THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE

DEPARTMENT OF ECONOMICS

ARE MAJOR LEAGUE BASEBALL PLAYERS PAID THEIR MARGINAL REVENUE PRODUCT?

BRIAN SCHANZENBACH Spring 2014

A thesis submitted in partial fulfillment of the requirements for a baccalaureate degree in Economics with honors in Economics

Reviewed and approved* by the following:

Ed Coulson Professor of Economics Professor of Real Estate Economics Jeffrey and Cindy King Fellow in Real Estate Thesis Supervisor

Russell Chuderewicz Senior Lecturer in Economics Honors Advisor

* Signatures are on file in the Schreyer Honors College.

i ABSTRACT

This thesis studies whether Major League Baseball players are paid their marginal revenue product. In other words, the goal is to determine whether the players are paid the amount of money they make for their franchise. Major League Baseball wants a competitive league because a competitively balanced league makes the most amount of money. In order to have a competitively balanced league, the players must be paid their marginal revenue product. Thus, it is in the league’s best interest for the players to receive their marginal revenue product as compensation. In order to calculate the marginal revenue product, I ran winning regressions and attendance regressions. The winning regressions calculated what factors into a team’s winning percentage, while the attendance regressions calculated what factors into attendance at a ball game. The winning regressions gave me the marginal product and the attendance regressions gave me the marginal revenue. I was then able to calculate the marginal revenue product, as it is simply the marginal revenue multiplied by the marginal product. The next step was to regress the player salary on MRP, so that I could determine whether players are paid their marginal revenue product. I found that, on average, all Major League Baseball players are underpaid. Therefore, my recommendation to the league would be to increase the average salaries of each position accordingly to create a more competitively balanced league. ii

TABLE OF CONTENTS

List of Tables ...... iii

Acknowledgements ...... iv

Chapter 1 Introduction ...... 1

Chapter 2 Literature Review ...... 5

Chapter 3 Data and Methodology ...... 13

Chapter 4 Results ...... 22

Chapter 5 Conclusion ...... 28

Appendix A Data ...... 30 BIBLIOGRAPHY ...... 91

iii LIST OF TABLES

Table 1. Winning Regression Data ...... 17

Table 2. Attendance Regression Data ...... 20

Table 3. Winning Regression ...... 22

Table 4. Attendance Regression ...... 23

Table 5. Salary and Marginal Revenue Product Regression Data ...... 26

Table 6. Determination of Whether a Player is Paid Their MRP ...... 27

Table 7. Team Data ...... 30

Table 8. City Population and Team Attendance Data ...... 31

Table 9. Player Salary Data ...... 32

Table 10. Field Player Statistics Data ...... 60

Table 11. Pitcher Statistics Data ...... 75

iv ACKNOWLEDGEMENTS

Professor Edward Coulson,

For your advice throughout the completion of this thesis.

Professor James Tybout,

For your wisdom and help through the duration of the Honors Program in Economics. Chapter 1

Introduction

Professional athletes are paid incredibly high salaries to play a game that is typically reserved for children. Some people believe athletes are paid far too much since their occupation is nothing more than entertainment. Others believe their salaries are perfectly just given the utility of entertainment for each individual can be substantial. The fact of the matter is professional sports franchises are cash cows. They make a huge amount of money, create many jobs, and are responsible for a great deal of cash flow, whether it is advertising campaigns during games or internal financing. The goal of this paper is to attempt to determine whether each position in major league baseball is paid, on average, the amount they make for their team. In a competitive market, as professional sports intend to be, a player will be paid the amount they make for their team given complete free agency. However, complete free agency does not exist because players sign contracts that are longer than one year. However, we will assume that when each player signs their contract they were provided with a salary equal to their marginal revenue product. If they are paid less than their marginal revenue product then they are underpaid, while if they are paid more they are overpaid.

In order to go about solving this problem, first I did research to discover what work had already been completed in this area and what models were used. The major piece of literature was Scully’s paper on how performance influences salaries in Major

League Baseball. He wrote the first major article on pay and performance in baseball and 2 his model has been used in various papers (Scully 1974). I based my model on Scully’s with a few changes. The other major literature I used was a thesis written by Brian Fields entitled “Estimating the Value of Major League Baseball Players.” His paper is an analysis similar to Scully’s paper, but uses more recent data (Fields 2001). My paper is similar as I am attempting to determine a player’s marginal revenue product to compare it to his salary. I will then be able to determine whether they are overpaid, underpaid, or paid at the margin by comparing these two values. However, there are a few major differences that make my study unique and interesting. First, my paper looks at the 2011

Major League Baseball season. Since it is an analysis of the 2011 season, it is the most recent analysis of this kind. This is important, as salaries, statistics, and franchise values have exploded in recent years. Second, my paper uses attendance to measure marginal revenue product. I believe that this will give a more accurate value because it is easier to measure attendance, since those numbers are readily available, while revenue numbers are not. However, there are a few problems with using attendance to measure marginal revenue product. First off, there are things that affect attendance that players do not have any control over. A few examples of these would be the weather, other events going on in the area, and promotional giveaways. I attempted to account for these as best I could in the regression model, but not everything was accounted for. Another major issue with using attendance is there is a maximum capacity at each stadium. There will be some bias when calculating the affect of different statistics on attendance because there are a finite number of fans capable of viewing the game. There may be more fans that want to see a given game, but we cannot account for that because they were unable to purchase tickets. 3 The paper will begin with a literature review of all relevant information that was used to write this thesis. That includes the two sources discussed above, as well as many other resources. The next step is the data work. I have run many regression models to determine the marginal product, marginal revenue, and affect of salary on MRP. I analyzed the data from the regressions to further understand which statistics matter and how much they matter. I analyze the data and explain the calculations. I then perform the marginal revenue product and salary calculations to get the numbers I will compare.

Next, I compare those numbers to determine whether players are paid effectively or not. I then am able to determine which positions are overpaid and which are underpaid.

The other interesting thing about this analysis is it is not simply a sports paper, but can be used to understand the labor market economically. It is difficult to measure the marginal revenue product of employees because so many factors do not have numbers associated with them. For example, the only true way to measure someone’s marginal revenue product is by looking at how much work they do. However, this leaves out a lot of factors including the work performed, the value of each project, and various intangibles that have no measurable data. Thus, one of the few occupations that can be analyzed to see whether the workers are underpaid or overpaid is professional athletics.

There are data and statistics that can show how valuable a player is to the team and how much the team benefits from said player. Therefore, the analysis of Major League

Baseball players is essentially a representation of the labor market in the economy. “The

Sports Business as a Labor Market Laboratory” goes into detail about how professional athletics are a perfect way to analyze the labor market because the employees’ performance grades are so readily available to see. He is able to examine monopsony in 4 the labor force, discrimination, how ownership does not have any impact on the allocation of resources and instead only affects how wealth is distributed, and the impacts of supervision and incentives on behavior. Lawrence M. Kahn states,

However, taken as a whole, this line of research produces additional evidence that making the labor market more competitive leads to higher salaries than would be the case under monopsony. Nonetheless, during the 1980s there still appeared to be widespread monopsonistic exploitation in baseball…. Whatever the cause of segregation, it can have long-term consequences. In baseball, for example, managers are drawn from the middle infield positions, which have been disproportionately white…. The mobility evidence appears to contradict the stereotype that while in the old days (before free agency), players stayed with one team, but now players are mercenaries and will move at the drop of $50 million. However, we often forget that there were many trades before free agency, and once a player moves nowadays to a team offering him a long-term contract—the mercenary stereotype—that player then typically becomes relatively immobile…. In the major team sports that have been the primary focus of this paper, free agency has brought with it an increased incidence of long-term contracts, a finding Lehn (1990) argued was consistent with wealth effects, as players in essence buy long-term income insurance. He noted that as the incidence of long- term contracts went from virtually zero during the days of the reserve clause to 42 percent of baseball players with at least two years pay guaranteed as of 1980 (Kahn, 2000).

The article is a good examination of professional athletics as a representation of the labor market.

Chapter 2

Literature Review

Major League Baseball is a game driven by statistics. This makes it easier to analyze when compared to other sports, as you can truly understand what is going on and account for almost everything. There has also been an abundance of research done previously on baseball statistics. This has given me a great database of research I am able to use to pursue my goal of determining whether Major League Baseball players are paid their marginal revenue product.

Gerald Scully wrote the first important paper in 1974 (Scully 1974). He attempted to estimate the marginal revenue product for players to determine how much they should be paid. He used a model with two equations. The first equation is a team revenue function that attempts to relate the winning percentage of a team and the characteristics of the market in the team’s area to the revenues for that team. The second equation that goes into the model is a production function that attempts to relate team winning percentage and output to various team inputs. Scully then analyzed a season in the late 1960’s to determine player’s wage compared to their MRP. He determined, through his model, players were paid about 10-20% of their MRP, which shows the reserve clause did cause a large economic loss to the players.

This leads me to Anthony Krautmann’s paper entitled “What’s Wrong with

Scully-Estimates of a Player’s Marginal Revenue Product” (Krautmann 1999).

Krautmann claims that Scully’s assertion that even the highest paid athletes are grossly 6 underpaid compared to their marginal revenue product cannot possibly be correct. He cites the huge bidding wars during free agency as the main reason he finds it difficult to believe owners are swimming considerably below the marginal revenue product line for player salaries. Thus, in this paper he suggests an alternative method to estimate a player’s economic value. Krautmann explains Scully’s approach is proportional, where if a player accounts for a given percentage of the team’s at bats you multiply that by their slugging percentage to determine their MRP. However, Krautmann uses an approach that he terms the free market returns approach. This method utilizes the belief that intense free agent bidding wars cause salaries to grow until they reach the MRP. The main difference in Krautmann’s model is he only looks at free agents. He is not concerned with every single player in Major League Baseball. He believes only incorporating free agents will allow the model to truly determine whether players are paid their marginal revenue product because the only instance where this is a possibility is when a bidding war ensues. This theoretically pushes the salary higher and higher until it reaches the player’s marginal revenue product, where the bidding will cease. The only time this theory exists is when a player is a free agent. Thus, Krautmann estimates the competitive return to performance based on this “intuitive notion”. Krautmann’s results show players are underpaid, but less than by the amount Scully claims. His results show players that are considered journeymen are paid a salary about 85% of their marginal revenue product.

Journeymen’s salary is closest to their marginal revenue product since they have moderate production and have substantial negotiating rights, since they have been around a while. Thus, the other players are paid a salary that is less than 85% of their MRP. 7 In order to represent Scully’s method fairly, a paper that supports his model will also be analyzed. The paper is written by John Bradbury and is entitled “What's Right with Scully-Estimates of a Player's Marginal Revenue Product” (Bradbury). Bradbury claims while Scully’s approach is not perfect; it is not as far off as Krautmann claims. He feels Krautmann’s method is no better as it eliminates the problems from Scully’s method only to create new issues. The major difference between Scully’s method and

Krautmann’s method is Scully examines all players, while Krautmann only examines free agents. Krautmann argues Scully’s method cannot possibly measure marginal revenue product accurately because there is not pure competition to sign every player every year.

Many players sign contracts for more than one year, so there is not a bidding war after every season to accurately measure their new marginal revenue product every year.

Krautmann believes Scully’s model underestimates marginal revenue product because of the lack of competition to sign each player every year, so he examines only free agents to account for this. He believes he will more accurately measure marginal revenue product through this method because of the constant bidding war for these players. The first supposed problem of Scully’s method is the marginal revenue product estimates are far too high. Scully initially claimed they were well above salaries due to collusion among the owners through the reserve clause, which was a major problem when Scully first created his model. Krautmann claims the model was wrong because when it was run again about a decade later the salaries were still much lower. However, Bradbury claims the numbers were so unrealistic due to a unique sample or improper specification, as when he used a Scully-inspired approach the numbers were much more realistic. Another problem with the Scully-estimates was the correlation between them and salaries were 8 not strong. Bradbury proved this was not the case, and the correlation was strong.

However, Bradbury claims Krautmann’s method is fundamentally wrong. He believes competition in the free-agent market will not cause wages to effectively approximate the marginal revenue products. Krautmann does state a lack of competition would cause salaries to not equal the marginal revenue products, but he believes the free-agent market is competitive. However, Bradbury claims substitutes cause salaries to fall. There are so many good players that can fill a role it brings down everyone’s salary. The other main salary depressant is when there are exceptionally talented young athletes that are free agents. They do not have the experience when first entering the league to garner a huge wage, thus creating cheap substitutes that are just as good as the expensive players if not better. Another issue is players often sacrifice a higher salary for other benefits. These benefits could be playing on a better team, playing for a hometown team, getting a guaranteed salary to protect them if they get injured, or various other reasons. Another issue with the free agent pool is there are very few free agents signed each year compared to the amount of players in the league. Thus, the small number of observations magnifies any problems. Therefore, Bradbury claims, while Scully’s model does have some issues, it is the best model to estimate marginal revenue products for each Major League

Baseball player.

I will examine a paper from 1994 that studies whether baseball players are paid their marginal revenue products. The paper is called “Are Baseball Players Paid their

Marginal Products?” and is written by Don MacDonald and Morgan Reynolds

(MacDonald and Reynolds 1994). MacDonald and Reynolds note that previously researchers have concluded players are not paid their marginal revenue product, but they 9 wonder if the new contractual system of free agency and arbitration has pushed the salaries closer to the MRP. They use data from 1986 and 1987 and determine players are paid much closer to their marginal revenue product. However, they claim there is variation and it appears as though the more experienced players are paid close to their marginal revenue product, while younger players are not. MacDonald and Reynolds blame this fact on the market structure existing in professional baseball.

I will begin to look into literature that explains which pieces of the data are most useful in reaching my goal. The first paper I will examine is “Which Baseball Statistic Is the Most Important When Determining Team Success?” by Adam Houser (Houser 2005).

Houser attempts to determine the most important statistic for winning percentage prediction. Houser explains theory would suggest the best team in the league has the best statistics in the league, which means they must have the best players in the league leading to the highest team salary. However, this has not been the case on most occasions. Houser wants to determine which statistic is the most important to winning to see if that is the cause for Major League Baseball not aligning with the human capital theory, “which states players will be paid for their productivity.” He used mostly offensive statistics because, according to his literature review, offensive statistics are by far the best way of finding significant test statistics. He also used a few pitching and defensive statistics.

Houser ran regressions of winning percentage on a variety of offensive statistics to determine which had the most importance. The three statistics that were significant were on-base percentage, slugging percentage, and WHIP (WHIP = (walks+hits)/innings pitched). This is an interesting revelation, as it seems from the fan perspective home run 10 hitters and flashy players are paid the most because they are the most exciting. In reality, if you want more wins these are not the players necessary according to Adam Houser.

The next study observed is “What Makes a Winning Baseball Team and What

Makes a Playoff Team?” by Javier Lopez, Daniel Mundfrom, and Jay Schaffer (Lopez,

Mundfrom, and Schaffer 2011). This paper continues with the theme of determining what matters for the creation of a winning team. The data is from the 1995-2009 seasons.

Lopez used 32 different statistics in a multiple linear regression and discriminant analyses. The intent of the regression was to determine what makes a winning team, while the discriminant analyses were used to discover the components of a playoff team.

Batting, fielding, and pitching statistics were all used. Lopez ran a regression on wins with all 32 variables and eliminated the variable with the smallest, non-significant contribution to the explanation of the variance in order to remove multicollinearity from the data. They continued to do this until there were only significant values left. By the time the procedure was completed, the only variables remaining were OPS (on-base percentage plus slugging percentage) and ERA (earned run average). Lopez concluded that this made sense, as there was one variable to measure offense (OPS) and one variable to measure defense (ERA). Thus, they determined out of all statistics for a team, the most important statistics for determining wins are OPS and ERA.

Continuing with determining what contributes most to winning, I will briefly address “The Numbers Behind the MLB” by Kevin Rader (Rader). The data that Rader used is from the 2010 season, which is the most recent season someone has performed data analysis. Rader first ran simple linear regression models, and then ran a multiple regression model he based his analysis and conclusions on. After running all of the 11 regressions, the significant variables were batting average, strikeouts, quality starts, and errors. Rader states the fact these statistics were significant was not a surprise, but it was surprising that payroll, home runs, and the league each team plays in were not significant determinants of the winning percentage. Rader also examines how these results will affect the future and what this could mean. Rader suggested in his conclusion teams should pursue players that have high batting averages and a low amount of errors committed. He also suggests teams should pursue pitchers with many quality starts and strikeouts. However, Rader determines quality starts and strikeouts are less important than high batting averages and low amounts of errors because batting average and errors were more significant.

The next topic examined is the factors affecting attendance. Thus, “Factors

Affecting Attendance of Major League Baseball” by John Marcum and Theodore

Greenstein was the next paper analyzed (Marcum and Greenstein 1985). This study was performed on one National League team and one American League team for the 1982 season to determine what the major factors that contributed to attendance at a professional baseball game. The two teams were the St. Louis Cardinals and the Texas

Rangers. Multiple regression analyses were performed to discover the affects. The variables included day of the week, home team’s record, away team’s record, the last 10 results, weather, and promotions. Interestingly, attendance seems to be mostly based on opponent, day of the week, and promotions. Day of the week is the most important factor for attendance at Cardinals games, while promotions are the most important factor for attendance at Rangers games. Also, the opponent record is much more important for

Rangers fans than it is for Cardinals fans. This is perhaps due to the fact that the Rangers 12 had a poor season in 1982 and suggests an interesting conclusion that fans with struggling home teams are more interested in a baseball game when their team plays a better opponent. If a home team is performing well, attendance will be strong no matter what, but if the home team is struggling there must be promotions or high caliber opponents to bring fans into the stadium.

Chapter 3

Data and Methodology

My objective is to determine whether Major League Baseball players are paid their marginal revenue product. Thus, we must first understand what marginal revenue product is. According to Rodney Fort’s textbook “Sports Economics,” the marginal revenue product is equal to the marginal product multiplied by the marginal revenue (Fort

2011).

MRP(W) = MP(W) x MR(W)

The marginal product is the player’s contribution to winning percentage, while the marginal revenue, in this case, is the amount of attendance on the margin generated by the player’s contribution to winning. Marginal revenue decreases with output because of the law of diminishing returns. Thus, the marginal revenue is downward sloping for the individual team at any level of W in the long run. Since marginal revenue product is the other side of the equation, it is the input’s contribution to the revenues earned by the team owner. In order to calculate the marginal revenue product, we will use Scully’s method

(Scully 1974).

The first step is to estimate the contribution of a player’s statistics in the production of wins (the marginal product). Thus,

Winning = f(H, P, F) 14 Winning for the team is the clear objective and it depends on H, which is the hitter performance, P, which is the pitcher performance, and F, which is the fielding performance. The model for winning will be as follows:

Winning = α0+β1*R+β2*HR+β3*H+β4*ERA+β5*IP+β6*CG+β7*SHO+β7*SO+β7*FPCT+e where Winning = percentage of games won by a team R = runs scored during the season H = hits recorded by the team during the season ERA = team earned run average per 9-inning game over the course of the season IP = innings pitched during the season CG = number of complete games pitched by a team during the season SHO = number of shutouts recorded by a team during the season SO = number of strikeouts pitched by a team during the season FPCT = team fielding percentage during the season

The winning percentage of a major league baseball team is obviously affected by many things over the course of the season. I have attempted to capture as many as seemed relevant to the regression. The hitting statistics I believed had the most affect on winning percentage were runs scored and hits recorded. My belief is the more runs a team records, the higher their team’s winning percentage because to win a ball game you must score more runs than your opponent. Therefore, it makes sense that typically the teams scoring the most runs would have the best winning percentage. Hits should also positively influence winning percentage because a team needs to get on base to score runs. The most common way of getting on base is to get a hit, so I would think more hits would mean more runs. The pitching statistics I chose to use in my regression are team earned run average, innings pitched, number of complete games, number of shutouts, and number of strikeouts recorded. Earned run average is the statistic I expect to have the most impact on winning percentage because it is the calculation of how many runs a team’s pitching stuff allows per nine innings. I expect the relationship between ERA and 15 winning percentage to be negative because I would expect that when a team allows more runs their winning percentage should decrease. I believe the innings pitched should positively affect winning percentage because a team pitches more innings when they are winning as the home team and do not need to take their final at bat or when they are in extra innings. If they are in extra innings they are in a close game where their chance of winning is decent. Thus, more innings should mean a higher winning percentage.

However, I believe this affect will be small because the number of innings pitched over the course of the season does not differ much from team to team. There may be a slightly positive impact on winning percentage, but most games are completed in nine innings, meaning the difference between teams will be slim. The CG variable represents the number of complete games pitched for one team during the season. A complete game is when only one pitcher is used throughout the entirety of a game. The only way a pitcher is able to complete a game is if he has a low number of pitches thrown, which most likely signifies the other team is struggling against him. Therefore, I expect a positive correlation between complete games and winning percentage. The more complete games teams have, the higher their winning percentage. However, this may also be an ineffective variable because there are not many complete games over the course of a season. The variable SHO represents the number of shutouts a team earns over the course of the season. A shutout means the team wins the game because a game cannot end in a tie and they have allowed zero runs. Thus, shutouts should correlate positively with winning percentage because one extra shutout means one extra win. While, there tends not to be an abundance of shutouts over the course of the season, every extra shutout means an extra win. The variable SO represents the number of strikeouts a team pitches 16 over the course of the season. I expect there to be a positive correlation between strikeouts and winning percentage because a strikeout is an out where no runners are able to advance. When there are more strikeouts, it means that there are fewer batters capable of running the bases and advancing others along the base paths. Therefore, more strikeouts mean less possibility of scoring runs. The only fielding statistic that I am using in my regression is fielding percentage because I think that it is a broad statistic that is able to represent how well a team fields. I expect fielding percentage to affect winning percentage positively because a team that has a better fielding percentage does not allow as many runners on base. This means opposing teams are less likely to score than if they had more base runners, so the winning percentage of a good fielding team should be higher than the winning percentage of a poor fielding team.

My study is modeled after Scully’s examination of the pay for performance rate in

Major League Baseball in the 1970’s. I have collected data from the 2011 Major League

Baseball season to attempt to determine whether players are paid their marginal revenue product. I think it is interesting to do this study during the tail-end of the steroid era because there are so many stars in the game that seem to have inflated contracts.

I will use the modified winning regression that includes logarithmic variables because it has a larger r-squared. This means that the regression explains more of what affects attendance than the regression that contains no logarithmic variables. The following table shows the results of the logarithmic winning regression:

Winning1 = α0+β1*R+β2*HR+β3*H+β4*ERA+β5*IP+β6*CG+β7*SHO+β7*SO+β7*FPCT+e

17

Winning2 = α0+β1*R+β2*HR+β3*Log(H)+β4*ERA+β5*Log(IP)+β6*Log(CG)+β7*Log(SHO)+β7*Log(SO) +β7*FPCT+e

Table 1. Winning Regression Data

Variable WP1 WP2 Intercept 0.5325 0.7987 p-value 0.808 0.851 R 0.0003 0.0004 p-value 0.061 0.016 HR 0.0007 0.0005 p-value 0.019 0.087 H 0.0000 p-value 0.853 Log(H) -0.0286 p-value 0.861 ERA -0.1017 -0.1135 p-value 0.000 0.000 IP 0.0000 p-value 0.936 Log(IP) -0.0911 p-value 0.892 CG -0.0005 p-value 0.741 Log(CG) -0.0107 p-value 0.187 SHO 0.0030 p-value 0.142 Log(SHO) 0.0243 p-value 0.168 SO -0.0001 p-value 0.299 Log(SO) -0.0920 p-value 0.198 FPCT 0.0002 1.2906 p-value 1.000 0.570 R2 0.9151 0.9309

18 The next step is to calculate the marginal revenue. I will be unable to get broadcast revenue because franchises do not readily give this information out, so the revenue gained will be understated. However, my model will attempt to circumvent this as best it can. Thus, the equation for the marginal revenue is:

Attendance = f(W, OCF)

Attendance will be the main contributor to revenue without broadcast revenue.

Attendance will depend on W or winning, which was described above and OCF, which is other city factors. These include city population, whether the opponent is a contender, and whether the opponent makes the playoffs, among others. The model for attendance will be as follows:

Attendance = α0+β1*POP+β2*W+β3*CONT+β4*PLAYOFFS+β5*PRICE+β6*GB+e where Attendance = the number of fans that attended all home games for each team POP = home team’s city population W = winning percentage over the course of the 2011 season CONT = 1 if team finished within 3 games of first place in the division, 0 otherwise PLAYOFFS = opponent made the playoffs at the end of the season PRICE = the average price per ticket GB = number of games behind the division winner at the end of the season

Attendance is an important measure of team success. It gives a decent idea of how much money an organization earns. Thus, attendance is increasingly important in determining how much a player is paid. There are a few variables that go into determining attendance. I have attempted to choose the statistics that I believe to be the most important determinants of attendance. The amount of people living in the city where the team plays should be positively associated with attendance because the more people in a given city means there are more potential attendees. Also, given that large population cities tend to win more games, more people will want to attend in order to see a good 19 team play. Winning percentage is an obvious foundation of attendance because people want to go to a game where their team wins, so I expect there to be a positive relationship with attendance. The variable CONT is a variable that determines whether each team was a contender based on their final winning percentage. A team that is three games or less behind the division winner is considered a contender. Presumably, the attendance will increase when the visiting team is a contender because people want to see better teams play. PLAYOFFS represents when the opponent was a team that ended up making the playoffs in 2011. I expect a positive correlation between PLAYOFF and Attendance because fans want to see the best players and ball clubs, even if it is not their home team.

The PRICE variable is a measure of the average ticket price for each stadium. Obviously, one would think the lower a ticket price is, the higher the attendance. However, I believe lower ticket prices lead to lower attendance because there must have been a reason for the price dropping so much. Lastly, GB represents games behind the division winner the home team ends up at the end of the season. I expect a negative correlation because as a team gets further behind there is little to play for and the team is most likely not good.

Thus, fans would not want to see the game as much because there is no excitement.

The data I have used to analyze my question comes from a few different sources. I used the well-known sports network, ESPN, to get the wins and losses for every team during the 2011 season, as well as all of the team statistics (ESPN 2011). The average ticket prices for each team came from the daily sports list written by Getz (Getz 2012). I found data on the population of each city that has a baseball team from two different sources. The first source was from the Government of Canada, which gave me the population of the only Canadian city with a baseball team, Toronto (Government of 20 Canada 2014). The remaining population data came for the United States Census Bureau

(United State Census Bureau 2012). Newsday was my source for all of the player salaries during the 2011 MLB season (Newsday 2014). Fielding statistics were gathered from the

Sports Reference LLC. These statistics included fielding percentage, errors, and plays for each player (Sports Reference LLC 2013). I used a sports database created by The Guru to find data for various baseball statistics. This includes all major statistics of field players and pitchers. Games, at bats, runs, hits, homeruns, RBI, stolen bases, and walks among others were included in the statistics provided for the field players. Meanwhile, statistics provided for pitchers included wins, loses, complete games, strikeouts, walks,

ERA, earned runs, and homeruns allowed among others. This data was the primary source for my calculations (The Guru 2014).

The following table shows the results of the attendance regression.

Attendance = α0+β1*POP+β2*W+β3*CONT+β4*PLAYOFFS+β5*PRICE+β6*GB+e Log(Attendance) = α0+β1*Log(POP)+β2*W+β3*CONT+β4*PLAYOFFS+β5*Log(PRICE)+β6*GB+e

Table 2. Attendance Regression Data

Variable Attendance Log(Attendance) Intercept 1617491.00 14.225 p-value 0.000 0.000 POP 0.021 p-value 0.000 Log(POP) 0.014 p-value 0.000 W 2157348.00 0.835 p-value 0.000 0.000 CONT 62048.00 0.008 p-value 0.014 0.379 21

PLAYOFFS 3514.76 0.001 p-value 0.488 0.655 PRICE 3011.75 p-value 0.001 Log(PRICE) 0.005 p-value 0.000 GB -3603.77 -0.001 p-value 0.000 0.000 R2 0.3280 0.3171

We are able to calculate marginal revenue product since we have marginal product and marginal revenue. Chapter 4

Results

I have performed the winning regressions based on how a team’s statistics affect their winning percentage. The regression that I will use for the remainder of this paper for winning is the second regression in Table 1 because it has greater coverage, which means that the second regression describes what affects winning percentage better than the first regression. The winning regression can be viewed in Table 3 and the estimated equation is:

Winning = 0.7987 + 0.0004*R + 0.0005*HR – 0.0286*Log(H) – 0.1135*ERA – 0.0911*Log(IP)

– 0.0107*Log(CG) + 0.0243*Log(SHO) – 0.0920*Log(SO) + 1.2906*FPCT + e

Table 3. Winning Regression wp Coefficient Std. Err. t P>|t| [95% Conf. Interval] r 0.0004 0.00 2.63 0.02 0.00 0.00 hr 0.0005 0.00 1.80 0.09 0.00 0.00 lh -0.0286 0.16 -0.18 0.86 -0.37 0.31 era -0.1135 0.02 -6.56 0.00 -0.15 -0.08 lip -0.0911 0.66 -0.14 0.89 -1.48 1.30 lcg -0.0107 0.01 -1.37 0.19 -0.03 0.01 lsho 0.0243 0.02 1.43 0.17 -0.01 0.06 lso -0.0920 0.07 -1.33 0.20 -0.24 0.05 fpct 1.2906 2.23 0.58 0.57 -3.38 5.96 _cons 0.7987 4.20 0.19 0.85 -8.00 9.59

There are three test statistics that are significant at the 10% level, as can be seen in the above equation. They are R, with a p-value of 0.02, HR, with a p-value of 0.09, and 23 ERA, with a p-value of 0.00. Thus, a one-run increase in the amount of runs the team scores increase the winning percentage by 0.0004 units. A one-homerun increase in the amount of homeruns the team hits increases the winning percentage by 0.0005 units. A one-run increase in the team’s earned run average causes a decrease in the winning percentage of 0.1135 units.

The next results that must be shown are the attendance regressions. I will use the non-logarithmic model from Table 2 because it has a greater r-squared value. The results of the regression can be viewed in Table 4 and the equation can be estimated as:

Attendance =

1617491+0.021*POP+2157348*W+62048*CONT+3514.76*PLAYOFFS+3011.75*PRICE–

3603.77*GB+e

Table 4. Attendance Regression attendance Coefficient Std. Err. t P>|t| [95% Conf. Interval] citypop 0.02 0.00 6.61 0.00 0.01 0.03 w 2157348.00 147257.90 14.65 0.00 1868584.00 2446113.00 cont 62048.00 25102.32 2.47 0.01 12823.77 111272.20 playoffs 3514.76 5068.24 0.69 0.49 -6423.78 13453.30 price 3011.75 889.02 3.39 0.00 1268.42 4755.07 gb -3603.77 539.04 -6.69 0.00 -4660.79 -2546.75 _cons 1617491.00 81464.06 19.86 0.00 1457744.00 1777237.00

There are six test statistics that are significant at the 10% level as can be seen in Table 2.

The variables are city population, with a p-value of 0.00, winning percentage, with a p- value of 0.00, contender, with a p-value of 0.01, ticket price, with a p-value of 0.00, and number of games back with a p-value of 0.00. Thus, a one-person increase in city population causes an increase of 0.02 in attendance. A one-unit increase in winning 24 percentage causes an increase of 2,157,348 in attendance. A team that is considered a contender causes an increase of 62,048 in attendance. A one-unit increase in ticket price causes an increase of 3,011.75 in attendance. A one-game increase in games back of the division winner in 2011 causes a decrease of 3,603.77 in attendance.

We are able to calculate the average marginal revenue product for each position.

The marginal revenue product is the player’s individual contribution to winning percentage multiplied by the amount of attendance, on the margin, generated by the individual player’s contribution to winning. In order to find the MRP, we calculate the change in winning divided by the change in player statistics to determine how a change in an individual player’s statistics will change the winning percentage of their team. The difference between marginal product for pitchers and hitters comes in the statistics included in each marginal product calculation. The marginal revenue includes all variables that were significant in the winning regression. This includes runs, homeruns, earned run average, and the amount of walks allowed by each pitcher. Essentially, the coefficient of each variable from the regression is multiplied by the individual statistic for each player and combined with all of the other variables. The marginal revenue equation is calculated below:

MR = 0.0004 * Player R + 0.0005 * Player HR – 0.1135 * Player ERA

We can see from the attendance regression equation that a one-point increase in the winning percentage of a team raises the season attendance by 2,157,348. The marginal revenue is the affect winning has on attendance. In this model, the marginal revenue is 25 the change in attendance over the change in winning percentage, which is the coefficient for winning in the attendance regression. This value is above, 2,157,348. However, we must multiply the winning coefficient in the attendance regression by the average ticket price for each team. The reasoning behind this calculation is that when we compare attendance to salary, they must have equivalent units. In order to make the MRP a dollar value, we must multiply the calculation by the average price per ticket. The marginal revenue product is the marginal revenue multiplied by the marginal product. In addition, the marginal revenue must be multiplied by 81 for hitters because the MR is a single game calculation and there are 81 home games that a hitter could play. For pitchers, the

MR must be multiplied by half of innings pitched because half of the games that teams play are at home. Thus, the MRP of hitters is:

MRP Hitters = 81* (0.0004 * Player R + 0.0005 * Player HR) * (2,157,348 * Avg. Ticket

Price per Team)

Similarly, the MRP of pitchers is:

MRP Pitchers = 0.5*(innings pitched) * (– 0.1135 * Player ERA) * (2,157,348 * Avg.

Ticket Price per Team)

Now that I have the MRP for pitchers and hitters, I can calculate whether players are paid their MRP. In order to calculate this, I will run a regression model where player salary is regressed on MRP. The model is: 26

Salaryi = α + β*MRPi + ei

where Salaryi is the salary of player i, MRPi is the MRP of player i, and ei is the error of

player i. In order for players to be paid their MRP, β must be equal to one after the

regression is run, assuming that MRP is estimated accurately. This states that for every

one-unit increase in MRP there is a one-unit increase in salary. I ran regressions using

this model for every position to calculate whether the average player at each position is

paid their marginal revenue product. The regression results can be seen in Table 3.

Table 5. Salary and Marginal Revenue Product Regression Data Variable Pitcher Catcher First Base Second Base Third Base Shortstop Outfield Intercept 387357.60 491000.20 156651.00 870610.40 108121.60 -545980.30 787845.70 p-value 0.04 0.06 0.87 0.201 0.85 0.48 0.05 MRP 0.01 0.01 0.03 0.01 0.03 0.03 0.02 p-value 0.00 0.00 0.00 0.00 0.00 0.00 0.00 R2 0.2274 0.2607 0.3791 0.1835 0.3594 0.3823 0.2027

We see that all position’s β values are smaller than one. Also, every β, which is the

coefficient of the marginal revenue product, is statistically significant. This means that

they are statistically different from the hypotheses. Thus, every position is underpaid

according to my MRP calculation.

We have the regression data from the model that compares salary to marginal

revenue product. I can compare the coefficients to the hypotheses to determine whether a

position is overpaid, underpaid, or paid what the average player should make for a salary.

In order to determine whether a position is overpaid, underpaid, or paid at the margin, we

simply compare the marginal revenue product coefficient to the hypothesis. If the

marginal revenue product is larger than the hypothesis then the position is overpaid. If the 27 marginal revenue product is smaller than the hypothesis then the position is underpaid. If the marginal revenue product is equal to the hypothesis then the position is paid at the margin. The value comparison can be viewed in Table 31.

Table 6. Determination of Whether a Player is Paid Their MRP Position MRP Coefficient Comparison Catcher 0.01 Underpaid First Base 0.03 Underpaid Outfield 0.02 Underpaid Shortstop 0.03 Underpaid Second Base 0.01 Underpaid Third Base 0.03 Underpaid Pitcher 0.01 Underpaid !

We can see in Table 6 that all positions are grossly underpaid. This is unsurprisingly similar to the literature outlined throughout this paper, as the other papers have consistently found that players are underpaid. The major difference is that the previous papers were written based on data from the early 1990’s and the early 1970’s, while my paper uses the 2011 Major League Baseball season. There has been a huge increase in player salaries over recent years, including a contract signed this past week where a player is paid over $30 million dollars per year. Thus, it is surprising that the average player is so substantially underpaid. Chapter 5

Conclusion

This study determines whether Major League Baseball players are paid their marginal revenue product. I estimated a winning regression, an attendance regression, and a salary regression. I was able to calculate the marginal revenue product by using the data gathered from the winning regression and attendance regression. I then compared it to the salary regression to determine whether players were paid their marginal revenue product. The data suggests that all Major League Baseball players are substantially underpaid.

The goal of professional sports leagues is to be competitive. When they are competitive they make the most money, which is obviously the ultimate goal. In order to be as competitive as possible players must be paid their marginal revenue product. This is how much they make for the team, and is how much the team should pay them. If teams overpay or underpay their players, then the league is not as competitive as it could be and is not maximizing their profits. Thus, not paying the players correctly harms the whole of the league.

Major League Baseball should use this information to increase the salaries of their players in all positions. This study reveals that players are underpai based on their value to their teams. Therefore, if Major League Baseball wants to maximize their profits they must adjust the salaries of their players to the margin. When players are paid their marginal value the league is at its most competitive state. The league will have the most 29 revenue when it is at its highest level of competition. Thus, it is in the interest of Major

League Baseball as a whole to use this study to justify adjusting player salaries as described. Appendix A

Data

Table 7. Team Data TEAM GP WP AB R H HR ERA CG SHO IP SO FPCT Arizona 162 0.580 5421 731 1357 172 3.8 5 12 1443.1 1058 0.985 Atlanta 162 0.549 5528 641 1345 173 3.48 3 16 1479.2 1332 0.987 Baltimore 162 0.426 5585 708 1434 191 4.89 3 7 1446.2 1044 0.982 Boston 162 0.556 5710 875 1600 203 4.2 2 13 1457.1 1213 0.985 Chicago Cubs 162 0.438 5549 654 1423 148 4.33 4 5 1434.1 1224 0.978 Chicago Sox 162 0.488 5502 654 1387 154 4.1 6 14 1460 1220 0.987 Cincinnati 162 0.488 5612 735 1438 183 4.16 4 5 1467.2 1112 0.985 Cleveland 162 0.494 5509 704 1380 154 4.23 2 4 1453.1 1024 0.982 Colorado 162 0.451 5544 735 1429 163 4.43 5 7 1447.2 1118 0.984 Detroit 162 0.586 5563 787 1540 169 4.04 4 14 1440 1115 0.983 Florida 162 0.444 5508 625 1358 149 3.95 7 11 1459.2 1218 0.985 Houston 162 0.346 5598 615 1442 95 4.51 2 6 1435 1191 0.981 Kansas City 162 0.438 5672 730 1560 129 4.44 2 6 1451.1 1080 0.985 LA Angels 162 0.531 5513 667 1394 155 3.57 12 11 1465 1058 0.985 LA Dodgers 161 0.509 5436 644 1395 117 3.54 7 17 1432 1265 0.986 Milwaukee 162 0.593 5447 721 1422 185 3.63 1 13 1441.2 1257 0.982 Minnesota 162 0.389 5487 619 1357 103 4.58 7 8 1421.2 940 0.98 NY Mets 162 0.475 5600 718 1477 108 4.19 6 9 1448 1126 0.981 NY Yankees 162 0.599 5518 867 1452 222 3.73 5 8 1458.1 1222 0.983 Oakland 162 0.457 5452 645 1330 114 3.71 6 12 1447.2 1160 0.979 Philadelphia 162 0.630 5579 713 1409 153 3.02 18 21 1477 1299 0.988 Pittsburgh 162 0.444 5421 610 1325 107 4.04 5 11 1449.1 1031 0.982 San Diego 162 0.438 5417 593 1284 91 3.42 0 10 1449.1 1139 0.985 San Francisco 162 0.531 5486 570 1327 121 3.2 3 12 1468 1316 0.983 Seattle 162 0.414 5421 556 1263 109 3.9 12 10 1433 1088 0.982 St. Louis 162 0.556 5532 762 1513 162 3.74 7 9 1462 1098 0.982 Tampa Bay 162 0.562 5436 707 1324 172 3.58 15 13 1449 1143 0.988 Texas 162 0.593 5659 855 1599 210 3.79 10 19 1441.1 1179 0.981 Toronto 162 0.500 5559 743 1384 186 4.32 7 10 1458.2 1169 0.982 Washington 161 0.497 5441 624 1319 154 3.58 3 10 1449.1 1049 0.983

31 Table 8. City Population and Team Attendance Data Team City Population Total Attendance in 2011 Season ARI 1,469,471 2,579,486.00 ATL 432,427 2,687,942.00 BAL 619,493 2,520,803.00 BOS 625,087 3,055,879.00 CHC 2,707,120 3,038,530.00 CHW 2,707,120 2,143,991.00 CIN 296,223 2,629,012.00 CLE 393,806 2,426,549.00 COL 619,968 2,934,723.00 DET 706,585 2,745,765.00 FLA 408,750 2,459,238.00 HOU 2,145,146 2,113,016.00 KC 463,202 2,439,250.00 LAA 3,819,702 3,186,165.00 LAD 3,819,702 2,954,891.00 MIL 597,867 3,103,203.00 MIN 387,753 3,187,863.00 NYM 8,244,910 2,395,863.00 NYY 8,244,910 3,669,242.00 OAK 395,817 2,595,923.00 PHI 1,536,471 3,681,112.00 PIT 307,484 2,530,065.00 SD 1,326,179 2,522,361.00 SEA 620,778 2,549,055.00 SF 812,826 3,476,259.00 STL 318,069 3,106,066.00 TB 346,037 2,436,946.00 TEX 373,698 2,958,327.00 TOR 5,841,100 2,696,057.00 WAS 617,996 2,388,538.00

32 Table 9. Player Salary Data

first name last name salary team Brandon Allen $413,000.00 ARI Henry Blanco $1,000,000.00 ARI Willie Bloomquist $750,000.00 ARI Geoff Blum $1,350,000.00 ARI Russell Branyan $1,000,000.00 ARI Josh Collmenter $414,000.00 ARI Ryan Cook $414,000.00 ARI Collin Cowgill $414,000.00 ARI Sam Demel $417,000.00 ARI Stephen Drew $4,650,000.00 ARI Zach Duke $3,500,000.00 ARI Barry Enright $418,000.00 ARI Armando Galarraga $2,300,000.00 ARI Cole Gillespie $414,000.00 ARI Paul Goldschmidt $414,000.00 ARI Juan Gutierrez $430,500.00 ARI Aaron Heilman $2,000,000.00 ARI David Hernandez $423,500.00 ARI Aaron Hill $5,000,000.00 ARI Daniel Hudson $419,000.00 ARI Kelly Johnson $5,850,000.00 ARI Ian Kennedy $423,000.00 ARI Zach Kroenke $414,000.00 ARI Jason Marquis $7,500,000.00 ARI John McDonald $1,500,000.00 ARI Kam Mickolio $417,000.00 ARI Wade Miley $414,000.00 ARI Juan Miranda $420,000.00 ARI Miguel Montero $3,200,000.00 ARI Melvin Mora $2,000,000.00 ARI Xavier Nady $1,750,000.00 ARI Jarrod Parker $414,000.00 ARI Gerardo Parra $426,000.00 ARI Joe Paterson $414,000.00 ARI J.J. Putz $4,000,000.00 ARI Ryan Roberts $423,500.00 ARI Joe Saunders $5,500,000.00 ARI Bryan Shaw $414,000.00 ARI Justin Upton $4,250,000.00 ARI Esmerling Vasquez $414,000.00 ARI 33

Josh Wilson $725,000.00 ARI Chris Young $5,000,000.00 ARI Brad Ziegler $1,250,000.00 ARI Jairo Asencio $414,000.00 ATL Brandon Beachy $416,500.00 ATL J.C. Boscan $414,000.00 ATL Michael Bourn $4,400,000.00 ATL Brooks Conrad $427,250.00 ATL Jose Constanza $414,000.00 ATL Randall Delgado $414,000.00 ATL Matt Diaz $2,000,000.00 ATL Freddie Freeman $414,000.00 ATL Cory Gearrin $414,000.00 ATL Alex Gonzalez $2,500,000.00 ATL Tommy Hanson $456,500.00 ATL Diory Hernandez $414,000.00 ATL Jason Heyward $496,500.00 ATL Brandon Hicks $414,000.00 ATL Eric Hinske $1,350,000.00 ATL Tim Hudson $9,000,000.00 ATL Chipper Jones $14,000,000.00 ATL Jair Jurrjens $3,250,000.00 ATL Craig Kimbrel $419,000.00 ATL Scott Linebrink $5,500,000.00 ATL Derek Lowe $15,000,000.00 ATL Cristhian Martinez $419,000.00 ATL Joe Mather $414,000.00 ATL Brian McCann $6,500,000.00 ATL Nate McLouth $6,500,000.00 ATL Kris Medlen $429,500.00 ATL Mike Minor $414,000.00 ATL Peter Moylan $2,000,000.00 ATL Eric O'Flaherty $895,000.00 ATL Martin Prado $3,100,000.00 ATL Wilkin Ramirez $414,000.00 ATL Antoan Richardson $414,000.00 ATL David Ross $1,625,000.00 ATL Jordan Schafer $414,000.00 ATL George Sherrill $1,200,000.00 ATL Julio Teheran $414,000.00 ATL Dan Uggla $9,000,000.00 ATL Anthony Varvaro $414,000.00 ATL Jonny Venters $429,500.00 ATL 34

Arodys Vizcaino $414,000.00 ATL Jack Wilson $5,000,000.00 ATL Matthew Young $414,000.00 ATL Jeremy Accardo $1,080,000.00 BAL Ryan Adams $414,000.00 BAL Robert Andino $421,500.00 BAL Matt Angle $414,000.00 BAL Jake Arrieta $419,000.00 BAL Mitch Atkins $414,000.00 BAL Josh Bell $414,000.00 BAL Brad Bergesen $426,500.00 BAL Jason Berken $426,500.00 BAL Zach Britton $414,000.00 BAL Blake Davis $414,000.00 BAL Pedro Florimon $414,000.00 BAL Jake Fox $424,000.00 BAL Michael Gonzalez $6,000,000.00 BAL Kevin Gregg $4,200,000.00 BAL Vladimir Guerrero $8,000,000.00 BAL Jeremy Guthrie $5,750,000.00 BAL J.J. Hardy $5,850,000.00 BAL Kyle Hudson $414,000.00 BAL Cesar Izturis $1,080,000.00 BAL Chris Jakubauskas $414,000.00 BAL Jim Johnson $975,000.00 BAL Adam Jones $3,250,000.00 BAL Derrek Lee $7,250,000.00 BAL Nick Markakis $10,250,000.00 BAL Brian Matusz $1,400,000.00 BAL Zach Phillips $414,000.00 BAL Felix Pie $985,000.00 BAL Nolan Reimold $414,000.00 BAL Jo-Jo Reyes $439,100.00 BAL Mark Reynolds $5,000,000.00 BAL Brian Roberts $10,000,000.00 BAL Josh Rupe $475,000.00 BAL Luke Scott $6,400,000.00 BAL Alfredo Simon $455,000.00 BAL Brandon Snyder $414,000.00 BAL Pedro Strop $414,000.00 BAL Craig Tatum $414,000.00 BAL Chris Tillman $417,000.00 BAL Koji Uehara $3,000,000.00 BAL 35

Pedro Viola $414,000.00 BAL Matt Wieters $452,250.00 BAL Alfredo Aceves $650,000.00 BOS Matt Albers $875,000.00 BOS Lars Anderson $414,000.00 BOS Scott Atchison $440,000.00 BOS Mike Aviles $640,000.00 BOS Daniel Bard $505,000.00 BOS Josh Beckett $15,750,000.00 BOS Erik Bedard $1,000,000.00 BOS Michael Bowden $455,000.00 BOS Clay Buchholz $550,000.00 BOS Mike Cameron $7,250,000.00 BOS Carl Crawford $14,000,000.00 BOS Felix Doubront $417,000.00 BOS J.D. Drew $14,000,000.00 BOS Jacoby Ellsbury $2,400,000.00 BOS Adrian Gonzalez $6,300,000.00 BOS Tommy Hottovy $414,000.00 BOS Jose Iglesias $2,060,000.00 BOS Bobby Jenks $6,000,000.00 BOS John Lackey $15,250,000.00 BOS Ryan Lavarnway $414,000.00 BOS Jon Lester $5,750,000.00 BOS Jed Lowrie $450,000.00 BOS Daisuke Matsuzaka $10,333,333.00 BOS Darnell McDonald $470,000.00 BOS Yamaico Navarro $414,000.00 BOS Hideki Okajima $1,750,000.00 BOS David Ortiz $12,500,000.00 BOS Jonathan Papelbon $12,000,000.00 BOS Dustin Pedroia $5,500,000.00 BOS Josh Reddick $414,000.00 BOS Dennys Reyes $900,000.00 BOS Jarrod Saltalamacchia $750,000.00 BOS Marco Scutaro $5,000,000.00 BOS Nate Spears $414,000.00 BOS Drew Sutton $414,000.00 BOS Junichi Tazawa $1,500,000.00 BOS Jason Varitek $2,000,000.00 BOS Tim Wakefield $2,000,000.00 BOS Kyle Weiland $414,000.00 BOS Dan Wheeler $3,000,000.00 BOS 36

Kevin Youkilis $12,250,000.00 BOS Jeff Baker $1,175,000.00 CHC Darwin Barney $417,000.00 CHC Justin Berg $414,000.00 CHC Marlon Byrd $5,500,000.00 CHC Tony Campana $414,000.00 CHC Chris Carpenter $414,000.00 CHC Andrew Cashner $427,500.00 CHC Welington Castillo $414,000.00 CHC Starlin Castro $440,000.00 CHC Steve Clevenger $414,000.00 CHC Casey Coleman $414,000.00 CHC Tyler Colvin $440,000.00 CHC Ryan Dempster $14,500,000.00 CHC Blake DeWitt $460,000.00 CHC Rafael Dolis $414,000.00 CHC Kosuke Fukudome $14,500,000.00 CHC Matt Garza $5,950,000.00 CHC John Gaub $414,000.00 CHC John Grabow $4,800,000.00 CHC Koyie Hill $850,000.00 CHC Reed Johnson $900,000.00 CHC Bryan LaHair $414,000.00 CHC DJ LeMahieu $414,000.00 CHC Rodrigo Lopez $625,000.00 CHC Scott Maine $414,000.00 CHC Carlos Marmol $3,200,000.00 CHC Sean Marshall $1,600,000.00 CHC Marcos Mateo $417,000.00 CHC Carlos Pena $10,000,000.00 CHC Aramis Ramirez $14,600,000.00 CHC James Russell $427,500.00 CHC Jeff Samardzija $3,300,000.00 CHC Brad Snyder $414,000.00 CHC Alfonso Soriano $19,000,000.00 CHC Geovany Soto $3,000,000.00 CHC Jeff Stevens $414,000.00 CHC Randy Wells $475,000.00 CHC Kerry Wood $1,500,000.00 CHC Carlos Zambrano $18,875,000.00 CHC Dylan Axelrod $414,000.00 CHW Gordon Beckham $485,000.00 CHW Mark Buehrle $14,000,000.00 CHW 37

Ramon Castro $1,200,000.00 CHW Jesse Crain $4,000,000.00 CHW John Danks $6,000,000.00 CHW Adam Dunn $12,000,000.00 CHW Eduardo Escobar $414,000.00 CHW Tyler Flowers $414,000.00 CHW Gavin Floyd $5,000,000.00 CHW Lucas Harrell $414,000.00 CHW Philip Humber $500,000.00 CHW Edwin Jackson $8,750,000.00 CHW Paul Konerko $12,000,000.00 CHW Brent Lillibridge $430,000.00 CHW Shane Lindsay $414,000.00 CHW Lastings Milledge $500,000.00 CHW Brent Morel $414,000.00 CHW Will Ohman $1,500,000.00 CHW Jake Peavy $16,000,000.00 CHW Tony Pena $1,600,000.00 CHW Juan Pierre $8,500,000.00 CHW A.J. Pierzynski $2,000,000.00 CHW Carlos Quentin $5,050,000.00 CHW Alexei Ramirez $2,750,000.00 CHW Addison Reed $414,000.00 CHW Alex Rios $12,500,000.00 CHW Chris Sale $425,000.00 CHW Hector Santiago $414,000.00 CHW Sergio Santos $435,000.00 CHW Zach Stewart $414,000.00 CHW Mark Teahen $4,750,000.00 CHW Matt Thornton $3,000,000.00 CHW Dayan Viciedo $2,250,000.00 CHW Omar Vizquel $1,750,000.00 CHW Yonder Alonso $1,000,000.00 CIN Jose Arredondo $480,000.00 CIN Bronson Arroyo $11,500,000.00 CIN Homer Bailey $441,000.00 CIN Bill Bray $645,000.00 CIN Jay Bruce $2,791,666.00 CIN Jared Burton $750,000.00 CIN Miguel Cairo $1,000,000.00 CIN Aroldis Chapman $3,708,333.00 CIN Francisco Cordero $12,125,000.00 CIN Zack Cozart $414,000.00 CIN 38

Johnny Cueto $3,400,000.00 CIN Carlos Fisher $414,000.00 CIN Juan Francisco $414,000.00 CIN Todd Frazier $414,000.00 CIN Jonny Gomes $1,750,000.00 CIN Ryan Hanigan $550,000.00 CIN Chris Heisey $419,000.00 CIN Ramon Hernandez $3,000,000.00 CIN Jeremy Horst $414,000.00 CIN Paul Janish $437,500.00 CIN Mike Leake $425,000.00 CIN Sam LeCure $414,000.00 CIN Fred Lewis $900,000.00 CIN Matt Maloney $415,000.00 CIN Nick Masset $1,725,000.00 CIN Devin Mesoraco $414,000.00 CIN Logan Ondrusek $418,000.00 CIN Brandon Phillips $11,437,500.00 CIN Edgar Renteria $2,100,000.00 CIN Scott Rolen $8,166,667.00 CIN Dave Sappelt $414,000.00 CIN Jordan Smith $416,500.00 CIN Drew Stubbs $450,000.00 CIN Daryl Thompson $414,000.00 CIN Chris Valaika $414,000.00 CIN Edinson Volquez $1,625,000.00 CIN Joey Votto $7,500,000.00 CIN Dontrelle Willis $3,000,000.00 CIN Travis Wood $422,500.00 CIN Michael Brantley $421,800.00 CLE Travis Buck $625,000.00 CLE Asdrubal Cabrera $2,025,000.00 CLE Orlando Cabrera $1,000,000.00 CLE Carlos Carrasco $415,800.00 CLE Ezequiel Carrera $414,000.00 CLE Lonnie Chisenhall $414,000.00 CLE Shin-Soo Choo $3,975,000.00 CLE Trevor Crowe $435,700.00 CLE Jason Donald $423,200.00 CLE Shelley Duncan $500,000.00 CLE Chad Durbin $800,000.00 CLE Adam Everett $700,000.00 CLE Justin Germano $415,600.00 CLE 39

Jeanmar Gomez $414,000.00 CLE Travis Hafner $13,000,000.00 CLE Nick Hagadone $414,000.00 CLE Jack Hannahan $500,000.00 CLE Jerad Head $414,000.00 CLE Roberto Hernandez $6,287,500.00 CLE Frank Herrmann $419,800.00 CLE David Huff $414,000.00 CLE Josh Judy $414,000.00 CLE Austin Kearns $1,300,000.00 CLE Jason Kipnis $414,000.00 CLE Corey Kluber $414,000.00 CLE Matt LaPorta $431,400.00 CLE Lou Marson $424,300.00 CLE Justin Masterson $468,400.00 CLE Zach McAllister $414,000.00 CLE Chris Perez $2,225,000.00 CLE Rafael Perez $1,330,000.00 CLE Vinnie Pestano $414,100.00 CLE Cord Phelps $414,000.00 CLE Zach Putnam $414,000.00 CLE Carlos Santana $416,600.00 CLE Tony Sipp $436,800.00 CLE Grady Sizemore $7,666,666.00 CLE Joe Smith $870,000.00 CLE Mitch Talbot $431,700.00 CLE Josh Tomlin $417,200.00 CLE Alex White $414,000.00 CLE Bruce Billings $414,000.00 COL Charlie Blackmon $414,000.00 COL Rex Brothers $414,000.00 COL Jhoulys Chacin $414,000.00 COL Matt Daley $414,000.00 COL Jorge De $10,000,000.00 COL Edgmer Escalona $414,000.00 COL Tommy Field $414,000.00 COL Cole Garner $414,000.00 COL Jason Giambi $1,000,000.00 COL Hector Gomez $414,000.00 COL Carlos Gonzalez $1,428,600.00 COL Todd Helton $20,275,000.00 COL Ubaldo Jimenez $2,800,000.00 COL Alan Johnson $414,000.00 COL 40

Matt Lindstrom $2,800,000.00 COL Jose Lopez $3,600,000.00 COL Kevin Millwood $1,100,000.00 COL Franklin Morales $424,000.00 COL Clay Mortensen $414,000.00 COL Chris Nelson $414,000.00 COL Juan Nicasio $414,000.00 COL Jordan Pacheco $414,000.00 COL Matt Pagnozzi $414,000.00 COL Felipe Paulino $790,000.00 COL Drew Pomeranz $414,000.00 COL Matt Reynolds $414,000.00 COL Esmil Rogers $417,000.00 COL Wilin Rosario $414,000.00 COL Seth Smith $429,000.00 COL Huston Street $7,300,000.00 COL Troy Tulowitzki $5,500,000.00 COL Alex White $414,000.00 COL Eric Young $414,000.00 COL Al Alburquerque $414,000.00 DET Alex Avila $414,000.00 DET Duane Below $414,000.00 DET Joaquin Benoit $5,500,000.00 DET Brennan Boesch $414,000.00 DET Miguel Cabrera $20,000,000.00 DET Phil Coke $440,000.00 DET Andy Dirks $414,000.00 DET Doug Fister $414,000.00 DET Charlie Furbush $414,000.00 DET Enrique Gonzalez $435,000.00 DET Carlos Guillen $12,922,231.00 DET Brandon Inge $5,500,000.00 DET Austin Jackson $414,000.00 DET Don Kelly $423,000.00 DET Luis Marte $414,000.00 DET Victor Martinez $12,000,000.00 DET Andrew Oliver $414,000.00 DET Lester Oliveros $414,000.00 DET Magglio Ordonez $10,000,000.00 DET Brad Penny $3,000,000.00 DET Jhonny Peralta $4,300,000.00 DET Ryan Perry $414,000.00 DET Rick Porcello $1,536,000.00 DET 41

Ryan Raburn $1,300,000.00 DET Will Rhymes $414,000.00 DET Chance Ruffin $414,000.00 DET Ramon Santiago $1,350,000.00 DET Omir Santos $414,000.00 DET Max Scherzer $1,600,000.00 DET Daniel Schlereth $414,000.00 DET Scott Sizemore $414,000.00 DET Brad Thomas $800,000.00 DET Jacob Turner $414,000.00 DET Jose Valverde $7,000,000.00 DET Justin Verlander $12,850,000.00 DET Brayan Villarreal $414,000.00 DET Robbie Weinhardt $414,000.00 DET Casper Wells $414,000.00 DET Adam Wilk $414,000.00 DET Danny Worth $414,000.00 DET Burke Badenhop $505,000.00 FLA John Baker $417,000.00 FLA Emilio Bonifacio $425,000.00 FLA John Buck $5,000,000.00 FLA Jay Buente $414,000.00 FLA Jose Ceda $414,000.00 FLA Randy Choate $1,000,000.00 FLA Steve Cishek $414,000.00 FLA Chris Coghlan $490,000.00 FLA Scott Cousins $414,000.00 FLA Greg Dobbs $600,000.00 FLA Matt Dominguez $414,000.00 FLA Mike Dunn $414,000.00 FLA Brad Hand $414,000.00 FLA Chris Hatcher $414,000.00 FLA Brett Hayes $414,000.00 FLA Wes Helms $1,000,000.00 FLA Clay Hensley $1,400,000.00 FLA Omar Infante $2,500,000.00 FLA Josh Johnson $7,750,000.00 FLA Osvaldo Martinez $414,000.00 FLA Logan Morrison $414,000.00 FLA Edward Mujica $800,000.00 FLA Donnie Murphy $425,000.00 FLA Ricky Nolasco $6,000,000.00 FLA Bryan Petersen $414,000.00 FLA 42

Hanley Ramirez $11,000,000.00 FLA Sandy Rosario $414,000.00 FLA Alex Sanabia $414,000.00 FLA Brian Sanches $425,000.00 FLA Anibal Sanchez $3,700,000.00 FLA Gaby Sanchez $431,000.00 FLA Giancarlo Stanton $416,000.00 FLA Javier Vazquez $7,000,000.00 FLA Elih Villanueva $414,000.00 FLA Chris Volstad $445,000.00 FLA Ryan Webb $414,000.00 FLA Fernando Abad $418,000.00 HOU Juan Abreu $414,000.00 HOU Jose Altuve $414,000.00 HOU Clint Barmes $3,925,000.00 HOU Brian Bogusevic $414,000.00 HOU Jason Bourgeois $423,000.00 HOU Michael Bourn $4,400,000.00 HOU David Carpenter $414,000.00 HOU Xavier Cedeno $414,000.00 HOU Carlos Corporan $414,000.00 HOU Enerio Del $417,000.00 HOU Matt Downs $421,000.00 HOU Luis Durango $414,000.00 HOU Sergio Escalona $414,000.00 HOU Nelson Figueroa $900,000.00 HOU Jeff Fulchino $467,000.00 HOU Bill Hall $3,000,000.00 HOU J.A. Happ $474,000.00 HOU Lucas Harrell $414,000.00 HOU Chris Johnson $424,000.00 HOU Jeff Keppinger $2,300,000.00 HOU Carlos Lee $19,000,000.00 HOU Wilton Lopez $442,000.00 HOU Jordan Lyles $414,000.00 HOU Brandon Lyon $5,250,000.00 HOU J.D. Martinez $414,000.00 HOU Mark Melancon $421,000.00 HOU Jason Michaels $900,000.00 HOU Brett Myers $8,000,000.00 HOU Bud Norris $437,500.00 HOU Jimmy Paredes $414,000.00 HOU Hunter Pence $6,900,000.00 HOU 43

Lance Pendleton $414,000.00 HOU Humberto Quintero $1,000,000.00 HOU Aneury Rodriguez $414,000.00 HOU Fernando Rodriguez $414,000.00 HOU Wandy Rodriguez $7,500,000.00 HOU Angel Sanchez $432,500.00 HOU J.B. Shuck $414,000.00 HOU Henry Sosa $414,000.00 HOU J.R. Towles $424,000.00 HOU Jose Valdez $414,000.00 HOU Brett Wallace $418,000.00 HOU Wesley Wright $504,500.00 HOU Nathan Adcock $414,000.00 KC Mike Aviles $640,000.00 KC Wilson Betemit $1,000,000.00 KC Billy Butler $3,500,000.00 KC Melky Cabrera $1,250,000.00 KC Lorenzo Cain $414,000.00 KC Jesse Chavez $424,000.00 KC Bruce Chen $2,000,000.00 KC Louis Coleman $414,000.00 KC Tim Collins $414,000.00 KC Aaron Crow $1,400,000.00 KC Kyle Davies $3,200,000.00 KC Danny Duffy $414,000.00 KC Jarrod Dyson $414,000.00 KC Alcides Escobar $428,000.00 KC Jeff Francis $2,000,000.00 KC Jeff Francoeur $2,500,000.00 KC Chris Getz $443,000.00 KC Johnny Giavotella $414,000.00 KC Alex Gordon $1,400,000.00 KC Kelvin Herrera $414,000.00 KC Luke Hochevar $1,760,000.00 KC Greg Holland $414,000.00 KC Eric Hosmer $414,000.00 KC Jeremy Jeffress $414,500.00 KC Kila Ka'aihue $419,000.00 KC Mitch Maier $459,000.00 KC Vin Mazzaro $414,000.00 KC Luis Mendoza $414,000.00 KC Mike Moustakas $414,000.00 KC Yamaico Navarro $414,000.00 KC 44

Sean O'Sullivan $420,500.00 KC Brayan Pena $660,000.00 KC Salvador Perez $414,000.00 KC Manny Pina $414,000.00 KC Joakim Soria $4,000,000.00 KC Everett Teaford $414,000.00 KC Robinson Tejeda $1,550,000.00 KC Kanekoa Texeira $426,000.00 KC Blake Wood $414,000.00 KC Bobby Abreu $9,000,000.00 LAA Alexi Amarista $414,000.00 LAA Erick Aybar $3,000,000.00 LAA Trevor Bell $414,000.00 LAA Peter Bourjos $414,000.00 LAA Jason Bulger $423,000.00 LAA Alberto Callaspo $2,000,000.00 LAA Bobby Cassevah $414,000.00 LAA Tyler Chatwood $414,000.00 LAA Hank Conger $414,000.00 LAA Scott Downs $5,000,000.00 LAA Dan Haren $12,750,000.00 LAA Torii Hunter $18,500,000.00 LAA Maicer Izturis $3,266,666.00 LAA Kevin Jepsen $435,000.00 LAA Scott Kazmir $12,000,000.00 LAA Howard Kendrick $3,300,000.00 LAA Michael Kohn $414,000.00 LAA Jeff Mathis $1,700,000.00 LAA Jeremy Moore $414,000.00 LAA Efren Navarro $414,000.00 LAA Chris Pettit $414,000.00 LAA Joel Pineiro $8,000,000.00 LAA Garrett Richards $414,000.00 LAA Fernando Rodney $5,500,000.00 LAA Francisco Rodriguez $414,000.00 LAA Andrew Romine $414,000.00 LAA Ervin Santana $8,000,000.00 LAA Hisanori Takahashi $3,800,000.00 LAA Rich Thompson $418,000.00 LAA Mike Trout $414,000.00 LAA Mark Trumbo $414,000.00 LAA Jordan Walden $414,000.00 LAA Jered Weaver $7,365,000.00 LAA 45

Vernon Wells $26,642,857.00 LAA Reggie Willits $775,000.00 LAA Bobby Wilson $416,000.00 LAA Brandon Wood $420,000.00 LAA Rod Barajas $3,250,000.00 LAD Chad Billingsley $6,275,000.00 LAD Casey Blake $5,500,000.00 LAD Jonathan Broxton $7,000,000.00 LAD Jamey Carroll $2,285,677.00 LAD Lance Cormier $1,200,000.00 LAD Ivan De $414,000.00 LAD Rubby De $414,000.00 LAD A.J. Ellis $421,000.00 LAD John Ely $414,000.00 LAD Nathan Eovaldi $414,000.00 LAD Andre Ethier $9,500,000.00 LAD Tim Federowicz $414,000.00 LAD Rafael Furcal $13,000,000.00 LAD Jon Garland $4,442,023.00 LAD Jay Gibbons $650,000.00 LAD Dee Gordon $425,000.00 LAD Javy Guerra $464,000.00 LAD Matt Guerrier $2,461,499.00 LAD Tony Gwynn $675,000.00 LAD Blake Hawksworth $426,000.00 LAD Jamie Hoffmann $414,000.00 LAD Kenley Jansen $416,000.00 LAD Matt Kemp $7,100,000.00 LAD Clayton Kershaw $500,000.00 LAD Hong-Chih Kuo $2,725,000.00 LAD Hiroki Kuroda $11,765,724.00 LAD Ted Lilly $8,166,666.00 LAD Josh Lindblom $414,000.00 LAD James Loney $4,875,000.00 LAD Aaron Miles $500,000.00 LAD Russ Mitchell $500,000.00 LAD Dioner Navarro $1,000,000.00 LAD Trent Oeltjen $414,000.00 LAD Vicente Padilla $2,000,000.00 LAD Xavier Paul $414,000.00 LAD Juan Rivera $419,500.00 LAD Jerry Sands $414,000.00 LAD Justin Sellers $414,000.00 LAD 46

Marcus Thames $1,000,000.00 LAD Juan Uribe $5,295,910.00 LAD Erick Almonte $414,000.00 MIL John Axford $442,500.00 MIL Yuniesky Betancourt $4,375,000.00 MIL Zach Braddock $424,000.00 MIL Ryan Braun $4,287,500.00 MIL Craig Counsell $1,400,000.00 MIL Marco Estrada $414,000.00 MIL Eric Farris $414,000.00 MIL Prince Fielder $15,500,000.00 MIL Michael Fiers $414,000.00 MIL Yovani Gallardo $3,500,000.00 MIL Carlos Gomez $1,500,000.00 MIL Sean Green $875,000.00 MIL Taylor Green $414,000.00 MIL Zack Greinke $13,500,000.00 MIL Corey Hart $6,833,333.00 MIL LaTroy Hawkins $4,250,000.00 MIL Brandon Kintzler $414,000.00 MIL Mark Kotsay $800,000.00 MIL George Kottaras $440,000.00 MIL Kameron Loe $1,250,000.00 MIL Jonathan Lucroy $424,000.00 MIL Martin Maldonado $414,000.00 MIL Shaun Marcum $3,950,000.00 MIL Mike McClendon $414,000.00 MIL Casey McGehee $468,000.00 MIL Sergio Mitre $900,000.00 MIL Nyjer Morgan $450,000.00 MIL Chris Narveson $441,500.00 MIL Wil Nieves $775,000.00 MIL Jeremy Reed $550,000.00 MIL Takashi Saito $1,750,000.00 MIL Logan Schafer $414,000.00 MIL Mitch Stetter $427,000.00 MIL Rickie Weeks $4,500,000.00 MIL Randy Wolf $9,500,000.00 MIL Scott Baker $5,000,000.00 MIN Joe Benson $414,000.00 MIN Nick Blackburn $3,000,000.00 MIN Alex Burnett $414,000.00 MIN Drew Butera $429,000.00 MIN 47

Matt Capps $7,150,000.00 MIN Alexi Casilla $865,000.00 MIN Michael Cuddyer $10,500,000.00 MIN Scott Diamond $414,000.00 MIN Brian Dinkelman $414,000.00 MIN Brian Duensing $462,500.00 MIN Phil Dumatrait $414,000.00 MIN Eric Hacker $414,000.00 MIN Liam Hendriks $414,000.00 MIN Jim Hoey $414,000.00 MIN Steve Holm $414,000.00 MIN Dusty Hughes $429,000.00 MIN Luke Hughes $414,000.00 MIN Jason Kubel $5,250,000.00 MIN Francisco Liriano $4,300,000.00 MIN Jeff Manship $419,000.00 MIN Joe Mauer $23,000,000.00 MIN Jose Mijares $445,000.00 MIN Justin Morneau $15,000,000.00 MIN Joe Nathan $11,250,000.00 MIN Tsuyoshi Nishioka $3,000,000.00 MIN Chris Parmelee $414,000.00 MIN Carl Pavano $8,000,000.00 MIN Glen Perkins $700,000.00 MIN Trevor Plouffe $414,000.00 MIN Jason Repko $600,000.00 MIN Ben Revere $414,000.00 MIN Rene Rivera $414,000.00 MIN Anthony Slama $414,000.00 MIN Kevin Slowey $2,700,000.00 MIN Denard Span $1,000,000.00 MIN Anthony Swarzak $414,000.00 MIN Jim Thome $3,000,000.00 MIN Matt Tolbert $425,000.00 MIN Rene Tosoni $414,000.00 MIN Danny Valencia $437,500.00 MIN Kyle Waldrop $414,000.00 MIN Delmon Young $5,375,000.00 MIN Mike Baxter $414,000.00 NYM Jason Bay $18,125,000.00 NYM Pedro Beato $414,000.00 NYM Carlos Beltran $19,325,436.00 NYM Blaine Boyer $725,000.00 NYM 48

Taylor Buchholz $600,000.00 NYM Tim Byrdak $900,000.00 NYM Chris Capuano $1,500,000.00 NYM D.J. Carrasco $1,200,000.00 NYM Ike Davis $414,000.00 NYM R.A. Dickey $2,750,000.00 NYM Lucas Duda $414,000.00 NYM Brad Emaus $414,000.00 NYM Dillon Gee $414,000.00 NYM Scott Hairston $1,100,000.00 NYM Willie Harris $800,000.00 NYM Chin-lung Hu $420,000.00 NYM Ryota Igarashi $1,750,000.00 NYM Jason Isringhausen $725,000.00 NYM Fernando Martinez $414,000.00 NYM Daniel Murphy $422,000.00 NYM Mike Nickeas $414,000.00 NYM Jon Niese $452,000.00 NYM Angel Pagan $3,500,000.00 NYM Bobby Parnell $433,500.00 NYM Mike Pelfrey $3,925,000.00 NYM Jose Reyes $11,000,000.00 NYM Francisco Rodriguez $12,166,666.00 NYM Josh Satin $414,000.00 NYM Chris Schwinden $414,000.00 NYM Josh Stinson $414,000.00 NYM Ruben Tejada $414,000.00 NYM Dale Thayer $414,000.00 NYM Josh Thole $414,000.00 NYM Justin Turner $414,000.00 NYM David Wright $14,250,000.00 NYM Chris Young $1,100,000.00 NYM Luis Ayala $650,000.00 NYY Dellin Betances $414,000.00 NYY Andrew Brackman $414,000.00 NYY A.J. Burnett $16,500,000.00 NYY Robinson Cano $10,000,000.00 NYY Francisco Cervelli $455,700.00 NYY Joba Chamberlain $1,400,000.00 NYY Eric Chavez $1,500,000.00 NYY Bartolo Colon $900,000.00 NYY Freddy Garcia $1,500,000.00 NYY Brett Gardner $529,500.00 NYY 49

Steve Garrison $414,000.00 NYY Curtis Granderson $8,250,000.00 NYY Phil Hughes $2,700,000.00 NYY Derek Jeter $14,729,364.00 NYY Andruw Jones $1,500,000.00 NYY George Kontos $414,000.00 NYY Brandon Laird $414,000.00 NYY Boone Logan $1,200,000.00 NYY Jeffrey Marquez $414,000.00 NYY Russell Martin $4,000,000.00 NYY Gustavo Molina $455,000.00 NYY Jesus Montero $414,000.00 NYY Hector Noesi $414,000.00 NYY Ivan Nova $432,900.00 NYY Eduardo Nunez $419,300.00 NYY Ramiro Pena $414,000.00 NYY Lance Pendleton $414,000.00 NYY Jorge Posada $13,100,000.00 NYY Mariano Rivera $14,911,700.00 NYY David Robertson $460,450.00 NYY Alex Rodriguez $32,000,000.00 NYY Austin Romine $414,000.00 NYY CC Sabathia $24,285,714.00 NYY Amauri Sanit $414,000.00 NYY Rafael Soriano $10,000,000.00 NYY Nick Swisher $9,100,000.00 NYY Mark Teixeira $23,125,000.00 NYY Raul Valdes $414,000.00 NYY Kevin Whelan $414,000.00 NYY Brandon Allen $414,000.00 OAK Brett Anderson $1,250,000.00 OAK Andrew Bailey $465,000.00 OAK Grant Balfour $3,750,000.00 OAK Daric Barton $425,000.00 OAK Bruce Billings $414,000.00 OAK Jerry Blevins $420,000.00 OAK Dallas Braden $3,350,000.00 OAK Craig Breslow $1,400,000.00 OAK Trevor Cahill $440,000.00 OAK Andrew Carignan $414,000.00 OAK Chris Carter $414,000.00 OAK Bobby Cramer $414,000.00 OAK Coco Crisp $5,750,000.00 OAK 50

Fautino De $414,000.00 OAK David DeJesus $6,000,000.00 OAK Joey Devine $557,500.00 OAK Mark Ellis $6,000,000.00 OAK Brian Fuentes $5,000,000.00 OAK Graham Godfrey $414,000.00 OAK Gio Gonzalez $420,000.00 OAK Rich Harden $1,500,000.00 OAK Conor Jackson $3,200,000.00 OAK Kevin Kouzmanoff $4,750,000.00 OAK Andy LaRoche $600,000.00 OAK Trystan Magnuson $414,000.00 OAK Hideki Matsui $4,250,000.00 OAK Brandon McCarthy $1,000,000.00 OAK Guillermo Moscoso $414,000.00 OAK Jordan Norberto $414,000.00 OAK Josh Outman $457,000.00 OAK Cliff Pennington $420,000.00 OAK Landon Powell $420,000.00 OAK Anthony Recker $414,000.00 OAK Adam Rosales $425,000.00 OAK Tyson Ross $414,000.00 OAK Scott Sizemore $414,000.00 OAK Eric Sogard $414,000.00 OAK Kurt Suzuki $3,437,500.00 OAK Ryan Sweeney $1,400,000.00 OAK Michael Taylor $414,000.00 OAK Neil Wagner $414,000.00 OAK Jemile Weeks $414,000.00 OAK Josh Willingham $6,000,000.00 OAK Michael Wuertz $2,800,000.00 OAK Brad Ziegler $1,250,000.00 OAK Danys Baez $2,750,000.00 PHI Antonio Bastardo $419,000.00 PHI Joe Blanton $10,500,000.00 PHI Domonic Brown $414,000.00 PHI Jose Contreras $2,500,000.00 PHI Justin De $414,000.00 PHI Ben Francisco $1,175,000.00 PHI Ross Gload $1,600,000.00 PHI Roy Halladay $20,000,000.00 PHI Cole Hamels $9,500,000.00 PHI David Herndon $425,000.00 PHI 51

Ryan Howard $20,000,000.00 PHI Raul Ibanez $12,166,666.00 PHI Kyle Kendrick $2,450,000.00 PHI Erik Kratz $414,000.00 PHI Cliff Lee $11,000,000.00 PHI Brad Lidge $12,000,000.00 PHI Ryan Madson $4,833,333.00 PHI Michael Martinez $414,000.00 PHI John Mayberry $414,000.00 PHI Pete Orr $600,000.00 PHI Roy Oswalt $16,000,000.00 PHI Placido Polanco $5,416,666.00 PHI Jimmy Rollins $8,500,000.00 PHI J.C. Romero $1,350,000.00 PHI Carlos Ruiz $2,750,000.00 PHI Joe Savery $414,000.00 PHI Brian Schneider $1,625,000.00 PHI Michael Schwimer $414,000.00 PHI Michael Stutes $414,000.00 PHI Chase Utley $15,285,714.00 PHI Wilson Valdez $560,000.00 PHI Shane Victorino $7,500,000.00 PHI Vance Worley $414,000.00 PHI Pedro Alvarez $2,050,000.00 PIT Jose Ascanio $416,000.00 PIT John Bowker $441,500.00 PIT Dusty Brown $414,000.00 PIT Ronny Cedeno $1,850,000.00 PIT Pedro Ciriaco $414,000.00 PIT Kevin Correia $4,000,000.00 PIT Michael Crotta $414,000.00 PIT Chase d'Arnaud $414,000.00 PIT Matt Diaz $2,125,000.00 PIT Ryan Doumit $5,200,000.00 PIT Eric Fryer $414,000.00 PIT Jason Grilli $900,000.00 PIT Joel Hanrahan $1,400,000.00 PIT Josh Harrison $414,000.00 PIT Jared Hughes $414,000.00 PIT Jason Jaramillo $422,500.00 PIT Garrett Jones $455,500.00 PIT Jeff Karstens $1,100,000.00 PIT Chris Leroux $414,000.00 PIT 52

Brad Lincoln $414,500.00 PIT Jeff Locke $414,000.00 PIT Paul Maholm $6,250,000.00 PIT Andrew McCutchen $452,500.00 PIT Daniel McCutchen $414,000.00 PIT James McDonald $443,000.00 PIT Michael McKenry $414,000.00 PIT Charlie Morton $441,000.00 PIT Daniel Moskos $414,000.00 PIT Ross Ohlendorf $2,025,000.00 PIT Garrett Olson $430,000.00 PIT Lyle Overbay $5,000,000.00 PIT Matt Pagnozzi $414,000.00 PIT Xavier Paul $414,000.00 PIT Steve Pearce $427,500.00 PIT Alex Presley $414,000.00 PIT Chris Resop $431,500.00 PIT Josh Rodriguez $414,000.00 PIT Chris Snyder $6,250,000.00 PIT Jose Tabata $428,000.00 PIT Aaron Thompson $414,000.00 PIT Wyatt Toregas $414,000.00 PIT Jose Veras $1,214,000.00 PIT Neil Walker $437,000.00 PIT Tony Watson $414,000.00 PIT Tim Wood $414,000.00 PIT Mike Adams $2,535,000.00 SD Jason Bartlett $4,000,000.00 SD Anthony Bass $414,000.00 SD Heath Bell $7,500,000.00 SD Kyle Blanks $424,700.00 SD Brad Brach $414,000.00 SD Everth Cabrera $414,000.00 SD Jorge Cantu $850,000.00 SD Aaron Cunningham $414,000.00 SD James Darnell $414,000.00 SD Samuel Deduno $414,240.00 SD Chris Denorfia $800,000.00 SD Logan Forsythe $414,000.00 SD Ernesto Frieri $421,700.00 SD Alberto Gonzalez $600,000.00 SD Luke Gregerson $447,800.00 SD Jesus Guzman $414,000.00 SD 53

Erik Hamren $414,000.00 SD Aaron Harang $3,500,000.00 SD Brad Hawpe $2,000,000.00 SD Chase Headley $2,325,000.00 SD Orlando Hudson $4,000,000.00 SD Nick Hundley $439,900.00 SD Cedric Hunter $414,000.00 SD Rob Johnson $421,700.00 SD Mat Latos $460,700.00 SD Wade LeBlanc $454,000.00 SD Ryan Ludwick $6,775,000.00 SD Cory Luebke $415,600.00 SD Luis Martinez $414,000.00 SD Cameron Maybin $429,100.00 SD Dustin Moseley $900,000.00 SD Pat Neshek $625,000.00 SD Andy Parrino $414,000.00 SD Eric Patterson $423,300.00 SD Kyle Phillips $414,000.00 SD Chad Qualls $1,500,000.00 SD Clayton Richard $468,800.00 SD Anthony Rizzo $414,000.00 SD Evan Scribner $414,000.00 SD Josh Spence $414,000.00 SD Tim Stauffer $1,075,000.00 SD Blake Tekotte $414,000.00 SD Joe Thatcher $433,900.00 SD Will Venable $444,400.00 SD Dustin Ackley $414,000.00 SEA Blake Beavan $414,000.00 SEA Mike Carp $414,000.00 SEA Dan Cortes $414,000.00 SEA Steve Delabar $414,000.00 SEA Doug Fister $414,000.00 SEA Charlie Furbush $414,000.00 SEA Chris Gimenez $414,000.00 SEA Greg Halman $400,000.00 SEA Felix Hernandez $15,500,000.00 SEA Shawn Kelley $414,000.00 SEA Brandon League $2,250,000.00 SEA Alex Liddi $414,000.00 SEA Josh Lueke $414,000.00 SEA Adam Moore $414,000.00 SEA 54

David Pauley $422,000.00 SEA Carlos Peguero $414,000.00 SEA Michael Pineda $414,000.00 SEA Trayvon Robinson $414,000.00 SEA Chance Ruffin $414,000.00 SEA Michael Saunders $414,000.00 SEA Kyle Seager $414,000.00 SEA Justin Smoak $414,000.00 SEA Ichiro Suzuki $18,000,000.00 SEA Anthony Vasquez $414,000.00 SEA Casper Wells $414,000.00 SEA Tom Wilhelmsen $414,000.00 SEA Mike Wilson $414,000.00 SEA Jamey Wright $900,000.00 SEA Jeremy Affeldt $4,500,000.00 SF Brandon Belt $414,000.00 SF Madison Bumgarner $450,000.00 SF Pat Burrell $1,000,000.00 SF Matt Cain $7,333,333.00 SF Santiago Casilla $1,300,000.00 SF Brandon Crawford $414,000.00 SF Steven Edlefsen $414,000.00 SF Mike Fontenot $1,050,000.00 SF Darren Ford $414,000.00 SF Aubrey Huff $10,000,000.00 SF Waldis Joaquin $414,000.00 SF Tim Lincecum $14,000,000.00 SF Javier Lopez $2,375,000.00 SF Guillermo Mota $925,000.00 SF Brett Pill $414,000.00 SF Buster Posey $575,000.00 SF Ramon Ramirez $1,650,000.00 SF Sergio Romo $450,000.00 SF Cody Ross $6,300,000.00 SF Aaron Rowand $13,600,000.00 SF Dan Runzler $418,500.00 SF Freddy Sanchez $6,000,000.00 SF Hector Sanchez $414,000.00 SF Jonathan Sanchez $4,800,000.00 SF Pablo Sandoval $500,000.00 SF Nate Schierholtz $432,500.00 SF Eric Surkamp $414,000.00 SF Miguel Tejada $6,500,000.00 SF 55

Andres Torres $2,200,000.00 SF Ryan Vogelsong $1,500,000.00 SF Eli Whiteside $425,000.00 SF Brian Wilson $6,500,000.00 SF Barry Zito $18,500,000.00 SF Bryan Augenstein $414,000.00 STL Miguel Batista $750,000.00 STL Lance Berkman $8,000,000.00 STL Mitchell Boggs $431,000.00 STL Andrew Brown $414,000.00 STL Chris Carpenter $15,000,000.00 STL Matt Carpenter $414,000.00 STL Adron Chambers $414,000.00 STL Maikel Cleto $414,000.00 STL Allen Craig $414,000.00 STL Tony Cruz $414,000.00 STL Daniel Descalso $414,000.00 STL Brandon Dickson $414,000.00 STL Ryan Franklin $3,250,000.00 STL David Freese $416,000.00 STL Jaime Garcia $437,000.00 STL Tyler Greene $415,500.00 STL Mark Hamilton $414,000.00 STL Matt Holliday $17,000,000.00 STL Jon Jay $416,000.00 STL Pete Kozma $414,000.00 STL Gerald Laird $1,000,000.00 STL Kyle Lohse $12,187,500.00 STL Lance Lynn $414,000.00 STL Kyle McClellan $1,375,000.00 STL Trever Miller $2,000,000.00 STL Yadier Molina $5,312,500.00 STL Jason Motte $435,000.00 STL Albert Pujols $16,000,000.00 STL Nick Punto $750,000.00 STL Colby Rasmus $443,000.00 STL Shane Robinson $414,000.00 STL Fernando Salas $414,000.00 STL Eduardo Sanchez $414,000.00 STL Skip Schumaker $2,750,000.00 STL Ryan Theriot $3,300,000.00 STL Raul Valdes $414,000.00 STL P.J. Walters $414,000.00 STL 56

Jake Westbrook $8,000,000.00 STL Reid Brignac $425,400.00 TB Jay Buente $414,000.00 TB Russ Canzler $414,000.00 TB Robinson Chirinos $414,000.00 TB Alex Cobb $414,000.00 TB Juan Cruz $850,000.00 TB Johnny Damon $5,250,000.00 TB Wade Davis $434,100.00 TB Dane De $414,000.00 TB Rob Delaney $414,000.00 TB Kyle Farnsworth $2,600,000.00 TB Sam Fuld $418,300.00 TB Brandon Gomes $414,000.00 TB Brandon Guyer $414,000.00 TB Jeremy Hellickson $418,400.00 TB J.P. Howell $1,100,000.00 TB John Jaso $427,200.00 TB Desmond Jennings $414,000.00 TB Dan Johnson $1,000,000.00 TB Elliot Johnson $414,900.00 TB Matt Joyce $426,500.00 TB Jose Lobaton $414,000.00 TB Evan Longoria $2,000,000.00 TB Jake McGee $415,200.00 TB Matt Moore $414,000.00 TB Jeff Niemann $903,000.00 TB Joel Peralta $925,000.00 TB David Price $2,084,671.00 TB Manny Ramirez $2,020,000.00 TB Cesar Ramos $416,700.00 TB Sean Rodriguez $428,600.00 TB Adam Russell $420,800.00 TB Kelly Shoppach $3,000,000.00 TB Andy Sonnanstine $914,500.00 TB Alex Torres $414,000.00 TB B.J. Upton $4,825,000.00 TB Ben Zobrist $4,687,300.00 TB Elvis Andrus $452,180.00 TEX Adrian Beltre $14,000,000.00 TEX Andres Blanco $520,000.00 TEX Julio Borbon $490,000.00 TEX Dave Bush $1,000,000.00 TEX 57

Nelson Cruz $3,650,000.00 TEX Chris Davis $418,290.00 TEX Cody Eppley $414,000.00 TEX Scott Feldman $4,400,000.00 TEX Neftali Feliz $457,160.00 TEX Craig Gentry $414,000.00 TEX Mark Hamburger $414,000.00 TEX Josh Hamilton $8,750,000.00 TEX Matt Harrison $428,830.00 TEX Derek Holland $431,810.00 TEX Tommy Hunter $442,000.00 TEX Ian Kinsler $6,200,000.00 TEX Michael Kirkman $414,000.00 TEX Colby Lewis $2,000,000.00 TEX Mark Lowe $1,200,000.00 TEX Leonys Martin $414,000.00 TEX Mitch Moreland $426,000.00 TEX David Murphy $2,400,000.00 TEX Mike Napoli $5,800,000.00 TEX Darren O'Day $1,250,000.00 TEX Alexi Ogando $430,150.00 TEX Darren Oliver $3,250,000.00 TEX Omar Quintanilla $750,000.00 TEX Arthur Rhodes $3,900,000.00 TEX Pedro Strop $416,000.00 TEX Yoshinori Tateyama $414,000.00 TEX Mason Tobin $414,000.00 TEX Yorvit Torrealba $3,000,000.00 TEX Matt Treanor $850,000.00 TEX Ryan Tucker $414,000.00 TEX C.J. Wilson $7,000,000.00 TEX Michael Young $16,000,000.00 TEX Henderson Alvarez $414,000.00 TOR J.P. Arencibia $417,000.00 TOR Jose Bautista $8,000,000.00 TOR Chad Beck $414,000.00 TOR Shawn Camp $2,250,000.00 TOR Joel Carreno $414,000.00 TOR Brett Cecil $443,100.00 TOR David Cooper $414,000.00 TOR Rajai Davis $2,500,000.00 TOR Octavio Dotel $3,000,000.00 TOR Kyle Drabek $416,000.00 TOR 58

Edwin Encarnacion $2,500,000.00 TOR Yunel Escobar $2,900,000.00 TOR Danny Farquhar $414,000.00 TOR Frank Francisco $4,000,000.00 TOR Jason Frasor $3,500,000.00 TOR Aaron Hill $5,000,000.00 TOR Casey Janssen $1,095,000.00 TOR Brett Lawrie $414,000.00 TOR Rommie Lewis $414,000.00 TOR Adam Lind $5,150,000.00 TOR Jesse Litsch $830,000.00 TOR Darin Mastroianni $414,000.00 TOR Mike McCoy $422,300.00 TOR John McDonald $1,500,000.00 TOR Dustin McGowan $450,000.00 TOR Brad Mills $414,000.00 TOR Jose Molina $1,200,000.00 TOR Brandon Morrow $2,300,000.00 TOR Jayson Nix $438,000.00 TOR Corey Patterson $900,000.00 TOR Luis Perez $414,000.00 TOR David Purcey $436,500.00 TOR Colby Rasmus $414,000.00 TOR Jon Rauch $3,500,000.00 TOR Jo-Jo Reyes $439,000.00 TOR Juan Rivera $5,250,000.00 TOR Ricky Romero $1,000,000.00 TOR Marc Rzepczynski $429,600.00 TOR Travis Snider $435,800.00 TOR Zach Stewart $414,000.00 TOR Eric Thames $414,000.00 TOR Carlos Villanueva $1,415,000.00 TOR P.J. Walters $414,000.00 TOR Rick Ankiel $1,500,000.00 WAS Collin Balester $414,000.00 WAS Brian Broderick $414,000.00 WAS Corey Brown $414,000.00 WAS Sean Burnett $1,350,000.00 WAS Tyler Clippard $443,000.00 WAS Todd Coffey $1,350,000.00 WAS Alex Cora $900,000.00 WAS Ian Desmond $441,500.00 WAS Danny Espinosa $415,000.00 WAS 59

Chad Gaudin $900,000.00 WAS Tom Gorzelanny $2,100,000.00 WAS Jerry Hairston $2,000,000.00 WAS Livan Hernandez $1,250,000.00 WAS Cole Kimball $414,000.00 WAS John Lannan $2,750,000.00 WAS Adam LaRoche $7,000,000.00 WAS Steve Lombardozzi $414,000.00 WAS Jason Marquis $7,500,000.00 WAS Christopher Marrero $414,000.00 WAS Ryan Mattheus $414,000.00 WAS Yunesky Maya $414,000.00 WAS Tommy Milone $414,000.00 WAS Mike Morse $1,050,000.00 WAS Laynce Nix $700,000.00 WAS Brad Peacock $414,000.00 WAS Wilson Ramos $415,000.00 WAS Henry Rodriguez $415,000.00 WAS Ivan Rodriguez $3,000,000.00 WAS Atahualpa Severino $414,000.00 WAS Doug Slaten $695,000.00 WAS Matt Stairs $850,000.00 WAS Craig Stammen $414,000.00 WAS Drew Storen $418,000.00 WAS Stephen Strasburg $4,375,000.00 WAS Chien-Ming Wang $1,200,000.00 WAS Jayson Werth $10,571,428.00 WAS Ryan Zimmerman $9,025,000.00 WAS Jordan Zimmermann $415,000.00 WAS

60 Table 10. Field Player Statistics Data nameFirst nameLast POS team R H HR RBI STL% BB AVG OBP SLG K Brandon Allen 1B ARI 23 35 6 18 1 18 0.200 0.277 0.377 68

Lars Anderson 1B BOS 2 0 0 0 . 0 . . . 3 Daric Barton 1B OAK 27 50 0 21 0.67 39 0.212 0.325 0.267 47 Russell Branyan 1B SEA 11 25 5 14 1.00 18 0.197 0.295 0.370 41 Billy Butler 1B KC 74 174 19 95 0.67 66 0.291 0.361 0.461 95 Melky Cabrera 1B ATL 102 201 18 87 0.67 35 0.305 0.339 0.470 94 Jorge Cantu 1B TEX 8 28 3 16 . 7 0.194 0.232 0.285 28 Matt Carpenter 1B STL 0 1 0 0 . 4 0.067 0.263 0.133 4 Chris Carter 1B NYM 2 6 0 0 . 2 0.136 0.174 0.136 20 David Cooper 1B TOR 9 15 2 12 . 7 0.211 0.284 0.394 14 Rajai Davis 1B OAK 44 76 1 29 0.76 15 0.238 0.273 0.350 63 Adam Dunn 1B WAS 36 66 11 42 0.00 75 0.159 0.292 0.277 177 Nick Evans 1B NYM 26 45 4 25 0.00 15 0.256 0.314 0.403 48 Thomas Field 1B COL 4 13 0 3 . 3 0.271 0.314 0.271 14 Freddie Freeman 1B ATL 67 161 21 76 0.50 53 0.282 0.346 0.448 142 Jason Giambi 1B COL 20 34 13 32 . 17 0.260 0.355 0.603 45 Ross Gload 1B PHI 3 29 0 8 . 3 0.257 0.276 0.327 23 Paul Goldschmidt 1B ARI 28 39 8 26 1.00 20 0.250 0.333 0.474 53 Adrian Gonzalez 1B SD 108 213 27 117 1.00 74 0.338 0.410 0.548 119 Jesus Guzman 1B SF 33 77 5 44 0.82 22 0.312 0.369 0.478 43 Josh Hamilton 1B TEX 80 145 25 94 0.89 39 0.298 0.346 0.536 93 Brad Hawpe 1B TB 19 45 4 19 . 19 0.231 0.301 0.344 68 Todd Helton 1B COL 59 127 14 69 0.00 59 0.302 0.385 0.466 71 Eric Hosmer 1B KC 66 153 19 78 0.69 34 0.293 0.334 0.465 82 Ryan Howard 1B PHI 81 141 33 116 1.00 75 0.253 0.346 0.488 172 Aubrey Huff 1B SF 45 128 12 59 0.63 47 0.246 0.306 0.370 90 Rob Johnson 1B SEA 9 34 3 16 1.00 14 0.190 0.259 0.285 58 Kila Ka'aihue 1B KC 6 16 2 6 . 12 0.195 0.295 0.317 26 Paul Konerko 1B CHW 69 163 31 105 0.50 77 0.300 0.388 0.517 89 Casey Kotchman 1B SEA 44 153 10 48 0.50 48 0.306 0.378 0.422 66 Ryan Langerhans 1B SEA 6 9 3 6 0.00 11 0.173 0.317 0.346 22 Matt LaPorta 1B CLE 34 87 11 53 1.00 23 0.247 0.299 0.412 87 Carlos Lee 1B HOU 66 161 18 94 0.57 59 0.275 0.342 0.446 60 Brent Lillibridge 1B CHW 38 48 13 29 0.63 17 0.258 0.340 0.505 62 Steve Lombardozzi 1B WAS 3 6 0 1 . 1 0.194 0.219 0.226 4 Nick Markakis 1B BAL 72 182 15 73 0.80 62 0.284 0.351 0.406 75 Jai Miller 1B KC 2 3 1 2 . 0 0.250 0.250 0.500 5 Brent Morel 1B CHW 44 101 10 41 0.56 22 0.245 0.287 0.366 57 Nyjer Morgan 1B WAS 61 115 4 37 0.76 19 0.304 0.357 0.421 70 Logan Morrison 1B FLA 54 114 23 72 0.67 54 0.247 0.330 0.468 99 61

David Murphy 1B TEX 46 111 11 46 0.65 33 0.275 0.328 0.401 61 Donnie Murphy 1B FLA 10 17 2 9 . 4 0.185 0.240 0.315 21 Efren Navarro 1B LAA 1 2 0 0 . 1 0.200 0.273 0.300 1 David Ortiz 1B BOS 84 162 29 96 0.50 78 0.309 0.398 0.360 83 Lyle Overbay 1B TOR 43 92 9 47 0.67 42 0.234 0.310 0.369 88 Jordan Pacheco 1B COL 5 24 2 14 . 3 0.286 0.318 0.372 9 Chris Parmelee 1B MIN 8 27 4 14 . 12 0.355 0.443 0.427 13 Valentino Pascucci 1B NYM 1 3 1 2 . 0 0.273 0.273 0.359 3 Steve Pearce 1B PIT 7 19 1 10 . 7 0.202 0.260 0.474 21 Brayan Pena 1B KC 17 55 3 24 . 12 0.248 0.288 0.462 24 Brett Pill 1B SF 7 15 2 9 0.00 2 0.300 0.321 0.392 8 Jorge Posada 1B NYY 34 81 14 44 0.00 39 0.235 0.315 0.225 76 Albert Pujols 1B STL 105 173 37 99 0.90 61 0.299 0.366 0.499 58 Anthony Rizzo 1B SD 9 18 1 9 0.67 21 0.141 0.281 0.427 46 Freddy Sanchez 1B SF 21 69 3 24 0.00 13 0.289 0.332 0.323 35 Joshua Satin 1B NYM 3 5 0 2 . 1 0.200 0.259 0.315 11 Justin Smoak 1B SEA 38 100 15 55 . 55 0.234 0.323 0.111 105 Brad Snyder 1B CHC 1 1 0 0 . 0 0.111 0.111 0.396 6 Matt Stairs 1B WAS 4 10 0 2 . 9 0.154 0.257 0.309 23 Mark Teixeira 1B NYY 90 146 39 111 0.80 76 0.248 0.341 0.335 110 Mark Trumbo 1B LAA 65 137 29 87 0.69 25 0.254 0.291 0.356 120 Joey Votto 1B CIN 101 185 29 103 0.57 110 0.309 0.416 0.369 129 Brett Wallace 1B HOU 37 87 5 29 0.50 36 0.259 0.334 0.468 91 Dustin Ackley 2B SEA 39 91 6 36 1.00 40 0.273 0.348 0.417 79 Ryan Adams 2B BAL 9 25 0 7 . 6 0.281 0.333 0.326 25 Jose Altuve 2B HOU 26 61 2 12 0.70 5 0.276 0.297 0.357 29 Alexi Amarista 2B LAA 2 8 0 5 . 2 0.154 0.182 0.250 8 Alfredo Amezaga 2B FLA 6 14 0 4 . 7 0.182 0.247 0.195 14 Robert Andino 2B BAL 63 120 5 36 0.81 41 0.263 0.327 0.344 83 John Baker 2B FLA 0 2 0 1 . 2 0.154 0.267 0.154 3 Darwin Barney 2B CHC 66 146 2 43 0.82 22 0.276 0.313 0.353 67 Gordon Beckham 2B CHW 60 115 10 44 0.63 35 0.230 0.296 0.337 111 Emmanuel Burriss 2B SF 14 28 0 4 0.79 6 0.204 0.253 0.212 17 Miguel Cabrera 2B DET 111 197 30 105 0.67 108 0.344 0.448 0.586 89 Robinson Cano 2B NYY 104 188 28 118 0.80 38 0.302 0.349 0.533 96 Jamey Carroll 2B LAD 52 131 0 17 1.00 47 0.290 0.359 0.347 58 Alexi Casilla 2B MIN 52 84 2 21 0.79 28 0.260 0.322 0.368 45 Starlin Castro 2B CHC 91 207 10 66 0.71 35 0.307 0.341 0.432 96 Brooks Conrad 2B ATL 11 23 4 13 1.00 15 0.223 0.325 0.388 41 Blake Davis 2B BAL 6 15 1 6 0.50 6 0.254 0.323 0.390 13 David DeJesus 2B KC 60 106 10 46 0.57 45 0.240 0.323 0.376 86 Brian Dinkelman 2B MIN 5 22 0 4 1.00 4 0.301 0.346 0.315 14 Jason Donald 2B CLE 13 42 1 8 0.60 7 0.318 0.364 0.402 35 62

Matt Downs 2B HOU 29 55 10 41 . 17 0.276 0.347 0.518 47 A.J. Ellis 2B LAD 8 23 2 11 0.00 14 0.271 0.392 0.376 16 Brad Emaus 2B NYM 2 6 0 1 . 4 0.162 0.262 0.162 9 Danny Espinosa 2B WAS 72 135 21 66 0.74 57 0.236 0.323 0.414 166 Esteban German 2B TEX 6 5 1 4 1.00 1 0.455 0.462 0.818 1 Chris Getz 2B KC 50 97 0 26 0.75 30 0.255 0.313 0.287 45 Johnny Giavotella 2B KC 20 44 2 21 0.71 6 0.247 0.273 0.376 32 Carlos Gonzalez 2B COL 91 142 26 92 0.80 48 0.295 0.363 0.526 105 Tyler Greene 2B STL 22 22 1 11 1.00 13 0.212 0.322 0.288 31 Carlos Guillen 2B DET 8 22 3 13 0.50 5 0.232 0.265 0.368 16 Bill Hall 2B BOS 24 39 2 14 0.60 11 0.211 0.261 0.314 63 Jonathan Herrera 2B COL 28 68 3 14 0.50 28 0.242 0.313 0.299 40 Koyie Hill 2B CHC 15 26 2 9 1.00 14 0.194 0.268 0.276 40 Chin-lung Hu 2B LAD 2 1 0 1 1.00 1 0.050 0.091 0.050 11 Kyle Hudson 2B BAL 3 4 0 2 1.00 0 0.143 0.143 0.143 6 Luke Hughes 2B MIN 31 64 7 30 0.60 24 0.223 0.289 0.338 79 Omar Infante 2B ATL 55 160 7 49 0.67 34 0.276 0.315 0.382 67 Joe Inglett 2B MIL 3 6 0 1 . 0 0.222 0.222 0.259 7 Maicer Izturis 2B LAA 51 124 5 38 0.60 33 0.276 0.334 0.388 65 Elliot Johnson 2B TB 20 31 4 17 0.46 14 0.194 0.257 0.338 53 Howard Kendrick 2B LAA 86 153 18 63 0.70 33 0.285 0.338 0.464 119 Adam Kennedy 2B WAS 36 89 7 38 0.80 22 0.234 0.277 0.355 67 Jeff Keppinger 2B HOU 39 105 6 35 0.00 12 0.277 0.300 0.377 24 Ian Kinsler 2B TEX 121 158 32 77 0.88 89 0.255 0.355 0.477 71 Jason Kipnis 2B CLE 24 37 7 19 1.00 11 0.272 0.333 0.507 34 Derrek Lee 2B ATL 55 116 19 59 0.67 33 0.267 0.325 0.446 110 Adam Loewen 2B TOR 4 6 1 4 . 3 0.188 0.297 0.313 13 Jason Michaels 2B HOU 10 31 2 10 1.00 11 0.199 0.256 0.295 31 Dioner Navarro 2B TB 13 34 5 17 . 20 0.193 0.276 0.324 35 Pete Orr 2B WAS 7 21 0 4 1.00 6 0.219 0.279 0.554 19 Dustin Pedroia 2B BOS 102 195 21 91 0.76 86 0.307 0.387 0.371 85 Cord Phelps 2B CLE 10 11 1 6 1.00 8 0.155 0.241 0.289 17 Brandon Phillips 2B CIN 94 183 18 82 0.61 44 0.300 0.353 0.280 85 Nick Punto 2B MIN 21 37 1 20 0.50 25 0.278 0.388 0.317 21 Will Rhymes 2B DET 13 20 0 2 1.00 11 0.235 0.323 0.348 12 Brian Roberts 2B BAL 18 36 3 19 0.86 12 0.221 0.273 . 21 Gaby Sanchez 2B FLA 72 152 19 78 0.75 74 0.266 0.352 0.552 97 Ramon Santiago 2B DET 29 67 5 30 . 17 0.260 0.311 0.318 38 Skip Schumaker 2B STL 34 104 2 38 0.00 27 0.283 0.333 0.423 50 Miguel Tejada 2B SD 28 77 4 26 0.50 12 0.239 0.270 0.265 35 Joe Thurston 2B STL 0 1 0 0 1.00 0 0.250 0.250 . 1 Matt Tolbert 2B MIN 22 41 0 11 0.60 11 0.198 0.252 0.330 31 Justin Turner 2B NYM 49 113 4 51 0.78 39 0.260 0.334 0.429 59 63

Dan Uggla 2B FLA 88 140 36 82 0.25 62 0.233 0.311 0.529 156 Chase Utley 2B PHI 54 103 11 44 1.00 39 0.259 0.344 0.279 47 Luis Valbuena 2B CLE 4 9 1 1 1.00 1 0.209 0.227 0.383 9 Wilson Valdez 2B PHI 39 68 1 30 0.50 18 0.249 0.294 0.423 41 Gil Velazquez 2B BOS 0 3 0 1 . 0 0.500 0.429 0.395 0 Eugenio Velez 2B SF 5 0 0 1 1.00 2 . 0.075 0.314 11 Neil Walker 2B PIT 76 163 12 83 0.60 54 0.273 0.334 0.421 112 Jemile Weeks 2B OAK 50 123 2 36 0.67 21 0.303 0.340 0.442 62 Rickie Weeks 2B MIL 77 122 20 49 0.82 50 0.269 0.350 0.412 107 Josh Wilson 2B SEA 13 19 2 5 1.00 4 0.224 0.258 0.303 22 Chris Woodward 2B SEA 3 0 0 0 . 0 . . 0.427 4 Danny Worth 2B DET 6 10 0 3 . 2 0.270 0.308 0.459 9 Ben Zobrist 2B TB 99 158 20 91 0.76 77 0.269 0.353 . 128 Pedro Alvarez 3B PIT 18 45 4 19 1.00 24 0.191 0.272 0.289 80 Mike Aviles 3B KC 31 73 7 39 0.78 13 0.255 0.289 0.409 44 Josh Bell 3B BAL 6 10 0 6 . 4 0.164 0.215 0.164 25 Carlos Beltran 3B NYM 78 156 22 84 0.67 71 0.300 0.385 0.525 88 Wilson Betemit 3B KC 40 92 8 46 0.80 31 0.285 0.343 0.452 105 Casey Blake 3B LAD 32 51 4 26 0.33 26 0.252 0.342 0.371 50 Geoff Blum 3B HOU 8 11 2 10 . 5 0.224 0.309 0.408 9 Sean Burroughs 3B ARI 8 30 1 8 1.00 3 0.273 0.289 0.336 15 Miguel Cairo 3B CIN 33 65 8 33 0.43 18 0.265 0.330 0.412 36 Alberto Callaspo 3B LAA 54 137 6 46 0.89 58 0.288 0.366 0.375 48 Mike Carp 3B SEA 27 80 12 46 0.00 19 0.276 0.326 0.466 81 Eric Chavez 3B OAK 16 42 2 26 . 14 0.263 0.320 0.356 34 Lonnie Chisenhall 3B CLE 27 54 7 22 1.00 8 0.255 0.284 0.415 49 Alex Cora 3B TEX 12 35 0 6 1.00 12 0.224 0.287 0.276 23 James Darnell 3B SD 2 10 1 7 1.00 5 0.222 0.294 0.333 7 Ike Davis 3B NYM 20 39 7 25 . 17 0.302 0.383 0.543 31 Mark DeRosa 3B SF 9 24 0 12 0.50 8 0.279 0.351 0.302 18 Daniel Descalso 3B STL 35 86 1 28 0.50 33 0.264 0.334 0.353 65 Greg Dobbs 3B PHI 38 113 8 49 . 22 0.275 0.311 0.389 83 Matt Dominguez 3B FLA 2 11 0 2 . 2 0.244 0.292 0.333 8 Edwin Encarnacion 3B TOR 70 131 17 55 0.80 43 0.272 0.334 0.453 77 Adam Everett 3B DET 9 13 0 1 1.00 5 0.217 0.277 0.233 14 Chone Figgins 3B SEA 24 54 1 15 0.65 21 0.188 0.241 0.243 42 Logan Forsythe 3B SD 12 32 0 12 0.75 12 0.213 0.281 0.287 33 Jeff Francoeur 3B TEX 77 171 20 87 0.69 37 0.285 0.329 0.476 123 Todd Frazier 3B CIN 17 26 6 15 1.00 7 0.232 0.289 0.438 27 David Freese 3B STL 41 99 10 55 1.00 24 0.297 0.350 0.441 75 Mat Gamel 3B MIL 1 3 0 2 . 1 0.115 0.148 0.154 4 Conor Gillaspie 3B SF 2 5 1 2 . 2 0.263 0.333 0.421 1 Taylor Green 3B MIL 2 10 0 1 . 0 0.270 0.270 0.351 6 64

Scott Hairston 3B SD 20 31 7 24 0.50 11 0.235 0.303 0.470 34 Jack Hannahan 3B SEA 38 80 8 40 0.67 38 0.250 0.331 0.388 80 Willie Harris 3B WAS 36 59 2 23 0.56 36 0.246 0.351 0.317 62 Chase Headley 3B SD 43 110 4 44 0.87 52 0.289 0.374 0.399 92 Wes Helms 3B FLA 10 21 0 6 . 11 0.191 0.276 0.236 35 Ramon Hernandez 3B CIN 28 84 12 36 . 23 0.282 0.341 0.446 41 Brandon Inge 3B DET 29 53 3 23 0.50 24 0.197 0.265 0.283 74 Chris Johnson 3B HOU 32 95 7 42 0.50 16 0.251 0.291 0.378 97 Andruw Jones 3B CHW 27 47 13 33 . 29 0.247 0.356 0.495 62 Don Kelly 3B DET 35 63 7 28 0.67 14 0.245 0.291 0.381 32 Kevin Kouzmanoff 3B OAK 24 55 7 33 1.00 12 0.235 0.284 0.372 46 Bryan LaHair 3B CHC 9 17 2 6 . 9 0.288 0.377 0.508 18 Andy LaRoche 3B PIT 10 23 0 5 . 8 0.247 0.320 0.333 19 Ryan Lavarnway 3B BOS 5 9 2 8 . 4 0.231 0.302 0.436 10 Fred Lewis 3B TOR 20 42 3 19 0.29 22 0.230 0.321 0.317 38 James Loney 3B LAD 56 153 12 65 1.00 42 0.288 0.339 0.416 67 Evan Longoria 3B TB 78 118 31 99 0.60 80 0.244 0.355 0.495 93 Jose Lopez 3B SEA 23 50 8 21 1.00 7 0.216 0.245 0.372 28 Ryan Ludwick 3B SD 56 116 13 75 0.50 51 0.237 0.310 0.363 124 Osvaldo Martinez 3B FLA 0 3 0 1 . 0 0.130 0.130 0.130 9 Darnell McDonald 3B BOS 26 37 6 24 0.40 14 0.236 0.303 0.401 33 Nate McLouth 3B ATL 35 61 4 16 0.67 44 0.228 0.344 0.333 52 Juan Miranda 3B NYY 18 37 7 23 0.00 23 0.213 0.315 0.402 48 Melvin Mora 3B COL 5 29 0 16 0.00 2 0.228 0.244 0.276 24 Jose Morales 3B MIN 6 16 0 7 0.00 9 0.267 0.352 0.317 12 Brandon Moss 3B PIT 0 0 0 0 . 0 . . . 2 Yamaico Navarro 3B BOS 8 15 1 9 . 5 0.250 0.303 0.350 14 Tsuyoshi Nishioka 3B MIN 14 50 0 19 0.33 15 0.226 0.278 0.249 43 Jimmy Paredes 3B HOU 16 48 2 18 0.56 9 0.286 0.320 0.592 47 Andy Parrino 3B SD 3 8 0 4 1.00 9 0.182 0.327 0.545 17 Carlos Pena 3B TB 72 111 28 80 0.50 101 0.225 0.357 0.175 161 Placido Polanco 3B PHI 46 130 5 50 1.00 42 0.277 0.335 0.389 44 Aramis Ramirez 3B CHC 80 173 26 93 0.50 43 0.306 0.361 0.059 69 Mark Reynolds 3B ARI 84 118 37 86 0.60 75 0.221 0.323 0.500 196 Ryan Roberts 3B ARI 86 120 19 65 0.67 66 0.249 0.341 0.336 98 Josh Rodriguez 3B PIT 1 1 0 1 . 1 0.083 0.214 0.357 8 Scott Rolen 3B CIN 31 61 5 36 1.00 10 0.242 0.279 0.125 36 Pablo Sandoval 3B SF 55 134 23 70 0.33 32 0.315 0.357 0.457 63 Kyle Seager 3B SEA 22 47 3 13 0.75 13 0.258 0.312 0.339 36 Scott Sizemore 3B DET 50 90 11 56 0.63 53 0.245 0.342 0.396 112 Ian Stewart 3B COL 14 19 0 6 0.60 14 0.156 0.243 0.444 37 Drew Sutton 3B CLE 11 17 0 7 . 3 0.315 0.362 0.385 13 Mark Teahen 3B CHW 14 32 4 14 0.00 16 0.200 0.273 0.326 45 65

Ruben Tejada 3B NYM 31 93 0 36 0.83 35 0.284 0.360 0.456 50 Juan Uribe 3B SF 21 55 4 28 1.00 17 0.204 0.264 0.400 60 Chris Valaika 3B CIN 3 7 0 0 . 2 0.280 0.333 0.341 3 Danny Valencia 3B MIN 63 139 15 72 0.25 40 0.246 0.294 0.500 102 Omar Vizquel 3B CHW 18 42 0 8 0.33 9 0.251 0.287 0.408 18 Ty Wigginton 3B BAL 52 97 15 47 0.89 38 0.242 0.315 0.091 84 Brandon Wood 3B LAA 26 54 7 31 . 19 0.216 0.270 0.324 73 David Wright 3B NYM 60 99 14 61 0.87 52 0.254 0.345 0.420 97 Kevin Youkilis 3B BOS 68 111 17 80 1.00 68 0.258 0.373 0.393 100 Michael Young 3B TEX 88 213 11 106 0.75 47 0.338 0.380 0.469 78 Ryan Zimmerman 3B WAS 52 114 12 49 0.75 41 0.289 0.355 . 73 Eliezer Alfonzo C SEA 2 20 1 9 . 3 0.267 0.304 0.320 13 J.P. Arencibia C TOR 47 97 23 78 0.50 36 0.219 0.282 0.438 133 Alex Avila C DET 63 137 19 82 0.75 73 0.295 0.389 0.506 131 Jeff Baker C CHC 20 54 3 23 . 10 0.269 0.302 0.383 46 Rod Barajas C LAD 29 70 16 47 . 22 0.230 0.287 0.430 71 Josh Bard C SEA 5 17 2 11 . 5 0.210 0.256 0.333 20 Andres Blanco C TEX 9 17 2 3 0.00 4 0.224 0.263 0.342 14 J.C. Boscan C ATL 0 3 0 0 . 0 0.333 0.333 0.333 5 Domonic Brown C PHI 28 45 5 19 0.75 25 0.245 0.333 0.391 35 Travis Buck C OAK 18 34 2 18 0.50 8 0.228 0.275 0.342 30 Drew Butera C MIN 19 39 2 23 . 11 0.167 0.210 0.239 42 Robinson Cancel C HOU 0 0 0 0 . 1 . 0.143 . 4 Welington Castillo C CHC 0 2 0 0 . 0 0.154 0.154 0.154 4 Ramon Castro C CHW 6 16 4 10 . 7 0.235 0.307 0.456 23 Francisco Cervelli C NYY 17 33 4 22 0.80 9 0.266 0.324 0.395 29 Robinson Chirinos C TB 4 12 1 7 . 5 0.218 0.283 0.309 13 Steve Clevenger C CHC 1 1 0 0 . 0 0.250 0.400 0.500 0 Hank Conger C LAA 14 37 6 19 . 17 0.209 0.282 0.356 37 Carlos Corporan C MIL 9 29 0 11 . 10 0.188 0.253 0.253 49 Nelson Cruz C TEX 64 125 29 87 0.64 33 0.263 0.312 0.509 116 Ryan Doumit C PIT 17 66 8 30 0.00 16 0.303 0.353 0.477 35 Mark Ellis C OAK 54 119 7 41 0.74 22 0.248 0.288 0.346 75 Tim Federowicz C LAD 0 2 0 1 . 2 0.154 0.313 0.154 4 Jesus Flores C WAS 5 18 1 2 . 5 0.209 0.253 0.314 27 Tyler Flowers C CHW 13 23 5 16 0.00 14 0.209 0.310 0.409 38 Jake Fox C BAL 8 15 2 6 . 4 0.246 0.313 0.443 8 Eric Fryer C PIT 5 7 0 0 0.50 3 0.269 0.345 0.269 7 Chris Gimenez C CLE 6 12 1 6 0.00 10 0.203 0.314 0.271 13 Hector Gimenez C LAD 0 1 0 0 . 0 0.143 0.143 0.143 3 Ryan Hanigan C CIN 27 71 6 31 . 35 0.267 0.356 0.357 32 Brett Hayes C FLA 19 30 5 16 . 11 0.231 0.291 0.415 39 Diory Hernandez C ATL 4 7 1 4 . 0 0.212 0.212 0.333 5 66

Aaron Hill C TOR 61 128 8 61 0.75 35 0.246 0.299 0.356 72 Steve Holm C SF 1 2 0 0 . 1 0.118 0.167 0.176 4 Nick Hundley C SD 34 81 9 29 0.50 22 0.288 0.347 0.477 74 Chris Iannetta C COL 50 82 14 55 0.67 70 0.238 0.370 0.414 89 Jason Jaramillo C PIT 1 14 0 6 1.00 2 0.326 0.356 0.395 12 John Jaso C TB 26 55 5 27 0.33 25 0.224 0.298 0.354 36 Kelly Johnson C ARI 75 121 21 58 0.73 60 0.222 0.304 0.413 163 George Kottaras C MIL 15 28 5 17 0.00 10 0.252 0.311 0.459 26 Gerald Laird C DET 11 22 1 12 0.50 9 0.232 0.302 0.358 19 Adam LaRoche C ARI 15 26 3 15 1.00 25 0.172 0.288 0.258 37 Adam Lind C TOR 56 125 26 87 0.50 32 0.251 0.295 0.439 107 Jed Lowrie C BOS 40 78 6 36 0.50 23 0.252 0.303 0.382 60 Jonathan Lucroy C MIL 45 114 12 59 0.67 29 0.265 0.313 0.391 99 Chris Marrero C WAS 6 27 0 10 . 4 0.248 0.274 0.294 27 J.D. Martinez C HOU 29 57 6 35 0.00 13 0.274 0.319 0.423 48 Luis Martinez C SD 7 12 1 10 1.00 8 0.203 0.309 0.305 14 Victor Martinez C BOS 76 178 12 103 1.00 46 0.330 0.380 0.470 51 Joe Mather C STL 4 16 1 9 0.00 6 0.213 0.272 0.307 23 Hideki Matsui C LAA 58 130 12 72 0.50 56 0.251 0.321 0.375 84 Cameron Maybin C FLA 82 136 9 40 0.83 44 0.264 0.323 0.393 125 Casey McGehee C MIL 46 122 13 67 0.00 45 0.223 0.280 0.346 104 Dallas McPherson C CHW 1 2 0 0 . 0 0.133 0.133 0.133 7 Russ Mitchell C LAD 5 8 2 3 . 7 0.157 0.259 0.294 10 Gustavo Molina C BOS 0 1 0 0 . 0 0.167 0.167 0.333 0 Jose Molina C TOR 19 48 3 15 0.67 15 0.281 0.342 0.415 44 Lou Montanez C BAL 6 12 1 9 0.00 2 0.222 0.263 0.352 9 Jesus Montero C NYY 9 20 4 12 . 7 0.328 0.406 0.590 17 Miguel Montero C ARI 65 139 18 86 0.50 47 0.282 0.351 0.469 97 Jeremy Moore C LAA 3 1 0 0 . 0 0.125 0.125 0.125 2 Xavier Nady C CHC 26 51 4 35 1.00 10 0.248 0.287 0.359 46 Mike Napoli C LAA 72 118 30 75 0.67 58 0.320 0.414 0.631 85 Chris Nelson C COL 20 45 4 16 0.75 7 0.250 0.280 0.383 35 Mike Nickeas C NYM 4 10 1 6 0.00 4 0.189 0.246 0.264 11 Jayson Nix C CLE 15 23 4 16 0.80 12 0.169 0.245 0.309 42 Miguel Olivo C COL 54 107 19 62 0.55 20 0.224 0.253 0.331 140 Matt Pagnozzi C STL 2 8 0 3 . 1 0.276 0.323 0.393 10 Xavier Paul C LAD 30 62 2 20 0.73 13 0.255 0.292 0.255 62 Wily Mo Pena C SEA 15 23 7 15 . 5 0.204 0.250 0.502 39 Salvador Perez C KC 20 49 3 21 . 7 0.331 0.361 0.387 20 Kyle Phillips C TOR 9 13 2 10 . 8 0.171 0.259 0.327 19 A.J. Pierzynski C CHW 38 133 8 48 . 23 0.287 0.323 0.357 33 Manuel Pina C KC 2 3 0 0 . 1 0.214 0.267 0.339 2 Buster Posey C SF 17 46 4 21 1.00 18 0.284 0.368 0.385 30 67

Landon Powell C OAK 10 19 1 4 . 11 0.171 0.246 0.465 32 Omar Quintanilla C COL 3 1 0 2 . 0 0.045 0.045 0.399 9 Wilson Ramos C WAS 48 104 15 52 0.00 38 0.267 0.334 0.391 76 Anthony Recker C OAK 3 3 0 0 . 4 0.176 0.333 . 7 Juan Rivera C LAA 46 120 11 74 0.63 43 0.258 0.319 0.202 76 Mike Rivera C FLA 0 2 0 0 . 0 0.333 0.333 0.242 1 Luis Rodriguez C SD 10 23 2 14 0.33 16 0.197 0.299 . 21 Austin Romine C LAA 2 3 0 0 . 1 0.158 0.200 0.463 5 Wilin Rosario C COL 6 11 3 8 . 2 0.204 0.228 0.428 20 Cody Ross C SF 54 97 14 52 0.71 49 0.240 0.325 0.167 96 Carlos Ruiz C PHI 49 116 6 40 1.00 48 0.283 0.371 0.450 48 Jarrod Saltalamacchia C BOS 52 84 16 56 1.00 24 0.235 0.288 0.397 119 Hector Sanchez C SF 0 8 0 1 . 3 0.258 0.324 0.389 6 Carlos Santana C CLE 84 132 27 79 0.63 97 0.239 0.351 0.227 133 Omir Santos C NYM 1 5 0 0 . 0 0.227 0.227 0.250 4 Dane Sardinha C PHI 8 7 0 1 . 10 0.219 0.419 0.217 13 Brian Schneider C PHI 11 22 2 9 . 11 0.176 0.246 0.402 35 Kelly Shoppach C TB 23 39 11 22 . 19 0.176 0.268 0.422 79 Chris Snyder C PIT 13 26 3 17 0.00 17 0.271 0.376 0.469 23 Geovany Soto C CHC 46 96 17 54 . 45 0.228 0.310 . 124 Chris Stewart C SD 20 33 3 10 . 16 0.204 0.283 0.364 18 Kurt Suzuki C OAK 54 109 14 44 0.50 38 0.237 0.301 0.449 64 Craig Tatum C BAL 7 17 0 7 1.00 6 0.195 0.245 0.294 21 Taylor Teagarden C TEX 3 8 0 2 . 2 0.235 0.278 0.494 13 Josh Thole C NYM 22 91 3 40 0.00 38 0.268 0.345 0.250 47 Wyatt Toregas C CLE 0 0 0 0 . 0 . . 0.399 1 Andres Torres C SF 50 77 4 19 0.76 42 0.221 0.312 0.343 95 J.R. Towles C HOU 11 27 3 11 . 13 0.184 0.256 0.390 26 Matt Treanor C TEX 24 42 3 22 0.50 34 0.214 0.338 0.477 53 Jason Varitek C BOS 32 49 11 36 . 21 0.221 0.300 . 67 Eli Whiteside C SF 14 42 4 17 0.67 18 0.197 0.264 0.416 59 Matt Wieters C BAL 72 131 22 68 1.00 48 0.262 0.328 0.477 84 Jack Wilson C SEA 25 52 0 11 0.71 10 0.243 0.274 0.185 39 Bobby Abreu OF LAA 54 127 8 60 0.81 78 0.253 0.353 0.365 113 Erick Almonte OF MIL 1 3 1 3 . 0 0.103 0.103 0.207 4 Yonder Alonso OF CIN 9 29 5 15 . 10 0.330 0.398 0.545 21 Matt Angle OF BAL 12 14 1 7 0.92 12 0.177 0.293 0.266 13 Rick Ankiel OF ATL 46 91 9 37 0.77 29 0.239 0.296 0.363 96 Jose Bautista OF TOR 105 155 43 103 0.64 132 0.302 0.447 0.608 111 Mike Baxter OF NYM 6 8 1 4 . 5 0.235 0.350 0.441 9 Jason Bay OF NYM 59 109 12 57 0.92 56 0.245 0.329 0.374 109 Brandon Belt OF SF 21 42 9 18 0.60 20 0.225 0.306 0.412 57 Adrian Beltre OF BOS 82 144 32 105 0.50 25 0.296 0.331 0.561 53 68

Joe Benson OF MIN 3 17 0 2 0.50 3 0.239 0.270 0.352 21 Lance Berkman OF NYY 90 147 31 94 0.25 92 0.301 0.412 0.547 93 Roger Bernadina OF WAS 40 75 7 27 0.85 22 0.243 0.301 0.362 63 Brian Bixler OF PIT 9 17 0 2 0.57 7 0.205 0.267 0.265 19 Charlie Blackmon OF COL 9 25 1 8 0.83 3 0.255 0.277 0.296 8 Kyle Blanks OF SD 21 39 7 26 1.00 16 0.229 0.300 0.406 51 Brennan Boesch OF DET 75 121 16 54 0.63 35 0.283 0.341 0.458 83 Brandon Boggs OF TEX 4 3 2 2 1.00 3 0.158 0.273 0.474 8 Brian Bogusevic OF HOU 22 47 4 15 0.67 15 0.287 0.348 0.457 40 Julio Borbon OF TEX 10 24 0 11 0.75 3 0.270 0.305 0.348 9 Jason Bourgeois OF HOU 30 70 1 16 0.84 10 0.294 0.323 0.357 24 Peter Bourjos OF LAA 72 136 12 43 0.71 32 0.271 0.327 0.438 124 Michael Bourn OF HOU 94 193 2 50 0.81 53 0.294 0.349 0.386 140 John Bowker OF PIT 0 4 0 2 0.00 2 0.133 0.188 0.167 11 Milton Bradley OF SEA 12 22 2 13 1.00 13 0.218 0.313 0.356 31 Michael Brantley OF CLE 63 120 7 46 0.72 34 0.266 0.318 0.384 76 Ryan Braun OF MIL 109 187 33 111 0.85 58 0.332 0.397 0.597 93 Andrew Brown OF STL 1 4 0 3 . 0 0.182 0.182 0.227 8 Corey Brown OF WAS 0 0 0 0 . 0 . . . 2 Jay Bruce OF CIN 84 150 32 97 0.53 71 0.256 0.341 0.474 158 John Buck OF TOR 41 106 16 57 0.00 54 0.227 0.316 0.367 115 Pat Burrell OF SF 17 42 7 21 . 33 0.230 0.352 0.404 67 Marlon Byrd OF CHC 51 123 9 35 0.60 25 0.276 0.325 0.396 78 Asdrubal Cabrera OF CLE 87 165 25 92 0.77 44 0.273 0.332 0.460 119 Lorenzo Cain OF MIL 4 6 0 1 . 1 0.273 0.304 0.318 4 Mike Cameron OF BOS 27 48 9 27 1.00 28 0.203 0.285 0.359 59 Tony Campana OF CHC 24 37 1 6 0.92 8 0.259 0.303 0.301 30 Ezequiel Carrera OF CLE 27 49 0 14 0.67 16 0.243 0.301 0.312 35 Brett Carroll OF FLA 0 0 0 0 . 0 . . . 1 Adron Chambers OF STL 2 3 0 4 . 0 0.375 0.375 0.625 1 Endy Chavez OF SEA 37 77 5 27 0.67 10 0.301 0.323 0.426 30 Shin-Soo Choo OF CLE 37 81 8 36 0.71 36 0.259 0.344 0.390 78 Justin Christian OF SF 6 12 0 4 0.60 2 0.255 0.286 0.362 8 Chris Coghlan OF FLA 33 62 5 22 0.54 22 0.230 0.296 0.368 49 Tyler Colvin OF CHC 17 31 6 20 . 14 0.150 0.204 0.306 58 Jose Constanza OF ATL 21 33 2 10 0.64 6 0.303 0.339 0.385 14 Scott Cousins OF FLA 5 7 1 4 0.50 6 0.135 0.224 0.212 21 Collin Cowgill OF ARI 8 22 1 9 0.67 8 0.239 0.300 0.304 28 Allen Craig OF STL 33 63 11 40 1.00 15 0.315 0.362 0.555 40 Carl Crawford OF TB 65 129 11 56 0.75 23 0.255 0.289 0.405 104 Coco Crisp OF OAK 69 140 8 54 0.84 41 0.264 0.314 0.379 65 Trevor Crowe OF CLE 6 6 0 2 1.00 4 0.214 0.313 0.250 9 Tony Cruz OF STL 8 17 0 6 0.00 6 0.262 0.333 0.338 13 69

Michael Cuddyer OF MIN 70 150 20 70 0.92 48 0.284 0.346 0.459 95 Aaron Cunningham OF SD 12 16 3 9 1.00 9 0.178 0.257 0.367 17 Jack Cust OF OAK 19 48 3 23 . 44 0.213 0.344 0.329 87 Johnny Damon OF DET 79 152 16 73 0.76 51 0.261 0.326 0.418 92 Chris Davis OF TEX 25 53 5 19 1.00 11 0.266 0.305 0.402 63 Alejandro De Aza OF CHW 29 50 4 23 0.71 17 0.329 0.400 0.520 34 Ivan De Jesus OF LAD 2 6 0 1 . 2 0.188 0.235 0.188 11 Chris Denorfia OF SD 38 85 5 19 0.65 28 0.277 0.337 0.381 49 Blake DeWitt OF CHC 21 61 5 26 1.00 12 0.265 0.305 0.413 31 Matt Diaz OF ATL 16 66 0 20 0.71 12 0.263 0.302 0.323 52 Chris Dickerson OF MIL 9 13 1 7 1.00 2 0.260 0.296 0.360 17 Andy Dirks OF DET 34 55 7 28 0.71 11 0.251 0.296 0.406 36 J.D. Drew OF BOS 23 55 4 22 0.00 33 0.222 0.315 0.302 58 Lucas Duda OF NYM 38 88 10 50 1.00 33 0.292 0.370 0.482 57 Shelley Duncan OF CLE 29 58 11 47 0.00 19 0.260 0.324 0.484 56 Luis Durango OF SD 0 1 0 1 . 1 0.167 0.286 0.167 1 Jarrod Dyson OF KC 8 9 0 3 0.92 7 0.205 0.308 0.227 14 Jacoby Ellsbury OF BOS 119 212 32 105 0.72 52 0.321 0.376 0.552 98 Andre Ethier OF LAD 67 142 11 62 0.00 58 0.292 0.368 0.421 103 Eric Farris OF MIL 0 0 0 0 . 0 . . . 0 Darren Ford OF SF 7 4 0 0 0.58 1 0.286 0.375 0.286 5 Dexter Fowler OF COL 84 128 5 45 0.57 68 0.266 0.363 0.432 130 Ben Francisco OF PHI 24 61 6 34 0.50 33 0.244 0.340 0.364 42 Juan Francisco OF CIN 10 24 3 15 1.00 4 0.258 0.289 0.452 24 Kosuke Fukudome OF CHC 59 139 8 35 0.40 61 0.262 0.342 0.370 110 Sam Fuld OF CHC 41 74 3 27 0.71 32 0.240 0.313 0.360 49 Brett Gardner OF NYY 87 132 7 36 0.79 60 0.259 0.345 0.369 93 Cole Garner OF COL 1 2 0 3 . 1 0.222 0.300 0.222 6 Joey Gathright OF BOS 1 0 0 0 0.50 1 . 1.000 . 0 Craig Gentry OF TEX 26 36 1 13 1.00 10 0.271 0.347 0.346 27 Jay Gibbons OF LAD 5 14 1 5 . 5 0.255 0.323 0.345 14 Cole Gillespie OF ARI 2 2 1 4 . 1 0.333 0.429 0.833 1 Greg Golson OF NYY 1 2 0 0 1.00 1 0.182 0.250 0.182 2 Jonny Gomes OF CIN 41 65 14 43 0.70 48 0.209 0.325 0.389 105 Hector Gomez OF COL 1 2 0 0 . 1 0.333 0.429 0.333 2 Alex Gonzalez OF ATL 59 136 15 56 1.00 22 0.241 0.270 0.372 126 Alex Gordon OF KC 101 185 23 87 0.68 67 0.303 0.376 0.502 139 Curtis Granderson OF NYY 136 153 41 119 0.71 85 0.262 0.364 0.552 169 Vladimir Guerrero OF TEX 60 163 13 63 0.50 17 0.290 0.317 0.416 56 Franklin Gutierrez OF SEA 26 72 1 19 0.87 16 0.224 0.261 0.273 56 Brandon Guyer OF TB 7 8 2 3 . 1 0.195 0.214 0.366 9 Tony Gwynn OF SD 37 80 2 22 0.79 23 0.256 0.308 0.353 61 Travis Hafner OF CLE 41 91 13 57 . 36 0.280 0.361 0.449 78 70

Jerry Hairston OF SD 43 91 5 31 0.60 33 0.270 0.344 0.383 46 Greg Halman OF SEA 7 20 2 6 0.83 2 0.230 0.256 0.345 32 Mark Hamilton OF STL 5 10 0 4 . 4 0.213 0.275 0.277 16 Robby Hammock OF ARI 0 0 0 0 . 0 . . . 0 Josh Harrison OF PIT 21 53 1 16 0.80 3 0.272 0.281 0.374 24 Corey Hart OF MIL 80 140 26 63 0.54 51 0.285 0.356 0.510 114 Jerad Head OF CLE 2 3 0 1 1.00 0 0.125 0.160 0.167 5 Chris Heisey OF CIN 44 71 18 50 0.86 19 0.254 0.309 0.487 78 Jeremy Hermida OF OAK 5 11 2 9 . 7 0.190 0.288 0.362 26 Jason Heyward OF ATL 50 90 14 42 0.82 51 0.227 0.319 0.389 93 Eric Hinske OF ATL 24 55 10 28 0.00 26 0.233 0.311 0.403 71 Jamie Hoffmann OF LAD 0 0 0 0 . 0 . . . 1 Matt Holliday OF STL 83 132 22 75 0.67 60 0.296 0.388 0.525 93 Orlando Hudson OF MIN 54 98 7 43 0.86 49 0.246 0.329 0.352 84 Cedric Hunter OF SD 1 1 0 0 0.00 1 0.250 0.400 0.250 0 Torii Hunter OF LAA 80 152 23 82 0.42 62 0.262 0.336 0.429 125 Raul Ibanez OF PHI 65 131 20 84 1.00 33 0.245 0.289 0.419 106 Austin Jackson OF DET 90 147 10 45 0.81 56 0.249 0.317 0.374 181 Conor Jackson OF OAK 32 86 5 43 0.75 32 0.244 0.310 0.341 53 Jon Jay OF STL 56 135 10 37 0.46 28 0.297 0.344 0.424 81 Desmond Jennings OF TB 44 64 10 25 0.77 31 0.259 0.356 0.449 59 Dan Johnson OF TB 7 10 2 4 . 6 0.119 0.187 0.202 18 Adam Jones OF BAL 68 159 25 83 0.75 29 0.280 0.319 0.466 113 Chipper Jones OF ATL 56 125 18 70 0.50 51 0.275 0.344 0.470 80 Garrett Jones OF PIT 51 103 16 58 0.67 48 0.243 0.321 0.433 104 Matt Joyce OF TB 69 128 19 75 0.93 49 0.277 0.347 0.478 106 Austin Kearns OF NYY 18 30 2 7 0.00 18 0.200 0.302 0.287 48 Matt Kemp OF LAD 115 195 39 126 0.78 74 0.324 0.399 0.586 159 Mark Kotsay OF CHW 18 63 3 31 1.00 21 0.270 0.329 0.373 27 Erik Kratz OF PIT 0 2 0 0 . 0 0.333 0.333 0.385 1 Jason Kubel OF MIN 37 100 12 58 0.50 32 0.273 0.332 0.434 86 Brandon Laird OF NYY 3 4 0 1 . 3 0.190 0.292 0.190 4 Brett Lawrie OF TOR 26 44 9 25 0.88 16 0.293 0.373 0.580 31 DJ LeMahieu OF CHC 3 15 0 4 . 1 0.250 0.262 0.283 12 Alex Liddi OF SEA 7 9 3 6 1.00 3 0.225 0.295 0.525 17 Jose Lobaton OF SD 2 4 0 0 . 4 0.118 0.231 0.147 8 Donny Lucy OF CHW 1 2 0 1 . 1 0.200 0.273 0.300 5 Julio Lugo OF BAL 3 6 0 3 . 4 0.136 0.208 0.136 11 Mitch Maier OF KC 19 22 0 7 1.00 16 0.232 0.345 0.337 32 Martin Maldonado OF MIL 0 0 0 0 . 0 . . . 1 Lou Marson OF CLE 26 56 1 19 0.67 24 0.230 0.300 0.296 68 Leonys Martin OF TEX 2 3 0 0 . 0 0.375 0.375 0.500 1 Russell Martin OF LAD 57 99 18 65 0.80 50 0.237 0.324 0.408 81 71

Michael Martinez OF PHI 25 41 3 24 1.00 18 0.196 0.258 0.282 35 Darin Mastroianni OF TOR 0 0 0 0 . 0 . . . 1 Jeff Mathis OF LAA 18 43 3 22 0.33 15 0.174 0.225 0.259 75 Joe Mauer OF MIN 38 85 3 30 . 32 0.287 0.360 0.368 38 John Mayberry OF PHI 37 73 15 49 0.73 26 0.273 0.341 0.513 55 Mike McCoy OF TOR 26 39 2 10 0.86 25 0.198 0.291 0.269 41 Andrew McCutchen OF PIT 87 147 23 89 0.70 89 0.257 0.362 0.455 126 Michael McKenry OF COL 17 40 2 11 0.00 14 0.222 0.276 0.322 49 Devin Mesoraco OF CIN 5 9 2 6 . 3 0.180 0.226 0.360 10 Aaron Miles OF STL 49 125 3 45 0.57 25 0.275 0.314 0.346 49 Lastings Milledge OF PIT 1 1 0 0 . 0 0.250 0.250 0.500 1 Yadier Molina OF STL 55 145 14 65 0.44 33 0.305 0.349 0.465 44 Adam Moore OF SEA 0 1 0 0 . 0 0.167 0.167 0.333 2 Mitch Moreland OF TEX 60 120 16 51 0.50 39 0.259 0.320 0.414 92 Justin Morneau OF MIN 19 60 4 30 . 19 0.227 0.285 0.333 44 Michael Morse OF WAS 73 158 31 95 0.40 36 0.303 0.360 0.550 126 Mike Moustakas OF KC 26 89 5 30 1.00 22 0.263 0.309 0.367 51 Laynce Nix OF CIN 38 81 16 44 0.50 23 0.250 0.299 0.451 82 Trent Oeltjen OF LAD 10 14 2 6 1.00 13 0.197 0.322 0.388 30 Magglio Ordonez OF DET 33 84 5 32 0.67 23 0.255 0.303 0.250 41 Angel Pagan OF NYM 68 125 7 56 0.80 44 0.262 0.322 0.276 62 Gerardo Parra OF ARI 55 130 8 46 0.94 43 0.292 0.357 0.205 82 Corey Patterson OF BAL 49 88 6 36 0.59 17 0.239 0.273 0.292 77 Eric Patterson OF BOS 8 16 2 8 0.80 12 0.180 0.272 0.351 22 Ronny Paulino OF FLA 19 61 2 19 . 15 0.268 0.312 0.346 38 Carlos Peguero OF SEA 14 28 6 19 0.00 8 0.196 0.252 0.338 54 Ramiro Pena OF NYY 5 4 1 4 . 2 0.100 0.159 0.416 11 Hunter Pence OF HOU 84 190 22 97 0.80 56 0.314 0.370 0.369 124 Bryan Petersen OF FLA 18 54 2 10 0.88 26 0.265 0.357 0.254 49 Chris Pettit OF LAA 0 0 0 0 . 0 . . 0.457 0 Felix Pie OF BAL 15 36 0 7 0.60 10 0.220 0.264 0.405 32 Juan Pierre OF CHW 80 178 2 50 0.61 43 0.279 0.329 0.560 41 Martin Prado OF ATL 66 143 13 57 0.33 34 0.260 0.302 0.370 52 Alex Presley OF PIT 27 64 4 20 0.75 13 0.298 0.339 0.541 40 Jason Pridie OF NYM 28 48 4 20 0.88 24 0.231 0.309 0.421 64 Carlos Quentin OF CHW 53 107 24 77 0.50 34 0.254 0.340 0.136 84 Ryan Raburn OF DET 53 99 14 49 0.50 21 0.256 0.297 0.510 114 Manny Ramirez OF CHW 0 1 0 1 . 0 0.059 0.059 0.445 4 Wilkin Ramirez OF DET 5 6 0 2 0.00 4 0.231 0.333 0.303 11 Colby Rasmus OF STL 75 106 14 53 0.71 50 0.225 0.298 0.457 116 Josh Reddick OF BOS 41 71 7 28 0.33 19 0.280 0.327 0.453 50 Jeremy Reed OF TOR 0 0 0 0 . 0 . . 0.348 2 Nolan Reimold OF BAL 40 66 13 45 0.78 28 0.247 0.328 0.286 57 72

Jason Repko OF MIN 21 30 2 11 0.78 6 0.226 0.270 0.493 38 Ben Revere OF MIN 56 120 0 30 0.79 26 0.267 0.310 0.483 41 Antoan Richardson OF ATL 2 2 0 0 1.00 0 0.500 0.500 0.382 0 Alex Rios OF CHW 64 122 13 44 0.65 27 0.227 0.265 0.333 68 Rene Rivera OF MIN 9 15 1 5 . 8 0.144 0.211 0.331 32 Shane Robinson OF STL 0 0 0 0 . 1 . 0.125 0.461 2 Trayvon Robinson OF SEA 12 30 2 14 1.00 8 0.210 0.250 0.323 61 Ryan Rohlinger OF SF 0 0 0 0 . 0 . . 0.399 1 David Ross OF ATL 14 40 6 23 0.00 16 0.263 0.333 0.347 51 Vinny Rottino OF MIL 1 2 0 0 . 2 0.167 0.286 0.400 4 Aaron Rowand OF SF 34 77 4 21 0.40 10 0.233 0.274 0.383 84 Justin Ruggiano OF TB 11 26 4 13 0.50 4 0.248 0.273 0.326 26 Jerry Sands OF LAD 20 50 4 26 0.50 25 0.253 0.338 0.384 51 Dave Sappelt OF CIN 14 26 0 5 0.50 7 0.243 0.289 0.240 17 Michael Saunders OF SEA 16 24 2 8 0.75 12 0.149 0.207 0.333 56 Jordan Schafer OF ATL 46 73 2 13 0.85 28 0.242 0.309 0.430 70 Logan Schafer OF MIL 1 1 0 0 . 1 0.333 0.500 0.256 1 Nate Schierholtz OF SF 42 93 9 41 0.64 21 0.278 0.326 0.351 61 Luke Scott OF BAL 24 46 9 22 0.50 24 0.220 0.301 0.379 54 J.B. Shuck OF HOU 9 22 0 3 1.00 11 0.272 0.359 0.399 7 Grady Sizemore OF CLE 34 60 10 32 0.00 18 0.224 0.285 0.483 85 Seth Smith OF COL 67 135 15 59 0.83 46 0.284 0.347 0.348 93 Travis Snider OF TOR 23 42 3 30 0.75 11 0.225 0.269 0.308 56 Brandon Snyder OF BAL 2 3 0 1 . 3 0.231 0.412 0.329 4 Alfonso Soriano OF CHC 50 116 26 88 0.67 27 0.244 0.289 0.359 113 Denard Span OF MIN 37 75 2 16 0.86 27 0.264 0.328 0.305 36 Nate Spears OF BOS 0 0 0 0 . 0 . . 0.169 1 Ryan Spilborghs OF COL 22 42 3 22 0.50 19 0.210 0.283 0.537 49 Michael Stanton OF FLA 79 135 34 87 0.50 70 0.262 0.356 0.221 166 Drew Stubbs OF CIN 92 147 15 44 0.80 63 0.243 0.321 0.335 205 Ichiro Suzuki OF SEA 80 184 5 47 0.85 39 0.272 0.310 0.341 69 Ryan Sweeney OF OAK 34 70 1 25 0.50 33 0.265 0.346 0.362 48 Nick Swisher OF NYY 81 137 23 85 0.50 95 0.260 0.374 0.230 125 Jose Tabata OF PIT 53 89 4 21 0.70 40 0.266 0.349 0.300 61 Michael Taylor OF OAK 4 6 1 1 . 5 0.200 0.314 0.300 11 Blake Tekotte OF SD 1 6 0 1 0.67 4 0.176 0.263 0.333 21 Eric Thames OF TOR 58 95 12 37 0.67 23 0.262 0.313 0.342 88 Marcus Thames OF NYY 4 13 2 7 . 4 0.197 0.243 0.344 16 Jim Thome OF MIN 32 71 15 50 . 46 0.256 0.361 0.266 92 Yorvit Torrealba OF SD 40 108 7 37 0.00 20 0.273 0.306 0.293 65 Rene Tosoni OF MIN 20 35 5 22 0.00 14 0.203 0.275 0.291 42 Mike Trout OF LAA 20 27 5 16 1.00 9 0.220 0.281 0.544 30 B.J. Upton OF TB 82 136 23 81 0.75 71 0.243 0.331 0.293 161 73

Justin Upton OF ARI 105 171 31 88 0.70 59 0.289 0.369 0.425 126 Will Venable OF SD 49 91 9 44 0.90 31 0.246 0.310 0.491 92 Dayan Viciedo OF CHW 11 26 1 6 1.00 9 0.255 0.327 0.305 23 Shane Victorino OF PHI 95 145 17 61 0.86 55 0.279 0.355 0.531 63 Casper Wells OF DET 30 51 11 27 0.60 18 0.237 0.317 0.389 71 Vernon Wells OF TOR 60 110 25 66 0.69 20 0.218 0.248 0.310 86 Jayson Werth OF PHI 69 130 20 58 0.86 74 0.232 0.330 0.450 160 Josh Willingham OF WAS 69 120 29 98 0.80 56 0.246 0.332 0.288 150 Reggie Willits OF LAA 0 1 0 1 . 4 0.045 0.192 0.285 7 Bobby Wilson OF LAA 5 21 1 8 0.00 10 0.189 0.252 0.353 16 Dewayne Wise OF TOR 10 20 2 7 0.75 3 0.202 0.231 . 36 Chris Young OF ARI 89 134 20 71 0.71 80 0.236 0.331 0.298 139 Delmon Young OF MIN 54 127 12 64 1.00 23 0.268 0.302 0.229 85 Eric Young OF COL 34 49 0 10 0.87 26 0.247 0.342 0.474 38 Matt Young OF ATL 4 10 0 1 0.00 4 0.208 0.269 0.443 6 Dusty Brown PH BOS 2 3 0 0 1.00 1 0.107 0.138 0.107 10 Russ Canzler PH TB 0 1 0 1 . 1 0.333 0.400 0.333 1 Elvis Andrus SS TEX 96 164 5 60 0.76 56 0.279 0.347 0.361 74 Erick Aybar SS LAA 71 155 10 59 0.83 31 0.279 0.322 0.421 68 Clint Barmes SS COL 47 109 12 39 0.75 38 0.244 0.312 0.386 88 Jason Bartlett SS TB 61 136 2 40 0.70 48 0.245 0.308 0.307 98 Yuniesky Betancourt SS KC 51 140 13 68 0.50 16 0.252 0.271 0.381 63 Henry Blanco SS NYM 12 25 8 12 0.00 12 0.250 0.330 0.540 21 Willie Bloomquist SS CIN 44 93 4 26 0.67 23 0.266 0.317 0.340 51 Emilio Bonifacio SS FLA 78 167 5 36 0.78 59 0.296 0.360 0.393 129 Reid Brignac SS TB 18 48 1 15 0.75 10 0.193 0.227 0.221 63 Everth Cabrera SS SD 1 1 0 0 1.00 1 0.125 0.222 0.125 3 Orlando Cabrera SS CIN 39 107 5 51 0.67 17 0.238 0.267 0.307 57 Juan Castro SS LAD 2 4 0 1 . 1 0.286 0.333 0.286 4 Ronny Cedeno SS PIT 43 103 2 32 0.29 30 0.249 0.297 0.339 93 Pedro Ciriaco SS PIT 4 10 0 6 0.67 1 0.303 0.324 0.424 6 Craig Counsell SS MIL 19 28 1 9 0.67 20 0.178 0.280 0.223 21 Zack Cozart SS CIN 6 12 2 3 . 0 0.324 0.324 0.486 6 Brandon Crawford SS SF 22 40 3 21 0.25 23 0.204 0.288 0.296 31 Chase d'Arnaud SS PIT 17 31 0 6 0.86 4 0.217 0.242 0.287 36 Ian Desmond SS WAS 65 148 8 49 0.71 35 0.253 0.298 0.358 139 Stephen Drew SS ARI 44 81 5 45 0.50 30 0.252 0.317 0.396 74 Alcides Escobar SS MIL 69 139 4 46 0.74 25 0.254 0.290 0.343 73 Eduardo Escobar SS CHW 0 2 0 0 . 0 0.286 0.286 0.286 1 Yunel Escobar SS TOR 77 149 11 48 0.50 61 0.290 0.369 0.413 70 Prince Fielder SS MIL 95 170 38 120 0.50 107 0.299 0.415 0.566 106 Pedro Florimon Jr. SS BAL 1 1 0 2 . 1 0.125 0.222 0.250 6 Mike Fontenot SS SF 22 50 4 21 0.83 25 0.227 0.304 0.377 48 74

Rafael Furcal SS LAD 44 77 8 28 0.64 28 0.231 0.298 0.348 39 Carlos Gomez SS MIL 37 52 8 24 0.89 15 0.225 0.276 0.403 64 Alberto Gonzalez SS WAS 18 53 1 32 0.33 13 0.215 0.256 0.283 37 Dee Gordon SS LAD 34 68 0 11 0.77 7 0.304 0.325 0.362 27 J.J. Hardy SS MIN 76 142 30 80 . 31 0.269 0.310 0.491 92 Brandon Hicks SS ATL 1 1 0 1 . 1 0.048 0.091 0.048 9 Jose Iglesias SS BOS 3 2 0 0 . 0 0.333 0.333 0.333 2 Cesar Izturis SS BAL 4 6 0 1 . 2 0.200 0.250 0.200 10 Paul Janish SS CIN 27 72 0 23 0.60 18 0.214 0.259 0.262 46 Derek Jeter SS NYY 84 162 6 61 0.73 46 0.297 0.355 0.388 81 Reed Johnson SS LAD 33 76 5 28 0.67 5 0.309 0.348 0.467 63 Peter Kozma SS STL 2 3 0 1 . 4 0.176 0.333 0.235 4 Felipe Lopez SS BOS 12 29 2 11 0.50 8 0.206 0.247 0.277 35 Fernando Martinez SS NYM 3 5 1 2 . 1 0.227 0.261 0.455 7 Brian McCann SS ATL 51 126 24 71 0.60 57 0.270 0.351 0.466 89 John McDonald SS TOR 21 52 2 22 0.33 12 0.229 0.269 0.308 27 Daniel Murphy SS NYM 49 125 6 49 0.50 24 0.320 0.362 0.448 42 Wil Nieves SS WAS 2 7 0 0 . 3 0.140 0.189 0.180 12 Eduardo Nunez SS NYY 38 82 5 30 0.79 22 0.265 0.313 0.324 37 Cliff Pennington SS OAK 57 136 8 58 0.61 42 0.264 0.319 0.478 104 Jhonny Peralta SS DET 68 157 21 86 0.00 40 0.299 0.345 0.473 95 Trevor Plouffe SS MIN 47 68 8 31 0.50 25 0.238 0.305 0.398 71 Humberto Quintero SS HOU 22 63 2 25 1.00 6 0.240 0.258 0.432 53 Alexei Ramirez SS CHW 81 165 15 70 0.58 51 0.269 0.328 0.376 84 Hanley Ramirez SS FLA 55 81 10 44 0.67 44 0.240 0.331 0.308 66 Cody Ransom SS PHI 3 5 1 4 1.00 3 0.152 0.243 0.235 9 Edgar Renteria SS SF 34 75 5 36 0.67 24 0.251 0.306 0.309 65 Jose Reyes SS NYM 101 181 7 44 0.85 43 0.337 0.384 0.271 41 Alex Rodriguez SS NYY 67 103 16 62 0.80 47 0.276 0.362 0.083 80 Ivan Rodriguez SS WAS 14 27 2 19 . 10 0.218 0.281 0.333 28 Sean Rodriguez SS TB 45 83 8 36 0.61 38 0.223 0.323 0.397 87 Jimmy Rollins SS PHI 87 152 16 63 0.79 58 0.268 0.338 0.158 59 Andrew Romine SS LAA 2 2 0 0 1.00 1 0.125 0.176 0.197 6 Adam Rosales SS OAK 5 6 2 8 . 4 0.098 0.162 0.405 13 Brendan Ryan SS STL 51 108 3 39 0.81 34 0.248 0.313 0.285 87 Angel Sanchez SS HOU 35 69 1 28 1.00 27 0.240 0.305 0.427 44 Marco Scutaro SS BOS 59 118 7 54 0.67 38 0.299 0.358 0.301 36 Justin Sellers SS LAD 20 25 1 13 1.00 12 0.203 0.283 0.321 21 Eric Sogard SS OAK 7 14 2 4 . 4 0.200 0.243 0.411 13 Ryan Theriot SS LAD 46 120 1 47 0.40 29 0.271 0.321 0.477 41 Troy Tulowitzki SS COL 81 162 30 105 0.75 59 0.302 0.372 0.453 79 Mike Wilson SS SEA 0 4 0 3 . 1 0.148 0.179 0.340 7

75 Table 11. Pitcher Statistics Data

nameFirst nameLast POS team ip SOA K/9 ERA WHIP

Fernando Abad P HOU 19.7 15 6.86 7.32 1.88 Juan Abreu P HOU 6.7 12 16.20 2.70 1.35 Jeremy Accardo P BAL 37.7 23 5.50 5.73 1.62 Alfredo Aceves P NYY 114.0 80 6.32 2.61 1.11 Manny Acosta P NYM 47.0 46 8.81 3.45 1.38 Mike Adams P SD 73.7 74 9.04 1.47 0.79 Nathan Adcock P KC 60.3 36 5.37 4.18 1.48 Jeremy Affeldt P SF 61.7 54 7.88 2.63 1.15 Matt Albers P BOS 64.7 68 9.46 4.73 1.45 Al Alburquerque P DET 43.3 67 13.92 1.66 1.15 Henderson Alvarez P TOR 63.7 40 5.65 3.39 1.13 Brett Anderson P OAK 83.3 61 6.59 4.00 1.33 Jose Arredondo P CIN 53.0 48 8.15 3.23 1.40 Jake Arrieta P BAL 119.3 93 7.01 4.98 1.46 Bronson Arroyo P CIN 199.0 108 4.88 5.07 1.37 Jose Ascanio P PIT 6.3 5 7.11 5.68 1.89 Jairo Asencio P ATL 10.3 8 6.97 6.10 2.03 Scott Atchison P 30.3 17 5.04 2.97 1.22

Mitch Atkins P BAL 10.7 7 5.91 8.44 2.25 Bryan Augenstein P STL 5.7 6 9.53 9.53 2.47 Dylan Axelrod P CHW 18.7 19 9.16 2.89 1.45 John Axford P MIL 73.7 86 10.51 1.83 1.14 Luis Ayala P MIN 56.0 39 6.27 2.09 1.27 Burke Badenhop P FLA 63.7 51 7.21 4.10 1.40 Danys Baez P PHI 36.0 18 4.50 6.25 1.56 Andrew Bailey P OAK 41.7 41 8.86 3.24 1.10 Homer Bailey P CIN 132.0 106 7.23 4.36 1.28 Scott Baker P MIN 134.7 123 8.22 3.07 1.17 Collin Balester P WAS 35.7 34 8.58 4.54 1.46 Grant Balfour P TB 62.0 59 8.56 2.47 1.03 Daniel Bard P BOS 73.0 74 9.12 3.33 0.96 Anthony Bass P SD 48.3 24 4.47 1.68 1.28 Antonio Bastardo P PHI 58.0 70 10.86 2.64 0.93 Miguel Batista P WAS 60.0 31 4.65 3.60 1.37 Brandon Beachy P ATL 141.7 169 10.74 3.62 1.21 Pedro Beato P NYM 67.0 39 5.24 4.30 1.28 Blake Beavan P SEA 97.0 42 3.90 4.27 1.25 Chad Beck P 2.3 3 11.57 - 0.43

Josh Beckett P BOS 193.0 175 8.16 2.84 1.03 Erik Bedard P SEA 129.3 125 8.70 3.62 1.28 76

Joe Beimel P COL 25.3 17 6.04 4.97 1.70 Matt Belisle P COL 72.0 58 7.25 3.25 1.26 Heath Bell P SD 62.7 51 7.32 2.30 1.15 Trevor Bell P LAA 34.3 17 4.46 3.15 1.43 Duane Below P DET 29.0 14 4.34 4.03 1.34 Joaquin Benoit P TB 61.0 63 9.30 2.80 1.05 Justin Berg P CHC 12.0 6 4.50 3.75 1.42 Brad Bergesen P BAL 101.0 61 5.44 5.61 1.50 Jason Berken P BAL 47.0 41 7.85 5.36 1.79 Dellin Betances P NYY 2.7 2 6.75 6.75 2.63 Rafael Betancourt P COL 62.3 73 10.54 2.89 0.87 Bruce Billings P COL 7.0 7 9.00 10.29 2.71 Chad Billingsley P LAD 188.0 152 7.28 4.16 1.45 Nick Blackburn P MIN 148.3 76 4.61 4.43 1.60 Joe Blanton P PHI 41.3 35 7.62 4.79 1.48 Jerry Blevins P OAK 28.3 26 8.26 2.54 1.34 Mitchell Boggs P STL 60.7 48 7.12 3.56 1.37 Michael Bowden P BOS 20.0 17 7.65 4.05 1.50 Blaine Boyer P ARI 6.7 1 1.35 10.80 2.10 Brad Brach P SD 7.0 11 14.14 3.86 2.29 Andrew Brackman P NYY 2.3 0 - - 1.71 Zach Braddock P MIL 17.3 18 9.35 6.75 1.56 Dallas Braden P OAK 18.0 15 7.50 3.00 1.28 Bill Bray P CIN 48.3 44 8.19 2.79 1.08 Yhency Brazoban P 6.0 8 12.00 6.00 2.00

Craig Breslow P OAK 59.3 44 6.67 3.64 1.52 Zach Britton P BAL 154.3 97 5.66 4.61 1.45 Brian Broderick P WAS 12.3 4 2.92 5.84 1.54 Rex Brothers P COL 40.7 59 13.06 2.88 1.30 Jonathan Broxton P LAD 12.7 10 7.11 5.68 1.89 Brian Bruney P NYM 19.7 16 7.32 6.86 1.93 Clay Buchholz P BOS 82.7 60 6.53 3.38 1.29 Taylor Buchholz P BOS 26.0 26 9.00 3.12 1.12 Mark Buehrle P CHW 205.3 109 4.78 3.55 1.30 Jay Buente P TB 5.0 2 3.60 9.00 2.40 Jason Bulger P LAA 9.3 7 6.75 - 1.71 Madison Bumgarner P SF 204.7 191 8.40 3.21 1.21 A.J. Burnett P NYY 190.3 173 8.18 5.11 1.43 Alex Burnett P MIN 50.7 33 5.86 5.51 1.40 Sean Burnett P WAS 56.7 33 5.24 3.81 1.32 Brian Burres P PIT 14.0 10 6.43 3.86 1.14 Jared Burton P CIN 4.7 3 5.79 3.86 1.93 Dave Bush P MIL 37.3 23 5.54 5.54 1.50 77

Tim Byrdak P HOU 37.7 47 11.23 3.82 1.41 Trevor Cahill P OAK 207.7 147 6.37 4.16 1.43 Matt Cain P SF 221.7 179 7.27 2.84 1.08 Shawn Camp P TOR 66.3 32 4.34 4.21 1.52 Matt Capps P MIN 65.7 34 4.66 4.25 1.20 Chris Capuano P MIL 186.0 168 8.13 4.55 1.35 Andrew Carignan P OAK 6.3 5 7.11 2.84 1.58 Buddy Carlyle P NYY 7.7 9 10.57 4.70 1.57 Fausto Carmona P CLE 188.7 109 5.20 5.30 1.40 Chris Carpenter P STL 9.7 8 7.45 2.79 1.97 Chris Carpenter P STL 237.3 191 7.24 3.41 1.26 David Carpenter P HOU 27.7 29 9.43 2.93 1.48 Drew Carpenter P PHI 14.7 16 9.82 7.98 1.77 Carlos Carrasco P CLE 124.7 85 6.14 4.62 1.36 D.J. Carrasco P NYM 49.3 27 4.93 5.84 1.68 Joel Carreno P TOR 15.7 14 8.04 1.15 0.96 Andrew Cashner P CHC 10.7 8 6.75 1.69 0.66 Santiago Casilla P SF 51.7 45 7.84 1.57 1.12 Bobby Cassevah P LAA 39.7 24 5.45 2.50 1.18 Alberto Castillo P BAL 11.7 6 4.63 2.31 1.46 Brett Cecil P TOR 123.7 87 6.33 4.73 1.33 Jose Ceda P FLA 20.3 21 9.30 3.98 1.38 Xavier Cedeno P HOU 1.7 0 - 27.00 4.20 Jhoulys Chacin P COL 194.0 150 6.96 3.62 1.31 Joba Chamberlain P NYY 28.7 24 7.53 2.83 1.05 Aroldis Chapman P CIN 50.0 71 12.78 3.60 1.30 Tyler Chatwood P LAA 142.0 74 4.69 4.69 1.67 Jesse Chavez P KC 7.7 8 9.39 10.57 2.22 Bruce Chen P KC 155.0 97 5.63 3.72 1.30 Randy Choate P FLA 24.7 31 11.31 1.46 1.01 Steve Cishek P FLA 54.7 55 9.05 2.47 1.17 Maikel Cleto P STL 4.3 6 12.46 10.38 2.54 Tyler Clippard P WAS 88.3 104 10.60 1.73 0.84 Alex Cobb P TB 52.7 37 6.32 3.42 1.33 Todd Coffey P MIL 59.7 46 6.94 3.62 1.26 Phil Coke P DET 108.7 69 5.71 4.39 1.45 Casey Coleman P CHC 84.3 75 8.00 6.30 1.75 Louis Coleman P KC 59.7 64 9.65 2.87 1.17 Tim Collins P KC 67.0 60 8.06 3.63 1.49 Josh Collmenter P ARI 154.3 100 5.83 3.32 1.07 Bartolo Colon P NYY 164.3 135 7.39 4.00 1.29 Jose Contreras P PHI 14.0 13 8.36 3.86 1.36 Aaron Cook P COL 97.0 48 4.45 5.94 1.69 78

Ryan Cook P ARI 7.7 7 8.22 7.04 2.48 Francisco Cordero P CIN 69.7 42 5.43 2.33 1.02 Lance Cormier P TB 13.7 7 4.61 9.88 1.98 Kevin Correia P PIT 154.0 77 4.50 4.73 1.39 Dan Cortes P SEA 10.7 3 2.53 5.91 1.78 Jesse Crain P CHW 65.3 70 9.64 2.62 1.24 Bobby Cramer P OAK 8.3 6 6.48 - 0.84 Michael Crotta P PIT 10.7 7 5.91 9.28 2.34 Aaron Crow P 62.0 65 9.44 2.76 1.39

Juan Cruz P KC 48.7 46 8.51 3.70 1.32 Johnny Cueto P CIN 156.0 104 6.00 2.31 1.09 Matt Daley P COL 6.0 7 10.50 10.50 1.67 John Danks P CHW 170.3 135 7.13 4.28 1.34 Kyle Davies P KC 61.3 50 7.34 6.60 1.79 Doug Davis P MIL 45.7 36 7.09 6.50 1.86 Wade Davis P TB 184.0 105 5.14 4.40 1.38 Justin De Fratus P PHI 4.0 3 6.75 2.25 1.00 Eulogio De La Cruz P SD 13.0 9 6.23 2.77 1.15 Dane De La Rosa P TB 7.3 8 9.82 9.82 1.77 Jorge De La Rosa P COL 59.0 52 7.93 3.51 1.19 Rubby De La Rosa P LAD 60.7 60 8.90 3.71 1.40 De Los Fautino Santos P OAK 33.3 43 11.61 4.32 1.32 Samuel Deduno P COL 3.0 4 12.00 3.00 2.67 Enerio Del Rosario P CIN 53.0 31 5.26 4.42 1.70 Steve Delabar P SEA 7.0 7 9.00 2.57 1.29 Rob Delaney P MIN 5.0 3 5.40 10.80 2.20 Randall Delgado P ATL 35.0 18 4.63 2.83 1.23 Sam Demel P ARI 25.7 15 5.26 4.21 1.71 Ryan Dempster P CHC 202.3 191 8.50 4.76 1.45 Ross Detwiler P WAS 66.0 41 5.59 3.00 1.26 Joey Devine P OAK 23.0 20 7.83 3.13 1.26 Scott Diamond P MIN 39.0 19 4.38 5.08 1.74 R.A. Dickey P NYM 208.7 134 5.78 3.28 1.23 Brandon Dickson P STL 8.3 7 7.56 2.16 1.44 Mark DiFelice P MIL 3.0 3 9.00 12.00 1.67 Tim Dillard P MIL 28.7 27 8.48 4.08 1.05 Rafael Dolis P CHC 1.3 1 6.75 - 0.75 Octavio Dotel P TOR 54.0 62 10.33 3.50 0.98 Felix Doubront P BOS 10.3 6 5.23 5.23 1.94 Scott Downs P LAA 53.7 35 5.87 1.17 1.01 Kyle Drabek P TOR 78.7 51 5.83 5.95 1.81 Brian Duensing P MIN 161.7 115 6.40 5.18 1.52 79

Danny Duffy P KC 105.3 87 7.43 5.55 1.61 Zach Duke P PIT 76.7 32 3.76 4.93 1.57 Phil Dumatrait P MIN 41.3 29 6.31 3.70 1.69 Michael Dunn P FLA 63.0 68 9.71 3.43 1.30 Chad Durbin P PHI 68.3 59 7.77 5.40 1.64 Steve Edlefsen P SF 11.3 6 4.76 8.74 2.38 Mike Ekstrom P TB 1.0 1 9.00 - 1.00 Scott Elbert P LAD 33.3 34 9.18 2.16 1.23 John Ely P LAD 12.7 13 9.24 4.26 1.50 Barry Enright P ARI 37.7 21 5.02 7.41 1.73 Nathan Eovaldi P LAD 34.7 23 5.97 3.38 1.38 Cody Eppley P TEX 9.0 6 6.00 8.00 1.78 Edgmer Escalona P COL 25.7 14 4.91 1.40 0.94 Sergio Escalona P PHI 27.7 25 8.13 2.93 1.27 Marco Estrada P MIL 92.7 88 8.55 4.08 1.21 Dana Eveland P PIT 29.7 16 4.85 2.73 1.15 Willie Eyre P OAK 18.3 10 4.91 2.95 0.93 Kyle Farnsworth P TB 57.7 51 7.96 2.03 0.99 Danny Farquhar P OAK 2.0 1 4.50 13.50 3.00 Scott Feldman P TEX 32.0 22 6.19 3.94 1.09 Neftali Feliz P TEX 62.3 54 7.80 2.60 1.16 Michael Fiers P MIL 2.0 2 9.00 - 2.50 Nelson Figueroa P HOU 29.0 17 5.28 8.69 2.10 Carlos Fisher P CIN 24.0 17 6.38 4.50 1.50 Doug Fister P SEA 216.3 146 6.07 2.83 1.06 Gavin Floyd P CHW 193.7 151 7.02 4.37 1.16 Jeff Francis P COL 183.0 91 4.48 4.82 1.44 Frank Francisco P TEX 50.7 53 9.41 3.38 1.32 Ryan Franklin P STL 27.7 17 5.53 8.46 1.84 Jason Frasor P TOR 60.0 57 8.55 3.60 1.40 Ernesto Frieri P SD 63.0 76 10.86 2.57 1.35 Brian Fuentes P MIN 58.3 42 6.48 3.55 1.23 Jeff Fulchino P HOU 34.7 33 8.57 5.71 1.70 Charlie Furbush P SEA 85.3 67 7.07 5.38 1.49 Armando Galarraga P DET 42.7 28 5.91 5.91 1.62 Yovani Gallardo P MIL 207.3 207 8.99 3.52 1.22 Freddy Garcia P CHW 146.7 96 5.89 3.62 1.34 Jaime Garcia P STL 194.7 156 7.21 3.56 1.32 Jon Garland P LAD 54.0 28 4.67 4.17 1.39 Steve Garrison P NYY 0.7 0 - - - Matt Garza P CHC 198.0 197 8.95 3.32 1.26 John Gaub P CHC 2.7 3 10.13 6.75 1.50 Chad Gaudin P WAS 8.3 10 10.80 5.40 2.40 80

Cory Gearrin P ATL 18.3 25 12.27 7.36 1.58 Dillon Gee P NYM 160.7 114 6.39 4.43 1.38 Justin Germano P CLE 12.7 5 3.55 5.68 1.58 Graham Godfrey P OAK 25.0 13 4.68 3.96 1.48 Brandon Gomes P TB 37.0 32 7.78 2.92 1.35 Jeanmar Gomez P CLE 58.3 31 4.78 4.32 1.51 Edgar Gonzalez P OAK 2.0 1 4.50 9.00 3.00 Enrique Gonzalez P DET 9.0 3 3.00 10.00 2.11 Gio Gonzalez P OAK 202.0 197 8.78 3.12 1.32 Michael Gonzalez P BAL 53.3 51 8.61 4.22 1.35 Brian Gordon P NYY 10.3 4 3.48 4.35 1.45 Tom Gorzelanny P CHC 105.0 95 8.14 4.03 1.29 John Grabow P CHC 62.3 38 5.49 4.62 1.52 Jeff Gray P CHC 48.3 23 4.28 4.10 1.51 Sean Green P MIL 11.7 7 5.40 5.40 1.71 Luke Gregerson P SD 55.7 34 5.50 2.75 1.37 Kevin Gregg P BAL 59.7 53 7.99 4.22 1.64 Zack Greinke P MIL 171.7 201 10.54 3.83 1.20 Jason Grilli P COL 32.7 37 10.19 2.48 1.19 Javy Guerra P LAD 46.7 38 7.33 2.12 1.18 Matt Guerrier P LAD 66.3 50 6.78 3.93 1.27 Jeremy Guthrie P BAL 208.0 130 5.63 4.33 1.34 Juan Gutierrez P ARI 18.3 23 11.29 4.91 1.69 Eric Hacker P MIN 5.3 2 3.38 - 1.50 Nick Hagadone P CLE 11.0 11 9.00 3.27 0.91 Roy Halladay P PHI 233.7 220 8.47 2.35 1.04 Mark Hamburger P TEX 8.0 6 6.75 4.50 1.00 Cole Hamels P PHI 216.0 194 8.08 2.75 0.99 Jason Hammel P COL 170.3 94 4.97 4.76 1.43 Erik Hamren P SD 12.3 10 7.30 3.65 1.54 Brad Hand P FLA 60.0 38 5.70 4.20 1.47 Joel Hanrahan P PIT 68.7 61 8.00 1.70 1.05 Tommy Hanson P ATL 130.0 142 9.83 3.60 1.17 J.A. Happ P HOU 156.3 134 7.71 5.30 1.54 Aaron Harang P SD 170.7 124 6.54 3.64 1.37 Rich Harden P OAK 82.7 91 9.91 5.12 1.43 Dan Haren P LAA 238.3 192 7.25 3.13 1.02 Lucas Harrell P CHW 18.0 15 7.50 4.50 1.72 Matt Harrison P TEX 185.7 126 6.11 3.34 1.28 Chris Hatcher P 10.3 8 6.97 6.10 1.74

LaTroy Hawkins P MIL 48.3 28 5.21 2.23 1.26 Blake Hawksworth P LAD 53.0 43 7.30 4.08 1.17 Aaron Heilman P ARI 35.3 33 8.41 6.62 1.67 81

Jeremy Hellickson P TB 189.0 117 5.57 2.90 1.15 Mark Hendrickson P BAL 11.0 5 4.09 5.73 1.91 Liam Hendriks P MIN 23.3 16 6.17 5.79 1.50 Clay Hensley P FLA 67.7 46 6.12 5.19 1.36 David Hernandez P ARI 69.3 77 10.00 3.38 1.14 Felix Hernandez P SEA 233.7 222 8.55 3.47 1.22 Livan Hernandez P WAS 175.3 99 5.08 4.47 1.40 David Herndon P PHI 57.0 39 6.16 3.32 1.37 Daniel Ray Herrera P NYM 9.7 5 4.66 4.66 1.66 Kelvin Herrera P 2.0 0 - 13.50 1.00

Frank Herrmann P CLE 56.3 34 5.43 4.95 1.54 Rich Hill P BOS 8.0 12 13.50 - 0.75 Luke Hochevar P KC 198.0 128 5.82 4.64 1.28 Jim Hoey P MIN 24.7 14 5.11 5.47 1.91 Derek Holland P TEX 198.0 162 7.36 3.91 1.35 Greg Holland P KC 60.0 74 11.10 1.80 0.93 Jeremy Horst P CIN 15.3 9 5.28 2.35 1.57 Tommy Hottovy P BOS 4.0 2 4.50 6.75 1.75 J.P. Howell P TB 30.7 26 7.63 6.16 1.57 Daniel Hudson P ARI 222.0 169 6.85 3.49 1.20 Tim Hudson P ATL 215.0 158 6.61 3.18 1.14 David Huff P CLE 50.7 36 6.39 4.09 1.42 Dusty Hughes P KC 12.7 11 7.82 9.95 2.13 Jared Hughes P PIT 11.0 10 8.18 3.27 1.18 Phil Hughes P NYY 74.7 47 5.67 5.79 1.49 Philip Humber P OAK 163.0 116 6.40 3.70 1.18 Tommy Hunter P TEX 84.7 45 4.78 4.68 1.36 Ryota Igarashi P NYM 38.7 42 9.78 4.66 1.84 Jason Isringhausen P TB 46.7 44 8.49 4.05 1.29 Edwin Jackson P CHW 199.7 148 6.67 3.79 1.44 Chris Jakubauskas P PIT 72.3 52 6.47 5.60 1.69 Chuck James P 10.3 8 6.97 5.23 1.55

Kenley Jansen P LAD 53.7 96 16.10 2.85 1.04 Casey Janssen P TOR 55.7 53 8.57 2.10 1.10 Jeremy Jeffress P MIL 15.3 13 7.63 4.11 1.50 Bobby Jenks P BOS 15.7 17 9.77 6.32 2.17 Kevin Jepsen P LAA 13.0 6 4.15 7.62 2.31 Cesar Jimenez P SEA 6.7 7 9.45 5.40 1.35 Ubaldo Jimenez P COL 188.3 180 8.60 4.64 1.40 Waldis Joaquin P SF 6.3 3 4.26 2.84 1.42 Alan Johnson P COL 4.0 3 6.75 9.00 2.25 Jim Johnson P BAL 91.0 58 5.74 2.57 1.11 Josh Johnson P FLA 60.3 56 8.35 1.49 0.98 82

Josh Judy P CLE 14.0 10 6.43 6.43 1.57 Jair Jurrjens P ATL 152.0 90 5.33 2.90 1.22 Jeff Karstens P PIT 162.3 96 5.32 3.33 1.21 Scott Kazmir P LAA 1.7 0 - 27.00 4.20 Shawn Kelley P SEA 12.7 10 7.11 - 0.79 Kyle Kendrick P PHI 114.7 59 4.63 3.22 1.23 Ian Kennedy P ARI 222.0 198 8.03 2.88 1.09 Clayton Kershaw P LAD 233.3 248 9.57 2.28 0.98 Cole Kimball P WAS 14.0 11 7.07 1.93 1.36 Craig Kimbrel P ATL 77.0 127 14.84 1.99 1.04 Josh Kinney P CHW 17.7 20 10.19 6.62 1.70 Brandon Kintzler P MIL 14.7 15 9.20 3.68 1.16 Michael Kirkman P TEX 27.3 21 6.91 6.26 1.39 Corey Kluber P CLE 4.3 5 10.38 6.23 2.08 Michael Kohn P LAA 12.3 9 6.57 6.57 1.86 George Kontos P SD 6.0 6 9.00 3.00 1.17 Zach Kroenke P ARI 4.0 3 6.75 9.00 1.75 Hong-Chih Kuo P LAD 27.0 36 12.00 9.00 1.74 Hiroki Kuroda P LAD 202.0 161 7.17 3.12 1.21 John Lackey P BOS 160.0 108 6.08 6.36 1.62 Aaron Laffey P CLE 53.3 30 5.06 3.71 1.65 John Lannan P WAS 184.7 106 5.17 3.66 1.46 Mat Latos P SD 194.3 185 8.57 3.43 1.18 Brandon League P SEA 61.3 45 6.60 2.79 1.08 Mike Leake P CIN 167.7 118 6.33 3.81 1.17 Wade LeBlanc P SD 79.7 51 5.76 4.63 1.41 Sam LeCure P CIN 77.7 73 8.46 3.71 1.00 Wil Ledezma P PIT 6.0 6 9.00 15.00 3.00 Cliff Lee P PHI 232.7 238 9.21 2.40 1.03 Chris Leroux P PIT 25.0 24 8.64 2.88 1.32 Jon Lester P BOS 191.7 182 8.55 3.43 1.26 Colby Lewis P TEX 200.3 169 7.59 4.36 1.21 Rommie Lewis P TOR 5.0 5 9.00 9.00 2.80 Brad Lidge P PHI 19.3 23 10.71 1.40 1.50 Ted Lilly P LAD 192.7 158 7.38 3.97 1.16 Tim Lincecum P SF 217.0 220 9.12 2.74 1.21 Brad Lincoln P PIT 47.7 29 5.48 4.72 1.47 Josh Lindblom P LAD 29.7 28 8.49 2.73 1.04 Shane Lindsay P 6.0 6 9.00 12.00 2.67

Matt Lindstrom P COL 54.0 36 6.00 3.00 1.22 Scott Linebrink P ATL 54.3 42 6.96 3.48 1.45 Francisco Liriano P MIN 134.3 112 7.50 5.02 1.49 Jesse Litsch P TOR 75.0 66 7.92 4.44 1.29 83

Jeff Locke P PIT 16.7 5 2.70 6.48 1.86 Kameron Loe P MIL 72.0 61 7.63 3.50 1.13 Boone Logan P NYY 41.7 46 9.94 3.46 1.34 Kyle Lohse P STL 188.3 111 5.30 3.35 1.17 Javier Lopez P SF 53.0 40 6.79 2.72 1.28 Rodrigo Lopez P ARI 97.7 54 4.98 4.33 1.49 Wilton Lopez P HOU 71.0 56 7.10 2.79 1.27 Derek Lowe P ATL 187.0 137 6.59 5.01 1.51 Mark Lowe P TEX 45.0 42 8.40 3.40 1.42 Cory Luebke P SD 139.7 154 9.92 3.29 1.07 Josh Lueke P SEA 32.7 29 7.99 6.06 1.44 Jordan Lyles P HOU 94.0 67 6.41 5.27 1.41 Lance Lynn P STL 34.7 40 10.38 3.12 1.04 Brandon Lyon P HOU 13.3 6 4.05 10.80 2.40 Mike MacDougal P STL 57.0 41 6.47 1.89 1.46 Ryan Madson P PHI 60.7 62 9.20 2.23 1.15 Trystan Magnuson P OAK 14.7 11 6.75 6.14 1.36 Paul Maholm P PIT 162.3 97 5.38 3.66 1.29 Scott Maine P CHC 7.0 5 6.43 10.29 2.29 Matt Maloney P CIN 18.7 13 6.27 9.16 2.14 Jeff Manship P MIN 3.3 2 5.40 5.40 2.70 Shaun Marcum P TOR 200.7 158 7.09 3.50 1.15 Carlos Marmol P CHC 74.0 99 12.04 3.89 1.38 Jeffrey Marquez P CHW 4.0 2 4.50 2.25 1.25 Jason Marquis P WAS 132.0 76 5.18 4.36 1.49 Sean Marshall P CHC 75.7 79 9.40 2.26 1.10 Luis Marte P DET 3.7 3 7.36 2.45 1.91 Cristhian Martinez P ATL 77.7 58 6.72 3.36 0.97 Nick Masset P CIN 70.3 62 7.93 3.58 1.52 Justin Masterson P CLE 216.0 158 6.58 3.21 1.28 Marcos Mateo P CHC 23.0 25 9.78 3.91 1.48 Scott Mathieson P PHI 5.0 5 9.00 - 2.40 Daisuke Matsuzaka P BOS 37.3 26 6.27 5.06 1.47 Ryan Mattheus P WAS 32.0 12 3.38 2.53 1.28 Brian Matusz P BAL 49.7 38 6.89 10.51 2.09 Yunesky Maya P WAS 32.7 15 4.13 5.23 1.53 Vin Mazzaro P KC 28.3 10 3.18 7.94 1.91 Zach McAllister P CLE 17.7 14 7.13 6.11 1.87 Brandon McCarthy P OAK 170.7 123 6.49 3.27 1.13 Kyle McClellan P STL 141.7 76 4.83 4.13 1.31 Mike McClendon P MIL 13.7 10 6.59 2.63 1.32 Daniel McCutchen P PIT 84.7 47 5.00 3.72 1.42 James McDonald P PIT 171.0 142 7.47 4.16 1.49 84

Jake McGee P TB 28.0 27 8.68 4.50 1.50 Dustin McGowan P TOR 21.0 20 8.57 6.43 1.57 Kris Medlen P ATL 2.3 2 7.71 - 0.43 Evan Meek P PIT 20.7 17 7.40 3.48 1.89 Mark Melancon P HOU 74.3 66 7.99 2.66 1.22 Luis Mendoza P KC 14.7 7 4.30 1.23 1.09 Kam Mickolio P ARI 6.7 7 9.45 6.75 1.95 Jose Mijares P MIN 49.0 30 5.51 4.41 1.69 Wade Miley P ARI 40.0 25 5.63 4.50 1.65 Andrew Miller P BOS 65.0 50 6.92 5.54 1.82 Jim Miller P COL 7.0 5 6.43 1.29 1.00 Trever Miller P STL 21.3 12 5.06 3.38 1.73 Brad Mills P TOR 18.3 18 8.84 9.33 1.91 Kevin Millwood P BAL 54.3 36 5.96 3.98 1.21 Tom Milone P WAS 26.0 15 5.19 3.81 1.23 Mike Minor P ATL 82.7 77 8.38 4.14 1.49 Pat Misch P NYM 7.0 5 6.43 10.29 2.14 Sergio Mitre P NYY 38.3 16 3.76 4.23 1.38 Matt Moore P TB 9.3 15 14.46 1.93 1.29 Franklin Morales P COL 46.3 42 8.16 3.50 1.27 Brandon Morrow P TOR 179.3 203 10.19 4.72 1.29 Clay Mortensen P OAK 58.3 30 4.63 3.86 1.35 Charlie Morton P PIT 171.7 110 5.77 3.83 1.53 Guillermo Moscoso P TEX 128.0 74 5.20 3.38 1.09 Dustin Moseley P SD 120.0 64 4.80 3.30 1.28 Daniel Moskos P PIT 24.3 11 4.07 2.59 1.56 Guillermo Mota P SF 80.3 77 8.63 3.70 1.26 Jason Motte P STL 68.0 63 8.34 2.25 0.96 Peter Moylan P ATL 8.3 10 10.80 2.16 1.80 Edward Mujica P FLA 76.0 63 7.46 2.84 1.03 Brett Myers P HOU 216.0 160 6.67 4.46 1.31 Chris Narveson P MIL 161.7 126 7.01 4.40 1.39 Joe Nathan P MIN 44.7 43 8.66 4.84 1.16 Pat Neshek P MIN 24.7 20 7.30 4.01 1.66 Juan Nicasio P COL 71.7 58 7.28 4.02 1.27 Jeff Niemann P TB 135.3 105 6.98 4.06 1.24 Jon Niese P NYM 157.3 138 7.89 4.35 1.41 Hector Noesi P NYY 56.3 45 7.19 4.31 1.51 Ricky Nolasco P FLA 206.0 148 6.47 4.63 1.40 Jordan Norberto P ARI 6.7 4 5.40 8.10 2.25 Bud Norris P HOU 186.0 176 8.52 3.73 1.33 Ivan Nova P NYY 165.3 98 5.33 3.65 1.33 Leo Nunez P FLA 64.3 55 7.69 4.06 1.23 85

Michael O'Connor P NYM 6.7 8 10.80 2.70 1.20 Darren O'Day P TEX 16.7 18 9.72 5.40 1.32 Eric O'Flaherty P ATL 73.7 67 8.19 0.98 1.09 Alexi Ogando P TEX 169.0 126 6.71 3.46 1.14 Ross Ohlendorf P PIT 38.7 27 6.28 8.15 1.94 Will Ohman P CHW 53.3 54 9.11 4.05 1.31 Hideki Okajima P BOS 8.3 6 6.48 3.24 1.44 Andrew Oliver P DET 9.7 5 4.66 6.52 1.97 Darren Oliver P TEX 51.0 44 7.76 2.12 1.14 Lester Oliveros P MIN 21.3 13 5.48 4.22 1.50 Garrett Olson P SEA 4.3 4 8.31 - 1.15 Logan Ondrusek P CIN 61.3 41 6.02 3.08 1.35 Ramon Ortiz P TB 33.3 25 6.75 4.59 1.26 Sean O'Sullivan P KC 58.3 19 2.93 7.10 1.78 Roy Oswalt P PHI 139.0 93 6.02 3.63 1.34 Josh Outman P OAK 58.3 35 5.40 3.55 1.46 Micah Owings P CIN 63.0 44 6.29 3.43 1.25 Vicente Padilla P LAD 8.7 9 9.35 4.15 1.38 Matt Palmer P LAA 15.7 7 4.02 5.74 1.47 Jonathan Papelbon P BOS 64.3 87 12.17 2.94 0.93 Jarrod Parker P ARI 5.7 1 1.59 - 0.88 Bobby Parnell P NYM 59.3 64 9.71 3.49 1.47 Joe Paterson P ARI 34.0 28 7.41 2.65 1.26 Troy Patton P BAL 30.0 22 6.60 3.00 1.00 David Pauley P SEA 74.0 44 5.35 3.04 1.16 Felipe Paulino P COL 139.3 133 8.59 4.46 1.44 Carl Pavano P MIN 222.0 102 4.14 4.30 1.36 Bradley Peacock P WAS 12.0 4 3.00 0.75 1.08 Jake Peavy P CHW 111.7 95 7.66 4.92 1.26 Mike Pelfrey P NYM 193.7 105 4.88 4.74 1.47 Tony Pena P CHW 20.3 17 7.52 5.75 1.72 Lance Pendleton P NYY 18.7 13 6.27 6.75 1.66 Brad Penny P STL 181.7 74 3.67 5.30 1.56 Joel Peralta P TB 67.7 61 8.11 2.93 0.92 Chris Perez P CLE 59.7 39 5.88 3.32 1.21 Juan Perez P PHI 5.0 8 14.40 3.60 1.20 Luis Perez P TOR 65.0 54 7.48 4.98 1.55 Rafael Perez P CLE 63.0 33 4.71 3.00 1.24 Glen Perkins P MIN 61.7 65 9.49 2.48 1.23 Ryan Perry P DET 37.0 24 5.84 5.11 1.62 Vinnie Pestano P CLE 62.0 84 12.19 2.18 1.05 Zachary Phillips P BAL 8.0 8 9.00 1.13 1.00 Michael Pineda P SEA 171.0 173 9.11 3.74 1.10 86

Joel Pineiro P LAA 145.7 62 3.83 5.13 1.51 Drew Pomeranz P 18.3 13 6.38 4.91 1.31

Rick Porcello P DET 182.0 104 5.14 4.75 1.41 David Price P TB 224.3 218 8.75 3.45 1.14 Scott Proctor P ATL 40.3 29 6.47 6.92 2.01 David Purcey P TOR 33.7 22 5.88 5.61 1.78 Zach Putnam P CLE 7.3 9 11.05 4.91 1.36 J.J. Putz P ARI 58.0 61 9.47 2.02 0.91 Chad Qualls P SD 74.3 43 5.21 3.39 1.25 Horacio Ramirez P KC 9.0 4 4.00 6.00 2.00 Ramon Ramirez P SF 68.7 66 8.65 2.49 1.17 Cesar Ramos P TB 43.7 31 6.39 3.92 1.40 Clay Rapada P TEX 16.3 18 9.92 6.06 1.35 Jon Rauch P MIN 52.0 36 6.23 4.85 1.35 Chris Ray P SF 32.7 22 6.06 4.68 1.38 Addison Reed P 7.3 12 14.73 2.45 1.50

Chad Reineke P OAK 6.7 3 4.05 6.75 1.65 Chris Resop P PIT 69.7 79 10.21 4.26 1.48 Dennys Reyes P STL 1.7 1 5.40 16.20 2.40 Jo-Jo Reyes P TOR 140.7 87 5.57 5.57 1.59 Greg Reynolds P COL 32.0 18 5.06 6.19 1.56 Matt Reynolds P COL 50.7 50 8.88 4.09 1.30 Arthur Rhodes P TEX 33.0 21 5.73 4.64 1.36 Clayton Richard P SD 99.7 53 4.79 3.79 1.42 Garrett Richards P LAA 14.0 9 5.79 5.79 1.64 Scott Richmond P TOR 0.3 0 - - - Mariano Rivera P NYY 61.3 60 8.80 1.91 0.90 David Robertson P NYY 66.7 100 13.50 1.08 1.13 Fernando Rodney P LAA 32.0 26 7.31 4.50 1.69 Aneury Rodriguez P HOU 85.3 64 6.75 5.17 1.34 Fernando Rodriguez P LAA 52.3 57 9.80 3.96 1.55 Francisco Rodriguez P LAA 13.7 7 4.61 4.61 1.32 Francisco Rodriguez P MIL 71.7 79 9.92 2.64 1.30 Henry Rodriguez P WAS 65.7 70 9.59 3.43 1.51 Wandy Rodriguez P HOU 191.0 166 7.82 3.49 1.31 Josh Roenicke P TOR 16.7 12 6.48 3.78 1.26 Esmil Rogers P COL 82.7 63 6.86 7.08 1.90 J.C. Romero P PHI 24.7 19 6.93 4.01 1.74 Ricky Romero P TOR 225.0 178 7.12 2.92 1.14 Sergio Romo P SF 48.0 70 13.13 1.50 0.71 Sandy Rosario P FLA 3.7 2 4.91 2.45 1.91 Tyson Ross P OAK 36.0 24 6.00 2.75 1.28 Chance Ruffin P 17.7 18 9.17 4.08 1.53

87

Dan Runzler P SF 27.3 25 8.23 5.93 1.65 Josh Rupe P KC 14.3 7 4.40 5.02 1.53 Adam Russell P TB 32.7 13 3.58 3.03 1.56 James Russell P CHC 67.7 43 5.72 3.99 1.33 Marc Rzepczynski P TOR 62.0 61 8.85 3.34 1.23 CC Sabathia P NYY 237.3 230 8.72 3.00 1.23 Takashi Saito P ATL 26.7 23 7.76 2.03 1.13 Fernando Salas P STL 75.0 75 9.00 2.28 0.95 Chris Sale P CHW 71.0 79 10.01 2.79 1.11 Jeff Samardzija P CHC 88.0 87 8.90 2.97 1.30 Alex Sanabia P FLA 11.0 8 6.55 2.45 1.45 Brian Sanches P FLA 61.7 53 7.74 3.94 1.43 Anibal Sanchez P FLA 196.3 202 9.26 3.67 1.28 Eduardo Sanchez P STL 30.0 35 10.50 1.80 1.00 Jonathan Sanchez P SF 101.3 102 9.06 4.17 1.44 Amaury Sanit P NYY 7.0 4 5.14 12.86 2.14 Ervin Santana P LAA 228.7 178 7.01 3.35 1.22 Hector Santiago P 5.3 2 3.38 - 0.38

Sergio Santos P CHW 63.3 92 13.07 3.41 1.11 Joe Saunders P ARI 212.0 108 4.58 3.74 1.31 Max Scherzer P DET 195.0 174 8.03 4.34 1.34 Daniel Schlereth P DET 49.0 44 8.08 3.49 1.37 Michael Schwimer P PHI 14.3 16 10.05 4.40 1.53 Christopher Schwinden P NYM 21.0 17 7.29 4.29 1.38 Evan Scribner P SD 14.0 10 6.43 6.43 1.57 Atahualpa Severino P WAS 4.7 7 13.50 3.86 1.29 Bryan Shaw P ARI 28.3 24 7.62 2.22 1.34 George Sherrill P ATL 36.0 38 9.50 2.75 1.25 James Shields P TB 249.3 225 8.12 2.82 1.04 Alfredo Simon P BAL 115.7 83 6.46 4.82 1.45 Tony Sipp P CLE 62.3 57 8.23 2.89 1.11 Anthony Slama P MIN 2.3 3 11.57 - 0.86 Doug Slaten P WAS 16.3 13 7.16 3.86 2.14 Kevin Slowey P MIN 59.3 34 5.16 6.52 1.40 Joe Smith P CLE 67.0 45 6.04 1.88 1.09 Jordan Smith P CIN 20.0 13 5.85 7.20 2.00 Andy Sonnanstine P TB 35.7 12 3.03 5.55 1.46 Joakim Soria P KC 60.3 60 8.95 4.03 1.28 Rafael Soriano P NYY 39.3 36 8.24 3.89 1.30 Henry Sosa P SF 53.3 38 6.41 5.06 1.44 Joshua Spence P 29.7 31 9.40 2.73 1.11

Craig Stammen P WAS 10.3 12 10.45 - 0.68 Tim Stauffer P SD 185.7 128 6.20 3.68 1.25 88

Mitch Stetter P MIL 7.0 7 9.00 3.86 1.29 Jeff Stevens P CHC 7.0 4 5.14 3.86 1.57 Zach Stewart P 67.3 45 6.01 5.75 1.60

Josh Stinson P NYM 13.0 8 5.54 6.23 1.62 Drew Storen P WAS 75.3 74 8.84 2.75 1.02 Stephen Strasburg P WAS 24.0 24 9.00 1.50 0.71 Huston Street P COL 58.3 55 8.49 3.86 1.22 Pedro Strop P TEX 22.0 21 8.59 2.05 1.14 Eric Stults P COL 12.0 7 5.25 6.00 1.25 Michael Stutes P PHI 62.0 58 8.42 3.63 1.23 Eric Surkamp P SF 26.7 13 4.39 5.74 1.84 Anthony Swarzak P MIN 102.0 55 4.85 4.24 1.34 Hisanori Takahashi P NYM 68.0 52 6.88 3.31 1.22 Mitch Talbot P CLE 63.7 36 5.09 6.50 1.85 Brian Tallet P STL 13.3 10 6.75 8.78 2.18 Yoshinori Tateyama P TEX 44.0 43 8.80 4.50 1.09 Junichi Tazawa P BOS 3.0 4 12.00 6.00 1.33 Everett Teaford P KC 44.0 28 5.73 3.07 1.14 Julio Teheran P ATL 19.7 10 4.58 5.03 1.47 Robinson Tejeda P KC 7.3 2 2.45 4.91 2.05 Kanekoa Texeira P KC 6.3 0 - 1.42 2.53 Joe Thatcher P SD 10.0 9 8.10 4.50 1.50 Dale Thayer P TB 10.3 5 4.35 2.61 1.16 Brad Thomas P DET 11.0 7 5.73 9.00 2.09 Aaron Thompson P PIT 7.7 1 1.17 7.04 2.48 Daryl Thompson P CIN 3.0 0 - 15.00 3.67 Rich Thompson P LAA 54.0 56 9.33 3.00 1.22 Matt Thornton P CHW 59.7 63 9.50 3.32 1.36 Chris Tillman P BAL 62.0 46 6.68 5.52 1.65 Mason Tobin P TEX 5.3 0 - 5.06 1.88 Brett Tomko P NYY 17.7 14 7.13 4.58 1.42 Josh Tomlin P CLE 165.3 89 4.84 4.25 1.08 Alexander Torres P TB 8.0 9 10.13 3.38 1.88 Ramon Troncoso P LAD 22.7 14 5.56 6.75 1.85 Ryan Tucker P TEX 5.0 4 7.20 7.20 2.00 Jacob Turner P 12.7 8 5.68 8.53 1.66

Koji Uehara P BAL 65.0 85 11.77 2.22 0.72 Raul Valdes P NYM 12.0 15 11.25 3.00 1.67 Jose Valdez P HOU 14.0 15 9.64 9.00 1.71 Merkin Valdez P TOR 4.3 6 12.46 4.15 1.85 Jose Valverde P DET 72.3 69 8.59 2.12 1.19 Rick VandenHurk P BAL 9.0 7 7.00 8.00 2.22 Jason Vargas P SEA 201.0 131 5.87 4.21 1.31 89

Anthony Varvaro P SEA 24.0 23 8.63 2.63 1.08 Anthony Vasquez P SEA 29.3 13 3.99 8.59 1.91 Esmerling Vasquez P ARI 30.3 20 5.93 3.86 1.32 Javier Vazquez P FLA 192.7 162 7.57 3.69 1.18 Jonny Venters P ATL 88.0 96 9.82 1.74 1.09 Jose Veras P FLA 71.0 79 10.01 3.68 1.24 Justin Verlander P DET 251.0 250 8.96 2.37 0.92 Carlos Villanueva P TOR 107.0 68 5.72 4.04 1.26 Elih Villanueva P FLA 3.0 2 6.00 24.00 3.33 Brayan Villarreal P DET 16.0 14 7.88 6.75 2.00 Pedro Viola P BAL 3.7 4 9.82 9.82 2.18 Arodys Vizcaino P ATL 17.3 17 8.83 4.15 1.44 Ryan Vogelsong P SF 179.7 139 6.96 2.71 1.25 Edinson Volquez P CIN 108.7 104 8.61 5.63 1.57 Chris Volstad P FLA 165.7 117 6.36 4.89 1.42 Cory Wade P LAD 39.7 30 6.81 1.82 1.03 Neil Wagner P OAK 5.0 4 7.20 7.20 1.80 Tim Wakefield P BOS 154.7 93 5.41 5.12 1.36 Jordan Walden P LAA 60.3 67 9.99 2.83 1.24 Kyle Waldrop P 11.0 5 4.09 5.73 1.45

P.J. Walters P STL 5.0 4 7.20 7.20 1.20 Chien-Ming Wang P WAS 62.3 25 3.61 3.90 1.28 Tony Watson P PIT 41.0 37 8.12 3.73 1.32 Jered Weaver P LAA 235.7 198 7.56 2.41 1.01 Ryan Webb P FLA 50.7 31 5.51 3.20 1.34 Kyle Weiland P BOS 24.7 13 4.74 7.66 1.66 Robbie Weinhardt P DET 1.7 1 5.40 10.80 2.40 Randy Wells P CHC 135.3 82 5.45 4.99 1.38 Jake Westbrook P STL 183.3 104 5.11 4.61 1.53 Dan Wheeler P BOS 49.3 39 7.11 4.20 1.11 Kevin Whelan P NYY 1.7 1 5.40 5.40 3.00 Alex White P 51.3 37 6.49 6.84 1.69

Tom Wilhelmsen P 32.7 30 8.27 3.31 1.16

Adam Wilk P DET 13.3 10 6.75 4.73 1.28 Jerome Williams P LAA 44.0 28 5.73 3.48 1.36 Randy Williams P BOS 8.3 6 6.48 5.40 1.80 Dontrelle Willis P CIN 75.7 57 6.78 5.00 1.52 Brian Wilson P SF 55.0 54 8.84 3.11 1.47 C.J. Wilson P TEX 223.3 206 8.30 2.94 1.19 Randy Wolf P MIL 212.3 134 5.68 3.69 1.32 Blake Wood P KC 69.7 62 8.01 3.75 1.41 Kerry Wood P CHC 51.0 57 10.06 3.18 1.29 Tim Wood P WAS 8.0 2 2.25 5.63 2.00 90

Travis Wood P CIN 106.0 76 6.45 4.84 1.49 Vance Worley P PHI 131.7 119 8.13 3.01 1.23 Mark Worrell P BAL 2.0 3 13.50 36.00 4.00 Jamey Wright P SEA 68.3 48 6.32 3.03 1.33 Wesley Wright P HOU 12.0 11 8.25 1.50 0.92 Michael Wuertz P OAK 33.7 32 8.55 6.68 1.87 Chris Young P SD 24.0 22 8.25 1.88 0.96 Mike Zagurski P PHI 3.3 4 10.80 2.70 2.10 Carlos Zambrano P CHC 145.7 101 6.24 4.82 1.44 Brad Ziegler P OAK 58.3 44 6.79 2.01 1.23 Jordan Zimmermann P WAS 161.3 124 6.92 3.12 1.15 Barry Zito P SF 53.7 32 5.37 5.87 1.40

BIBLIOGRAPHY

Bradbury, J. C. (n.d.). What's Right with Scully-Estimates of a Player's Marginal Revenue

Product. Informally published manuscript, Department of Health, Physical

Education, and Sport Science, Kennesaw State University, Kennesaw, GA.

Baseball Almanac. (2014). Year-by-year baseball history. Retrieved from

http://www.baseball-almanac.com/yearmenu.shtml

C., J. (2004, April 12). Winning and attendance in mlb. Retrieved from

http://www.sabernomics.com/sabernomics/index.php/2004/04/winning-and-

attendance-in-mlb/

Depken, C. A. I., & Wilson, D. P. (2004). Labor markets in the classroom: Marginal

revenue product in major league baseball. JOURNAL OF ECONOMICS AND

FINANCE EDUCATION, 3(1), 12-24.

ESPN. (2011, Sept 28). Mlb standings - 2011. Retrieved from

http://espn.go.com/mlb/standings/_/year/2011

Fields, B. (2001). Estimating the value of major league baseball players. (Unpublished

master's thesis, East Carolina University).

Fort, R. (2013, May 23). Rod's sports business data: Mlb. Retrieved from

https://umich.app.box.com/s/41707f0b2619c0107b8b/1/320021673

Fort, R. (2011). Sports economics. (3rd ed.). Upper Saddle River, New Jersey: Pearson

Education, Inc.

Galletti, A. (2010, July 07). Arturo's silly little stats. Retrieved from

http://arturogalletti.wordpress.com/2010/07/01/win-regression-for-the-nba-2/ 92 Gaugler, T. (n.d.). Predicting attendance at major league baseball games. Informally

published manuscript, Computer Science, Boston University, Boston, MA.

Getz, V. (2012, Sept 07). Mlb: Average ticket prices and fan cost index for all 30

baseball teams (2008-2012). Retrieved from

http://sportslistoftheday.com/2012/09/07/mlb-average-ticket-prices-and-fan-cost-

index-for-all-30-baseball-teams-2008-2012/

Government of Canada. (2013, Feb 06). Population of census metropolitan areas .

Retrieved from http://www.statcan.gc.ca/tables-tableaux/sum-

som/l01/cst01/demo05a-eng.htm

Grabiner, D. (2012). The sabermetric manifesto. Retrieved from

http://www.seanlahman.com/baseball-archive/sabermetrics/sabermetric-

manifesto/

Houser, A. (2005). Which baseball statistic is the most important when determining team

success?. The Park Place Economist, 13, 29-36.

Kahn, L. (2000). The sports business as a labor market laboratory. Journal of Economic

Perspectives, 14(3), 75-94. Retrieved from

http://people.ucsc.edu/~lkletzer/econ104/kahn_sportsbusiness_JEP.pdf

Krautmann, A. C. (1999). What's wrong with scully-estimates of a player's marginal

revenue product. Economic Inquiry, 37(2), 369-381.

Lemke, R. J., Leonard, M., & Tlhokwane, K. (2010). Estimating attendance at major

league baseball games for the 2007 season. Journal of Sports Economics, 11(3),

316-348. 93 Lopez, J., Mundfrom, D. J., & Schaffer, J. R. (2011). What makes a winning baseball

team and what makes a playoff team?. Multiple Linear Regression Viewpoints,

32(2), 23-28.

Macdonald, D. N., & Reynolds, M. O. (2006). Are baseball players paid their marginal

products?. Managerial and Decision Economics, 15(5), 443-457.

Marcum, J. P., & Greenstein, T. N. (1985). Factors Affecting Attendance of Major

League Baseball: 11. A Within-Season Analysis. Sociology of Sport Journal, 2,

314-322.

Moy, D. (2006). A regression model using common baseball statistics to project offensive

and defensive efficiency. Informally published manuscript, Statistics, University

of California - Berkeley, Berkeley, CA.

Newsday. (2014). Baseball salaries for 2013. Retrieved from

http://sports.newsday.com/long-island/data/baseball/mlb-salaries-2013/

Rader, K. (n.d.). The numbers behind the mlb. (Informally published manuscript, Harvard

University).

Scully, G. W. (1974). Pay and performance in major league baseball. The American

Economic Review, 64(6), 915-930.

Sports Reference LLC. (2013). 2011 american league standard fielding. Retrieved from

http://www.baseball-reference.com/leagues/AL/2011-standard-fielding.shtml

The Guru. (2014, January 23). Data archive: The guru's private stock. Retrieved from

http://baseballguru.com/bbdata1.html

United States Census Bureau. (2012, June 27). City and town totals: Vintage 2011.

Retrieved from http://www.census.gov/popest/data/cities/totals/2011/ 94 Watnik, M. R. (1998). Pay for play: Are baseball salaries based on performance?. Journal

of Statistics Education, 6(2), Retrieved from

http://www.amstat.org/publications/jse/v6n2/datasets.watnik.html

ACADEMIC VITA

Brian Schanzenbach [email protected] 1393 Nathan Hale Drive, Phoenixville, PA 19460 ______

Education The Pennsylvania State University, Class of May 2014 Bachelor of Science in Economics and Minor in Business and Liberal Arts Schreyer Honors College for Economics Dean’s List every full-time semester

Achievements and Affiliations 2013 Schreyer Gateway Scholar Orientation Leader Schreyer Consulting Group, Member • Participate in case studies, phone interviews with top consulting firm employees, and various group projects Danglers Deck Hockey, Co-Captain and Treasurer • Manage team’s funds including collecting fees owed to the league and payments for team uniforms • Communicate the rules and requirements of the league to the players and act as liaison between the team and league The National Society of Leadership and Success, Member Side-by-Side Organization of Pennsylvania and Camp Jump Start, Mentor Pennsylvania State University Association for Computing Machinery, Member

Work Experience Limestone Pension Associates, LLC Wilmington, DE Consulting Internship May 2013 – August 2013 • Proposed and executed a conversion of over 950 plans from paper to electronic • Performed advanced data entry tasks, including working with arrays on Excel • Exceeded company goals, while completing tasks effectively and efficiently

David M. Frees Insurance Inc. Phoenixville, PA Marketing and General Insurance Internship September 2011 – August 2012 • Part-time while enrolled at Pennsylvania State University • Obtained, reviewed, and verified over 200 clients’ insurance policies • Contributed to determining the best policy for each client • Exceeded company goals, while completing all projects assigned

Brian Schanzenbach, Independent Contractor Phoenixville, PA Designed and Constructed a Basement May 2011 – August 2011

• Completed the project before the proposed deadline of September 1, 2011 • Budgeted material costs and labor, keeping them below the expected total costs of $5,000 • Managed and motivated a team of two workers to successfully complete the project

Technical Skills Software: Microsoft Office (including advanced knowledge of Excel), STATA Hardware: Macintosh and PC Operating Systems: Mac OS X and Windows 7