ARE MLB SIGNING BONUSES TOO HIGH? AN ANALYSIS OF PLAYER

PRODUCTION AND INVESTMENTS IN DRAFT PICKS

A THESIS

Presented to

The Faculty of the Department of Economics and Business

The Colorado College

In Partial Fulfillment of the Requirements for the Degree

Bachelor of Arts

By

Timmy Hall

May 2012

ARE MLB SIGNING BONUSES TOO HIGH? AN ANALYSIS OF PLAYER

PRODUCTION AND INVESTMENS IN DRAFT PICKS

Timmy Hall

May 2012

Mathematical Economics

Abstract

Major League Baseball (MLB) teams are often criticized for overspending on amateur

draft picks. Sports business professionals and the casual baseball fan argue that awarding

unproven amateur baseball players millions dollar signing bonuses is not a sound

investment. However, previous studies in sports economics have found that teams are

willing to sign better players to higher salaries or signing bonuses in order to increase

their production or team wins. Similar to the labor market, baseball teams will attempt to

create a product (team wins) by employing signing inputs (players who provide skills and

services). This paper attempts to quantify or place monetary values on a single total value

baseball statistic, Wins Above Replacement (WAR), to determine the Market Value of a

baseball player’s production. WAR represents the additional amount of wins a player

contributes to his team over a season. This paper then tests the overall performance and

investment of baseball draftees from the 1996 through the 2000 Rule IV Draft. Results of

this study reveal that professional baseball teams on average receive a very positive

return on their investment in amateur drafts picks.

KEYWORDS: (Major League Baseball, Rule IV Draft, Wins Above Replacement)

ACKNOWLEDGEMENTS

I would like to recognize and thank Dr. Kristina Lybecker for her aid and advisement

throughout this senior thesis project. Without her help, I would never have been able to complete

this project. Her guidance and support has made both this project and my academic experience at

Colorado College a fun and enjoyable four years. I would also like to thanks my parents, Tim

and Renee, for their unwavering support and patience with me. I cannot stress enough how much

I have appreciated your help and feedback throughout this project and my academic career.

TABLE OF CONTENTS

ABSTRACT

ACKNOWLEDGEMENTS

1 INTRODUCTION 1

Obstacle to Economic Analysis…………………………………………………. 2

Pioneering Economic Analysis…………………………………………………. 3

Conventional Wisdom and the Collective Bargaining Agreement……………... 4

Literature Review……………………………………………………………….. 6

Measurements of a Player’s Value…………………………………………. 6

The Value or Importance of a Win to a Team……………………………… 9

Classification of Major League Baseball Players………………………….. 12

Introduction Summary…………………………………………………………... 20

2 THEORY 21

Market Value of Win Shares……………………………………………………. 21

Market Value of WAR………………………………………………………….. 23

Adjustment Factor Model…………………………………………………... 26

Linear Regression Model…………………………………………………... 27

Market Value of WAR Model……………………………………………… 30

Conclusion………………………………………………………………………. 30

3 APPLICATION OF THE DATA 31

Data Set and Sources……………………………………………………………. 31

Description of the Data Set……………………………………………………... 38

Conclusion………………………………………………………………………. 40

4 RESULTS AND ANALYSIS 41

Discussion of Variables…………………………………………………………. 43

Draft Investment Analysis………………………………………………………. 46

Draft Investment Results………………………………………………………... 50

Conclusion………………………………………………………………………. 52

5 CONCLUSION 53

Future Research…………………………………………………………………. 54

Implications……………………………………………………………………... 56

SOURCES CONSULTED…………………………………………………………... 57

LIST OF TABLES

3.1 Summary Statistics of Regression Variables………………………………... 39

3.2 Summary Statistics of Market Value Variables……………………………... 40

4.1 Table of Results for Linear Regression Model………………………….. 42

4.2 Calculations for the Market Value of WAR………………………………… 44

4.3 Market Value of WAR………………………………………………………. 45

4.4 Success of Top 100 Draft Picks and Average Salary………………………... 46

4.5 Positive Draft Pick Evaluation: Kris Benson………………………………... 47

4.6 Negative Draft Pick Evaluation: Jim Parque………………………………… 48

4.7 First Overall Pick Evaluation: Pat Burell……………………………………. 49

4.8 1996-2000 Draft Analysis…………………………………………………… 50

LIST OF FIGURES

3.1 Free Agent Players and Salary………………………………………………... 32

3.2 Salary Versus WAR…………………………………………………………... 34

3.3 Free Agent Players and WAR………………………………………………… 35

1

CHAPTER I

INTRODUCTION

Major League Baseball (MLB) teams are notorious for spending large amounts of

money on their amateur draft picks. The general perception among the sport’s business

professionals and every-day fans is that it is money not well spent. Moreover, it is

popular notion that small and middle-market franchises suffer from this practice. We will

first examine the challenges of measuring the performance of baseball draftees and the

respective return on the bet or investment made on them. Additionally, we will assess

whether large market teams benefit at the expense of smaller markets. At the center of

our analysis will be a study previously undertaken by a current major league player that

demonstrates professional baseball teams on average receive a very positive return on

their investment in their amateur draft picks. Finally, a new measure of a baseball

player’s productivity will be substituted in the above referenced model to improve its

predictive capability.

A good example of the largess in baseball occurred recently in the 2011 June

Amateur Draft where the Pittsburgh Pirates awarded the first overall pick, UCLA pitcher

Gerrit Cole, an $8,000,000 signing bonus.1 This is an incredibly large investment in a

player who has yet to compete in a Major League game. Through economic analysis we

1 "Baseball-Reference.com - Major League Baseball Statistics and History " [cited 2011]. Available from

http://www.baseball-reference.com/.

2

will attempt to prove that this was not naïve wishful fancy but actually a rational

investment decision.

Obstacles to Economic Analysis

Even though the apparent financial excesses of the draft has been a controversial

topic for baseball General Managers (GM’s), players and fans for years, economists have

not rigorously analyzed it due to the difficulty in measuring the professional athletes

productivity. One such challenge is it usually takes years for a prospect to develop into a

legitimate major leaguer. It is a long time to fruition because after a player is drafted and

signs his professional contract he is sent to the Minor Leagues for years of skill

development. It is normally many years after he was selected in the draft before a special

player makes it to the Major Leagues. In fact, only “5 to 7 percent of all prospects who

sign professional contracts make it to the Major Leagues.”2 This long gestation period

makes it more difficult to track players progress and thus to study the ultimate

performance or outcome of recent amateur drafts.

Another challenge for baseball scientists has been the historical lack of a single

value statistic for analyzing a player’s production. Until recently economists had no

metric to analyze the market value of a player. To solve this problem, “Win Shares”

contribution was developed circa 2002 to measure the value of a position players and

pitchers in one single statistic. It combines classic sport specific statistics such as batting

average, on base percentage, fielding assists, and earned run average to evaluate a

player’s contribution to the team regardless of position.

2 Abrams, R.I. The money pitch: Baseball free agency and salary arbitration. Temple Univ Pr, 2000: 41.

3

Another reason why this topic has not been heavily researched previously is that

non-baseball people do not easily understand the draft and first-year contract system. The

historical arrangement has an indentured servitude element to it and is difficult to model.

Once a player is drafted and signed with the team he is bound to that team for a

maximum of twelve years and receives a minimal annual salary.3 The current Minor

League salary system awards first year players at its lowest level $850 per month,

regardless of their pick number in the draft. The salary of a first year player at the highest

level in the Minor Leagues, Triple-A, is only $2,150 per month.4

Pioneering Economic Analysis

In a 2006 study analyzing the success of financial investments in drafts, current

Major League Pitcher and Princeton alum, Ross Ohlendorf, calculated the market value

of a Win Share, the single unit measurement of a player’s contribution reference above.

To determine the market value of a Win Share, he compiled all players eligible for free

agency from the 1989 through the 2004 season along with their salaries and Win Shares

contribution for each season. After quantifying a player’s contribution or value to their

team using a linear regression of salary versus Win Shares, he analyzed the benefits of

each amateur player drafted from 1989 to 1993. Ohlendorf concluded the average rate of

return of all the draft picks was 60.30% and the average net gain from each pick was

3 12 years is the maximum amount of service time an organization can maintain the rights to a player as a

result of drafting him. The player is able to play six Minor League Seasons and six Major League

seasons but organizations typically lose or concede the rights more quickly.

4 "Pay Structure of Minor League Baseball Players." in National Sports and Entertainment Law Society

[database online]. [cited 11/21/2010]. Available from

http://nationalsportsandentertainment.wordpress.com/2010/03/17/pay-structure-of-minor-league-

baseball-players/.

4

$2,263,385.5 For this study, the same model will be used with WAR replacing Win

Shares as an independent variable. A new single statistic measuring player value, Wins

Above Replacement (WAR), has eclipsed Win Shares in popularity and effectiveness.

WAR compares players using offensive, defensive, and pitching statistics as well as the

year, the league, and the home ball park of the player.6 It is also very useful when looking

at the expected contribution of a player and his related salary. A tool previously

unavailable, this statistic enables researches to determine the market value of players and

measure the benefits of drafted players. WAR is the best tool available for comparing the

contributions of player and it will hopefully improve upon Ohlendorf’s previous analysis.

Conventional Wisdom and the Collective Bargaining Agreement

The majority opinion of the sports world is that signing bonuses are way too high

and they continue to get larger each year. Chris Holt, a former Major League pitcher for

the Houston Astros, stated, “It’s a lot to give to an unproven player. Some of those guys

are getting more than [players] in the All-Star Game.”7 One of the first steps toward

fixing a so-called flawed draft system was done this summer by Major League Baseball

and the Players’ Association. They signed a new Collective Bargaining Agreement

(CBA) this off-season with major changes aimed at reducing the size of player signing

bonuses. Proponents of the new deal believe the curbed spending on drafted players will

help level the playing field for smaller market teams. Opponents of the new system feel

5Ohlendorf, C. A. "Investing in Prospects: A Look at the Financial Successes of Major League Baseball

Rule IV Drafts from 1989 to 1993." (2006): 107.

6 "WAR | FanGraphs Sabermetrics Library " [cited 2011]. Available from

http://www.fangraphs.com/library/index.php/misc/war/.

7 Madden, W. C. Baseball's first-year player draft, team by team through 1999. McFarland & Co Inc Pub,

2001: 16.

5

that smaller to mid-market teams are at a disadvantage because aggressively signing draft

picks was one way for them to gain a competitive edge. The new CBA introduced

“Signing Bonus Pools” in an attempt to decrease spending on drafted players and level

the playing field. In short, teams will receive an allotted draft pool of money depending

on how many picks the team has in the first ten rounds and how early the team picks. For

example, the Astros who have the first pick in the 2012 draft will have the largest pool to

draw from.8 The introduction of “Signing Bonus Pools” clearly supports the popular

opinion that teams are overspending on amateurs.

While signing bonuses continue to increase for drafted players, average Major

League salaries also continue to increase, so signing players to large bonuses should still

be beneficial in today’s game. Also, more informative and updated statistics for

measuring a player’s quality or contribution to a team have been developed that will help

better analyze the player’s actual value to an organization reflected in the salary he

receives. This will improve the accuracy when comparing the market value of a free

agent versus the value of an amateur draft pick. Since a salary cap does not exist in

baseball, only a luxury tax is imposed on big-market teams. This study is especially

important for small to mid-market teams who are looking for a competitive advantage. If

these teams can make large initial investments to obtain Major League quality players in

the draft, they can field competitive teams with the most cost-efficient players. Moreover,

a team that loses a top free agent player is awarded a compensatory draft pick from the

8 "Impact of signing-bonus constraints on Draft remains to be seen | MLB.com: News " [cited 2011].

Available from

http://mlb.mlb.com/news/article.jsp?ymd=20111201&content_id=26066708&vkey=news_mlb&c

_id=mlb.

6

signing team.9 This study will enable teams to evaluate the true cost associated with

signing a free agent. Using updated total value statistics to measure player production,

this study will determine whether teams still receive positive financial returns on draft

picks and if the new collective bargaining agreement will aid or hinder smaller market

teams in the draft.

LITERATURE REVIEW

The purpose of this section is to review the literature on the financial success of

MLB draft picks. The chapter will be divided into three sections:

1) The first will address baseball statistics used to evaluate a player’s value or

contribution to a team,

2) The second will determine the value or importance of a win to a team, and

3) The third will discuss the methods for acquiring professional baseball players

focusing primarily on the First-Year Player Draft10

and free agency. It will

also discuss the service time classification of players and previous models

analyzing an organization’s returns on investing in players.

1. Measurements of a Player’s Value

Baseball has always been a game of statistics. When a hitter steps up to the plate,

it is common to see a display of his batting average, on-base percentage, and slugging

percentage. A pitcher’s success is normally evaluated by his earned run average and

strikeout-to-walk ratio (K/BB). Defensive statistics include errors and outfield assists. In

9 Under the new CBA signed in November 2011, teams are no longer guaranteed a compensation pick

after losing a free agent. In order to gain a compensation pick, a team has to offer a free agent a

one-year guaranteed contract equal to the average of the top 125 paid players in the MLB.

10

The first year player draft is also known as the Rule IV Draft. It is held in June and is the main source for

assigning amateur high school and college players to MLB teams.

7

2002, Bill James combined these and other statistics to create a single statistical

instrument to evaluate a player’s contribution to his team. James developed Win Shares

to quantify a player’s influence on the number of wins a team accrues over a season.

Each Win Share is worth a third of a win, so a team that wins 75 games during the season

is credited with 225 Win Shares. A player contributing 27 Win Shares is responsible for 9

of those wins according to James’ comprehensive formulas.11

A player’s Win Shares

explains his overall contribution to the team’s success during the season. The more Win

Shares a player has, the more valuable that player is to his team.

As introduced above, recently Win Shares has been replaced by another total

value statistic, WAR. Sean Smith’s WAR value calculates the added number of wins that

a player contributed to his team’s win total compared to the number of wins the team

would have received from a “replacement value” player or talent level player you would

pay the minimum salary on the open market.12

For example, WAR illustrates the value a

team would be losing if a player was injured and replaced by a minor leaguer or

minimum salary player. WAR quantifies a player’s contribution to his team by measuring

his offensive, defensive, and pitching statistics, adjusted for his defensive position,

playing time, the year, the home park, and the league context. An average starter at the

Major League level earns 2.0 WAR in a season.13

11

James, B., and J. Henzler. Win shares. STATS Pub., 2002.

12

"WAR | FanGraphs Sabermetrics Library " [cited 2011]. Available from

http://www.fangraphs.com/library/index.php/misc/war/; FanGraphs does an excellent job

explaining how WAR is calculated for position players and pitchers. Both FanGraphs and

Baseball-Reference.com have WAR statistics available. For this paper, Baseball-Reference.com

will be used to obtain WAR values.

13

Ibid.

8

Offensive statistics are expressed by a player’s contribution to the team in terms

of runs added. WAR examines a player’s total plate appearances during the season and

the number of runs he contributed to the team. The home park statistic accounts for the

number of runs certain parks are known to produce since players play the majority of

their games during the season at home. If a player plays in a home park known for

depressing run scoring, his offensive WAR value will be adjusted and increased. The

opposite occurs for a player with a home park that induces run scoring.

Defensive statistics attempt to quantify how many runs a player saved or gave up

in the field. For players in the infield and outfield, WAR looks at how large a player’s

range is or how many hit balls he is able to field cleanly. It also accounts for the number

of errors he makes above the average player. Outfielder assists and infielder double plays

are also measured. 14

Some defensive positions are significantly harder than others. A

catcher fields the ball in every play, calls the pitches, and manages the opposing team’s

base runners. The first basemen rarely fields the ball, turns double plays, or makes strong

throws so the value of his defensive position is much lower than a catcher or center

fielder who controls the outfield. If comparing a catcher and first baseball with similar

defensive statistics, a catcher will have a higher adjusted value.

A pitcher’s WAR values explain the pitcher’s responsibility for the runs he allows

based on his walks, strikeouts, and home runs. There are also league-specific factors that

adjust a pitcher’s values. For example, the American League has a designated hitter in the

lineup to replace the pitcher. The designated hitter is usually a skilled hitter with home

run potential who does not have to play in the field. Pitchers are typically horrible hitters

14

An outfield assist is when an outfielder throws a ball into the infield and creates an out at one of the

bases. A double play occurs when a number of infielders and /or outfielders create two outs in one

play.

9

with very little run producing potential. Therefore, a pitcher in the American League will

be valued higher than a pitcher in the National League with similar statistics.

Win Shares and WAR have greatly improved the ability of researchers to study

the value of a player’s contribution to his team and analyze the returns on investing large

signing bonuses on Major League Draft Picks. In this study, WAR values will determine

whether signings bonuses have been successful investments for organizations. If the

organizations see a positive return of their investments, a player’s negotiating power will

be greatly improved. However, if the cost of the investment exceeds the benefits, teams

will be likely to decrease their signing bonus offers or sign fewer individuals.

A team that does not re-sign a top free agent is rewarded with two additional draft

picks in the Rule IV Draft. One of the additional draft picks is from the signing team of

the free agent.15

The overall value statistic, WAR, will greatly aid in assessing a

monetary value of a lost draft pick when a team is looking to sign a top free agent.

Moreover, the model will help determine the full cost of signing top free agents for

organizations.

2. The Value or Importance of a Win to a Team

A team’s success, television contracts, media exposure, fan base, advertising

capabilities, and ticket and concessions sales are all important factors in generating

revenue. A fundamental question for all organizations in sports is which factors or

variables generate the most revenue? Sports economists, Scully, Zimbalist, and

Ohlendorf, have theories of their own as to which variables are most important in

15

Under the 2007-2011 Basic Agreement, up to two picks were awarded to teams that had lost a Type A or

Type B free Agent. Compensation for Type A free agents was a draft choice of the signing club and a

special draft choice from the league. Compensation for Type B free agents is a special draft choice

from the league.

10

generating revenue. The goal from the studies was to determine that as team success or

the number of wins in a season increases, team revenue will increase. The only way to

increase wins during a season is to sign players that will contribute wins to the team.

Therefore, signing players from the Rule IV Draft and through free agency is critical for

teams who want to win and generate revenue.

Scully’s 1989 model includes city population, a team’s record for the current

season, and its record for the previous season to estimate team revenues for the 1984

season. He concluded “Club receipts basically are determined by the quality of the team

and the size of the market.”16

The results showed that 75% of the team revenue for the

season was explained by these three variables.17

However, the model has a small sample

size of 24 teams and the data was only analyzed over one major league baseball season.

Zimbalist’s 1992 study analyzed data from the 1984 to 1989 baseball seasons. He

built upon Scully’s model by adding three more variable to explain team revenue. He

added per capita income, a Boolean variable that accounts for the disparity in average

revenue between leagues, and a trend variable that explains the annual rise league

revenues. Zimbalist’s results fared slightly better than Scully’s. 77% of the team revenue

for the 1984 through 1989 time period was explained by the six independent variables.18

The significance of these studies is that team winning percentages along with

population size, per capita income of the team’s city, and other Boolean and trend

variables have a fairly strong effect on team revenue. However, teams have little control

16

Scully, G. W. The business of major league baseball. University of Chicago Press Chicago, 1989: 119.

17

Ibid: 119.

18

Zimbalist, A. "Salaries and performance: Beyond the Scully model." Diamonds Are Forever: The

Business of Baseball, edited by P.Sommers.Washington, DC: Brookings Institution (1992): 116.

11

over the population and wealth of the city they play in. They do have control over the

quality of the player’s they sign. By increasing the quality of their team by signing

productive players, the team’s winning percentage will increase and the organization will

generate more revenue according to both Zimbalist and Scully studies.

Ohlendorf’s 2006 thesis found flaws in previous studies regarding team revenue.

He ran a linear regression using Scully’s model for the years 2001-2004 and found the R-

squared value to be significantly lower than the value from the 1984 study, and

concluded that the model no longer accurately estimates team revenues. The p-value for

the coefficient of winning percentage was very small which means the variable is

statistically significant and team wins still explain revenue. Ohlendorf reached similar

conclusions when he ran a linear regression on Zimbalist’s model for the more recent

years 2001 through 2004. He found that per capita income was the only variable that

decreased in significance since 1989 and that the variables, wins for the current season

and wins from the previous season, were significant at the 98% level.19

While the models

explained less of the variance in team revenue, a team’s success was still strongly and

positively correlated with generating team revenue. Ohlendorf created his own model

citing a variety of factors in addition to those used in the Zimbalist and Scully models

which influence a team’s revenue in more recent years. Two variables of concern

included city population to represent the market size of a team and the effect of win

percentage on short-term revenue changes. In addition to the explanatory variables, win

percentage of the current season and win percentage of the previous season, Ohlendorf

created a trend variable and Boolean variables to explain team-specific factors that

19

Ohlendorf, C. A. "Investing in Prospects: A Look at the Financial Successes of Major League Baseball

Rule IV Drafts from 1989 to 1993." (2006): 6.

12

generate revenue including market size, market quality, and media contracts. His

regressions showed that the Boolean variables measuring the market quality of teams

were far better indicators for generating team revenue that city population and per capita

income. For example, Boston had the third best baseball market when this model was run

but was the sixteenth largest city in the league.20

Ohlendorf’s analysis concludes that

market quality depends on team-specific factors rather than city population and per capita

income. Also, by isolating the majority of permanent factors causing variance in revenue

among teams and drawing on knowledge of historical revenues, he was able to test

whether a team with a winning tradition would continue earning the same revenue in a

losing season and that winning more games would not increase revenue for a team

accustomed to losing. The coefficients for winning percentage were both positive and

significant. This supports that improving a team’s record between consecutive seasons

does influence revenue regardless of the winning or losing traditions of a team.

Ohlendorf’s model concludes that teams can significantly increase revenue by increasing

team wins. In today’s game, managers are willing to sign players to multi-million dollar

deals in order to field competitive teams. As the value of winning percentage to revenue

increases, salaries are only going to increase since the greater a player’s production, the

better chance the team has of winning.

3. Classification of Major League Baseball Players

Salaries are determined not only by a player’s production and contribution to the

team’s success, but also by the amount of time he has played in the Major Leagues. MLB

20

Ohlendorf, C. A. "Investing in Prospects: A Look at the Financial Successes of Major League Baseball

Rule IV Drafts from 1989 to 1993." (2006): 7.

13

players can be divided into three different service time classifications: free agents,

arbitration eligible players (ARP’s), and pre-arbitration eligible players (PARP’s).

Free Agents: In 1976, a new Basic Agreement was signed by team owners and

the Major League Baseball Player’s Association (MLBPA) that granted free agency to

players after accumulating six years of major league service time. Prior to 1976, a

reserve clause bound players to a team for his entire career after signing his first contract

unless his rights were sold or traded.21

Players were unable to test the open market and

therefore had very little leverage in contract negotiations. Without competing offers from

other teams, players received lower salaries and were paid just enough money to prevent

them from switching professions. With players allowed to enter free agency after six

years of major league service time, average salaries increased dramatically.

Reserve Clause Players (ARP’s / PARPS’S): Before attaining free agency, a

player is bound by the reserve clause and can only negotiate contracts with his current

team. “Reserve clause players” can be further divided into ARP’s and PARP’s depending

on their service time. Under the current Basic Agreement, players can be eligible for

salary arbitration after two years of Major League service.22

The agreement under salary

arbitration is that both the team and the player must accept the salary agreed and decided

upon by a panel of three judges. If a player and team cannot finalize a contract agreement

by a specific date, both sides will submit their arguments for what they believe the

player’s salary should be for the upcoming season. ARP’s tend to have significantly

higher salaries than players who are not yet eligible regardless if a player agrees to a new

21

Abrams, R. I. The money pitch: Baseball free agency and salary arbitration. Temple Univ Pr, 2000: 28.

22

"Analysis: CBA makes several changes to Draft | MLB.com: News " [cited 2011]. Available from

http://mlb.mlb.com/news/article.jsp?ymd=20111122&content_id=26030810&vkey=news_mlb&c_id

=mlb.

14

contract or takes his team to arbitration. PARP’s normally earn around the league

minimum salary since they have very limited service time. These players typically do not

have guaranteed salaries and can be sent down to the minor leagues at any time.

Ohlendorf Model Classifications: Ohlendorf classified all the players from 1990

season into free agents, ARP’s, or PARP’s to show that salary not only is dependent on

the skill or production of a player but also his service time. He also found the salaries and

Win Shares of every player. Win Shares is the total value statistic mentioned above to

measure a player’s contribution to the team success. He found PARP’s were paid far less

on average than free agents and ARP’s and that ARP’s were significantly more cost

efficient than free agents in terms of production. Moreover, the average pay per Win

Share for PARP’s, ARP’s, and free agents were $21,835, $65,820, and $109,521

respectively.23

Ohlendorf then broke down each player type team by team and found that

the majority of players on each team are free agents. Over a third of most rosters

consisted of free agents. While a General Manager (GM) would certainly prefer a

“reserve clause player” to a similarly productive free agent due to cost-efficiency, these

results show to it is hard to find younger players of Major League quality so GM’s have

little choice but to sign free agents. This study will not look at the difference between

ARP’s and PARP’s. It will analyze players under their first Major League contract,

“reserve clause players”, and free agent eligible players. However, it is interesting to see

the increased marginal rate of return from ARP’s and PARP’s versus free agents.

Call-Ups: Major League Baseball teams can also obtain players through call-ups

and trades. If a player is doing very well in a team’s Minor League system, a GM can

23

Ohlendorf, C. A. "Investing in Prospects: A Look at the Financial Successes of Major League Baseball

Rule IV Drafts from 1989 to 1993." (2006): 20.

15

promote the player to the Major League roster. This will most likely be a “reserve clause

player” who the GM believes is ready to compete at the Major League level or is needed

to fill a position until the full-time starter is ready to resume his position again. Moreover,

these transactions typically occur throughout the year to temporary fill in for injured

players and do not result in full-time service for the team.

Trades: Trades occur when teams need to fill a vacancies or weak spots in their

rosters. Trades in Major League Baseball occur in slightly different for ways. For

example, it is most common to see a team trade a free agent for a group of Minor League

prospects in order to reduce current Major League talent and payroll and increase the

quality of their Minor League system and number of “reserve clause players”. Teams

seeking free agents through trade are normally only concerned with short-term winning.

These are teams in the playoff picture and want to strengthen their roster with the chance

of winning the World Series at the end of the year. Teams that want to get rid of free

agents are usually thinking about the long-term benefits of acquiring younger players and

are not in the playoff run.

By signing a free agent and not going through the trade process, a team is able to

improve its Major League talent without forfeiting Minor League prospects. While the

team’s payroll increases, the organization’s talent improves and the player immediately

advances the quality of the organization. Free agents are unique because their salaries

reflect the production they will give to a team in the upcoming year. Ohlendorf stated, “A

free agent’s salary resembles his expected value to the organization because it is

determined by competitive bidding on the open market.”24

His study looked at all players

24

Ohlendorf, C. A. "Investing in Prospects: A Look at the Financial Successes of Major League Baseball

Rule IV Drafts from 1989 to 1993." (2006): 26.

16

who declared for free agency starting after the 1989 season and going through the 2004

season and analyzed their salaries and Bill James’ Win Shares to determine the player’s

overall contribution to his team. The results determined that there is a strong positive

correlation between salaries and Win Shares for free agents. However, the relationship is

not exactly linear because salaries are determined before the season and solely reflect the

projected performance of a player. Using the average salaries of players with equal Win

Shares contribution, Ohlendorf created a model to predict the market value of Win

Shares. By doing this he could compare the salary of a player under “the reserve clause”

to the salary of a free agent with equal production or contributing amount of wins.

Results determined the internal rate of return from the total investments in draft picks was

60.30%. This suggests that organizations benefitted greatly from signing players in the

amateur draft.

Draft: The Major League Baseball Rule IV Draft is another way for teams to

acquire players. The draft process was initiated in 1965 to deter the increasing size of

signing bonuses awarded to amateur players.25

The goal was to inhibit players from

signing with the highest bidder to maintain a level playing field for all teams. However,

the draft is a complicated because of leverage and the “signability” of players.

Unlike free agency where a player can negotiate with all teams, the Rule IV Draft

limits a player’s options significantly. The player has the choice to sign with the team that

drafted him or wait until he is eligible for the draft process again. Most college players

are eligible after one full year of play but if a player is drafted out of high school and

chooses to attend a four-year university, he is not eligible to be drafted again until he

25

Madden, W. C. Baseball's first-year player draft, team by team through 1999. McFarland & Co Inc Pub,

2001: 9.

17

turns 21 or has attended three years of college. Refusing to sign with a team is a risky

option as a player’s stock or value can decrease in a year due to poor play, injuries, and

off-field issues. However, Major League teams do not penalize high school players or

college juniors who refuse to sign since they are attending school, developing their skills,

and maturing. College seniors have the least amount of leverage because they have to

sign with the team or pursue another profession. Ohlendorf studied the effects of leverage

on the draft by dividing amateur players into three different groups: college seniors,

college juniors that typically have one year of eligibility left, and players with at least two

years of eligibility left that include players that just finished high school, one year of

junior college, or their sophomores years at college. His results suggest that college

seniors are at a significant disadvantage since their signing bonuses are on average lower

than the other two groups throughout the entire draft. He found no significant difference

between college juniors and younger players in the first two rounds, but that younger

players may have a slight advantage in the later rounds.26

Signing bonuses are determined by a player’s leverage in negotiations and his

draft position. The higher the draft position of a player, the more money he is able to

demand. Organizations value higher picks and are more willing to sign them so signing

bonuses decrease as the pick number in the draft increases. However, this is not a linear

relationship and the best available player is not always drafted when the “signability” of a

player is questioned. If a player is thought to be hard to sign because he or his advisor has

hinted at demanding a very large signing bonus, he has “signability” issues. Players

labeled hard to sign will fall to lower picks in the draft. Typically, a large market team

26

Ohlendorf, C. A. "Investing in Prospects: A Look at the Financial Successes of Major League Baseball

Rule IV Drafts from 1989 to 1993." (2006): 35.

18

will sign this player in the lower rounds of the draft but award him with a large bonus that

is far greater than the bonuses of players near him in the draft order. This is major

problem and one that was addressed in the new CBA.27

By allotting teams “Signing

Bonus Pools”, players will hopefully get drafted in a position that their skill indicates

rather than their “signability”.

As discussed earlier, Major League Baseball has a three-tiered market system for

players. There are “reserve clause players” which include all players under their first

Major League contract and there are free agent eligible players. Miller (2000) studied the

comparison in how arbitration-eligible salaries and free agent salaries were negotiated.

After running regressions based on productivity for the two data sets, Miller concluded

that there is a difference in salary structure for arbitration-eligible and for free agent

position players. His results determined that the experience factor carries a higher weight

in the negotiations than a wear-and-tear factor for “reserve clause players” and the

opposite holds true for free agents.28

The wear-and-tear factors refer to an organizations

fear of losing a player due to age and past injury problems over the course of a 162 game

season. In addition, the arbitration-eligible system will result in lower negotiated salaries

relative to the free agent system for comparable position players because “reserve clause

players” in the arbitration-eligible system can only negotiate with their current team.

After free agency was granted in Major League Baseball in 1976, 25 Major

League and Minor League players chose to be eligible and were named the “first family”

of free agents. For labor economics, the world of sports is a fascinating topic because the

27

The new CBA was signed in November 2011. It introduced “Signing Bonus Pools” to the First-Year

Player Draft. This puts a limit on the amount of money each team can spend on signing bonuses.

28

Miller, P. A. "A theoretical and empirical comparison of free agent and arbitration-eligible salaries

negotiated in major league baseball." Southern Economic Journal (2000): 87-104.

19

pay structure of the productivity of athletes is mostly accessible. Sommers and Quinton

(1982) studied the marginal revenue products of the 25 baseball players who declared for

free agency at the close of the 1976 season. They compared the player’s salaries to the

effect the player’s statistics had on the team’s winning percentage for the season. For

hitters, they looked at their slugging percentage times his fraction of the team’s at-bats.

Pitchers had their strikeout-to-walk ratio analyzed. Of the 14 highest paid free agents,

the model concluded that they all earned their 1977 salaries. In fact, the highest paid free

agent, Reggie Jackson of the New York Yankees, generated gross revenue of over

$1,000,000 for the team. Note that this was in 1977 and Jackson’s annual contract cost

was $580,000. 29

Wins are very important to a Major League organization. Similar to a labor

enterprise, a baseball team attempts to create a product (team wins) by employing or

signing inputs (players who provide skills and services). It is helpful for an organization

to understand its production function and marginal rate of return from its players. Do

individual skills in baseball including hitting, running, defense, and pitching produce

wins or does a power hitter create more runs than a singles hitter? For example, teams

should be able to place a value on different types of hitters and pitchers.

Zech (1981) used the Cobb-Douglas function below to determine which baseball

skills contribute to a team’s production and the value of a player:

Y = AL1/2

K1/2

Zech’s findings indicated that increasing returns to scale exist in the use of baseball skills

reinforcing the idea that teams are willing to sign better players to increase their

29

Sommers, P. M., and N. Quinton. "Pay and performance in major league baseball: The case of the first

family of free agents." The Journal of human resources 17, no. 3 (1982): 426-436.

20

production (team wins). In addition, hitting for average is by far the important factor

contributing to a team’s success.30

This is valued of pitching, defense, and running. This

explains the continued increase in salary demands for elite-level hitters in Major League

Baseball.

Summary

The theoretical literature pertaining to the financial success of draft picks is very

limited. Due to the complexity of MLB draft and vast amount of data needed to study the

benefits of signing drafted players, economists have not attempted to model this

phenomenon before Ohlendorf. Ohlendorf created a model so complex and intriguing that

it is worth building upon. This paper will proceed as follows. Chapter two will discuss

the theory of the market value of a player’s production. It will also analyze the original

model for determining the financial return of a draft pick’s signing bonus by looking at

his contribution at the Major League level prior to reaching free agency versus a player of

similar quality available on the open market. Chapter three will explore the data set and

analyze the investment on draft picks using the theory of market value. Chapter four will

be an analysis the results. Chapter five will provide the conclusion as well as ways to

improve this study, implications of this paper, and suggestions for future research.

30

Zech, C. E. "An empirical estimation of a production function: the case of major league baseball." The

American Economist 25, no. 2 (1981): 19-23.

21

CHAPTER II

THEORY

The theory discussed in this chapter is necessary to determine the success of the

financial investments in the amateur drafts. By determining the market value of WAR,

the production of a “reserve clause player”1 and a free agent player with similar WAR

values, the difference in their salaries can be analyzed. The difference in their salaries

was often very large and organizations saw large return on their investment in “reserve

clause players” who made it to the Major Leagues.2 The purpose of this chapter is to

discuss the theory behind the market value of a player’s contribution to the team’s

success. The chapter will be divided into two sections:

1) The first will discuss the original theory for determining the market value of

Win Shares.

2) The second will discuss the general theory that will be applied to a new model

to determine the market value of WAR contributions.

1. Market Value of Win Shares

Creating a model to determine the market value of Win Shares is critical for

comparing players against one another. Since players are paid based on their expected

contribution, their salary does not reflect their actual production on the field. The salaries

1 “Reserve clause players” are under their first professional contract and are property of their for a

maximum of six years. Their salaries are very small compared to free agents who can negotiate with

all 30 MLB teams.

2 Ohlendorf, C. A. "Investing in Prospects: A Look at the Financial Successes of Major League Baseball

Rule IV Drafts from 1989 to 1993." (2006): 125.

22

are determined before the season begins and many factors may hinder the success of a

player’s season including injury, off-field distractions, and slumps. So a salary only

represents the expected contribution a player will provide during a season.

The Win Shares and WAR formulas were created to effectively evaluate the

difference between position players and pitchers. However, the difference in the positions

is drastic and difficult to compare. They are three major reasons that support the

argument that position players and pitchers should be separated into groups when

calculating market value. First, Ohlendorf (2006) found the range of Win Share

contributions to be much greater for position players. Second, the average salaries of

starting pitchers tended to be significantly higher than those of comparable free agents.

Third, the cutoff value indicating a replacement player for position players and pitchers

was different for each group. For example, a position player earning a minimum 2 Win

Shares is considered a capable starter compared to a minimum of 4 Win Shares for a

pitcher.3 The three discrepancies will be checked using WAR to see if problems still

exist. Hopefully these discrepancies can be eliminated if WAR is a better measure of the

value of both position players and pitchers.

It is safe to assume that a linear regression with dummy variables adjusting for the

slope best represents the relationship between salaries and Win Shares or WAR

contribution. In a previous study, adjusting for the intercept of the regression by adding

dummy intercept variables for each year produced a regression produced negative outputs

for several of the years. This suggested that position players earning 2 Win Shares are

worth less than the league minimum. Also, the previous study showed that the regression

3 Ibid: 74.

23

did not appear quadratic as the results yielded a very low R-squared value. This supports

that the data is best explained by linear variables and that a linear regression is an

appropriate model for the data. The two final regressions from the previous model

consisted of position players earning 2 or more Win Shares and pitchers earning 4 or

more Win Shares. For this study, the regressions will consist of players earning at least a

2.0 WAR or better so the data can be better explained by the regressions. Also, negative

outputs will be reduced for players earning lower WAR values.

2. Market Value of WAR

Linear regressions will explain the variation in free agent salaries by the WAR

value a player earned in a season. The regression results will determine the market value

of a player’s performance during the season. The regression first must be weighted to

account for the uneven distribution of the number of players earning a WAR value. For

example, the average salary of 30 players earning 2.0 WAR is a much stronger estimate

of expected salary than one player earning 5.2 WAR. Position players and starting

pitchers earning at least 2.0 WAR are considered full time starters at the Major League

Level. Relief pitchers typically earn around 1.0 WAR if they have a very strong season.4

Relief pitchers are much harder to analyze using data because many of these players only

contribute a few outs in an entire game. For example, teams often bring in a relief pitcher

to get a single batter out and then they remove him from the game. While relief pitchers

are invaluable to a team, their contributions are not always reflected in their WAR values.

However, many closers who enter a game to get the last three outs earn at least a 2.0

4 "Baseball-Reference.com - Major League Baseball Statistics and History " [cited 2011]. Available from

http://www.baseball-reference.com/.

24

WAR or better. For this study, only pitchers and position players earning a 2.0 WAR or

better will be included in determining the Market Value of WAR.

To account for annual fluctuations in average salary and average salary per WAR

over multiple seasons, Boolean or dummy variables must be used. In Major League

Baseball, an organization’s willingness to pay for WAR differs yearly and a player’s

market value depends not only on his WAR production but also the year he played in. For

this model, average salary is the response variable, the explanatory variables are the

dummy variable year and WAR, the constant coefficient is m1, and the coefficients m2

through m16 represents the change in the monetary difference of WAR in a given year of

free agent players.5 The dummy variables are adjusted for the slope since average salaries

of the weakest players remained similar throughout the years. The dummy variables

represent years 1996 through 2010 (X96-X11). It is appropriate to suggest to a constant

intercept. For collinearity reasons, the 1997 season was omitted from the regression.

Salary = m1 + m2*X96+ m3*X98 +…+ m16*X11 + WAR 2.1

The regressions must be modified in order to determine the market value of WAR

for each season. It is under the assumption that the distribution of a player’s production is

roughly normal and the relationship between free agent salaries and WAR appears linear.

These two statements allow us to conclude a sample of salaries and contributions of a

group of free agents should reflect the organization’s willingness to pay for expected

contributions or WAR value. Minor League players can obtain 0.0 WAR and possibly 1.0

WAR in a season and their salaries are not accurate measurements of true free agent

5 All of the following equations in this chapter are based on Ohlendorf’s model for determining the

Market Value of Win Shares.

25

players and full-time starters at the Major League level.6 In order to estimate the market

value of free agents, only free agents with 2.0 WAR or better were included in the data.

To accurately adjust for the market value of WAR, many risk factors including

playing time need to be analyzed. The difference between a free agent’s expected

contributions and actual contributions are often different, so the sole knowledge of a

player’s salary is useful information. Free agent salaries as well as their corresponding

WAR value determine their actual contributions to organizations. A player’s salary is

determined by the equation:

Expected Salary = P(Active)*EV(Active) + P(Replaced)*EV(Replaced) 2.2

P(Active) and P(Replaced) are the probabilities of a player remaining active or being

replaced. EV(Active) is the expected value of an active player and is equal to the salary a

player should be rewarded if he stays active and plays in the majority of the games over a

season or if he becomes replaced. EV(Replaced) is the expected value of a replaced

player and is equal to a salary around the league minimum since replacement players

typically earn very low WAR values. Since player expectations and salaries are not

uniform, it is difficult to determine the “net loss” of an inactive player. A player with a

higher salary and the subsequent value if replaced will be different from a player with a

lower salary and his replacement value, thus more adjustments must be made. To

simplify, another assumption will be made stating that replaced value and the associate

probabilities, P(Active) and P(Replaced), are independent of salary and that these will be

constant values for all players.

6"Wins Above Replacement (WAR): Analyzing MLB Statistics using Sabermetrics: MLB reports " [cited

2012]. Available from http://mlbreports.com/2012/01/11/war/.

26

The adjustment factor is proportional to a player’s salary indicating that the larger

the contract, the greater the risk. The coefficient a1 represents (P(Active)/P(Replaced)).

The coefficient a1 represents the probability of a player becoming replaced divided by the

probability of a player remaining active. The coefficient a2 represents the expected value

of a replacement player, EV(Replaced). The expected value of a replacement player is

equal to the average salary of a substitute player or Minor League player. Due to time

constraints, previous values of a1 and a2 found in Ohlendorf’s model will be used in this

model. The coefficient a1 will remain the same throughout all years. The coefficient a2

will be adjusted from the year 2004. The value will be adjusted using average Major

League salary of each year to find its value over all the years in this study. For example,

the percentage increase or decrease from the average MLB salary in 2004 for a given

year was multiplied by a2 for the 2004 season to find a2 for the given year.

The adjustment factor is now given by:

Adjustment(x) = a1*(Salary(x) – a2) 2.3

where a1 = 0.134332 and

a2 = $357,062 for year 2004.

As mentioned earlier, a linear relationship exists between salary and WAR. The

greater a player’s WAR value, the more wins he contributes to the team, and the greater

the player’s value to the organization so his salary should increase.

The linear relationship between salary and WAR is determined by the following

equation. The constant coefficient is m1 and m2 through m16 represents the coefficients of

a dummy variables. Both coefficient values can be obtained by running a regression on

Equation 2.4. For this model, the coefficient m16 corresponds to the dummy variable year

27

2011 and the value of m16 represents the change in the monetary difference of WAR in

2011. The variables below, b1 and b2, will be used in the Market Value equations of

WAR. The linear regression model used in this study is the following Equation 2.5:

Salary = m1 + m2*X96 + m3*X98 + m4*X99 + m5*X00 + m6*X01 + m7*X02 +

m8*X03 + m9*X04 + m10*X05 + m11*X06 + m12*X07 + m13*X08 + m14*X09 +

m15*X10 + m16*X11 + WAR 2.4

where m1 = Intercept = b1 = -1,710,603,

m16 = Slope for year 2011 = b2 = 7,543,041, and

WAR = 1,347,422.

For simplicity’s sake, the coefficients m1 and m16 are replaced by b1 and b2

respectively. Using the coefficients a1 and a2 as well as the variables b1 and b2 found using

Equation 2.4, the complete list of Market Value Equations and the adjustment factor for a

given WAR value total can be written below. The variable x represents the WAR

contributions of free agent players.

Salary(x) = b1 +b2*x 2.5

Adjustment(x) = a1*(b1 + b2*x – a2) 2.6

Market Value(x) = Salary(x) + Adjustment(x) 2.7

Market Value(x) = b1 +b2*x + a1*(b1 + b2*x – a2) 2.8

Market Value(x) = Salary(x) + a1*Salary(x) – a1*a2 2.9

Market Value(x) = b1 + a1*(b1 – a2) + b2*(1 + a1)*x 2.10

Change in Market Value as a function of x = b2*(1 + a1) 2.11

From Equations 2.5-2.11, summing the adjustment for each player written as

“a1*Salary(x) – a1*a2” equals the amount that free agents were underpaid relative to their

28

contribution value due to the risk of replacement. This amount is equal to the cumulative

adjustment or expected loss from the chances that a free agent is replaced. The

cumulative adjustment can be estimated by finding the “net loss” of players contributing

less than a 2.0 WAR.

The market value of free agent contributions less than 2.0 WAR and their

corresponding salaries must be collected to find the “net loss” for a given year. This is an

entirely separate model of the linear regression model used in this study. A previous

study’s model will be used to determine the total net loss, which is equivalent to the

salaries earned by these players subtracted by the total value of their contributions.

Ohlendorf (2006) created a model with the following variables to find the total

net loss of players contributing less than the average Major League starter. The variable c

is equal to the minimum Major League salary for a given year and represents players

earning negative WAR. The variable c1 represents the salary of a Major League player

who obtained a WAR value between 0.0 and 2.0 and could be replaced by a Minor

League player or substitute.

The total “net loss” for a given year is the difference between the average salaries

of players earning less than 2.0 WAR and the market value of these WAR values

multiplied by the number of players earning these values. The expression Net Loss(0)

represents the “net loss” for players earning 0.0 WAR or less. The expression Net Loss(1)

represents the “net loss” players earning between a 0.0 and 2.0 WAR.

29

The following equations describe how “net loss” was calculated in Ohlendorf’s

model which enables the Market Value of WAR for free agent players for a given year to

finally be analyzed. Average salary (AS) is also used to determine “net loss”.

Net Loss(0) = (AS(0) – c)*N(0) 2.12

Net Loss(1) = (AS(1) – c1)*N(1) 2.13

where c = League Minimum,

c1 = Average salary of a free agent earning between a 0.0 and 2.0 WAR =

(c + b1 + 2b2)/2,

N(0) = Number of free agents earning 0.0 WAR,

N(1) = Number of free agents earning between a 0.0 and 2.0 WAR,

AS(0) = Average salary of free agents earning 0.0 WAR, and

AS(1) = Average salary of free agents earning between 0.0 and 2.0 WAR.

Ohlendorf (2006) used equations 2.12 and 2.13 to find the coefficients a1 and a2

respectively.

The model for the Market Value of player’s contributions in Equation 2.14 was

developed by Ohlendorf (2006). Ohlendorf also created the formulas for the variables, A

and B. The variable A represents the adjustment process for the Market Value of WAR

using the estimated replacement value for a given year. The variable B represents the

change in the Market Value of a WAR contribution from a specific year. The variable X

represents the WAR value of a player for a given season.

30

Market Value(X) = A + B*X for all X 2.14

where A = b1 + a1*(b1 – a2) and

B = b2*(1 + a1).

Equations 2.12-2.14 are examples of the entire process used for determining the Market

Value of a specific WAR contribution. The estimated benefits to organizations of the top

100 draft picks from the 1996 through 2000 drafts can now be determined.

This chapter provided an overview of the theories of the linear regression model

and previous models developed by Ohlendorf that will be tested in the following

chapters. The theory and methodology described in this chapter showed how the linear

regression model of salary versus years and WAR was used to find the changes in WAR

values as the year changes. The methodology describes the model Ohlendorf produced to

determine the Market Value of WAR. The following chapter will describe the data set

and make predictions about the variables and their influences on salary and Market

Value. Chapter 4 will utilize the theory and methodology in this chapter to analyze the

benefits organizations receive from amateur draft picks.

31

CHAPTER III

APPLICATION OF THE DATA

This chapter will provide detailed descriptions of the data set and time period of

the data collected. After computing the Market Value, the production or contribution of

drafted players or “reserve clause players” to their teams under their first Major League

contract will be compared to similar free agents market value. The net profit or loss of the

organization was then determined by subtracting the signing bonus and discounted salary

the “reserve clause player” received from the salary he would have received if he was

eligible for free agency. The purpose of this chapter is to explain the data set that will be

used to test the theories in the previous chapter and analyze the financial success of

investing in amateur draft picks. The primary econometric analysis will be to evaluate the

market value or monetary value of a Major League Baseball player’s production and then

determine whether organizations are overpaying or undervaluing amateur draft picks.

This chapter will begin by discussing the time frame and description of the data set. The

source of the data will also be explained as well descriptions of each individual variable

and explanations for the predictions made about expected results.

Data Set and Sources

The linear regression model that will be developed in this paper uses data from

the 1996 to 2011 MLB seasons. The model uses a data set consisting of all players who

filed for free agency at least once starting at the conclusion of the 1995 season through

32

the 2010 season. Players eligible for free agency after the 2011 season will not be

included in this paper because their statistics cannot be analyzed against their new salary.

511 free agent player seasons were analyzed over this time period so the data set is very

robust and will have a large number of degrees of freedom. The following figure shows

all 511 free agent seasons and their salary:

FIGURE 3.1

FREE AGENT PLAYERS AND SALARY

3150000027000000225000001800000013500000900000045000000

70

60

50

40

30

20

10

0

Salary

Fre

e A

ge

nt

Pla

ye

rs

Histogram of Salary

Source: Author’s calculation.

Each of the 511 data points consists of a free agent player earning at least a 2.0

WAR and his corresponding salary for one season. MLB.com provided all free agency

filings from 2004 through 2010. Retrosheet had a list of all free agent eligible players

from the 1995 season through 2003. Both of these sources were used since Retrosheet did

not have any record of free agent filings after the 2003 season. Each data entry will

33

consist of a free agent’s salary and his WAR contribution for the season. The WAR

statistic has been used to predict or determine a player’s salary by many analysts and

experts and is available at baseball-reference.com. Clearly, a strong positive correlation

exists between the two variables since better free agents tend to earn more money than

less productive ones. Retrosheet is a baseball archives website that has thorough free

agent and player salary information.1 MLB.com has a free agent tracker tool that lists

every free agent and additional details from the year 1996 to present day. The data

included the player position, signing status, former team, new team, and number of years

on his new contract.2

WAR is a powerful tool because it accounts for offensive and defensive aspects of

the game as well as the difficulty of position, the league of the player, and the home ball

park of the player in one single value. One of baseball’s major statistical databases

defines WAR for the average fan, “If this player got injured and their team had to replace

them with a Minor Leaguer or someone from their bench, how much value would the

team be losing.”3 For position players, WAR is calculated from two statistics, Weighted

Runs above Average (wRAA) and Ultimate Zone Rating (UZR). Offensively, wRAA

explains how many added runs a player contributes to the team when he is at the plate.

Defensively, UZR measures his defensive skills by how out a player contributes either by

fielding or catching balls. A pitcher’s WAR is measured with a statistic called Fielding

1 "FREE AGENT FILINGS, 1974-2003 " [cited 2011]. Available from

http://roadsidephotos.sabr.org/baseball/freeagts.htm.

2 "2011 MLB Baseball Free Agent Tracker - Major League Baseball - ESPN " [cited 2012]. Available from

http://espn.go.com/mlb/freeagents.

3"WAR | FanGraphs Sabermetrics Library " [cited 2011]. Available from

http://www.fangraphs.com/library/index.php/misc/war/.

34

Independent Pitching (FIP). FIP replaces the previous two statistics for position players

and measures a pitchers Earned Run Average (ERA) while accounting for the

“uncontrollable”. The uncontrollable refers to the potential errors or mishaps that could

have once the ball leaves the pitchers hands since he is at the mercy of his defensive

players.4 All of this information can be found at FanGraphs.com, one of more prominent

statistical databases analyzing Major League and Minor League players. Dave Cameron

of Fangraphs meticulously explains the definition of WAR and its components further on

the website in a chapter by chapter outline. The figure below is a fitted line plot of salary

versus WAR with a regression line. The data points are the 511 free agent player seasons.

FIGURE 3.2

SALARY VERSUS WAR

14121086420

35000000

30000000

25000000

20000000

15000000

10000000

5000000

0

WAR

Sa

lary

Scatterplot of Salary vs WAR

Source: Author’s calculation.

4 "WAR | FanGraphs Sabermetrics Library " [cited 2011]. Available from

http://www.fangraphs.com/library/index.php/misc/war/.

35

There are many factors of WAR that require mostly arbitrary ratings or

calculations. For example, centerfielders could potentially be worth 1.5 more wins per

season than first basemen because of the difficulty of both positions. Moreover, it is

extremely difficult to quantify the contribution of a catcher as one of his most critical

roles is calling and managing the game of a pitcher. The catcher sets the defense in place

and calls the pitches depending on the opposing batter. It is however the best single

statistic available that is able to quantify a player’s value. The figure below looks at the

511 free agent seasons and the frequency of WAR values obtained:

FIGURE 3.3

FREE AGENT PLAYERS AND WAR

12108642

120

100

80

60

40

20

0

WAR

Fre

e A

ge

nt

Pla

ye

rs

Histogram of WAR

Source: Author’s calculation.

36

The second data set will consist of the top 100 players selected in the draft

starting with the 1996 draft and ending with the 2000 draft. The data set includes a total

of 500 players consisting of each player’s pick number in the draft, his position, the

drafting team, the level the player was selected from, and the signing bonus. To find the

Market Value of WAR, another data set of 511 free agent seasons was collected. Also,

the player’s salary and WAR value was recorded for each season the player played in the

Major Leagues before attaining free agency. BaseballAmerica.com publishes the top 100

picks from each draft and their corresponding signing bonus data. TheBaseballCube.com

has a list of players that acquired Major League service time from each draft. All salary

and WAR information for drafted players that made it to the Major Leagues were again

obtained from baseball-reference.com. Some salaries are not included with their statistics

and WAR contribution. The salary omissions are almost always in the first few seasons

of a player’s career. Since these are all “reserve clause players”, it is safe to assume they

are receiving the league minimum salary for that season.5 It is only after two full seasons

of service time that these players will begin to receive salaries above the league

minimum.

Unfortunately, the 2000 draft is the latest draft possible to analyze due to current

reserve clause rules. As mentioned in the first chapter, once a draft pick signs his first

professional contract, he is property of that organization for a maximum of 12 years. The

organization has his rights for a maximum of 6 Minor League seasons and 6 Major

League seasons. Since this study is not only looking at the production of free agents, but

also of “reserve clause players” as well, 12 seasons must have concluded since the last

draft in the data set. For example, there was a player in a previous study that was selected

5 The league minimum salary for each year was listed at baseball-reference.com.

37

in the 1993 draft. At the conclusion of the 2004 season, he had acquired only 5 years and

135 days of service time at the Major League level. This case illustrates why this study is

limited to drafts prior to 2001. This insures that all players in this study will have

acquired enough service time to become free agents after the 2011 season.

In total, 500 players have been collected for this data set and classified into

several groups depending on their success within the organization that drafted them. It is

expected that the top 100 picks from each draft should make a good career out of baseball

and contribute positively at the Major League level. Organizations value these high picks

and reward them with significant signing bonuses. However, many of these players do

not make it to the Major Leagues or do not contribute enough at the Major League level

to have a positive contribution to the organization due to many factors including

increased foreign competition, lack of mental and physical ability, and injury. Players

who never make it to the Major Leagues consist of the first group and produce a negative

profit for the organization. The negative profit is equal to the signing bonus awarded to

the drafted player. The second group consists of players who made it to the Major

Leagues but did not benefit the team who drafted them. These players never contributed a

WAR value greater than 0 which means they were replacement level players while

earning the league minimum salary. They do not benefit the organization between any

Minor League replacement player can contribute 0 wins to a team. Since these players

were earning around the league minimum, the negative investment for the organization is

equal to the signing bonus the player was rewarded. The third and final group consists of

players who acquired Major League service team and contributed positively to the

organization. These players earn at least a WAR value of 1 which means they contribute

38

at least 1 win per season to the team during there “reserve clause” years. As mentioned

earlier, an average full-time player and starting pitcher is worth 2 wins (2.0 WAR) a

season while 1 win a season (1.0 WAR) represents a strong season for a relief pitcher.6

These players had to have at least one season with a WAR value equal to or above 1.0.

DESCRIPTION OF THE DATA SET

The description of the independent and dependent variables were already

discussed and defined in detail at the end of Chapter II. Here, the data sources behind

these variables will be discussed as well as the expected effect they will have on Market

Value. The coefficient m1 is equal to the intercept or constant coefficient from the linear

regression. It is expected that m1 should be a positive value about equal to the average

Major League minimum salary from the year 1996 to year 2011. This is because the

value of a player earning 0.0 WAR should earn the league minimum salary. It is possible

that m1 could be equal to zero or a negative value since players with a 0.0 WAR are

considered replacement players and can have a negative impact team wins for a year.

The coefficients, m2 through m16 holding all other things constant, represent the additional

increase or decrease in the Market Value of WAR as the year changes. The variables

should be large positive numbers in the millions and increase slightly year after year

because of inflation. This result is also expected because free agents are generally

rewarded more money after each year of their contract since organizations expect their

contributions to increase each year.

Some variables were very important to observe when data was being collected but

they were not included in the regression. The variable plate appearances (PA) for position

6 "Wins Above Replacement (WAR): Analyzing MLB Statistics using Sabermetrics « MLB reports " [cited

2012]. Available from http://mlbreports.com/2012/01/11/war/.

39

players or innings pitched (IP) for pitchers was used to determine if a Major Leaguer had

acquired enough playing time for his WAR to be included in the data set. For position

players, they needed to have at least 200 PA’s. Since some pitchers are relief pitchers and

may not always appear in a game, pitchers needed to have at least 60 IP. Table 3.1

displays the summary statistics of the variables used in this study. Year and the free agent

player age are also included. N is the number of observations recorded.

TABLE 3.1

SUMMARY STATISTICS OF REGRESSION VARIABLES

Variable N Mean StDev Minimum Maximum

Salary 510 8.09E+06 5.72E+06 1.09E+05 3.30E+07

WAR 511 3.73 1.66 0.60 12.40

year 511 1996.00 2011.00

Age 511 33.55 3.42 25.00 45.00

Pa/IP 511 425.00 222.00 20.00 749.00

Source: Author’s calculation.

The variable a1 will be assumed to stay the same throughout all years in this study

since it is equal to the chances of a player being replaced. It is expected that a2 will equal

a large positive number greater than the average league minimum salary of the MLB

because these players were obviously contributing WAR values less than the average

Major League started since they were replaced. The variables, A and B, are equal to the

summation of b1 + a1*(b1 – a2) and b2*(1 + a1) respectively.

40

Table 3.2 displays the summary statistics of the variables used to determine the

Market Value of WAR where N is the number of observations.

TABLE 3.2

SUMMARY STATISTICS OF MARKET VALUE VARIABLES

Variable N Mean StDev Minimum Maximum

a1 16 0.134 0.000 0.134 0.134

a2 16 3.486E+05 1.020E+05 1.690E+05 4.739E+05

b1 16 -1.711E+06 0.000E+00 -1.711E+06 -1.711E+06

b2 15 4.263E+06 2.384E+06 2.975E+05 7.543E+06

A 16 -1.987E+06 1.370E+04 -2.004E+06 -1.963E+06

B 15 4.836E+06 2.704E+06 3.374E+05 8.556E+06

Source: Author’s calculation.

This chapter has presented the data set and explained each of the variables in

depth. The predictions for the expected results of the variables will be analyzed in the

following chapter. Also, the benefits to organizations of the top 100 draft picks from the

1996 through 2000 drafts will be determined.

41

CHAPTER IV

RESULTS AND ANALYSIS

This chapter will provide the results of the linear regression model and Market

Value of WAR model that will determine the success of organizations investments on

amateur draft picks. The linear regression model of salary versus WAR and the dummy

variables, years, will determine the coefficients necessary to compute the Market Value

of WAR. The Market Value of WAR makes it possible to estimate the benefits

organizations receive from investing in draft picks. This chapter will discuss the

coefficients of each independent variable and the monetary values of WAR. The chapter

will conclude with an analysis of the success of draft picks for each draft year.

Table 4.1 summarizes the regression results from the linear regression model of

salary versus WAR and the dummy variables year. Since years were dummy variables,

one of the years had to be omitted because of collinearity. Year 2 or year 1997 was the

year that happened to be omitted. Luckily, no draft pick played in the year 1997. The first

draft analyzed in this study was the 1996 draft and it would be extremely rare and

difficult for a player to make it to the Major Leagues straight out of high school or

college. The most talented players spend at least a year or two in the Minors before

making the jump to the Majors. The t-stats that have an asterisk next to them indicate

variables significant at the 95% confidence level.

42

TABLE 4.1

TABLE OF RESULTS FOR LINEAR REGRESSION MODEL

salary Coef. t P>t

1996 2974670 0.15 0.88

1998 297685 0.16 0.87

1999 1318264 0.76 0.45

2000 3414654 1.76 0.08

2001 3154616 1.80 0.07

2002 3578605 2.03* 0.04

2003 3956459 2.29* 0.02

2004 3670538 2.22* 0.03

2005 4681863 2.90* 0

2006 6273546 3.87* 0

2007 5613937 3.52* 0

2008 6987803 4.29* 0

2009 7481324 4.52* 0

2010 5678589 3.50* 0

2011 7543041 4.53* 0

war 1347422 9.35* 0

constant -1710603 -1.08 0.28

R-Squared 0.22

Adj. R-Squared 0.20

Source: Author’s calculations.

*indicates significance at the 95% confidence level

Most of the variables are significant at the 95% confidence level. However, the constant

coefficient and a few of the earlier years are not found to be significant. It is interesting

that salary values are much larger in the later years of this study. This is consistent with

the prediction of the expected results increasing due to inflation. Also, this shows that

player’s production is either undervalued or organizations continued to pay more for free

agent players as time passed. Most likely, it is a combination of the two. The data

consistently showed players earning a very high WAR value and only earning a slightly

larger salary than a substitute player or average Major League starter. It is possible that

43

many free agent players that contribute All-Star and MVP caliber WAR values are being

underpaid. Also, many more organizations are emerging as big-market clubs and

subsequently increasing the bidding war for free agents. The New York Yankees, Boston

Red Sox, and Texas Rangers used to only have to compete with each other for free

agents. In recent years, teams like the Miami Marlins, Washington Nationals, Detroit

Tigers, and Los Angeles Angels have been willing to pay as much if not more for free

agent players.

Table 4.2 displays the variables used to determine the Market Value of WAR. The

coefficient b2 is equal to the slope of the explanatory variables from the linear regression

model for a given year. For example, if the Market Value of WAR in 2011 was being

determined, b2 would equal the slope for year 2011, m16. The coefficients m2 through m16

are given in the “Coef.” column in Table 4.1 with their corresponding year. The constant

coefficient from Table 4.1 is equal to the variable b1. The variables a2 and a1 represent the

expected replacement values and the chance of being replaced respectively found in the

previous chapter. Variables A and B help determine the Market Value of WAR. They

were from Ohlendorf’s model (2006) and are defined by a combination of the coefficients

a1, a2, b1, and b2 in Equation 4.1. For example, if a player earns a 2.0 WAR in 2000, the

Market Value is equal to the equation:

Market Value(X) = A + B*X = -1978932 + 3873351*2.0 = 5767770 (4.1)

44

TABLE 4.2

CALCULATIONS FOR THE MARKET VALUE OF WAR

Year a1 a2 b1 b2 A B

1996 0.13 169005 -1710603 297470 -1963095 337429

1997 0.13 198673 -1710603 Omitted -1967080 Omitted

1998 0.13 206977 -1710603 297685 -1968195 337674

1999 0.13 246989 -1710603 1318264 -1973570 1495349

2000 0.13 286906 -1710603 3414654 -1978932 3873351

2001 0.13 325155 -1710603 3154616 -1984070 3578382

2002 0.13 342218 -1710603 3578605 -1986363 4059326

2003 0.13 366951 -1710603 3956459 -1989685 4487938

2004 0.13 357062 -1710603 3670538 -1988357 4163609

2005 0.13 378033 -1710603 4681863 -1991174 5310787

2006 0.13 411618 -1710603 6273546 -1995685 7116284

2007 0.13 422820 -1710603 5613937 -1997190 6368068

2008 0.13 453017 -1710603 6987803 -2001246 7926489

2009 0.13 465274 -1710603 7481324 -2002893 8486305

2010 0.13 473548 -1710603 5678589 -2004004 6441405

2011 0.13 473860 -1710603 7543041 -2004046 8556313

Source: Author’s calculation.

The coefficients A and B are very large values. These variables may have skewed the

results of the benefits for organizations investing in draft picks. The Market Value of

WAR became increasingly larger as the years increased meaning that drafted players

benefitted the organization greatly if they produced at the Major League level. Not only

was their signing bonus nullified by their contribution, but they also rewarded

organizations with hundreds of millions of dollars.

Table 4.3 summarizes the results of the Market Value of WAR for each year and

is expressed in dollars. Using increments of one half, WAR values ranging from negative

two to eight were analyzed. If a player obtained a different WAR value, that value was

round to the nearest one half increment. For example, if a player had a 2.6 WAR in year

TABLE 4.3

MARKET VALUE OF WAR (ALL VALUES IN MILLIONS)

Source: Author’s Calculations.

year -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8

1996 -2.6 -2.5 -2.3 -2.1 -2.0 -1.8 -1.6 -1.5 -1.3 -1.1 -1.0 -0.8 -0.6 -0.4 -0.3 -0.1 0.1 0.2 0.4 0.6 0.7

1998 -2.6 -2.5 -2.3 -2.1 -2.0 -1.8 -1.6 -1.5 -1.3 -1.1 -1.0 -0.8 -0.6 -0.4 -0.3 -0.1 0.1 0.2 0.4 0.6 0.7

1999 -5.0 -4.2 -3.5 -2.7 -2.0 -1.2 -0.5 0.3 1.0 1.8 2.5 3.3 4.0 4.8 5.5 6.3 7.0 7.8 8.5 9.2 10.0

2000 -9.7 -7.8 -5.9 -3.9 -2.0 0.0 1.9 3.8 5.8 7.7 9.6 11.6 13.5 15.5 17.4 19.3 21.3 23.2 25.1 27.1 29.0

2001 -9.1 -7.4 -5.6 -3.8 -2.0 -0.2 1.6 3.4 5.2 6.7 8.6 10.5 12.3 14.1 15.9 17.7 19.5 21.3 23.1 24.9 26.6

2002 -10.1 -8.1 -6.1 -4.0 -2.0 0.0 2.1 4.1 6.1 8.1 10.2 12.2 14.3 16.3 18.3 20.3 22.4 24.4 26.4 28.5 30.5

2003 -11.0 -8.7 -6.5 -4.2 -2.0 0.3 2.5 4.7 7.0 9.2 11.5 13.7 16.0 18.2 20.5 22.7 24.9 27.2 29.4 31.7 33.9

2004 -10.3 -8.2 -6.2 -4.1 -2.0 0.1 2.2 4.3 6.3 8.4 10.5 12.6 14.7 16.7 18.8 20.9 23.0 25.1 27.2 29.2 31.3

2005 -12.6 -10.0 -7.3 -4.7 -2.0 0.7 3.3 6.0 8.6 11.3 13.9 16.6 19.3 21.9 24.6 27.2 29.9 32.5 35.2 37.8 40.5

2006 -16.2 -12.7 -9.1 -5.6 -2.0 1.6 5.1 8.7 12.2 15.8 19.4 22.9 26.5 30.0 33.6 37.1 40.7 44.3 47.8 51.4 54.9

2007 -14.7 -11.5 -8.4 -5.2 -2.0 1.2 4.4 7.6 10.7 13.9 17.1 20.3 23.5 26.7 29.8 33.0 36.2 39.4 42.6 45.8 48.9

2008 -17.9 -13.9 -9.9 -6.0 -2.0 2.0 5.9 9.9 13.9 17.8 21.8 25.7 29.7 33.7 37.6 41.6 45.6 49.5 53.5 57.4 61.4

2009 -19.0 -14.7 -10.5 -6.3 -2.0 2.2 6.5 10.7 15.0 19.2 23.5 27.7 31.9 36.2 40.4 44.7 48.9 53.2 57.4 61.6 65.9

2010 -14.9 -11.7 -8.5 -5.2 -2.0 1.2 4.4 7.7 10.9 14.1 17.3 20.5 23.8 27.0 30.2 33.4 36.6 39.9 43.1 46.3 49.5

2011 -19.1 -14.8 -10.6 -6.3 -2.0 2.3 6.6 10.8 15.1 19.4 23.7 27.9 32.2 36.5 40.8 45.1 49.3 53.6 57.9 62.2 66.4

45

46

2000, the Market Value of 2.5 WAR in year 2000 was used. The WAR values ranging

from negative two to eight are located on the top row of the table. All WAR values are in

millions of dollars.

Having estimated the Market Value of WAR, it is possible to evaluate the success

of organizations investing in drafts picks from 1996 to 2000. For each year, the top 100

draft selections were analyzed. Starting with 1996 draft, 18 out of the top 100 drafted

players contributed positively at the Major League level. For the 82 players who did not

make it to the Major Leagues or did not earn a WAR value that contributed positively to

the team at the Major League level, their signing bonus value was considered a loss.

Table 4.4 shows the number of draft picks from each year and the number of players that

produced at the Major League level from that draft as well as the average salaries of those

players. Average salary of successful drafts picks is in dollars.

TABLE 4.4

SUCCESS OF TOP 100 DRAFT PICKS AND AVERAGE SALARY

Year Draft Picks Successful Draft Picks Average Salary from Successful Draft Picks

1996 100 18 1,389,942

1997 100 15 1,281,701

1998 100 21 1,295,379

1999 100 18 1,666,575

2000 100 10 1,488,853

Source: Author’s calculations.

The signing bonus value lost was calculated by multiplying the number of players

not having contributed at the Major League level, 82, by the average first round signing

bonus in 1996. This method was used for each draft analyzed year after as well. Table 4.5

details how a drafted players benefits were recorded if he made it to the Major Leagues

47

and earned a 2.0 WAR or greater in one of his “reserve clause” seasons.1 Out of the top

100 picks from the 1996 through the 2000 draft, 72 players made it to the Major Leagues

and contributed positively to their organization that drafted by earning at least a 2.0 WAR

in one Major League season. Tables 4.5-4.7 are three examples of how individual players

benefit their organizations. The table shows the salary and WAR value earned by the

player, the market value of the WAR earned, and the benefit to the team from drafting the

player. Table 4.5 presents the results for a pitcher, Kris Benson, from the 1996 draft and

his first five seasons of Major League Baseball before attaining free agency. The column

of WAR values are the number of additional wins he contributed to his team over the

year and were inserted into the Market Value equation. The player’s signing bonus from

the amateur draft is in the Year 0 column. A player’s salary for each Major League

season is under the Salary column. The total benefit of the player to the organization is in

bold in the last column. Tables 4.5-4.7 are all under the same format but express different

players from different draft years.

TABLE 4.5

POSITIVE DRAFT PICK EVALUATION: KRIS BENSON

Year WAR Salary ($) Market Value ($) Benefit ($)

0 (Signing Bonus)

-2000000

1 2.5 200000 1764802 1564802

2 4.8 300000 17387824 17087824

3 0.2 2700000 -1986363 -4686363

4 -0.1 4300000 -1989685 -6289685

5 1.7 6150000 4257056 -1892944

3783636

Source: Author’s calculations.

1 A drafted player’s “reserve clause” years are his first six years of Major League service time. After he

completes six years of active service time, he is eligible for free agency. This study is only looking

player contribution in “reserve clause” years.

48

The benefit to the organization from signing Benson was $3,783,636. This is a

more modest benefit than other players who played in all six of their “reserve clause”

years and contributed All-Star caliber WAR values from the 1996 draft. Jimmy Rollins, a

shortstop and 46th

overall pick in the 1996 draft of the Philadelphia Phillies, contributed

two consecutive MVP like seasons saw his organization earn over $60 million from

signing him. Rollins’ signing bonus was only $340,000 which may have contributed to

such a large benefit for the Philadelphia organization that drafted him.

Table 4.6 represents a pitcher taken in the 1997 draft, Jim Parque, who produced a

negative benefit to the organization. Parque contributed negatively to the organization’s

investment because he had only one “reserve clause” season or one season under his first

professional contract with at least a 2.0 WAR, the value of an average Major League

starter. Parque earned WAR values of a replacement player or substitute in the rest of his

seasons.

TABLE 4.6

NEGATIVE DRAFT PICK EVALUATION: JIM PARQUE

Year WAR Salary ($) Market Value ($) Benefit ($)

0

(Signing Bonus)

-345500

1 0.2 170000 -1968195 -2138195

2 1.4 230000 269453 39453

3 2.6 475000 7704446 7229446

4 -0.7 550000 -3773261 -4323261

5 -1 300000 -6045689 -6345689

6 -1.2 400000 -6477623 -6877623

-12761369

Source: Author’s calculations.

49

Table 4.7 represents Pat Burell who was first overall in the 1998 amateur draft. As

a first overall pick, he was awarded a very large signing bonus. However, the investment

paid off for the organization because the player had some very productive seasons under

his first MLB contract.

TABLE 4.7

FIRST OVERALL PICK EVALUATION: PAT BURELL

Year WAR ($) Salary ($) Market Value ($) Benefit ($)

0

(Signing

Bonus)

-3150000

1 0.1 200000 -1978932 -2178932

2 0.8 1905000 1594311 -310689

3 4.1 1905000 14250942 12345942

4 0.8 1250000 2498253 1248253

5 1.6 4250000 4257056 7056

6 3.5 7250000 16596581 9346581

17308212

Source: Author’s calculations.

Table 4.8 summarizes the 500 draft picks total value added to organizations from

the 1996 to the 2000 draft. The total value added from each draft is in bold in the last

column of each table. The net value gained is very large and suggest that organizations

benefitted significantly as a whole from signing these draft picks. Moreover, these results

are consistent with Ohlendorf’s model using Win Shares instead of WAR. Ohlendorf’s

previous study found the average benefit from each draft pick from the 1989 through

1993 drafts was $2,263,385.2 Ohlendorf also researched the top 100 picks from each

draft. The average benefit from each draft pick in this study was $3,654,564. This value

2 Ohlendorf, C. A. "Investing in Prospects: A Look at the Financial Successes of Major League Baseball

Rule IV Drafts from 1989 to 1993." (2006): 107.

50

was found by summing the average benefit column in Table 4.8 and dividing by the five

drafts. The results from this study suggest that organizations benefitted greatly from

signing draft picks from the 1996 through 2000 draft.

TABLE 4.8

1996-2000 DRAFT ANALYSIS

Year Draft Picks Value Added Value Lost Gain Average Benefit

1996 100 264,657,557 77,441,128 187,216,429 1,872,164

1997 100 430,570,914 112,670,560 317,900,354 3,179,004

1998 100 560,163,765 129,375,693 430,788,072 4,307,881

1999 100 490,542,246 148,400,894 342,141,352 3,421,413

2000 100 717,768,316 168,532,740 549,235,576 5,492,356

Source: Author’s calculations.

From the five drafts that were analyzed in this study, the total value added to

organizations from investing in draft picks was $1.8 billion. At first glance, this value

seems too high and it is most likely is not a completely accurate measurement of the

organization’s actual benefit from awarding signing bonuses to drafted players. The

model used in this paper was previously developed to model the Market Value of a

different total value statistic, Win Shares. However, over each of the past two seasons the

MLB has invested over $200 million in draft signing bonuses and $2.7 billion in player’s

salary.3 It is realistic that organizations are benefitting significantly from the MLB draft

as teams are willing to invest over $200 million in draft picks. Also, if player salary

spending over each of the past two seasons in the MLB is in the billions, it is possible

3 "Why the MLB Draft Is the Best Bargain in the Game | Bleacher Report " [cited 2012]. Available from

http://bleacherreport.com/articles/593565-why-the-mlb-draft-is-the-best-bargain-in-the-game.

51

that organizations are benefitting close to that amount by developing players through the

draft.

The following example displays the disparity in salaries of free agent players

versus “reserve clause players”. Since the salary is non-negotiable for the first year and

still stays relatively small beyond a player’s first year at the minor league levels, the

primary investment for an organization is the signing bonus they reward a player when he

signs his first contract. If the player attains Major League service time, he is the property

of that team for six years at a discounted rate. For example, first year Major League

players earn around the league minimum, which was $414,000 for the 2010-2011

season.4 In 2011, the Chicago Cubs 21-year-old shortstop in his second season, Starlin

Castro, was a National League (NL) All-Star and finished 23rd

in NL Most Valuable

Player (MVP) voting while earning only $440,000. To put Castro’s salary in perspective,

the average Major League salary in 2011 was $3,014,572.5 Fellow NL All-Star and New

York Mets outfielder, Carlos Beltran, finished only slightly ahead of Castro in the NL

MVP voting and earned over $19 million for the season.6 The disparity in salaries

between players like Castro in their first few years of Major League service versus free

agent eligible players like Beltran illustrate the financial importance of the draft. If a

drafted player produces at the Major League level, the return on the signing bonus

investment for the team is extremely positive since the salary of the player is minimal

compared to even the average Major League salary.

4"Major League Baseball Players Association: Frequently Asked Questions " [cited 2012]. Available from

http://mlb.mlb.com/pa/info/faq.jsp.

5 Ibid.

6 "Baseball-Reference.com - Major League Baseball Statistics and History " [cited 2011]. Available from

http://www.baseball-reference.com/.

52

A past study done by Miller (2000) concluded that there is a difference in salary

structure for arbitration-eligible and free agent players and that arbitration-eligible system

will result in lower negotiated salaries relative to the free agent system. In this study, the

500 draft picks analyzed are part of the arbitration-eligible system since they were under

their first professional contract. Using Table 4.4, the average MLB salary of the draft

picks that contributed successfully at the Major League level was $1,424,490. The 511

free agent seasons analyzed are part of the free agent system. Using Excel files, the

average salary of the 511 free agent seasons was $8,087,207. The results from this study

are similar to Miller’s 2000 study in that free agent players are rewarded with a much

higher salary on average than players in the arbitrations eligible system. Sommers and

Quinton (1982) and Zech (1981) both determined that teams are willing to sign better

players to increase production or team wins. The results from this study are consistent

with the previous finding and are shown in Table 4.3. As the WAR values or player

production increased, the Market Value or monetary value of the player’s contribution

increased as well. The table is evidence that as a player contributes additional wins to his

team over a season, he is rewarded with a higher salary.

The purpose of this model was to evaluate the Market Value of WAR in order to

determine the benefit to organizations by awarding signing bonuses to amateur draft

picks. The model was tested and proved that organizations benefitted significantly from

investing in draft picks. Also, the results determined that increasing player production

that lead to increasing a team’s winning percentage lead to greater player salary. The

final chapter of this paper will present the conclusions as well as future research and

implications.

53

CHAPTER V

CONCLUSION

This study has attempted to measure the performance of baseball draftees and the

respective return on the organization’s investment in them. While the general perception

of sport’s business professionals and baseball fans is that MLB teams are overspending

on the draft, common findings demonstrate that professional baseball teams receive a

very positive return on their investment in the draft. These finding suggests that while

organizations continue to spend billions of dollars on player salary, signing bonus

investments in the draft are one of the best bargains in baseball.

This paper has incorporated the findings of past studies in sports economics,

which have used both linear regression and Cobb-Douglas models to evaluate the

determinants of salary versus player production and its effect on team success. Based on

these studies, this paper applied the linear regression model to quantify or place a

monetary value on a single total value baseball statistic that measures a player’s

contribution to the team success.

In order to run this linear regression, data was collected for the 1996 to 2011

MLB seasons. The model used a data set consisting of 511 free agent player seasons.

This regression determined the Market Value or monetary value of a player’s production

which was used to estimate the financial success of the organization’s investments in

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draft picks. Draft data consisted of the top 100 picks from each of the drafts from 1996 to

2000.

To test the hypothesis of having successful investments in draft picks, the new

model of a baseball player’s productivity was evaluated. The purpose of this model was

to reevaluate the Market Value of a free agent’s production at the Major League level.

This model made it possible to determine the drafts pick contributions to their respective

organizations. In total, 500 draft picks and their return on the organization’s investments

were analyzed.

At first glance, signing bonuses appear to be way too high. It is difficult to

comprehend such a large investment being handed out to a player who has yet to compete

in a Major League game. However, this paper finds that the signing bonuses paid to

players drafted in the in the top 100 picks of the 1996 to 2000 drafts proved to be very

rational and beneficial investment decisions.

Future Research

There are various improvements that could be made to this thesis in future studies.

The most obvious improvement is to continue to update the free agent and draft player

list at the conclusion of each Major League season. Minimum salary, average salary, and

average signing bonuses are ever changing in the MLB. The recent changes in the 2011

Collective Bargaining Agreement (CBA) have shown that the MLB has taken serious

interest in allowing team’s slotted bonuses for the draft. However, the CBA did not

address the overzealous spending of organizations on free agent players. While the MLB

is making changes to efficiently monitor the amount of money awarded to amateur

players in the draft, free agent spending continues to increases with no restrictions. As

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time goes on, and additional drafts can be analyzed, it is possible that the benefits from

the draft are going to continue to increase.

The second improvement would be to include more statistics for measuring player

production if more time was allotted for this extensive project. With more time, more

measuring devices could be included in the regressions. Since over 500 player seasons

were analyzed, it was very difficult to collect one statistic for each season let alone

multiple ones. Also, the analysis could be improved if a better alternative to WAR was

created. In today’s game, WAR is the single best total value statistic available. However,

the evolution of baseball statistics is fascinating and always in the pursuit of better

evaluation. Bill James (2002) revolutionized the game with his Win Shares measuring

device only to be overtaken by the WAR tool in 2007. Additional units as well as newly

created units for comparing the contributions of players would certainly improve the

accuracy of this study.

One last area of future research in the analysis of a baseball player’s productivity

is to incorporate the benefits and expenses of Minor League Baseball. After a player is

drafted and signed, he is allowed to play maximum of six years at the Minor League level

before the team loses his rights or has to promote him to the MLB. Almost all drafted

players acquire Minor League service time before making it to the MLB where they are

coached and trained to continuing developing as baseball players. Therefore, the success

and continuation of the amateur draft is dependent on the Minor League systems of

organizations. By ignoring the salaries of players, coaches, and staff as well as other

fixed costs, the organization’s investment in drafts picks comes exclusively from signing

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bonuses. By analyzing the success of the Minor League systems, the full analysis of a

drafted player’s contributions could be analyzed.

Implications

This study offers some important implications in terms of organization’s

motivations and the operation of MLB franchises. This paper offers an explanation as to

why organizations are willing to invest so much money in amateur players. Many sports

economists and business professionals look at the amateur draft and conclude that the

spending has spiraled out of control. This paper gives a plausible explanation in which

the MLB draft is actually doing exactly what it needs to do. It gives teams of all market

sizes a chance to sign and develop talented young baseball players while awarding the

most-hyped players the largest signing bonuses.

In addition to demonstrating that professional baseball teams on average receive a

positive return on their investment in domestic amateur draft picks, this study has also

provided a model of studying the performance of players through international free

agency. The results of this study reveal that teams are willing to invest large amounts of

money on unproven high school and college players domestically and the same is to be

said for international amateur free agents. These findings are important because they not

only state that organizations are benefitting from the amateur draft, but also international

free agents as well. This study represents an important starting point for testing the

benefits organizations receive from signing amateur players all over the world.

57

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