MIT Sloan Sports Analytics Conference 2011: The Broken MLB Free-Agent Compensation System

MIT Sloan Sports Analytics Conference 2011
March 4-5, 2011, Boston, MA, USA

The Broken MLB Free-Agent Compensation System:
The Valuation of Draft Picks and Free-Agents

Harry Raymond, Ethan Levitt, Professor Kenneth Segall
Colgate University,
Hamilton, NY, USA, 13346
Email: hraymond@colgate.edu

Abstract
In the current MLB free-agent compensation system, players are divided into five positional groups and ranked
on a 100-point scale based on an array of traditional statistics. The top 20 percent of players in each group are classified
as Type A. If a Type A free-agent turns down his team’s offer of arbitration and signs with another team, his former
team receives two compensatory first round draft picks. Draft picks are a valuable currency which is why certain Type A
free-agents have received less compensation. This study used First-Year Player Drafts from 1998 to 2003 to determine
the value of each pick by calculating the WAR (wins above replacement player) of each player for the 8 years following
the draft. The WAR for free agents from that time period was also calculated to compare. The numbers suggest that
the current system overcompensates teams that lose a player to free agency. This paper seeks to quantify in a more
definitive way the relationship between the value of free agents and corresponding draft picks in terms of wins. The
current MLB collective bargaining agreement expires in December 2011 and the negotiations concerning this issue will
be on the table.

1 Introduction
In November 2008, second baseman Orlando Hudson filed for free agency, expecting the biggest contract of
his career. The 30-year old was the top second baseman on the market, with three Gold Glove awards and a .346 on-
base percentage (OBP) over his 7-year career. Hudson started off the winter looking for a deal in the neighborhood of
five years, $50 million, with nearly a dozen teams showing interest. At least that was the idea, until the Elias Sports
Bureau Rankings were released in early November.1
The “Elias Rankings” is a collectively bargained item that assigns value to every player in the Major Leagues.
The Elias Sports Bureau, Major League Baseball’s (MLB’s) official statistician, calculates the rankings each November
using a formula provided by MLB. In theory, the Elias Rankings identify the best players so teams that lose certain
players in free agency can be rewarded with draft picks. It first became part of baseball’s Collective Bargaining
agreement in 1985 as a means, along with the Luxury Tax system later, to create a competitive balance between small
and large market teams.2
In order to calculate the Elias Rankings, all players (not just free agents) are divided into five position groups
and ranked on a 100-point scale based on an array of traditional statistics. The exact weight of each statistic has never
been released by MLB or the Elias Sports Bureau. Table 1 shows the five position groups and the different statistical
categories used for each position group.3

1 Jayson Stark, “Second Baseman Orlando Hudson, Los Angeles Dodgers Agree to Deal – ESPN,” ESPN, 21 Feb. 2009,
<http://sports.espn.go.com/mlb/news/story?id=3922546>.
2 “Elias Sports Bureau Player Rankings,” USA Today, 31 Oct. 2000, <http://www.usatoday.com/sports/baseball/eliasrankings.htm>.
3 Ibid.

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Table 1: Position groups and statistical categories used in calculation of current Elias Rankings

Position Group Statistics used for Position Group’s Rankings
first baseman, outfielders and designated hitters PA, AVG, OBP, HR, RBI
second baseman, third baseman and shortstops PA, AVG, OBP, HR, RBI, Fielding Percentage
Catchers PA, AVG, OBP, HR, RBI, Fielding Percentage, Assists
starting pitchers Total Games, IP, Wins, W-L percentage, ERA, strikeouts
relief pitchers Total Games, IP, Wins, Wins + saves, ERA, IP/H ratio, K/BB
Note: Elias calculates total games differently for starting pitchers (total starts + (0.5 * total relief appearances)) than total
games for relief pitchers (total relief appearances + (2 * total starts)).

The top 20 percent of players in each group are classified as Type-A. If a Type-A free agent turns down his
team’s offer of arbitration and signs with another team, his former team receives two compensatory first round draft
picks, the signing team’s pick and a supplemental (or sandwich) pick between the first and second rounds. 4 To
complicate matters, only teams picking 16 through 30 in the Rule 4 Draft (order is determined based on the previous
season's standings, with the team possessing the worst record receiving the first pick) must surrender their first round
pick, while teams picking 1 through 15 only surrender their second round pick. For example, if the worst team in
baseball signs a Type-A free agent, it does not have to surrender its #1 overall pick but the first pick of the second
round. Type-B players rank in the second quintile (21%-40%) and their signing as free agents fetch a supplemental pick
for the player’s former team but do not cost the signing team a pick. The remaining 60% of players are classified as
non-compensation players.5
In 2008, Hudson was listed as a “Type-A free agent” which meant two things: (1) Hudson ranked in the top 20
percent in his position group; and (2) signing Hudson would cost his new team a first round pick. In theory, being listed
among the top 20 percent in your profession ought to be considered an advantage but, in practice, for some players, a
Type-A ranking is a punishment that can cost the player money or even a contract all together. Teams now consider
draft picks a valuable currency, which is why the Elias Rankings greatly influence the strategies of teams during the
offseason and at the trade deadline. In the case of Hudson, this influence was clearly negative. Despite being the top
second baseman on the market, teams hesitated to give up a first round pick for Hudson, opting for free agent players
who did not have Type-A status. Hudson remained unsigned at the start of Spring Training, a rare occurrence for top
free agents who usually sign contracts 2-3 months before Spring Training.
Several other top free agents had similar experiences. Orlando Cabrera was considered the top free agent
shortstop in 2008, posting his fourth straight season with a Wins Above Replacement (WAR) greater than two. He
remained unsigned at the start of Spring Training. Set-up man Juan Cruz had the best year of his career in 2008, posting
career bests in fielding independent pitching (FIP), walks plus hits divided by innings pitched (WHIP) and WAR. Cruz
drew no interest from teams selecting 16-30 in the draft and remained unsigned at the start of Spring Training.
The situation became so dire for this group of players that The Minneapolis Star-Tribune reported that MLB and
the players’ union discussed a proposal that would allow remaining Type-A free agents to sign with the team they played

4 The majority of free agents turn down arbitration offers because, while arbitration usually results in a pay raise for the player, it is

always a one-year non-guaranteed contract. In the 2010 offseason, 33 Type-A and Type-B free agents were offered arbitration and
only two accepted.
5 The 2006 collective bargaining agreement eliminated Type-C free agents and changed the percentages for Type-A and Type-B

players.

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for in 2008, then waive the MLB provision that prevents players from being traded before June 15.6 This sign-and-trade
solution would allow a team to acquire Type-A free agents by giving up players in lieu of a draft pick. In the end, a sign-
and-trade solution was not necessary but the fact that it was discussed demonstrates the impact of the Elias Rankings
and draft pick compensation on the attractiveness of free agent players.
Hudson signed a one-year contract with the Los Angeles Dodgers for $3.4 million with $4.6 million in
performance incentives, far less than the five-year, $50 million contract Hudson was seeking. Many analysts criticized
the Dodgers because of Hudson’s Type-A status, claiming the team had given up too much.7 In March, Cabrera signed
a one-year, $4 million contract with the Oakland A’s, a substantial pay cut from the $10 million he earned in a productive
2008 for the Chicago White Sox. Cruz signed a two-year, $6 million contract with the Kansas City Royals, a team whose
low standing meant they only had to surrender a second round pick for signing a Type-A free agent. These are just three
examples within a larger trend of free agents presumably receiving less compensation because of their Type-A status.
This trend is particularly visible among relief pitchers because teams are particularly hesitant to surrender top
draft picks for a pitcher who will only pitch 2-6% of their team’s innings. In the 2010 offseason, 33 Type-A and Type-B
free agents were offered arbitration and only relievers Frank Francisco and Jason Frasor accepted their arbitration offers.
The two set-up men had career-years in 2010 but their Type-A status limited their ability to test the market. Similarly, in
the 2010 offseason, the New York Yankees made it a priority to add a left-handed reliever. The best available was Scott
Downs, a player the Yankees had shown interest in acquiring in the past. However, Jon Heyman of Sports Illustrated
reported that General Manager Brian Cashman “won’t go for Downs or other Type-A relievers because they want to
keep draft choices.”8 Even Michael Weiner, Executive Director of the Major League Baseball Players Association
(MLBPA), weighed in on the relief-pitcher ranking, saying “The parties should turn their attention to irrationalities
associated with the Elias rankings, such as the relief-pitcher rankings.”9
There are several other problems with the current free agent compensation system. The surrendering of draft
picks becomes more complicated if the signing team signs more than one Type-A free agent. When that happens, the
signing team surrenders its highest available pick to the team that had the better Type-A free agent, based upon Elias
rankings. For example, after the 2008 season, the New York Yankees signed CC Sabathia, Mark Teixeira and AJ
Burnett, all Type-A free agents. Based on the Elias Rankings, Teixeira had the highest score among the three players
(even though his score was calculated in a different position group) so the Los Angeles Angels (his previous team) was
awarded the Yankees first round pick. Then the Milwaukee Brewers received a second round pick for losing Sabathia,
while the Toronto Blue Jays received just a third round pick (#104 overall pick) for losing Burnett. Critics argue that
this does not fairly compensate teams who lose free agents and encourages market buyers to hoard free agents in a
particular offseason.10
There is also the problem of the “rental player.” Many teams have started to pursue projected Type-A and
Type-B players at the trade deadline with no intention of keeping the player after the season, but, rather, with the
intention of adding draft picks when they later lose the player to free agency. This strategy of hoarding draft picks is
sometimes called the “Money Ball Method” because A’s General Manager Billy Beane’s adoption of the strategy was
well-documented in Michael Lewis’ Money Ball. For example, in 2003, the A’s acquired second basemen Ray Durham at
the trade deadline. Money Ball reveals that Beane believed the move was only a minor upgrade but he wanted the two
compensation picks Durham would provide after his three months of service with the A’s.

6 Jeff Passan, “Draft-pick Compensation Rules Might Bend,” Yahoo! Sports, 16 Feb. 2009,
<http://sports.yahoo.com/mlb/news?slug=jp-cactusleaguenotes021609>.
7 Stark.
8 Jon Heyman, “Jon Heyman: #yankees Won't Go for Downs...,” Twitter, 7 Dec. 2010,

<http://twitter.com/SI_JonHeyman/statuses/12155945752727552#>.
9 Jeff Passan, “Free-agent Compensation System Is Unfair,” Yahoo! Sports, 5 Sept. 2009,

<http://sports.yahoo.com/mlb/news?slug=jp-typea090409>.
10 Eddie Bajek, “How and Why MLB’s Compensation System Should Be Reformed - Bless You Boys,” Detroit Tigers Thoughts, 16 May

2010, <http://www.blessyouboys.com/2010/6/9/1508715/how-and-why-mlbs-compensation>.

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R.J. Anderson of Fangraphs highlighted the problem in his fascinating study “First Round Compensation.”
Anderson argued that, if the free agent compensation system is designed, at least in part, to compensate teams losing
homegrown talent, then the system is “failing miserably.” He looked at how much time players spent with the teams
being rewarded draft picks by calculating the percentage of the player’s career plate appearances or innings pitched with
the compensated team. He analyzed, 219 first round compensatory picks (from 171 players lost to free agency) from the
2000-2010 drafts, and the average percentage of playing time spent with the compensated team was 37.9%. After a
further analysis, Anderson concludes, “If the league required that players had to spend at least 25% of their career
playing time…with a team to receive first round compensation, we would’ve essentially halved the actual player and pick
pool.”
Table 211 shows the free agents whose signing provided compensatory picks for the 2000 draft. Note that only
two of the thirteen ranked players lost to free agency were homegrown (Trombly and Rhodes). In addition, two of the
three Type-A players had been on the compensated team for less than a year (Zeile and Springer). In the case of Money
Ball’s example of the Durham acquisition, the A’s received two first round compensation picks for a player that had only
played 5% of his career to that point with the A’s. Using this data, Anderson concludes “compensation should be
altered so that players have to spend a certain amount of time with their last team to qualify for compensation; the
thought being that those compensatory matters only exist to help teams replace exiting homegrown talent.”

Table 2: Percentage of Playing time with Compensated team for Players lost to 2000 Free Agency

% Playing Time w/ Picks
Draft Year Player Lost to FA Compensated Team Compensated Team Awarded
2000 John Olerud Mets 35 2
2000 Mike Magnante Angels 14 1
2000 Darren Oliver Cardinals 30 1
2000 Mike Trombley Twins 100 1
2000 Arthur Rhodes Orioles 100 1
2000 Graeme Lloyd Blue Jays 20 1
2000 Juan Guzman Reds 5 1
2000 Aaron Sele Rangers 40 1
2000 Michael Jackson Indians 20 1
2000 Jose Hernandez Braves 8 1
2000 Todd Zeile Rangers 14 2
2000 Russ Springer Braves 15 2
Source: R.J. Anderson’s “First Round Compensation”

There is also a problem with the calculation of the rankings themselves. The growing Sabermetrics
community’s criticism of traditional baseball statistics has been well-developed over the last 30 years. With the exception
of OBP, the value of every statistic used in the Elias rankings has been extensively questioned, including batting average,

11R.JAnderson, “First Round Compensation (Part One - Four),” FanGraphs Baseball, 20 Aug. 2010,
<http://www.fangraphs.com/blogs/index.php/first-round-compensation-part-four/>.

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wins, and fielding percentage.12 These well-developed criticisms aside, even by traditional standards the Elias rankings
still seem oddly subjective. For example, why does Elias group first basemen with outfielders? It appears MLB wants to
group offensively similar positions. If this is the case, it seems odd that MLB did not differentiate outfield positions
since corner outfielders are traditionally better hitters while centerfielders are traditionally better defenders.
Furthermore, why do defensive statistics factor in the rankings for infielders and catchers but not outfielders and first
basemen? Another question: Is it fair that catchers are only ranked with other catchers while second basemen are ranked
with third basemen? In a 2009 column for Yahoo! Sports, Jeff Passan wrote that an MLB source told him that the ranking
system is so “blatantly outdated” that the Elias Sports Bureau is “embarrassed to compute them.”13

2 WAR Analysis of Draft Picks and Free-Agents
This research paper seeks to quantify in a more definitive way the relationship between the value of free agents
and corresponding draft picks. In addition, it will propose a new system for free-agent compensation that more
accurately assigns the value of draft picks and free agents in terms of WAR. The current MLB collective bargaining
agreement expires in December 2011 and this issue will be the subject of important negotiation, both from the point of
view of player and team interests and the league objectives of achieving “balance.”
In order to quantify the value relationship between free agents and draft picks, I had to identify statistics that
accurately reflect the value of a player’s performance. I settled on one statistic: WAR. My analysis relies heavily on
WAR so it is important to understand how it is calculated and why it is both the most widely used “total value” statistic
and the most reliable measure to use in establishing this value comparison. For those unfamiliar with reading and
calculating WAR, see my detailed explanation in Appendix A on page 10.
This study used the first three rounds of First-Year Player Drafts (since the first three rounds are the most
often compensated picks in the free-agent compensation system) from 1998 to 2002 to determine the value of each pick
by calculating the WAR of each player for the eight years following the draft. If a player did not make a Major League
roster, his WAR was considered 0. Why was WAR calculated for eight years following the draft? The goal was to pick an
amount of time that allows for comparison of these draft picks with the two-year WAR of free agents after they sign
(two years is the average length of a free agent contract and the standard used for the Elias rankings). In most cases, a
player is not eligible for free agency until his contract has expired with at least six years of service time on a Major
League 25-man roster. Therefore, free agents are established Major Leaguers and the goal of this calculation should be
to take into account the amount of time it takes a drafted player to become an established Major Leaguer.
To start, I used the 2001 draft to calculate the time in days for each drafted player in the first three rounds to
reach the Majors. Players’ signing dates marked the start of their Minor League career and their Major League debut
marked the beginning of their Major League career. In 2001, on average, it took 1197.81 days (or 3 years, 3 months and
12 days) for a drafted player to reach the Majors (see table 3). Players drafted in the first round reached the Majors
fastest (976.33 days) but, surprisingly, supplemental picks took considerably longer to reach the Majors than those
picked in the second round. This can be classified as a sample size error.

Table 3: Time for each drafted player in the first three rounds of 2001 Draft to reach Majors

1st
Players by Round 976.3333333
Days to Reach Majors 2.674885845
Yrs to Reach Majors

12 I have published several editorials that critique the use of traditional statistics in baseball including “The Myth of a Clutch Derek
Jeter” which can be accessed on www.maroon-news.com
13 Passan, “Free-agent Compensation System Is Unfair.”

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Supplemental 1827.714286 5.007436399
2nd 1162.583333 3.185159817
Average 1197.810811 3.281673454

I should note a few possible problems with using signing dates. The use of the signing date as the start of a
player’s career does not account for drafted players who sign a contract but do not immediately start playing. Should
this void in playing time be included in the player’s career? I considered using a Minor League debut date but that date
does not account for players who were drafted with an injury and younger players who spend time in instructional
leagues which is not considered a Minor League debut. Certainly, a year of instructional league or rehabilitation should
be included as time in a player’s career.
The use of the Major League debut date also poses some problems. Many players make their Major League
debut as a September call-up in some very limited role (pinch-runner, defensive replacement, etc.) or filling in for an
injured starter before returning to the Minors. Some of these players eventually become established Major League
players while others do not. My analysis of the first three rounds of the 2001 draft shows that 53.33% of players drafted
reached the Majors (see table 4). MLB has a Minor League turnover rate of 30% so it is difficult to imagine that 53.33%
reach the Majors and stay there.14
A University of Colorado study of 5,989 players who began their careers between 1902 and 1993 found that if a
drafted player reaches the Majors, his baseball career will last an average of 5.6 years. However, if a player reaches his
third season, he can expect to play six additional years.15 Thus, an established player can be defined as a player with
three years of Major League experience. Add three years to the average time it takes to make the Majors and we get
roughly six years as the average time it takes a drafted player to become an established Major Leaguer. Thus, an eight-
year calculation of WAR of drafted players gives us two years of WAR as an established Major Leaguer, allowing for
comparisons with the two-year WAR of free agents. Baseball-reference.com also uses an eight-year WAR calculation to
evaluate a draft pick. When reached for comment by e-mail in December 2010, Baseball-reference.com confirmed that
their choice of eight years was also based on the average time it takes a drafted player to become an established Major
Leaguer but did not elaborate further. The need for eight years of WAR statistics lead to this study’s selection of the
2002 draft as the most recent draft evaluated.

Table 4: Percentage of Players who Reached Majors in first Three Rounds of 2001 Draft

Reached Majors 53.33%
Reached Majors - 1st Rnd 63.33%
Reached Majors – Supplemental 50%
Reached Majors - 2nd Rnd 45.16%
Players that did not sign 5.26%
Table 5: Average 8-year WAR of Drafted Players in First, Supplemental and Second Rounds of 1998-2002 Drafts

Year 1st Round Supplemental 2nd Round
1998 6.426666667 3.076923077 1.503333333

14 Richard G. Rogers, Jarron S. Onge, and William D. Witnauer, Baseball Career Length in the Twentieth-Century: The Effects of Age,
Performance, and Era, University of Colorado, 21 Sept. 2005, <http://paa2006.princeton.edu>.
15 Ibid.

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1999 3.646666667 1.085714286 1.754545455
2000 1.313333333 1.2 0.28
2001 3.776666667 1.842857143 1.296875
2002 6.273333333 -0.172727273 2.403225806
4.287333333 1.406553447 1.447595919
WAR statistics derived from Baseball-reference.com

From 1998-2002, first round picks had an average WAR of 3.79. Picks from the supplemental round had an
average WAR of 1.41 and second round picks had an average WAR of 1.44 (see Table 5). The low average WAR of the
supplemental round can be credited to a negative WAR in the 2002 draft, an apparent outlier. A further analysis of first
round picks found that the value of a pick diminished later in the round, as one would expect (see table 6 and figure C in
Appendix D). Picks 1-10 had an average WAR of 6.87, picks 11-20 4.31, and picks 21-30 1.68.

Table 6: Average 8-year WAR of First Round by Picks from 1998-2002 Drafts

Year Picks 1-10 Picks 11-20 Pick 21-30
1998 11.45 7.28 0.55
1999 8.1 2.57 0.27
2000 1.3 2.23 0.41
2001 7.59 1.54 2.2
2002 5.88 7.96 4.98
6.864 4.316 1.682

The WAR for free agents from 1998-2002 was also calculated in order to evaluate the current free-agent
compensation system. One hundred free agents were randomly selected from each free agent class from 1998-2002.
Transaction logs were used to create the list. All top free agents were included. Players with club or player options were
not considered free agents. Players who signed Minor League contracts but did not return to the Major League level
(either because of performance or retirement) were not included in the statistical analysis.

Table 8: Average Draft WAR compared to Free Agent Class WAR 1998-2002

Year Draft WAR FA WAR (Two Prior Years) FA WAR (Two Years After)
1998 3.806849315 2.082 1.673
1999 2.263095238 1.515 0.976
2000 0.854285714 1.714141414 1.256
2001 2.376315789 1.852525253 1.150505051
2002 3.622222222 1.883838384 1.1
5-year Average 2.584553656 1.80950101 1.231

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The average WAR of free agents (two years prior to their free agency) from 1998-2002 was 1.81. The average
WAR for those same free agents the two years after they signed was 1.23 (see table 8 and figure A in Appendix D). This
suggests that free agents’ value diminished during the two years after they signed a contract. For comparison, the
average WAR for the first three rounds of the draft from 1998-2002 was 2.58. When a T-Test is performed on the free
agent and draft data, the p-value is 0.19. This p-value shows that the difference between the WAR of players selected in
the draft and the WAR of players signed as free agents is not statistically significant.
Finally, a baseball-equivalent discount rate was added to the numbers to account for the difference in current
value and long-term value. A discount rate was calculated in order to account for the value of time in WAR because
drafted players will not benefit the team until they are established major leaguers. I call this statistic WAR-DAD (Wins
above replacement discount adjusted for draft). Here is how WAR-DAD is calculated:

W0 = initial WAR = 1.23 (average FA two years after signing)
WF = final WAR = 5.7 (average WAR of supplemental and first round pick after eight years)
r = discount rate
x1 = cost after 1 year

First, calculate x1:

x1^6 = W0*WF
or, X1 = (W0*WF)^1/6

Then,

r = (x1-W0)/X1

Using this calculation, the discount rate is 10.8%. Factor in the discount rate into the average WAR of the 5-
year draft average and we get a WAR of 2.31, still significantly more than the average WAR for free agents the two years
after they signed (1.23).

3 Conclusions
The findings of this study suggest that the current free-agent system overcompensates teams that lose a player
to free agency. The five-year average WAR-DAD for a first round pick and supplemental pick (the current
compensation for the loss of a Type-A free agent) is 5.08 based on data from the 1998-2002 drafts This is significantly
greater than the five-year average free agent WAR (1.23 for each of the two years after signing) during that same time
period.
Based on these findings, MLB should consider a new system for free-agent compensation. The details of a new
system depend greatly on the goals of the free-agent compensation system (perhaps the MLB intends to overcompensate
teams that lose players to free agency). These are matters of policy that need to be addressed in collective bargaining, a
process that may be assisted by the use of WAR-DAD data. WAR-DAD analysis may provide a means for evaluating
and creating a compensation system that more accurately assigns the intended value.
Nonetheless, a new compensation system, regardless of its goals, should address some obvious issues. First, while
the Office of the Commissioner has been hesitant to look beyond traditional statistics, team front offices have not.
Director of MLBPA Michael Weiner recently said “It’s not in the interest of clubs or players for the Elias rankings to be

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out of touch with the way the market actually values players.”16 The proposed new ranking system described in this
article would eliminate the subjective assignment of position groups and the use of traditional statistics in the Elias
rankings and, instead, rank players based on their value in wins regardless of position. WAR accounts for relative
position strength anyway. Knowing the value of both free agents and each draft pick based on historical data, one could
accurately assign a specific draft pick (for example, the #35 pick) as compensation for a lost free agent with a certain
specific WAR. For example, if Orlando Hudson has a WAR of 3 over the prior two years and the MLBPA agrees to
compensate his team two-fold, the team can receive the number of supplemental picks that most closely equals a
cumulative WAR of 6 based on historical data.
Second, the authors agree with Anderson’s analysis that a playing time threshold needs to be implemented in
order to eliminate the rental player phenomenon. This phenomenon simply encourages teams to manipulate the system
(using the rules in place) which is bad for both the players and the fans.
Third, if the goal of free-agent compensation is to help teams replace lost free agents, it seems unnecessary to
punish teams by redistributing picks. There is a double standard when MLB criticizes teams like the Pittsburgh Pirates
and Florida Marlins for not raising payroll when their own collectively bargained compensation system is designed to
discourage or impose penalties on teams that spend. Small-market teams cannot afford to give up valuable draft picks
that provide cheap, young players.
With further research and applications of WAR-DAD, alternative approaches to these compensation issues can
be explored. Nonetheless, one thing is clear: the current free-agent compensation system does not value players fairly or
accurately.

4 Acknowledgments
1) John Hollinger of ESPN.com for reviewing this paper and suggesting that I add a discount rate.
2) Richard Thaler, Steven Glauberman, Jerry Raymond, Mike McCormick of Major League Baseball Publishing,
and Brian Richards of the Yankees Museum for helping along the way and nurturing my love for baseball.

5 References

[1] Aberle, Jeff. "WAR Lords of the Diamond." Beyond the Box Score. 12 June 2009. Web.
<http://www.beyondtheboxscore.com/2009/6/12/906943/war-lords-of-the-diamond-position>.

[2] Anderson, R.J. “First Round Compensation (Part One - Four).” FanGraphs Baseball. 20 Aug. 2010. Web.
<http://www.fangraphs.com/blogs/index.php/first-round-compensation-part-four/>.

[3] Bajek, Eddie. “How and Why MLB’s Compensation System Should Be Reformed - Bless You Boys.” Detroit Tigers
Thoughts. 16 May 2010. Web. <http://www.blessyouboys.com/2010/6/9/1508715/how-and-why-mlbs-
compensation>.

[4] “Elias Sports Bureau Player Rankings.” USA Today. 31 Oct. 2000. Web.
<http://www.usatoday.com/sports/baseball/eliasrankings.htm>.

[5] Heyman, Jon. “Jon Heyman: #yankees Won't Go for down ...” Twitter. 7 Dec. 2010. Web.
<http://twitter.com/SI_JonHeyman/statuses/12155945752727552#>.

16 Passan, “Free-agent Compensation System Is Unfair.”

9


[6] Passan, Jeff. “Draft-pick Compensation Rules Might Bend.” Yahoo! Sports. 16 Feb. 2009. Web. 05 Jan. 2011.
<http://sports.yahoo.com/mlb/news?slug=jp-cactusleaguenotes021609>.

[7] Passan, Jeff. “Free-agent Compensation System Is Unfair.” Yahoo! Sports. 5 Sept. 2009. Web.
<http://sports.yahoo.com/mlb/news?slug=jp-typea090409>.

[8] Pawlikowski, Joe. “The Stats We Use: FIP.” River Avenue Blues, A New York Yankees Blog. 5 Feb. 2010.
<http://riveraveblues.com/>.

[9] Pawlikowski, Joe. “The Stats We Use: wOBA.” River Avenue Blues, A New York Yankees Blog. 21 Jan. 2010.
<http://riveraveblues.com/>.

[10] Remington, Alex. “Everything You Always Wanted to Know About: FIP.” Yahoo! Sports. 2 Dec. 2009.
<http://sports.yahoo.com/mlb/blog/big_league_stew>.

[11] Slowinski, Steve. “Fielding Independent Pitching (FIP).” Sabermetrics Library.
<http://saberlibrary.com/pitching/woba/>.

[12] Slowinski, Steve. “Weighted On-Base Average (wOBA) « Sabermetrics Library.” Sabermetrics Library.
<http://saberlibrary.com/offense/woba/>.

[13] Stark, Jayson. “Second Baseman Orlando Hudson, Los Angeles Dodgers Agree to Deal.” ESPN. 21 Feb. 2009.
Web. 2011. <http://sports.espn.go.com/mlb/news/story?id=3922546>.

6 Appendices
Appendix A: How to Calculate and Read WAR

WAR attempts to quantify a player’s value in terms of wins into a single number by aggregating his offensive
and defensive value in the context of his position, park, year and league. As Alex Remington of Big League Stew explains,
at its root, WAR relies on traditional counting statistics (HRs, 2Bs, steals, outs, etc.) but converts these measures into
more advanced rate statistics: primarily weighted on-base average (wOBA), fielding independent pitching (FIP), and total
zone rating (TZ).17 Essentially, these rate statistics convey a player’s contributions in terms of runs added or runs
prevented, so they can be referred to as “Run Measures.”
After calculating Run Measures, there are several adjustments that are made in calculating WAR. First, a
positional adjustment is added to account for the relative value of different positions. Second, a league adjustment is
added to account for the relative strength of the American League due to a higher offensive output as a result of the
designated hitter and, currently, a greater concentration of talent than the National League. Third, a stadium adjustment
further normalizes WAR by removing the effect of a player’s home ballpark. Finally, since Run Measures are already
scaled to the league average, in effect, an era or league talent adjustment has already been added.
Run Measures are then adjusted to the player’s value above a replacement level player. Here we come to the
most debated aspect of total value statistics like WAR: What is a “replacement level” player?18 WAR’s creator, Tom

17 For a more detailed metric breakdown of FIP and wOBA, see appendices B and C.

18 This debate has led to the creation of other total value statistics, such as Win Shares Above Bench (WSAB).

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Tango, defines “replacement level” as “the talent level for which you would pay the minimum salary on the open
market, or for which you can obtain at minimal cost in a trade.” Replacement level players are generally fringe or AAAA
players (somewhere between AAA level and the Major League level). A team with all replacement level players would
win 20-25 games in a 162-game season. More specifically, the expected value of a replacement level player is 20 runs per
600 plate appearances. Jeff Berble, at Beyond the Boxscore, summarizes: “The reason that replacement players are used in
these value calculations is that we have a fixed baseline for exactly how much these players are worth--and that is the
MLB minimum ($400,000), the lowest possible cost to replace a Major League player.”19
Now that we have defined “replacement level” and adjusted our run measures to the player’s value above a
replacement player, we reach the last step in our calculation of WAR. For a position player, wOBA and TZ (our
replacement adjusted run measures) are added to get Runs Above Replacement (RAR). Then you scale all those
contributions to be expressed in terms of total team wins. The scale is 10 runs equals one team win, therefore WAR is
equal to RAR divided by 10. For a pitcher, this last step is slightly more complicated. In order to derive an equivalent of
RAR from FIP, the initial FIP calculation must be rescaled to runs allowed (RA) rather than Earned Run Average
(ERA). This is because unearned runs are counted in RAR. The rescaled FIP/RA number is then adjusted to the
pitcher’s innings pitched to complete the WAR calculation.
So, how do we read WAR? Baseball-Reference.com provides a scale for reading WAR that I have organized in
Table A below. For context, since 2000, only five players have posted a single-season WAR greater than 10 (Barry
Bonds did it four times). In 2010, Evan Longoria led the Majors with a 7.7 WAR. Again, this means that, by the WAR
calculation, Longoria contributed 7.7 more wins to his team than a replacement level player. In 2010, there were 11
qualifying position players and 10 qualifying pitchers with a negative WAR, meaning they were less valuable than a
replacement level player.20 For example, Carlos Lee had a WAR of -1.6, the worst in the Majors. The presence of players
with a negative WAR indicates that “replacement level” is more of a theoretical framework rather than a concrete
reality.21

Table A: Scale for Reading WAR

Type of Player WAR
MVP 8+
All-Star 5+
Starter 3+
Reserve 0 -2
Replacement <0
Source: Derived from Baseball-reference.com

19Jeff Aberle, “WAR Lords of the Diamond,” Beyond the Box Score, 12 June 2009,

<http://www.beyondtheboxscore.com/2009/6/12/906943/war-lords-of-the-diamond-position>.

20 For position players, qualifying means they had the minimum number of plate appeareances to qualify for the batting title. For

pitchers, qualifying means they had the minimum number of innings pitched to qualify for the ERA title.
21 For this study, I used Seth Smith’s calculations of WAR on baseball-reference.com. This calculation is slightly different than the

popular Fangraphs’ WAR because, instead of UZR, Smith uses “TotalZone” (TZ) to measure a player’s defensive value. Smith made
this substitution because UZR is only available after 2002 because UZR depends on batted-ball types and hit location. TZ
compensates for this lack of historical data by alternatively using both the percentage of the batter’s outs that were recorded by each
of the seven positions and the pitcher’s fly ball/ground ball ratio. Generally, the WAR calculations are similar but there are slight
variations between the two statistics.

11


12


Appendix B: Metric Breakdown - Weighted On-Base Average

Weighted on-base average (wOBA) attempts to reconcile two of the most important offensive statistics, on-
base percentage (OBP) and slugging percentage (SLG). OBP measures how many times a player reaches base (see figure
1) but it does not account for the value difference between a home run and a walk. SLG measures the power of hitter
by calculating total bases (see figure 1) but fails to account for walks. In addition, it’s weights are arbitrary since a home
run is not actually four times as valuable as a single. The first attempt to reconcile these limitations was OPS, calculated
as the sum of on-base percentage and slugging percentage. Since it was introduced by sabermatricians John Thorn and
Pete Palmer in 1984, OPS has grown in popularity, first appearing on Topps baseball cards in 2004 and then ESPN
baseball telecasts in 2008. While OPS represents both a player’s ability to get on base, hit for average and hit for power,
the metric has one major fault: it weighs OBP and SLG percentage equally even though OBP is more valuable than
SLG.

Figure A:

Sabermatrician Tom Tango tackled this issue by creating wOBA. Tango’s statistic is based on linear weights
(also called linear run estimators), designed to measure a player’s offensive contribution per plate appearance by
assigning a certain run value to each offensive event. Since we have run expectancy data for any given scenario, it is
possible to look at an offensive event (double, steal, out, etc.) and calculate the average change in run expectancy when
the offensive event occurs. Then add the average number of runs that scored on a play and the value in runs can be
found for any given offensive event (see table B).

Table B: Linear Weights of Offensive Event

Offensive Event Value
Single 0.46
Double 0.75
Triple 1.03
Home Run 1.40
Walk 0.30
Steal 0.19
Caught Stealing -0.44
Out -0.27
Source: Tom Tango

The last step for calculating wOBA is scaling it to OBP. This makes the final number easier to analyze since a
good OBP is also a good wOBA. The league average wOBA is typically around .335 as it was in 2009. Table C shows
wOBA for a cross-section of players in 2009. AVG, OBP and SLG were also included in figure 3 as reference points. It

13


is important to note that wOBA is a context-neutral statistic with no adjustment for position, park or league effects
though those can easily be added.
Tango created a statistic that combines multiple aspects of hitting (hitting for average, hitting for power and
plate discipline) into one metric. Since wOBA is based on linear weights, those hitting aspects are weighed
proportionally to their actual value in runs. This makes wOBA one of the most useful offensive statistics for evaluation.

Table C: wOBA Cross-Section in 2009

Player AVG OBP SLG wOBA
Albert Pujols* .327 .443 .658 .449
Joe Mauer .365 .444 .587 .438
Hanley Ramirez .342 .410 .543 .410
Evan Longoria .281 .364 .526 .380
Dustin Pedroia .296 .371 .447 .360
Marco Scutaro .282 .379 .409 .354
Ryan Ludwick .265 .329 .447 .336
B.J. Upton .241 .313 .373 .310
Yuniesky Betancourt .245 .274 .351 .271
*league leader
Source: FanGraphs

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Appendix C: Metric Breakdown – Fielding Independent Pitching

Fielding Independent Pitching (FIP) is the product of a Voros McCracken’s revolutionary research which
argued that pitchers only have complete control of home runs, walks and strikeouts. McCracken found that hits on balls
in play (BAPIP) fluctuated drastically from year to year, finding that “pitchers who are the best at preventing hits on
balls in play one year are often the worst at it in the next.” For example, in 2000 Pedro Martinez had a BAPIP of .253
which lead the Majors. In 1999, his BAPIP was third worst among qualifying starters. If pitchers have no control of
BAPIP, this implies that a pitcher has no control over hits, errors and runs (not scored on a home run). Therefore, in
evaluating a pitcher’s performance,
Inspired by McKracken’s research, Sabermatrician Tom Tango (also creator of wOBA) devised FIP. As Tom
Tango described it, “FIP helps you understand how well a pitcher pitched, regardless of how well his fielders fielded.” In
other words, FIP shows how well a pitcher should have performed, not how well he actually performed. Here is the
formula Tango created:

Figure B: formula for calculating FIP

The 13:3:2 ratio is the value between home runs, walks and strikeouts based on linear run estimators. Using
historical data, Tango’s linear weights put a value on play outcomes based on how they contribute to runs scored. The
13:3:2 ratio adjusts for how much each home run and walk contributes to the opponent’s runs scored and how much
each strikeout prevents the opponent’s runs scored.
There are some minor problems with FIP that are worth noting. First, FIP cannot tell you how many runs the
other team scored, a factor reflected in the more traditional ERA. While FIP proves to be a valuable tool for pitcher
evaluation, it does not measure whether runs were actually prevented. A second problem is that there are some
outcomes not included in FIP that the pitcher has some control over. These outcomes include wild pitches, passed balls
and stolen bases. However, since these outcomes depend partially on defense, they are not included in FIP. Again, FIP
focuses only on statistics over which the pitcher has complete control.
Expected Fielding Independent Pitching (xFIP) takes the idea of pitcher control even further, inferring that
pitchers have little control over the rate at which their fly balls are hit for home runs (HR/FB). xFIP normalizes this
variance by scaling the HR/FB to 11% which is the league average.
The last step in calculating FIP is scaling it to ERA by simply adding a constant of 3.20 (league-average ERA
minus league-average FIP) to the FIP value. This makes the number easier to interpret because a good ERA is also a
good FIP. Like ERA, the league average FIP is typically around 4.40. For context, I have compiled a table (see table D)
with six pitchers’ ERA and FIP in 2010. When a pitcher’s ERA is lower than his FIP (as in the case with CC Sabathia) it
indicates that the pitcher was lucky when it came to outcomes that were out of his control. Luck and defense are factors
of ERA but not in FIP. This is why FIP is such a valuable statistic in measuring how a pitcher actually pitched.

Table D: FIP Cross-Section in 2010

Pitcher ERA FIP

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Josh Johnson 2.30 2.41

Cliff Lee 3.18 2.58

C.C. Sabathia 3.18 3.54

John Lackey 4.40 3.86

Barry Zito 4.15 4.25

Rick Porcello 4.92 4.31

A.J. Burnett 5.26 4.83

Source: FanGraphs

16


Appendix D: Graphs

Figure C: Average 2-year Free Agent WAR before and after signing from 1998-2002

Figure D: Average 8-year WAR of First Round by Picks from 1998-2002

17

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