MIT Sloan Sports Analytics Conference 2011                                                                                ...
MIT Sloan Sports Analytics Conference 2011                                                                                ...
MIT Sloan Sports Analytics Conference 2011                                                                                ...
MIT Sloan Sports Analytics Conference 2011                                                                                ...
MIT Sloan Sports Analytics Conference 2011                                                                                ...
MIT Sloan Sports Analytics Conference 2011                                                                                ...
MIT Sloan Sports Analytics Conference 2011                                                                                ...
MIT Sloan Sports Analytics Conference 2011                                                                                ...
MIT Sloan Sports Analytics Conference 2011                                                                                ...
MIT Sloan Sports Analytics Conference 2011                                                                                ...
MIT Sloan Sports Analytics Conference 2011                                                                                ...
MIT Sloan Sports Analytics Conference 2011             March 4-5, 2011, Boston, MA, USA12
MIT Sloan Sports Analytics Conference 2011                                                                                ...
MIT Sloan Sports Analytics Conference 2011                                                                                ...
MIT Sloan Sports Analytics Conference 2011                                                                                ...
MIT Sloan Sports Analytics Conference 2011                                     March 4-5, 2011, Boston, MA, USAJosh Johnso...
MIT Sloan Sports Analytics Conference 2011                                                           March 4-5, 2011, Bost...
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The Broken MLB Free Agent Compensation System: The Valuation of Draft Picks and Free-Agents

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On November 23, MLB agreed to a new collective bargaining agreement which included an overhaul of the free agent compensation system. The merits of the new system remain to be seen. This paper (published in March 2011 at the MIT Sports Analytics Conference) seeks to quantify in a more definitive way the relationship between the value of free agents and draft picks in terms of wins.

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

  1. 1. 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 rankedon a 100-point scale based on an array of traditional statistics. The top 20 percent of players in each group are classifiedas Type A. If a Type A free-agent turns down his team’s offer of arbitration and signs with another team, his formerteam receives two compensatory first round draft picks. Draft picks are a valuable currency which is why certain Type Afree-agents have received less compensation. This study used First-Year Player Drafts from 1998 to 2003 to determinethe value of each pick by calculating the WAR (wins above replacement player) of each player for the 8 years followingthe draft. The WAR for free agents from that time period was also calculated to compare. The numbers suggest thatthe current system overcompensates teams that lose a player to free agency. This paper seeks to quantify in a moredefinitive way the relationship between the value of free agents and corresponding draft picks in terms of wins. Thecurrent MLB collective bargaining agreement expires in December 2011 and the negotiations concerning this issue willbe on the table.1 Introduction In November 2008, second baseman Orlando Hudson filed for free agency, expecting the biggest contract ofhis 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 offive years, $50 million, with nearly a dozen teams showing interest. At least that was the idea, until the Elias SportsBureau 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 Novemberusing a formula provided by MLB. In theory, the Elias Rankings identify the best players so teams that lose certainplayers in free agency can be rewarded with draft picks. It first became part of baseball’s Collective Bargainingagreement in 1985 as a means, along with the Luxury Tax system later, to create a competitive balance between smalland large market teams.2 In order to calculate the Elias Rankings, all players (not just free agents) are divided into five position groupsand ranked on a 100-point scale based on an array of traditional statistics. The exact weight of each statistic has neverbeen released by MLB or the Elias Sports Bureau. Table 1 shows the five position groups and the different statisticalcategories used for each position group.31 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. 1
  2. 2. MIT Sloan Sports Analytics Conference 2011 March 4-5, 2011, Boston, MA, USA Table 1: Position groups and statistical categories used in calculation of current Elias RankingsPosition Group Statistics used for Position Group’s Rankingsfirst baseman, outfielders and designated hitters PA, AVG, OBP, HR, RBIsecond baseman, third baseman and shortstops PA, AVG, OBP, HR, RBI, Fielding PercentageCatchers PA, AVG, OBP, HR, RBI, Fielding Percentage, Assistsstarting pitchers Total Games, IP, Wins, W-L percentage, ERA, strikeoutsrelief 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 seasons 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. 2
  3. 3. MIT Sloan Sports Analytics Conference 2011 March 4-5, 2011, Boston, MA, USAfor in 2008, then waive the MLB provision that prevents players from being traded before June 15.6 This sign-and-tradesolution 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 Rankingsand 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 inperformance incentives, far less than the five-year, $50 million contract Hudson was seeking. Many analysts criticizedthe Dodgers because of Hudson’s Type-A status, claiming the team had given up too much.7 In March, Cabrera signeda one-year, $4 million contract with the Oakland A’s, a substantial pay cut from the $10 million he earned in a productive2008 for the Chicago White Sox. Cruz signed a two-year, $6 million contract with the Kansas City Royals, a team whoselow standing meant they only had to surrender a second round pick for signing a Type-A free agent. These are just threeexamples 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 topdraft picks for a pitcher who will only pitch 2-6% of their team’s innings. In the 2010 offseason, 33 Type-A and Type-Bfree 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, inthe 2010 offseason, the New York Yankees made it a priority to add a left-handed reliever. The best available was ScottDowns, a player the Yankees had shown interest in acquiring in the past. However, Jon Heyman of Sports Illustratedreported that General Manager Brian Cashman “won’t go for Downs or other Type-A relievers because they want tokeep 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 irrationalitiesassociated 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 draftpicks becomes more complicated if the signing team signs more than one Type-A free agent. When that happens, thesigning team surrenders its highest available pick to the team that had the better Type-A free agent, based upon Eliasrankings. For example, after the 2008 season, the New York Yankees signed CC Sabathia, Mark Teixeira and AJBurnett, 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) wasawarded 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 thatthis does not fairly compensate teams who lose free agents and encourages market buyers to hoard free agents in aparticular offseason.10 There is also the problem of the “rental player.” Many teams have started to pursue projected Type-A andType-B players at the trade deadline with no intention of keeping the player after the season, but, rather, with theintention of adding draft picks when they later lose the player to free agency. This strategy of hoarding draft picks issometimes called the “Money Ball Method” because A’s General Manager Billy Beane’s adoption of the strategy waswell-documented in Michael Lewis’ Money Ball. For example, in 2003, the A’s acquired second basemen Ray Durham atthe trade deadline. Money Ball reveals that Beane believed the move was only a minor upgrade but he wanted the twocompensation 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 Wont 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 May2010, <http://www.blessyouboys.com/2010/6/9/1508715/how-and-why-mlbs-compensation>. 3
  4. 4. MIT Sloan Sports Analytics Conference 2011 March 4-5, 2011, Boston, MA, USA 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 losinghomegrown talent, then the system is “failing miserably.” He looked at how much time players spent with the teamsbeing rewarded draft picks by calculating the percentage of the player’s career plate appearances or innings pitched withthe compensated team. He analyzed, 219 first round compensatory picks (from 171 players lost to free agency) from the2000-2010 drafts, and the average percentage of playing time spent with the compensated team was 37.9%. After afurther analysis, Anderson concludes, “If the league required that players had to spend at least 25% of their careerplaying time…with a team to receive first round compensation, we would’ve essentially halved the actual player and pickpool.” Table 211 shows the free agents whose signing provided compensatory picks for the 2000 draft. Note that onlytwo of the thirteen ranked players lost to free agency were homegrown (Trombly and Rhodes). In addition, two of thethree Type-A players had been on the compensated team for less than a year (Zeile and Springer). In the case of MoneyBall’s example of the Durham acquisition, the A’s received two first round compensation picks for a player that had onlyplayed 5% of his career to that point with the A’s. Using this data, Anderson concludes “compensation should bealtered so that players have to spend a certain amount of time with their last team to qualify for compensation; thethought 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/ PicksDraft 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 Sabermetricscommunity’s criticism of traditional baseball statistics has been well-developed over the last 30 years. With the exceptionof 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/>. 4
  5. 5. MIT Sloan Sports Analytics Conference 2011 March 4-5, 2011, Boston, MA, USAwins, and fielding percentage.12 These well-developed criticisms aside, even by traditional standards the Elias rankingsstill seem oddly subjective. For example, why does Elias group first basemen with outfielders? It appears MLB wants togroup offensively similar positions. If this is the case, it seems odd that MLB did not differentiate outfield positionssince 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 firstbasemen? Another question: Is it fair that catchers are only ranked with other catchers while second basemen are rankedwith third basemen? In a 2009 column for Yahoo! Sports, Jeff Passan wrote that an MLB source told him that the rankingsystem is so “blatantly outdated” that the Elias Sports Bureau is “embarrassed to compute them.”132 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 agentsand corresponding draft picks. In addition, it will propose a new system for free-agent compensation that moreaccurately assigns the value of draft picks and free agents in terms of WAR. The current MLB collective bargainingagreement expires in December 2011 and this issue will be the subject of important negotiation, both from the point ofview 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 thataccurately reflect the value of a player’s performance. I settled on one statistic: WAR. My analysis relies heavily onWAR so it is important to understand how it is calculated and why it is both the most widely used “total value” statisticand the most reliable measure to use in establishing this value comparison. For those unfamiliar with reading andcalculating 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 mostoften compensated picks in the free-agent compensation system) from 1998 to 2002 to determine the value of each pickby calculating the WAR of each player for the eight years following the draft. If a player did not make a Major Leagueroster, his WAR was considered 0. Why was WAR calculated for eight years following the draft? The goal was to pick anamount 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, aplayer is not eligible for free agency until his contract has expired with at least six years of service time on a MajorLeague 25-man roster. Therefore, free agents are established Major Leaguers and the goal of this calculation should beto 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 toreach the Majors. Players’ signing dates marked the start of their Minor League career and their Major League debutmarked the beginning of their Major League career. In 2001, on average, it took 1197.81 days (or 3 years, 3 months and12 days) for a drafted player to reach the Majors (see table 3). Players drafted in the first round reached the Majorsfastest (976.33 days) but, surprisingly, supplemental picks took considerably longer to reach the Majors than thosepicked 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 Majors12 I have published several editorials that critique the use of traditional statistics in baseball including “The Myth of a Clutch DerekJeter” which can be accessed on www.maroon-news.com13 Passan, “Free-agent Compensation System Is Unfair.” 5
  6. 6. MIT Sloan Sports Analytics Conference 2011 March 4-5, 2011, Boston, MA, USA 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 aplayer’s career does not account for drafted players who sign a contract but do not immediately start playing. Shouldthis void in playing time be included in the player’s career? I considered using a Minor League debut date but that datedoes not account for players who were drafted with an injury and younger players who spend time in instructionalleagues which is not considered a Minor League debut. Certainly, a year of instructional league or rehabilitation shouldbe 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 Leaguedebut as a September call-up in some very limited role (pinch-runner, defensive replacement, etc.) or filling in for aninjured starter before returning to the Minors. Some of these players eventually become established Major Leagueplayers while others do not. My analysis of the first three rounds of the 2001 draft shows that 53.33% of players draftedreached 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 adrafted player reaches the Majors, his baseball career will last an average of 5.6 years. However, if a player reaches histhird season, he can expect to play six additional years.15 Thus, an established player can be defined as a player withthree years of Major League experience. Add three years to the average time it takes to make the Majors and we getroughly 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 forcomparisons with the two-year WAR of free agents. Baseball-reference.com also uses an eight-year WAR calculation toevaluate a draft pick. When reached for comment by e-mail in December 2010, Baseball-reference.com confirmed thattheir choice of eight years was also based on the average time it takes a drafted player to become an established MajorLeaguer but did not elaborate further. The need for eight years of WAR statistics lead to this study’s selection of the2002 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 1.50333333314 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. 6
  7. 7. MIT Sloan Sports Analytics Conference 2011 March 4-5, 2011, Boston, MA, USA 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 anaverage WAR of 1.41 and second round picks had an average WAR of 1.44 (see Table 5). The low average WAR of thesupplemental round can be credited to a negative WAR in the 2002 draft, an apparent outlier. A further analysis of firstround picks found that the value of a pick diminished later in the round, as one would expect (see table 6 and figure C inAppendix 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 WAR statistics derived from Baseball-reference.com The WAR for free agents from 1998-2002 was also calculated in order to evaluate the current free-agentcompensation 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 werenot 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 1.231WAR statistics derived from Baseball-reference.com 7
  8. 8. MIT Sloan Sports Analytics Conference 2011 March 4-5, 2011, Boston, MA, USA The average WAR of free agents (two years prior to their free agency) from 1998-2002 was 1.81. The averageWAR for those same free agents the two years after they signed was 1.23 (see table 8 and figure A in Appendix D). Thissuggests that free agents’ value diminished during the two years after they signed a contract. For comparison, theaverage WAR for the first three rounds of the draft from 1998-2002 was 2.58. When a T-Test is performed on the freeagent and draft data, the p-value is 0.19. This p-value shows that the difference between the WAR of players selected inthe 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 currentvalue and long-term value. A discount rate was calculated in order to account for the value of time in WAR becausedrafted players will not benefit the team until they are established major leaguers. I call this statistic WAR-DAD (Winsabove 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 ratex1 = cost after 1 yearFirst, calculate x1:x1^6 = W0*WFor, X1 = (W0*WF)^1/6Then,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 yearsafter they signed (1.23).3 Conclusions The findings of this study suggest that the current free-agent system overcompensates teams that lose a playerto free agency. The five-year average WAR-DAD for a first round pick and supplemental pick (the currentcompensation for the loss of a Type-A free agent) is 5.08 based on data from the 1998-2002 drafts This is significantlygreater than the five-year average free agent WAR (1.23 for each of the two years after signing) during that same timeperiod. Based on these findings, MLB should consider a new system for free-agent compensation. The details of a newsystem depend greatly on the goals of the free-agent compensation system (perhaps the MLB intends to overcompensateteams that lose players to free agency). These are matters of policy that need to be addressed in collective bargaining, aprocess that may be assisted by the use of WAR-DAD data. WAR-DAD analysis may provide a means for evaluatingand 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, whilethe 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 8
  9. 9. MIT Sloan Sports Analytics Conference 2011 March 4-5, 2011, Boston, MA, USAout of touch with the way the market actually values players.”16 The proposed new ranking system described in thisarticle would eliminate the subjective assignment of position groups and the use of traditional statistics in the Eliasrankings and, instead, rank players based on their value in wins regardless of position. WAR accounts for relativeposition strength anyway. Knowing the value of both free agents and each draft pick based on historical data, one couldaccurately assign a specific draft pick (for example, the #35 pick) as compensation for a lost free agent with a certainspecific WAR. For example, if Orlando Hudson has a WAR of 3 over the prior two years and the MLBPA agrees tocompensate his team two-fold, the team can receive the number of supplemental picks that most closely equals acumulative WAR of 6 based on historical data. Second, the authors agree with Anderson’s analysis that a playing time threshold needs to be implemented inorder 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 topunish teams by redistributing picks. There is a double standard when MLB criticizes teams like the Pittsburgh Piratesand Florida Marlins for not raising payroll when their own collectively bargained compensation system is designed todiscourage or impose penalties on teams that spend. Small-market teams cannot afford to give up valuable draft picksthat provide cheap, young players. With further research and applications of WAR-DAD, alternative approaches to these compensation issues canbe explored. Nonetheless, one thing is clear: the current free-agent compensation system does not value players fairly oraccurately.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 Wont Go for down ...” Twitter. 7 Dec. 2010. Web. <http://twitter.com/SI_JonHeyman/statuses/12155945752727552#>.16 Passan, “Free-agent Compensation System Is Unfair.” 9
  10. 10. MIT Sloan Sports Analytics Conference 2011 March 4-5, 2011, Boston, MA, USA[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 offensiveand 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 intomore advanced rate statistics: primarily weighted on-base average (wOBA), fielding independent pitching (FIP), and totalzone rating (TZ).17 Essentially, these rate statistics convey a player’s contributions in terms of runs added or runsprevented, so they can be referred to as “Run Measures.” After calculating Run Measures, there are several adjustments that are made in calculating WAR. First, apositional adjustment is added to account for the relative value of different positions. Second, a league adjustment isadded to account for the relative strength of the American League due to a higher offensive output as a result of thedesignated hitter and, currently, a greater concentration of talent than the National League. Third, a stadium adjustmentfurther normalizes WAR by removing the effect of a player’s home ballpark. Finally, since Run Measures are alreadyscaled 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 themost debated aspect of total value statistics like WAR: What is a “replacement level” player?18 WAR’s creator, Tom17 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). 10
  11. 11. MIT Sloan Sports Analytics Conference 2011 March 4-5, 2011, Boston, MA, USATango, defines “replacement level” as “the talent level for which you would pay the minimum salary on the openmarket, or for which you can obtain at minimal cost in a trade.” Replacement level players are generally fringe or AAAAplayers (somewhere between AAA level and the Major League level). A team with all replacement level players wouldwin 20-25 games in a 162-game season. More specifically, the expected value of a replacement level player is 20 runs per600 plate appearances. Jeff Berble, at Beyond the Boxscore, summarizes: “The reason that replacement players are used inthese value calculations is that we have a fixed baseline for exactly how much these players are worth--and that is theMLB 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 areplacement player, we reach the last step in our calculation of WAR. For a position player, wOBA and TZ (ourreplacement adjusted run measures) are added to get Runs Above Replacement (RAR). Then you scale all thosecontributions to be expressed in terms of total team wins. The scale is 10 runs equals one team win, therefore WAR isequal to RAR divided by 10. For a pitcher, this last step is slightly more complicated. In order to derive an equivalent ofRAR 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 thepitcher’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 inTable A below. For context, since 2000, only five players have posted a single-season WAR greater than 10 (BarryBonds did it four times). In 2010, Evan Longoria led the Majors with a 7.7 WAR. Again, this means that, by the WARcalculation, Longoria contributed 7.7 more wins to his team than a replacement level player. In 2010, there were 11qualifying position players and 10 qualifying pitchers with a negative WAR, meaning they were less valuable than areplacement level player.20 For example, Carlos Lee had a WAR of -1.6, the worst in the Majors. The presence of playerswith a negative WAR indicates that “replacement level” is more of a theoretical framework rather than a concretereality.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.com19Jeff 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. Forpitchers, 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 thepopular Fangraphs’ WAR because, instead of UZR, Smith uses “TotalZone” (TZ) to measure a player’s defensive value. Smith madethis substitution because UZR is only available after 2002 because UZR depends on batted-ball types and hit location. TZcompensates for this lack of historical data by alternatively using both the percentage of the batter’s outs that were recorded by eachof the seven positions and the pitcher’s fly ball/ground ball ratio. Generally, the WAR calculations are similar but there are slightvariations between the two statistics. 11
  12. 12. MIT Sloan Sports Analytics Conference 2011 March 4-5, 2011, Boston, MA, USA12
  13. 13. MIT Sloan Sports Analytics Conference 2011 March 4-5, 2011, Boston, MA, USA 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 figure1) but it does not account for the value difference between a home run and a walk. SLG measures the power of hitterby calculating total bases (see figure 1) but fails to account for walks. In addition, it’s weights are arbitrary since a homerun is not actually four times as valuable as a single. The first attempt to reconcile these limitations was OPS, calculatedas the sum of on-base percentage and slugging percentage. Since it was introduced by sabermatricians John Thorn andPete Palmer in 1984, OPS has grown in popularity, first appearing on Topps baseball cards in 2004 and then ESPNbaseball 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 thanSLG.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 byassigning a certain run value to each offensive event. Since we have run expectancy data for any given scenario, it ispossible to look at an offensive event (double, steal, out, etc.) and calculate the average change in run expectancy whenthe offensive event occurs. Then add the average number of runs that scored on a play and the value in runs can befound 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 agood OBP is also a good wOBA. The league average wOBA is typically around .335 as it was in 2009. Table C showswOBA for a cross-section of players in 2009. AVG, OBP and SLG were also included in figure 3 as reference points. It 13
  14. 14. MIT Sloan Sports Analytics Conference 2011 March 4-5, 2011, Boston, MA, USAis important to note that wOBA is a context-neutral statistic with no adjustment for position, park or league effectsthough those can easily be added. Tango created a statistic that combines multiple aspects of hitting (hitting for average, hitting for power andplate discipline) into one metric. Since wOBA is based on linear weights, those hitting aspects are weighedproportionally to their actual value in runs. This makes wOBA one of the most useful offensive statistics for evaluation. Table C: wOBA Cross-Section in 2009Player AVG OBP SLG wOBAAlbert Pujols* .327 .443 .658 .449Joe Mauer .365 .444 .587 .438Hanley Ramirez .342 .410 .543 .410Evan Longoria .281 .364 .526 .380Dustin Pedroia .296 .371 .447 .360Marco Scutaro .282 .379 .409 .354Ryan Ludwick .265 .329 .447 .336B.J. Upton .241 .313 .373 .310Yuniesky Betancourt .245 .274 .351 .271*league leaderSource: FanGraphs 14
  15. 15. MIT Sloan Sports Analytics Conference 2011 March 4-5, 2011, Boston, MA, USA Appendix C: Metric Breakdown – Fielding Independent Pitching Fielding Independent Pitching (FIP) is the product of a Voros McCracken’s revolutionary research whichargued that pitchers only have complete control of home runs, walks and strikeouts. McCracken found that hits on ballsin play (BAPIP) fluctuated drastically from year to year, finding that “pitchers who are the best at preventing hits onballs in play one year are often the worst at it in the next.” For example, in 2000 Pedro Martinez had a BAPIP of .253which lead the Majors. In 1999, his BAPIP was third worst among qualifying starters. If pitchers have no control ofBAPIP, this implies that a pitcher has no control over hits, errors and runs (not scored on a home run). Therefore, inevaluating a pitcher’s performance, Inspired by McKracken’s research, Sabermatrician Tom Tango (also creator of wOBA) devised FIP. As TomTango described it, “FIP helps you understand how well a pitcher pitched, regardless of how well his fielders fielded.” Inother words, FIP shows how well a pitcher should have performed, not how well he actually performed. Here is theformula 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. Usinghistorical data, Tango’s linear weights put a value on play outcomes based on how they contribute to runs scored. The13:3:2 ratio adjusts for how much each home run and walk contributes to the opponent’s runs scored and how mucheach 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 theother team scored, a factor reflected in the more traditional ERA. While FIP proves to be a valuable tool for pitcherevaluation, it does not measure whether runs were actually prevented. A second problem is that there are someoutcomes not included in FIP that the pitcher has some control over. These outcomes include wild pitches, passed ballsand stolen bases. However, since these outcomes depend partially on defense, they are not included in FIP. Again, FIPfocuses only on statistics over which the pitcher has complete control. Expected Fielding Independent Pitching (xFIP) takes the idea of pitcher control even further, inferring thatpitchers have little control over the rate at which their fly balls are hit for home runs (HR/FB). xFIP normalizes thisvariance 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 ERAminus league-average FIP) to the FIP value. This makes the number easier to interpret because a good ERA is also agood 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) itindicates that the pitcher was lucky when it came to outcomes that were out of his control. Luck and defense are factorsof 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 15
  16. 16. MIT Sloan Sports Analytics Conference 2011 March 4-5, 2011, Boston, MA, USAJosh Johnson 2.30 2.41 Cliff Lee 3.18 2.58C.C. Sabathia 3.18 3.54John Lackey 4.40 3.86 Barry Zito 4.15 4.25Rick Porcello 4.92 4.31A.J. Burnett 5.26 4.83 Source: FanGraphs 16
  17. 17. MIT Sloan Sports Analytics Conference 2011 March 4-5, 2011, Boston, MA, USA Appendix D: GraphsFigure 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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