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Analyzing the Effects of Revenue Sharing on Competitive Balance in Major League Baseball
Analyzing the Effects of Revenue Sharing on Competitive Balance in Major League Baseball
Analyzing the Effects of Revenue Sharing on Competitive Balance in Major League Baseball
Analyzing the Effects of Revenue Sharing on Competitive Balance in Major League Baseball
Analyzing the Effects of Revenue Sharing on Competitive Balance in Major League Baseball
Analyzing the Effects of Revenue Sharing on Competitive Balance in Major League Baseball
Analyzing the Effects of Revenue Sharing on Competitive Balance in Major League Baseball
Analyzing the Effects of Revenue Sharing on Competitive Balance in Major League Baseball
Analyzing the Effects of Revenue Sharing on Competitive Balance in Major League Baseball
Analyzing the Effects of Revenue Sharing on Competitive Balance in Major League Baseball
Analyzing the Effects of Revenue Sharing on Competitive Balance in Major League Baseball
Analyzing the Effects of Revenue Sharing on Competitive Balance in Major League Baseball
Analyzing the Effects of Revenue Sharing on Competitive Balance in Major League Baseball
Analyzing the Effects of Revenue Sharing on Competitive Balance in Major League Baseball
Analyzing the Effects of Revenue Sharing on Competitive Balance in Major League Baseball
Analyzing the Effects of Revenue Sharing on Competitive Balance in Major League Baseball
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Analyzing the Effects of Revenue Sharing on Competitive Balance in Major League Baseball

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This paper uses multiple regression models to conclude that revenue-sharing should promote competitive balance, but is hindered by the free agent-draft pick …

This paper uses multiple regression models to conclude that revenue-sharing should promote competitive balance, but is hindered by the free agent-draft pick
compensation system and the stadium-building boom.

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  • 1. Le vitt |1 Analyzing the Effects of the Revenue-Sharing Program on Competitive Balance in Major League Baseball Ethan Levitt Colgate University Abstract This paper seeks to add econometric analysis and insight to the discussion over the effects of the revenue- sharing program on competitive balance in Major League Baseball. It finds that the assumptions of the program hold if applied to the league as a whole, but differ dramatically if the teams are grouped based on spending habits. The relationships tested are between wins and revenue using attendance as an intermediate factor, and between payroll spending and winning. Using a combination of pooled OLS, fixed effects, and 2SLS regressions, we ultimately find evidence of simultaneity and significant categorical differences. Only upon further team-specific analysis does this paper offer some meaningful conclusions and ideas about how to improve competitive balance in the upcoming collective bargaining negotiations.Introduction The 1990s saw the emergence of major market teams, highlighted by the dominance of the AtlantaBraves and New York Yankees, as perennial winners in Major League Baseball. In response to a growingconcern that fans would lose interest in their team if it had no real chance at winning, the Commissioner ofMajor League Baseball, Bud Selig, commissioned a Blue Ribbon Panel on Baseball Economics to assess theproblem and develop potential solutions to create competitive balance. Their main finding was that:“The goal of a well-designed league is to produce adequate competitive balance. By this standard, MLB is not now well-designed.In the context of baseball, proper competitive balance should be understood to exist when there are no clubs chronically weakbecause of MLBs structural features. Proper competitive balance will not exist until every well-run club has a regularly recurringreasonable hope of reaching postseason play.” (Levin, Mitchell, Volcker, and Will; 2000) This conclusion was driven to a large degree by the perceived disparity in ability between teams togenerate revenue based predominantly on market size. Larger cities had more potential fans that could lead tomore interest in the team and thus more fans in attendance and watching on TV. This higher demand allowedmajor market teams to generate more revenue and thus provide them with more money to spend on payroll.In order to address this, a revenue sharing system was suggested and subsequently instituted. The goal of this program is relatively simple: by redistributing revenue from the wealthy, higherspending teams typically located in major metropolitan areas to the poorer, lower spending teams in smallermarkets, more teams would have the ability to field a competitive team and, by winning, increase theirrespective fan bases. This in turn would lead to greater interest in the League and generate more revenue forMajor League Baseball and the teams in it. Since its introduction in 1998, the revenue-sharing system hasevolved to penalize higher-spending teams more and redistribute more revenue across more teams. Theimplicit assumption behind such a system is clearly that there is a universally positive relationship between
  • 2. Le vitt |2spending, winning, and earning. Specifically, a team that spends more wins more, and a team that wins moremakes more money. However, leaked financial statements from several Major League teams suggest that some teams havebeen keeping the revenue-sharing money they receive instead of spending it on team payroll, as was theintention of the program. While the primary response to these leaked documents has been frustration andanger from the fans of the respective teams, many have also concluded that the money these teams collectfrom the revenue-sharing system exceeds the amount they expect to generate from an improved team andthus creates a disincentive for them to spend the money in an effort to win more games. Comparing thisfinding with the fact that 23 of the 30 MLB teams have reached the playoffs in the past decade and no teamhas won back to back World Series since the New York Yankees in 1999 and 2000 has led many to argue thatMLB is more competitively balanced and that the revenue-sharing program has played a substantial role inthat change. However, there is no conclusive evidence to corroborate the latter claim and thus a debate hasarisen as to what the true effects of revenue sharing are. With the current CBA set to expire at the end of2011, leaving these programs open to review, fans, owners, and analysts alike are challenging that the newCBA must address revenue sharing and potentially reform it.Theory This paper seeks to assess the effects (if they exist) of the revenue-sharing program on competitivebalance in Major League Baseball. Thus the economic theory focused on in this paper is the theory behindthe revenue-sharing program; namely that providing money to small market teams, or in a sense subsidizingthem, will allow them to be able to field competitive teams. This theory, and the revenue-sharing programbased on it, depends on two relationships: team revenue as a positive function of team wins and team wins asa positive function of team payroll. Furthermore, it seems to be an implicit assumption of the program thatthese relationships create a perpetual feedback mechanism in that more revenue provides teams with moremoney to spend on payroll which they thus use to win more games and generate more revenue.Literature Review Major League Baseball fills an interesting societal and economic niche as both America’s pastime anda rare monopoly with an antitrust exemption. This combination has made it the focus of a great deal ofsports-based economic research. With regards to the subjects of revenue-sharing and competitive balance, afew particular works have contributed valuable insight which helped inform the construction of myeconometric models. J.C. Bradbury, in his book The Baseball Economist, studied the effects of city population on the numberof games a team wins and found that not only do big market teams tend to win more games, but that “thedifference in market size explains about 40 percent of the difference in wins between the top and bottom
  • 3. Le vitt |3markets”(Bradbury, 2007). While Bradbury takes a relatively objective stance on whether the big marketadvantage is fair, he does assert that there is a definite and quantifiable advantage to being in a big city. Healso found that the “average team”, which I interpret as being defined by being based in a city with an averagepopulation relative to other MLB cities, faces a potential “revenue loss trap” caused by the costs of improvingfrom a mediocre team to an average team being outweighed by a minimal increase in fan interest and a lack ofsufficient revenue generation as a result. A study by Gustafson and Hadley (2007) corroborates this result. Using data from 1997-2001,Gustafson and Hadley used a four-equation simultaneous model of win percent, team payroll, team totalrevenue, and team local revenue to examine the impact of market size on competitive balance. Havingaccounted for simultaneity bias using the two-stage least squares technique, they found that local populationhas a statistically significant positive effect on local revenue, and that this effect can roughly translate, throughan increase in payroll, into an increase in wins. They measure this effect to be about 1 additional win for everymillion people in the population, which means up to a 10 win difference between the largest (New York) andsmallest (Milwaukee) markets.Data The data in this research spans the years 2003-2009, due to the fact that team revenue numbers for2010 have not yet been released. The decision to use 7 years worth of data was guided by the fact that themost recent Collective Bargaining Agreement, which changed the terms of the revenue-sharing system tobenefit the poorer teams even more, went into effect in 2007, giving us 4 years of data before and 3 yearsafter. This does not allow for a comparison of teams from the pre-revenue-sharing and during revenue-sharing periods, but may allow us to get an adequate estimate of whether changing the terms of the revenue-sharing program does or does not have an effect on a team. The data compiled from these 7 years consists ofOpening Day team payroll measured in millions of dollars, number of wins, average attendance per gamemeasured as a percent of total stadium capacity, and team revenue estimations calculated by Forbes. It isimportant to note that the revenue numbers used in this paper are estimations and that the actual revenuenumbers are not publicly available information. Thus, the coefficients from the regressions will not be entirelyaccurate, but it is accepted that the estimation technique Forbes uses is consistent so the relationship ofrevenue between teams should be fairly accurate (Zimbalist, 2003). The second thing worth mentioning is the inclusion of attendance rate as a way of gauging fanresponsiveness or interest. Whereas the two research papers discussed use local population to account fordifferences in fan base or fan interest, that approach seems quite broad for the effects I am trying to measure.Using attendance rate inherently controls for difference in stadium size and factors directly into team revenue.More concisely, given that cities with larger populations have an inherent advantage in the ability to generaterevenue, I am interested in focusing on how that translates to how that can directly affect a team’s finances.
  • 4. Le vitt |4Gustafson and Hadley (2007) use average ticket price (a disputed measure) and age of stadium to help controlfor similar stadium effects.Econometric Specification of the Model and Results Given that the data is panel data, a primary way to detect if there are significant team factors besideswins that affect revenue is through a fixed effects model. Thus I used the following fixed effects model to testa) the validity of Bradbury’s revenue loss trap assertion* and b) if team-specific effects affecting revenue arecorrelated to wins: (1) Revenue = β0 + β1wins + β2wins2 + β3wins3 + β4year + µ*The expectation is that β1>0, β2<0, β3>0, with year included as a control for league-wide changes in revenue over time. Following from the results of equation (1) and a Hausman test of fixed effects vs. random effects(see Appendix), it follows that there are significant team-specific factors that affect revenue besides wins, butthat not are correlated to wins. I used the following equation to test the hypothesis that attendance rate is oneof those factors: (2) Revenue = δ0 + δ1attend + ε (3) Attend = α0 + α1wins + ν Given the results of equation (3) and (4), with the latter helping to clarify the other half of therelationship between wins and revenue, I decided to add attendance rate to equation (1): (4) Revenue = β0 + β1wins + β2wins2 + β3wins3 + β4year + β5attend + µ In order to test the second relationship of the revenue-sharing program, I used the following model,with interaction terms designed to see if there is a difference in the effectiveness of payroll spending acrossgroups (the groups are categorized in the Appendix using each team’s average payroll over the 7 year period): (5) Wins = φ0 + φ1payroll + φ2small + φ3major + φ4payroll*small + φ5payroll*major + ω In order to test for the simultaneity bias that Gustafson and Hadley were concerned about in theirmodel, I used Equation (5) to instrument for the three win terms in Equation (4): (6) (1) Revenue = β0 + β1wins + β2wins2 + β3wins3 + β4year + β5attend + µ (2) Wins Wins2 Wins3 = φ0 + φ1payroll + φ2small + φ3major + φ4payroll*small + φ5payroll*major + ω
  • 5. Le vitt |5 Table 1Form of Fixed Effects OLS OLS OLS OLS 2SLSEstimation (1) (2) (3) (4) (5) (6)Dependent Revenue Revenue Attend Revenue Wins RevenueVariableConstant -23742.8** 55.745** .0801 -22349.81** 85.139** -29528.94** (976.3) (9.81) (.081) (1996.91) (7.54) (5541.89)Wins 2.071 .0074** 22.718** 89.457 (6.279) (.001) (11.544) (130.67)Wins2 -3.663 -35.254** -125.795 (8.322) (15.20) (179.418)Wins3 2.082 17.653** 61.01 (3.625) (6.593) (75.401)Year 11.899** 10.945** 13.696** (.484) (.993) (2.968)Attend 165.256** 129.942** -57.138 (14.003) (12.286) (81.141)Payroll -.1075 (.1128)Small -15.45* (8.63)Major -11.65 (8.328)Payroll*Small .2327* (.1381)Payroll*Major .2256* (.1173)R2 value .3154 .401 .2093 .6506 .2222 N/AF statistic 153.54 (model) 139.27 55.07 75.98 11.65 78.18 39.08 (on fixed (wald chi- effect) squared)Sample Size 210 210 210 210 210 210Notes: * and ** indicated statistical significance at the 10% and 5% significance levels, respectively. Values inparentheses are White-corrected robust standard errors and the significance levels reflect that adjustment. Beginning with Equation (1), it is clear that revenue is affected by much more than just how manygames a team wins, and that the significant team-specific effect (as determined by the F-statistic of 39.08 forthe fixed effect) is not correlated to wins. Previous literature posited that hometown population size was thatteam-specific effect. Based off the assumption that attendance rate is not only a reflection of population size,but also a more direct factor in team revenue generation, Equations (2) and (3) were included to observe theeffects of attendance rate and test for correlation with both revenue and wins. While the effect of attendanceon revenue is both statistically and economically significant, the effect of wins on attendance does not appearto be economically significant despite its statistical significance (this will be discussed further in the
  • 6. Le vitt |6Conclusion). It is important to add that while RESET and LM tests suggested that the functional form ofequations (2) and (3) could be improved by adding higher-order terms of the explanatory variables, doing somade the coefficients statistically insignificant due to high variance inflation and distorted the basic positiverelationship. Thus, I chose to leave them as they are presented. Equation (4) shows that when holding attendance rate constant, the coefficients on all the winvariables not only become statistically significant, but also follow the pattern proposed by the revenue losstrap at a economically significant level. Given the result of equation (3) indicating positive correlationbetween wins and attendance, it follows that when that effect is “partialled out”, the magnitude of the effectof wins will increase. While this result does indicate that even during the revenue-sharing period there is adisincentive for small market teams to try and win more games for fear of falling into the revenue loss trap, itdoes not take into account the simultaneity bias that Gustafson and Hadley found in their data. The underlying assumption behind any analysis of the revenue loss trap is that teams have the abilityto spend or buy their way out of the bottom tier. Equation (5) interestingly seems to indicate that the effect ofincreasing payroll for small budget teams and large budget teams is positive and statistically significant, butthat mid-range budget teams are not as effective payroll spenders, which is perhaps why they are in that losstrap to begin with. At the same time, the results of this regression also indicate that on average, a one milliondollar increase in payroll spending for a small market team is associated with about a .12 increase in thenumber of games won, holding other factors constant. However, spending 8 or 9 million dollars to winsomewhere in between zero and two more games would not be considered a solid investment by any MLBteam. Despite their statistical significance, the two coefficients do not indicate that, on average, MLB teamsare very good at buying more wins. In Equation (6), we see that the simultaneity bias substantially altered the coefficients and that thecoefficients on the win variables are no longer statistically significant. I maintain that this is due to incrediblyhigh variance inflation arising from collinearity of the explanatory variables (which is evidenced in theAppendix under Equation (4)), and that ultimately, equations (4), (5), and (6) show that: a) The revenue loss trap exists and is significant b) Simultaneity is present the model (see the Hausman test results in the Appendix) and thus teams should expect a positive feedback cycle for spending money on team payroll c) Since small market teams do not always increase payroll, there feedback effect must be small or different aspects of the cycle are failing for individual teams.Conclusion On the face of it, the results indicate that revenue-sharing should be an effective program forcreating competitive balance. The presence of simultaneity indicates that the key variables we focused on inthe regressions are interdependent and for the most part, we found that these interdependent relationships
  • 7. Le vitt |7are positive. The group that seems to be the main cause of distorting these simultaneously positiverelationships is the middle-spending group. They do not win a consistent (high or low) number of gamesannually and thus appear as neither efficient spenders nor effective revenue generators. However, this“problem” for our analysis can be viewed as a benefit to competitive balance. The constant rotation ofcompetitive teams may be preferable even if it means some teams have to lose money once in a while. While the issues facing middle-spending teams are worthy of inspection, the revenue-sharingprogram, and thus the focus of this paper, is really targeted at addressing the winning habits of small-spending teams. In order to get a better idea of how the individual team relationships of payroll spending towinning, winning to attendance, and attendance to revenue, play into the bigger picture of categorical andleague-wide differences; I ran single variable regressions for each team in each relationship using each team’s7 or 8 years of data, and compared those team-specific coefficients. The following conclusions rely on boththe multiple regressions and the single regressions. Payroll-Wins as an Indicator of Effective Spending While this relationship is not necessarily the most important to a team’s financial success, it is themost publicized and most heavily debated amongst writers and sabermetricians. As mentioned before, themost commonly held assumption (or at least expectation) is that this relationship is positive, i.e. that if a teamspends more money, it will win more games. However, results from the past few seasons, particularly fromthe 2010 season, provide evidence to the contrary. The correlation between winning and spending, asmeasured in Figure 2, has been generally decreasing since the 1998 season, from .71 in 1998 to below .20 in2010. A verage MLB P ayroll (in millions of dollars) by Y ear 10 0 90 80 70 60 50 40 30 20 10 0 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 Figure 2 Figure 3 This diminution in a team’s ability to “buy” wins has coincidentally been met with a leveling out ofaverage payroll in the past three years, as seen in Figure 3. Many attribute this shift to the recent economicdownturns forcing teams to be more conservative in their spending habits. It has also been suggested thatimprovements in scouting and developing younger players have allowed teams to make smaller investmentswith greater upsides and hold on to these players for many years before they can begin to demand expensivecontracts or become free agents. While the philosophy of building a team around a young core of home-
  • 8. Le vitt |8grown talent and then filling in the remaining pieces with established veterans has been growing in popularityand been met with great success over the past several seasons (see 2010 Rangers and Giants, 2008 Rays, andeven to a certain degree the 2009 Phillies), it is by no means the only philosophy employed. Other majormarket teams like the Yankees, Red Sox, and Cubs have continued to sign players to enormous contracts andhave also been successful in their efforts. I believe this suggests two factors are at play. The first relies on the work of Harry Raymond, who studied the draft pick-free agent compensationsystem. He found that the current system of rewarding teams that lose free agents with premium draft pickswas outdated. Raymond used sabermetric statistics to quantify in a more definitive way the relationshipbetween the value of free agents and corresponding draft picks in terms of wins. His findings suggested thatthe current system significantly overcompensates teams that lose a player to free agency, which encouragesteams to spend less money on free agents and focus more on developing young players. The second factor draws on the findings of Solow and Krautmann (2005). Their article findsstatistically and economically significant evidence that what they call the “Marginal Revenue of a win”increases as market size increases. I attribute this to a key distinction regarding the concept of payrollefficiency. I believe that the concept of payroll efficiency has two interdependent components to it: 1)spending the right amount on each player on the team, and 2) spending the right amount on total payroll. Inchoosing the appropriate level of spending, a team must consider the expected return on a player. Professorand author Vince Gennaro offers a dual approach to player valuation, asserting that a player has bothperformance and marquee/franchise value. This is meant to adjust for the fact that signing a player likeStephen Strasburg or Manny Ramirez has bigger implications than just what they do on the field since theyare incredibly marketable players. This is particularly true in major markets where advertising can have amuch greater impact. Thus, it can be argued that it is justified for major teams to spend more, or specificallyspend beyond performance value on a player since they will generate revenue for the franchise beyond therevenue through wins. Since a key clause of the report used to create revenue sharing was “well-run club”, Ithink it is important to distinguish that well-run can mean different things in different markets, in particularthat major market teams have reason to spend money not only on talent, but on fan appeal. This dichotomyin spending strategy combined with improvements in scouting and player development may offer someinsight as to why major market teams have not dominated baseball in the past decade. Attendance-Wins as an Indicator of Fan Interest Of the many fundamental changes Major League Baseball has undergone over the past decades, oneof the most observable is in how it is viewed. The advent, expansion and success of MLB Advanced Media(“MLBAM”), regional sports networks, and new theme park-like stadiums, has dramatically altered thebaseball fan’s landscape. However, one this has not changed over time: fans prefer winning teams to losing
  • 9. Le vitt |9teams. While there is substantial evidence to corroborate this claim, the recent indifference shown by the fansof the Tampa Bay Rays during their AL East Division title illustrates that success does not guaranteeattendance in every market. Seeing as how the revenue sharing program was designed to benefit those specificteams which suffer the most from a disinterested fan base, the system would only be working if the“preference for winning” premise held true universally. Surprisingly, while the league average for the slope of the attendance per wins relationship was alwayspositive and significant over time, it was not positive or significant for every team. This suggested externalfactors were influencing attendance. While it is intuitive to create the two categories of fans (namely thosewho are responsive to a winning team and those that aren’t), it is difficult to assess which fans (or cities)belong in those categories. On the one hand are the teams that have had substantial variation in performanceand attendance over the past 8 seasons (this was the case for 12 of the teams in MLB). In these cases therelationships were always positive and confirmed the long-standing assumption that winning brings in morefans. However, three groups of outliers presented an interesting issue for the model. First, and I supposeforemost, are the uncompetitive teams that didn’t draw many fans. For a number of these teams, there wasno significant fluctuation in wins, so it was impossible to tell if an improvement in their performance wouldhave generated the attendance spike that would be expected. Second was the category of teams with fanswho were so loyal they continued to attend in high numbers regardless of performance. Third is the categoryof teams that performed well throughout all 8 years of the study and drew a high number of fans because ofit, so that any potential effects of a diminution in performance did not have a chance to occur. I believe thatany attempt to disallow those teams would be subjective and inappropriate (one cannot assume that becauseit didn’t happen, it couldn’t), so I elected to keep them in the data set despite any clear method to control forthe varying conditions. It is a goal of my continued to research to develop a model that can accommodatedifferences between markets more adequately with regards to attendance. However, even solving this conundrum would not completely clarify the issue of how attendancerelates to winning. From an economic point view, a baseball team has a monopoly over tickets in that they arethe sole supplier of the product and control the prices. However, if a team cannot identify what the demandfor their tickets is, then they cannot set the “right” prices, whatever their conception of “right” (most likelyprofit-maximizing) is. This is only further exacerbated by the fact that baseball teams charge multiple pricesfor various ticket quantities and qualities (based on location in the stadium, giveaways, opponent, etc.). Ifticket prices are set too high, then they will prohibit fans from positively responding to a winning team byattending more games. It is important to note that not all winning teams are competitive teams, and not all competitiveteams necessarily win that many games. A team with a .500 winning percentage may be in the playoff hunt inSeptember one year while a team that ends up winning 90 games never really has a chance that same season.Given that playoff contention is not based solely on the number of wins a team has but rather the number of
  • 10. L e v i t t | 10wins a team has relative to other teams in its division or in Wild Card race, I think future analysis wouldbenefit from studying whether fans are interested in seeing a winning team or a competitive team. One goodexample of this the 82 win 2005 NL West Champion San Diego Padres that drew a proportionately highnumber of fans relative to their win total. One interesting observation associated with this relationship is that attendance has dropped the lastthree years (Figure 4). Average M LB Attendance 3 4 0 00 3 3 0 00 3 2 0 00 3 1 0 00 3 0 0 00 2 9 0 00 2 8 0 00 2 7 0 00 2 6 0 00 2 5 0 00 2003 2004 2005 2006 2007 2008 2009 2010 Figure 4 This is likely due to a combination of multiple factors including the general economic recession,smaller stadiums, and higher ticket prices. Regardless of the explanation though, it is an important trend toaccount for in looking at recent data since teams that maintained attendance levels actually increased relativeto the league. In addition, new stadiums always bring more fans to the ballpark and more interest to the team,and that effect usually lasts a few years, though the effect can vary. Stadiums have been developed and builtwith a greater frequency in the past decade than ever before. However, the new stadiums were not consideredwhen looking at changes in attendance and a method for accounting for differences in both size and age of aballpark should be addressed in future research. Revenue-Attendance as an Indicator of Front Office Operations This relationship was originally meant to connect the final dots between spending and earning, withthe assumption that the relationship between attendance and revenue would be similarly positive across allteams. In the broader sense, the coefficient for this relationship is supposed to represent roughly how much ateam makes in additional total revenue from one fan per game. Using the financial reports (see Figure 5below) as a sample to determine how much revenue from attendance comprises total revenue, I was able tosee a clear and distinct difference in revenue generation between teams depending on market size.
  • 11. L e v i t t | 11 Figure 5 Pittsburgh Pirates LA Angels of Anaheim 2007 2008 2008 2009Home GameReceipts 103.21 100.12(tickets) 34.42 32.13 43% 42%% of total 25% 22% 42.97 45.998Broadcasting 40.326 39.01% of total 29% 27% 18% 19%Total Revenue 138.636 145.99 237.87 240.824 Thus there is evidence that different markets rely on different factors to varying degrees, and aprogram which seeks to neutralize those differences runs the risk of poorly accounting for how thosedifferences play out over time. Some teams are more dependent on attendance and local based revenue andsome on rely on general broadcast revenue. The goal of revenue sharing was to redistribute local revenuesfrom rich teams to poor teams in order to mitigate the difference. An interesting trend has developed overthe past three years which may have a substantial impact on how effective that method of revenue sharing is.While revenues have continued to increase across MLB, attendance has dropped the past three years. This hascaused the coefficients for the Revenue-Attendance relationship to increase (Figure 6). This means that eachfan is technically generating more revenue in each subsequent year than in previous years. With this currenttrend, the incentive seems to be heading towards maximizing profit per fan rather than maximizing thenumber of fans, a dangerous path for Major League Baseball to be on. Figure 6 R evenue (in millions ) pe r F an in A ttendanc e per G ame 0 .0 0 6 0 .0 0 5 0 .0 0 4 0 .0 0 3 0 .0 0 2 0 .0 0 1 0 2003 2004 2005 2006 2007 2008 2009 A corresponding decrease in the R-squared values of the year-by-year regressions indicates that thismay not explain the full story. Given that 8 of the 30 MLB teams have a negative coefficient for theirattendance-revenue relationship in our model, which suggests that for some teams higher average attendancehas a negative effect on revenue, our model does affirm that in general more fans leads to more revenue. The
  • 12. L e v i t t | 12examples of teams with decreasing attendance and increasing revenue suffer from a variety of factors whichalter their situation and make them outliers. With the interaction of all these various factors at play indifferent ways and to different degrees, we turn to the conclusions that can be drawn.Conclusion Ultimately, having taken all these things into consideration, the fact remains that 25 teams in MajorLeague Baseball saw annual increases in revenue from 2003 to 2008 (including in 2008). Of these 25 teams,none of them, not one, increased team payroll every year or increased in wins every year. While this was neverthe assumption, it does provide sufficient evidence that there is more that goes into generating revenue thanspending and winning. Given that as a foundation, this paper proposes three further conclusions pertaining specifically tothe revenue sharing program. First, it is rational for major market teams to adopt different spending strategiesthan small market teams which includes signing elite players to lucrative deals. These are riskier investmentsfor teams and thus a “well-run” team will still have to make them efficiently. Second, the simultaneousstadium-building boom (which draws more fans to the game) and media outlet expansion (which makes iteasier for people to watch games without coming to the ballpark) have stagnated the effects of the truerelationships between winning, attendance, and revenue. New stadiums will not continue to be built at theirrecent rate and the effects of that as a revenue driver will diminish. As access to baseball media becomesmore widespread and accessible, teams will have to develop new ways to generate local revenue, which issomething revenue sharing cannot directly fix. However, with the current Collective Bargaining Agreement set to expire in December 2011, thereare two prescriptions I would recommend be considered in the negotiations of the new agreement: 1) Restructure the draft pick- free agent compensation system. Small market teams have been successful with growing regularity in the past few seasons by drafting good players and developing them, but competitive teams need experience players. If small market teams are reduced to signing old veterans to short contracts because they cannot compete with the big spending teams financially, then can not create the kind of long term performance consistency that is necessary to win over fans. 2) Stay away from excessive reform and continue with the type of oversight that we saw with the Florida Marlins. As many of the facts mentioned in this paper have made clear, more teams are competitive than there were in the years leading up to revenue-sharing. In the instances where that is not the case, the problem seems to lie not with the program, but with the team.
  • 13. L e v i t t | 13AppendixEquation 1 test -. h a u sm n a f i x e d r an d o m C e f o f i ci e nt s ( b) ( B) ( b- B ) s qr t ( di a g ( V_ b - V _B) ) f i x ed r a ndom Di f f e r en c e S. E . wi n s 2 . 0 7 14 4 6 2 . 8 01 2 9 1 - . 7 2 9 84 4 4 . wi n s 2 - 3 . 6 6 28 2 2 - 4 . 8 95 6 8 6 1 . 2 3 28 6 4 . wi n s 3 2 . 0 8 26 9 2 2 . 7 79 7 1 9 - . 6 9 7 02 7 5 . y e a r 1 1 . 8 99 5 8 1 1 . 93 5 2 5 - . 0 3 56 7 7 . b = c o n si s t e nt u n d er Ho an d Ha; obt ai ne d f r om xt r e g B = i nc ons i s t e nt unde r Ha , ef f i c i en t unde r Ho; obt ai ne d f r om xt r e g T es t : H : o di f f e r en c e i n c oe f f i c i e nt s not s y s t em t a i c c hi 2 (4 ) = ( b- B) [ ( V_ b - V_ B) ^ (- 1 ) ] ( b - B) = - 7 . 84 c h i 2 < 0 = = > m de l o f i t t e d o n t he s e da t a fa i l s t o m et e t h e a s y m t o p t i c a s s u m t p i o ns of t he Ha u sm a n t e s t ; s e e s ue s t f o r a ge n e r a l i z e d t e s tEquation 4 tests -Ramsey RESET test using powers of the fitted values of revenue Ho: model has no omitted variables F(3, 201) = 8.53 Prob > F = 0.0000Variable | VIF 1/VIF-------------+---------------------- wins2 | 19432.69 0.000051 wins3 | 5471.62 0.000183 wins | 4411.68 0.000227 attend | 1.29 0.772630 year | 1.04 0.964681-------------+---------------------- Mean VIF | 5863.66 150 100 Residuals 50 0 -50 100 150 200 250 300 Fitted valuesDM test. r e g r e v e nue wi ns wi n s2 wi n s3 y e a r a t t en d y h at l So u r c e SS df MS Nu m b e r of obs = 2 1 0 F( 6 , 2 0 3 ) = 63 . 0 7 M ode l 3 03 4 9 8 . 8 9 4 6 5 0 5 8 3. 1 4 9 Pr o b > F = 0 . 0 0 0 0 Res i d u a l 1 62 8 1 8 . 3 8 7 2 0 3 8 0 2 . 06 1 0 2 R- s q u a r ed = 0 . 6 5 0 8 Ad j R- s q ua r e d = 0 . 6 4 0 5 Tot a l 4 66 3 1 7 . 2 8 1 2 0 9 22 3 1 . 18 3 1 6 Ro ot M SE = 2 8. 3 2 1 re v e nue Co ef . St d. Er r . t P> | t | [ 9 5 % Co n f . I nt e rv a l ] wi n s 22 . 0 7 92 6 1 1 . 7 0 12 1 . 8 9 0 . 0 61 - . 9 9 2 21 3 3 4 5 . 15 0 7 4 wi n s 2 - 34 . 7 4 81 2 15 . 3 0 0 04 - 2 . 2 7 0 . 0 24 - 6 4 . 91 5 5 - 4 . 5 80 7 4 2 wi n s 3 17 . 4 9 78 3 6. 6 2 1 1 13 2 . 6 4 0 . 0 09 4 . 4 4 28 5 1 3 0 . 5 5 2 8 y e a r 8. 6 3 2 69 1 6. 4 2 0 2 11 1 . 3 4 0 . 1 80 - 4 . 0 26 1 6 2 1 . 29 1 5 4 a t t e n d 10 0 . 2 48 3 82 . 3 8 2 75 1 . 2 2 0 . 2 25 - 6 2 . 1 87 3 5 2 6 2 . 6 8 3 9 y ha t l . 2 2 3 6 03 9 . 6 1 3 4 0 36 0 . 3 6 0 . 7 16 - . 9 8 5 85 5 5 1 . 4 33 0 6 3 _ c o n s - 17 7 0 2 . 4 9 12 9 0 4 . 92 - 1 . 3 7 0 . 1 72 - 4 3 1 4 7. 3 6 7 7 4 2. 3 8 5
  • 14. L e v i t t | 14Equation 5 tests-Ramsey RESET test using powers of the fitted values of wins Ho: model has no omitted variables F(3, 201) = 0.94 Prob > F = 0.4242 20 0 Residuals -20 -40 70 80 90 100 Fitted valuesEquation 6 tests -Hausman test on 2SLS C e f o f i ci e nt s ( b) ( B) ( b- B ) s qr t ( di a g ( V_ b - V _B) ) s l s ol s Di f f e r en c e S. E . wi n s 8 9 . 4 56 8 4 2 2 . 71 8 3 3 6 6 . 7 38 5 1 1 4 0 . 19 5 3 wi n s 2 - 1 2 5 . 79 5 3 - 3 5 . 25 4 1 7 - 9 0 . 5 41 1 5 1 7 8 . 7 7 3 wi n s 3 6 1 . 01 3 6 1 7 . 65 3 1 3 4 3 . 3 60 4 7 7 5 . 1 12 3 8 y e a r 1 3 . 6 9 6 1 0 . 94 4 7 8 2 . 7 5 12 1 6 2 . 7 9 75 2 6 a t t e n d - 5 7 . 1 38 1 6 1 2 9. 9 4 2 - 1 8 7 . 08 0 1 8 0 . 2 05 1 8 b = c on s i s t en t un de r Ho a nd H ; a obt a i ne d f r om i v r e gr e s s B = i nc ons i s te nt un de r Ha, e f f i c i e nt unde r Ho ; obt a i ne d f rom r e g r e s s T es t : H : o di f f e r en c e i n c oe f fi c i e nt s not s y s t em t a i c c hi 2 (5 ) = ( b- B) [ ( V _ b - V _ B) ^ (- 1 ) ] ( b- B) = 17 . 3 7 Pr o b > c hi 2 = 0 . 0 0 3 8Single Regression Results - Revenue in Avg. number Millions per of fans in avg number of Wins per Million thousands per fans in Team Spent on Payroll Win thousands Product of Three = Rate of Return Philadelphia Phillies 0.177 1163.8 0.0055 1.1329593 Tampa Bay Rays 0.6258 245.1 0.0055 0.84360969 St. Louis Cardinals 0.758 158.06 0.0067 0.802723516 Milwaukee Brewers 0.2503 657.17 0.0042 0.690856534 Los Angeles Angels of Anaheim 0.497 129.9 0.0086 0.55521858 Baltimore Orioles -0.1709 931.93 -0.0029 0.461873827 double neg New York Yankees -0.131 -651.44 0.0042 0.358422288 double neg Oakland As -0.2245 361.6 -0.0044 0.35718848 double neg
  • 15. L e v i t t | 15 Detroit Tigers 0.234 348.09 0.0029 0.236213874 Texas Rangers -0.62 173.45 -0.002 0.215078 double neg Colorado Rockies 0.223 223.53 0.0038 0.189419322 Minnesota Twins 0.071 232.7 0.0093 0.15365181 Seattle Mariners -0.416 178.81 -0.0016 0.119015936 double neg Toronto Blue Jays 0.1236 140.09 0.0068 0.117742843 Boston Red Sox -0.051 -99.6 0.0215 0.1092114 double neg San Francisco Giants -0.0072 33.169 -0.0065 0.001552309 double neg Cincinnati Reds 0.147 -78.84 -0.005 0.0579474 Washington Nationals -0.2711 -49.708 0.0038 0.051208187 Arizona Diamondbacks -0.2977 44.216 -0.0024 0.031591448 Los Angeles Dodgers 0.196 14.063 0.0103 0.028390384 Pittsburgh Pirates 0.193 28.803 0.0014 0.007782571 Chicago White Sox -0.0596 -15.757 0.0045 0.004226027 Kansas City Royals 0.0116 74.83 0.0003 0.000260408 Atlanta Braves 0.0072 -89.014 0.0065 -0.004165855 Florida Marlins 0.12 51.979 -0.0013 -0.008108724 San Diego Padres -0.426 28.693 0.0017 -0.020779471 Chicago Cubs -0.03 50.931 0.0215 -0.032850495 Houston Astros -0.5242 147.8 0.0028 -0.216934928 New York Mets -0.121 580.85 0.0039 -0.274103115 Cleveland Indians -0.207 240 0.0056 -0.278208Bibliography and References Baseball Almanac, found at www.baseball-almanac.com. Baseball Refereece, found at www.Baseball-reference.com. Bradbury, J.C. The Baseball Economist: The Real Game Exposed. 
 New York: Dutton, 2007. Bradbury, J.C. “Revenue Sharing, Incentives, and Competitive Balance” www. Sabernomics.com.September 1, 2010. Brown, Maury. “Will leaked MLB financials alter Revenue-Sharing?” Fangraphs. August 25, 2010.http://www.fangraphs.com/blogs/index.php/will-leaked-mlb-financials-kill-revenue-sharing/ Elanjian, Michael R. and Dessislava A. Pachamanova. “Is Revenue Sharing Working for MajorLeague Baseball? A Historical Perspective”. The Sport Journal. Volume 12, No. 2.
  • 16. L e v i t t | 16http://www.thesportjournal.org/article/revenue-sharing-working-major-league-baseball-historical-perspective Futterman, Matthew. “The Year Money Didn’t Matter”. Wall Street Journal. September 16, 2010.http://online.wsj.com/article/SB10001424052748703743504575493942146685242.html Gennaro, Vince. Diamond Dollars: The Economics of Winning in Baseball. Hingham, Ma.: MapleStreet Press, 2007. Gustafson, Elizabeth and Lawrence Hadley. “Revenue, Population, and Competitive Balance inMajor League Baseball”. Contemporary Economic Policy. Volume 25, No. 2. April 2007. Hakes, Jahn K., and Raymond D. Sauer 2006. "An Economic Evaluation of the MoneyballHypothesis." Journal of Economic Perspectives, 20(3): 173–186. Jacobson, David. “The Revenue Model: Why Baseball is Booming”. Bnet website. July 11, 2008.http://www.bnet.com/article/the-revenue-model-why-baseball-is-booming/210671 Levin, R. C., Mitchell, G. J., Volcker, P. A., & Will, G. F. (2000). The Report of the independentmembers of the commissioners Blue Ribbon Panel on baseball economics. Retrieved December 10, 2010from Major League Baseball Website: http://www.mlb.com/mlb/downloads/blue_ribbon.pdf Solow, J., and A. C. Krautmann. “Leveling the Playing Field or Just Lowering Salaries?” WorkingPaper, University of Iowa, 2005. Zimbalist, Andrew. May the Best Team Win: Baseball Economics and Public Policy. Washington D.C. : TheBrookings Institution, 2003.

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