all black teams. Despite the many obstacles the

NNL faced over time, such as solvency issues experienced during the Great Depression that

disallowed the continuance of operations from 1930-1933, the league persevered. This is

arguably the platform that directly lead to the Dodger’s willingness to sign Jackie Robinson,

marking the historic, integral first step towards the desegregation of Major League Baseball.

The ethnic composition of the league reflects the diverse nature of the sport. Sport

Business News notes that of the players that were a part of the 2015 Opening Day roster 8.3%

identified themselves as black, 29.3% as Latino, 1.2% as Asian, and 58.8% as White. The

population of baseball has experienced concerning changes to minority advocates. As Jack

Oddonell states “since 1981, when African Americans represented 18.7% of all Major League


Baseball players, the sport has seen a steady decline in the demographic’s representation.

Heading into the 2015 MLB season, only 7.8% of the league is African-American. That’s a 70%

drop in 34 years”.

There are many possible root causes to this ethnic shift. For example, other sports like

basketball and football have become exponentially more popular in African American

communities over this time period. Furthermore, the steady increase of Latino players in the

league that resulted from the expansion of baseball programs in Hispanic countries has

potentially crowded out African American; however, the static percentage of white players over

this time period presents a strong doubt to this argument. The demographic changes might be

more attributable to the current distribution of collegiate sport scholarships. For instance, only

2.5% of college baseball players are black. This is potentially due to a poor degree of baseball

scholarships made available to this segment of the population or a more efficient recruiting

process practiced by the other major sports. Since college attendance is correlated with a player’s

ability to negotiate reasonable salary once he reaches the professional level, the current lack of

baseball scholarships relative to other sports offered to non-whites could hamper the external

validity of this paper. The table below shows the transitions the sport has experienced from 1947

to 2012.


From 1890-1975, all of the contracts in baseball were governed by the reserve clause.

The clause diminished the welfare of players by disallowing them from participating in

negotiations with a team other than the one they currently played for. As a result, teams held

complete discretion in a player’s ability to be traded, released, and signed by another team. The

clause was initially challenged in a court case known as Federal Baseball Club vs National

League in the grounds of violating the Sherman Antitrust Act, but the courts would rule in favor

of the teams by deciding that the nature of the baseball business did not fall under the umbrella

of interstate commerce. Eventually a former player known as Curt Flood, would pave the way

for the abolishment of this clause by challenging its reasonability in a case known as Flood v.

Kuhn. Flood famously noted “after 12 years in the major leagues, I do not feel that I am a piece

of property to be bought and sold irrespective of my wishes”. On the grounds of violating 13

amendment rights and antitrust laws, the case made its way all the way to the Supreme Court.

Although the court ruled against Kuhn, his efforts sparked negotiations between players and the

http://sabr.org/sites/default/files/images/Demographics1.JPG


league that would eventually terminate the clause in 1975. Afterwards baseball was governed by

a free agency system.

The free agency system had positive implications to the salary levels of players, as it

embodied the structure of a free labor market. From 1973-1975, players enjoyed a two percent

increase in their salary, whereas in 1976 their salary’s spiked by 10 percent (Kahn). Furthermore,

the integral effects of the new system became more apparent when in 1977, the first year where

the rules of the system were in full effect, player salaries rose exponentially by 38 percent.

Overshadowing the notion that these changes were caused by the higher total revenues of the

league was the lower profit margins team owners enjoyed during this time period. Economist

Andrew Zimbalist notes that salaries as a percentage of the revenue collected by teams increased

from 17.6% in 1974 to 20.5% 1977 to 41.1% in 1982 (Zimbalist). The effect of increased hiring

competition was transparent, and teams attempted to counteract the forces of the new system

through unlawful mechanisms.

In the fall of 1985 team owners held meetings where they agreed to cease the placement

of offers on free agents. Throughout the next three years this pact had adverse effects on the

salary levels the players earned. The tactical behavior was apparent. For example, reports show

that in 1984 sixteen of the year’s free agents signed with new teams whereas in 1985 there were

29 free agents available but all of them stayed with the same team (Brown). In 1986, the Players

Association filed its first of three grievance suits accusing teams of enabling the unfair market

practices, and in 1987 arbitrator Thomas Roberts ruled that the teams were engaging in illegal

collusion and granted 280 million dollars in restitution to players. In addition, worth noting is the

insight provided by sports economist Gerald Scully in his 1974 published article “Pay and

Permorance in Major League Baseball”. He conducted a study of how wages were associated


with a player’s revenue productivity. By using metrics to estimate the correlation between player

performance and team performance and then analyzing the relationship between team

performance and revenue totals, he produced the marginal revenue product of individual players.

His study showed that players only earned 10% of the total revenue they generated for their

teams (Scully).

Research Literature

There are numerous research papers that have studied salary discrimination. For example,

Maria Martinez and Inmaculada Bel-Oms, PHD researchers at the University of Jaume,

conducted a study to discover whether there was a gender wage discrepancy amongst the Board

of Directors of companies listed on the Madrid Stock Exchange from 2004 to 2011. Using a

multivariate analysis, the paper compiled data on 1392 firms and analyzed the impact that the

company’s specific sector, relative educational levels of men and women, director’s productivity

had on bottom line of their wages. The research encouraged regulatory institutions to mitigate

the existent gender pay gap (Martinez). The setting differs vastly from major league baseball due

to the nature of companies listed on the Madrid Stock Exchange. The bottom line profits of the

companies, a major indicator of the influential success of a director can be attributed to a number

of factors such as random shifts in consumer preferences and uncontrollable natural shocks,

whereas the success of a baseball player tends to be mainly attributed to personal effort which is

always under the players control if he remains free of injury. As a result, unlike major league

baseball, the majority of these companies do not provide a market with a framework where you

can discretionarily track the value of employees. Several scholars have taken advantage of this

framework.


Henry Raimondo, former professor of public policy at Rutgers University, focused on the

effects that the overhauled free agency market had on worker exploitation and level of racial

salary discrimination. The more competitive market served as a unique setting to analyze the

changes in salaries that resulted from the better opportunities Blacks and Hispanics experienced.

His analysis lead to the conclusion that the new system catalyzed higher salaries for Blacks and

Hispanics. Furthermore, the data showed that the two ethnicities earned as much as White

players and more than players who were not free agents. Finally, although his data pointed out to

possible salary discrimination, the results were not statistically significant.

An analysis on wage discrimination in baseball was offered by Matthew Palmer from Ohio

State University and Randall King from the University of Akron in 2016. They used a regression

analysis with data from 2001 that consisted of 362 major league non-pitching players: 171

White, 81 Black, and 110 Hispanic (Palmer). The research sought to find discrimination

specifically in the highest and lowest salary ranges; hypothesizing that the cost of discrimination

would be smaller if practiced against low earners since these players have the lowest impact on a

team’s potential to win. Their results indicated that there may be discrimination in the lowest

salary ranges. In this salary group, the difference in earnings between Hispanics and Blacks

relative to White players for the performance statistic such as slugging percentage and the

number of years a player was active in the league was statistically significant at the 5% level.

Evidence of discrimination was not found in any other salary group at significant levels. An

integral difference between the data examined is that players with less than three years of

experience were included in the sample for this study. They note that the main reason for this

omission is the fact that players with less experience tend to be paid less and their salary levels

are not necessarily indicative of their performance level. My sample includes these players, since


the majority of internationally recruited non-white players are commonly offered low salaries in

the initial years despite their apparent high level of production.

Kevin J. Christiano, University of Notre Dame sociology professor, wrote a scholarly paper

that analyzed wage discrimination in baseball in 1986. The data from his research included

performance statistics and salary levels of 277 players. He used a regression analysis which did

not find significant discrimination practiced against any of the used independent variables except

for Home Runs. His results showed that black players earn less on average for every additional

homerun they hit (Christiano). Although his paper included the variables Home Runs and Batting

Average as this paper does, it did not include the variable Wins Above Replacement which is

more commonly used in present times to access the value of a player.

The topic of interest was inspected in the article “Pay Discrimination in Baseball: Data from

the Seventies”. The article researched the degree of barriers to entree that black players

experienced in the league. Considering that black players statistically outperformed White

players at every position with the exception of third base, allowed for the conclusion that Blacks

experienced the barrier of entry of having to produce better numbers than Whites to play in the

league (Hill and Spellman). The results showed that White pitchers earned $1300 on average

more than Blacks, however White pitchers produced a lower average ERA, played more, and had

more experience, all of which could serve as justification for the higher salary. Furthermore, all

of the coefficient on the batting independent variables were reported to not be significantly

different from zero, meaning the results did not show evidence of discrimination practiced

against Blacks. In fact, the coefficient on the amount of experience for Black hitters was actually

significantly higher than for Whites. Given the year this data was collected; the research could

presently lack internal validity since the sample analyzed may naturally not be representative of


the present characteristics in the industry. The up to date data collected in my research helps

address this problem.

Results

Summary Table 1

Variable Observations Mean Std. Dev. Min Max

Homeruns 100 12.82 10.59577 0 44

Bat Avg. 100 .25918 .0381595 .12 .344

Hits 100 103.69 51.85522 6 205

Race 100 .51 .5024184 0 1

RBIs 100 48.81 28.17431 1 123

Steals 100 6.79 9.202871 0 58

WAR 100 1.849 2.238258 -1.9 9.9

Salary 100 $5,507,356 $6,247,804 $500,000 $24,000,000

Observable in Table 1, the 100 data points collected for this research produced results that

are comparable to the actual characteristics of the league as a whole. For instance, the data

generated 51% nonwhites and 49% White players, closely comparable to the current 58% Whites

and 42% Non-White demographics of the 2016 opening day roster. Espn.com reports that in

2015 the mean batting average was .254, in line with the .259. mean of the sample selection. The

website also reports that the average amount of homeruns per player was 12, and the collected

data resulted in a 12.82 average for this variable. The data produced a salary average of $5.5


million, which is $1 million above the actual league average, but given the large standard

deviation observed in the variable this difference can be expected.

Regression Table 2

(1)

Salary

(2)

Salary

(3)

Salary

(4)

Salary

(5)

Salary

(6)

Salary

Race

683.25

(0.55)

757.75

(0.61)

440.10

(0.37)

499.88

(0.42)

-151.54

(-0.13)

711.01

(0.62)

WAR

423.20

(1.45)

1005.20

(-1.83)

Hits

64.27

***(5.53)

43.41

(1.44)

76.82

**(2.56)

Steals

-176.926

**(-2.40)

-127.65

(-1.33)

-48.74

(-0.44)

Homeruns

67.97

(0.43)

262.05

(1.43)

245.98

***(4.13)

RBIs

16.43

(0.21)

-43.29

(-0.55)

Bat Avg.

(-15,552)

(-1.18)

Constant

5158.90

***(6.49)

4338.408

***(4.34)

-179.46

(0.17)

-54.52

(-0.05)

2593.596

(0.88)

1991.25

**(1.98)

Salary measured in thousands

T statistics in parenthesis

*p<0.1, **p<0.05, ***p<0.01


Table 2 shows the results of six regression tests. All of the tests assumed heteroscedasticity

because variability in salary levels can be expected to vary between different levels of

performance. This assumption was made to gauge against poor standard error estimates. Heavy

emphasis was placed throughout the analysis process on the changes effects the addition of a

regressor had on the adjusted R2 to maintain the fit of the models. The first regression indicates

that without the consideration of any performance statistic, non-white players on average make

$683,250 more than White players. This regression signals that non-white players were more

productive in 2015, which would justify the higher expected salary in 2016. The following five

regression served to diminish the existent omitted variable bias in this initial regression and to

observe the impact that is generated on the race coefficient as performance statistics were added

as regressors.

Wins above replacement (WAR), regarded by the industry’s statisticians as a respectable

measure of a player’s total value, was added as an independent variable in the second regression.

The coefficient on race increased by $74,500. This rise comes as a result of holding an important

determinant of a player’s value constant, suggesting that there is a bias against White players.

Furthermore, a run side regression where WAR was dependent on the variable race produced a

negative coefficient on race. The coefficient was not statistically significant, but signals that a

non-white player on averages produces a lower WAR. Both of these results hint that perhaps

salary discrimination is practiced against White players; polar to the hypothesis of this paper.

Also worth noting is that a F-stat of 1.15 generated, disallowing us from rejecting the hypothesis

that both variables have no impact on salary.

The sample indicated that non-whites had a higher average amount of stolen bases per season

than white players; 8.77 and 4.73 respectively. A regression test (not included in the table)


making the variable steals dependent on race showed that non-whites were expected to produce

4.03 more steals per season, and the result was statistically significant at the 5% level. The

regression in column 3 shows as hypothesized that for every extra hit a player hits he is expected

to earn $60,000. produced a negative coefficient on the steals variable, also significant at the 5%

level. This goes against economic intuition as more steals should be associated with higher

salaries. Given that non-whites steal more bases per year, the undervaluing of this variable can

be deemed as a potential source of discrimination. The main determinant of a player’s ability to

steal a base is speed. Since speed is negatively correlated with strength, the negative coefficient

on steals could be due to the fact that teams perhaps place a higher value on variables that

measure a player’s strength, such as total homeruns and RBIs (only four players in the history of

baseball have produced 40 homeruns and 40 steals in the same season). Column 4 shows that

there was a negative omitted bias associated with not including homeruns and RBIs.

The regression in column 6, used homerun as the only performance statistic to determine

wage levels for player. The test produced a coefficient that suggests players earn $245,982 for

every extra homerun; statistically significant at the 1% level. This validated one of the original

research assumptions. As stated earlier, Kevin Christiano’s data showed that Blacks earn less

than Whites for every additional homerun. Producing a positive coefficient on race, the results

imply the contrary: discrimination is not practiced on the basis of homeruns. Two regression

lines were created with the data from the sample and provided in the graph below to illustrate the

similar earning potential homeruns offers both ethnicities. Lastly given that the adjusted R2

decreased significantly in column 5 and the test only associated a statistically significant

coefficient on the variable number of hits, it can be concluded the results are probably not

accurate.


With every single one of these regressions there are several problems. The tests do not take

into account the amount of years a player has in the league. More experience is correlated with

higher salaries, all else equal, since players that have proven themselves in the league during a

longer period of time are regarded to be less risky. This creates the possibility that some players

performed better than certain players 2015, yet received lower salaries in 2016 for the lower

amount of experience. As a result, disregarding the salary premium applied to more years in the

league could have produced biased and inconsistent estimators on the coefficients of the

regressors in the analysis. Furthermore, panel data could have offered more accurate results than

the cross-sectional study presented in this paper. The performance levels in 2015 of players was

used as the predictor of salary for the following year. It is reasonable to expect the performance

of prior years is correlated with 2016 wages, consequently not including this data could have

impacted the error term (ε).

0

5000

10000

15000

20000

25000

30000

0 5 10 15 20 25 30 35 40 45 50

Sala

ry

Homeruns

Earnings per HR

White Players Non-White Players Linear (White Players) Linear (Non-White Players)


The purpose of this study was to provide an up to date analysis of whether statistically

significant racial discrimination is practiced in the billion-dollar industry of Major League

Baseball. 100 data points were collected randomly, and a sample distribution similar to the actual

characteristics of the total population was obtained. A thorough multivariate regression test,

considering the main performance variables of these 100 players in 2015 as the determinants of a

player’s expected salary in 2016, suggested that White and Non-white players earn equal wages

for similar levels of productivity. Noteworthy was the indication that stolen bases is

underappreciated by employers, and given that non-white players are significantly more likely to

produce more steals, unconscious bias might presently be exercised in the sport. The data

collecting process was predicated heavily on the limited time available to conduct this study. The

consequence was the omission of certain variables, such as experience levels and the

performance statistics of players in the years before 2015, that could have caused inconsistent

estimates in the results produced in the regression analysis. Although the results did not find

major evidence of salary discrimination, the external validity of this paper should be gauged in

the process of making conclusions about market dynamics in other major sports. There are

potential differences in the populations between sports since current scholarship distributions

make it more likely for a non-white players to avoid college before playing professional baseball,

resulting in different intelligence levels between non-white players in the MLB and non-white

players in the NBA and NFL. Researchers interested in exploring the topic of racial

discrimination in MLB further should include some of the variables omitted in this study and

consider the difference in intelligence levels that may exist between players if he/she wishes to

analyze another major sport.


References

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of Labor Research, 4(2), 183-193.


*100 data points collected

Player HR AVG Hits Race RBI Salary SB WAR

Justin Ruggiano 6 0.248 31 0 15 2,505,000 5 0.1

Chris Taylor 0 0.175 16 0 1 512,400 3 -0.8

Joey Votto 29 0.314 171 0 80 14,000,000 11 7.6

Evan Gattis 27 0.246 139 0 88 526,500 0 0.5

Yan Gomes 12 0.231 84 0 45 1,083,000 0 0.8

Bryce Harper 42 0.33 172 0 99 3,750,000 6 9.9

Kevin Pillar 12 0.278 163 0 56 512,000 25 5.2

Chase Utley 8 0.212 79 0 39 10,000,000 4 0.9

Chris Herrmann 2 0.146 15 0 10 510,000 0 0

Jason Castro 11 0.211 71 0 31 4,000,000 0 1.3

Craig Gentry 0 0.12 6 0 3 1,600,000 1 -0.5

Ryan Braun 25 0.285 144 0 84 19,000,000 24 3.8

Jay Bruce 26 0.226 131 0 87 12,041,667 9 0.8

Will Myers 8 0.253 57 0 29 519,800 1 1.1

Corey Dickerson 10 0.304 68 0 31 512,500 0 0.5

David Freese 14 0.257 109 0 56 6,425,000 1 2.3

Ryan Rua 4 0.193 16 0 7 508,500 0 -0.2

Ryan Raburn 8 0.301 52 0 29 2,500,000 0 1

Daniel Murphy 14 0.281 140 0 73 8,000,000 2 1.4

Jayson Worth 12 0.221 73 0 42 21,571,429 0 -1.6

Freddie Freeman 18 0.276 115 0 66 8,859,375 3 3.4

Brett Lawrie 16 0.26 146 0 60 1,925,000 5 1.9

Jason Kipnis 9 0.303 171 0 52 4,166,667 12 4.6

Neil Walker 16 0.269 146 0 71 8,000,000 4 2.4

Jedd Gyorko 16 0.247 104 0 57 2,000,000 0 0.6

George Springer 16 0.276 107 0 41 512,900 16 3.8

Dustin Ackley 10 0.231 55 0 30 2,600,000 2 0.2

Colby Rasmus 25 0.238 103 0 61 8,000,000.00 2 2.6

Mitch Moreland 23 0.278 131 0 85 2,950,000 1 2.2

John Jaso 5 0.286 53 0 22 3,175,000 1 1

Derek Norris 14 0.25 129 0 62 2,950,000 4 2.5

Cliff Pennington 3 0.21 44 0 21 3,275,000 3 0.2

Joe Panik 8 0.312 119 0 37 522,500 3 3.4

David Wright 8 0.269 144 0 63 19,347,171 8 2.8

Nick Hundley 10 0.301 110 0 43 3,100,000 5 1.8

Michael Saunders 8 0.273 63 0 34 2,875,000 4 2.4

Chris Johnson 3 0.255 62 0 18 6,000,000 2 -0.7

DJ Lemahieu 6 0.301 170 0 61 517,500 23 2.3

Jody Mercer 3 0.244 96 0 34 538,000 3 0.3


Josh Donaldson 41 0.297 184 0 123 11,650,000.00 6 8.8

Anthony Rizzo 31 0.278 163 0 101 5,000,000 17 6.3

Tyler Flowers 9 0.239 79 0 39 2,000,000 0 0.8

Josh Rutledge 1 0.284 21 0 10 500,000 0 0

Dustin Pedroia 12 0.291 111 0 42 13,125,000 2 2

Kyle Seager 26 0.266 166 0 74 8,000,000 6 4.3

Bryan Holaday 2 0.281 18 0 13 519,000 5 0.2

Matt Wieters 8 0.267 69 0 25 15,800,000 0 0.8

Kelly Johnson 14 0.263 82 0 47 2,000,000 2 0.3

Willie Bloomquist 0 0.159 11 0 4 2,800,000 1 -0.3

Mike Aviles 5 0.231 67 1 17 3,500,000 3 2

Chris Young 14 0.252 80 1 42 2,500,000 3 1.2

Angel Pagan 3 0.262 134 1 37 10,250,000 12 -1.9

Martin Prado 9 0.288 144 1 63 11,000,000 1 3.1

Robinson Chirinos 10 0.232 54 1 34 518,290 0 1.8

Robinson Cano 21 0.287 179 1 79 24,000,000 2 3.4

Ichiro Suzuki 1 0.229 91 1 21 2,000,000 11 -1.2

Christian Colon 0 0.29 31 1 6 509,525 3 0.6

Tony Cruz 2 0.204 29 1 11 775,000 0 -1

Albert Pujols 40 0.244 147 1 95 24,000,000 5 3.1

Asdrubal Cabrera 15 0.265 134 1 58 7,500,000 5 1.7

Jean Segura 6 0.257 144 1 50 534,000 25 0

Rajai Davis 8 0.258 88 1 30 5,000,000 8 1.6

Prince Fielder 23 0.305 187 1 98 24,000,000 0 1.9

Kolten Wong 11 0.262 146 1 61 520,000 15 2.2

David Peralta 17 0.312 144 1 78 512,000 9 3.7

Erick Aybar 3 0.27 161 1 44 8,750,000 15 2.3

Victor Martinez 11 0.245 108 1 64 14,000,000 0 -1.6

Dee Gordon 4 0.333 205 1 46 2,500,000 58 4.9

Jonathan Villar 2 0.284 33 1 11 513,000 7 0.9

Yasmani Grandal 16 0.234 83 1 47 693,000 0 1.4

Carlos Gonzalez 40 0.271 150 1 97 16,428,571 2 3.1

Manny Machado 35 0.344 181 1 86 548,000 20 7.1

JD Martinez 38 0.282 168 1 102 3,000,000 3 5

Jarrod Dyson 2 0.25 50 1 18 1,225,000 26 2.2

Anthony Rendon 5 0.264 82 1 25 2,800,000 1 0.3

Carlos Santana 19 0.231 127 1 85 4,200,000 11 1.1

Salvador Perez 21 0.26 138 1 70 1,750,000 1 2.3

Jason Heyward 13 0.293 160 1 60 8,800,000 23 6.5

Jose Iglesias 2 0.3 125 1 23 1,443,750 11 1.5

Oswaldo Arcia 2 0.276 16 1 8 532,500 0 -0.3

Eduardo Escobar 12 0.262 107 1 58 532,500 2 2

Lorenzo Cain 16 0.307 169 1 72 2,725,000 28 7.2

Starlin Castro 11 0.265 145 1 69 6,857,143 5 0.7

Rougned Odor 16 0.261 111 1 61 513,850 6 2.7


Nelson Cruz 44 0.302 178 1 93 14,250,000 3 5.2

Ian Desmond 19 0.233 136 1 62 11,000,000 13 2

Marlon Byrd 23 0.247 125 1 73 8,000,000 2 1.4

Hernan Perez 1 0.243 64 1 21 508,500 5 -0.5 Christian Betancourt 2 0.2 8 1 12 517,500 1 -0.1

Brandon Phillips 12 0.294 173 1 70 12,083,333 23 3.5

Alejandro De Aza 7 0.262 85 1 35 5,750,000 7 1

Jesus Aguilar 19 0.267 136 1 93 507,600 0 -0.2

Juan Uribe 6 0.219 28 1 20 4,000,000 0 1

Hanley Ramirez 19 0.249 100 1 53 22,000,000 6 -1.3

Leonys Martin 5 0.219 63 1 25 4,150,000 14 1.1

Luis Sardinas 0 0.196 19 1 4 512,000 0 -0.4

Delino Shields 2 0.261 111 1 37 517,130 25 1.1

Elvis Andrus 7 0.258 154 1 62 15,000,000 25 2.1

Jonathan Schoop 15 0.279 85 1 39 522,500 2 1.4

Welington Castillo 17 0.243 77 1 55 3,700,000 0 1.1

a test for salary discrimination

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