Free Agency and Competitive Balance. in Major League Baseball

Transcription

1 - 1 - Free Agency and Competitive Balance in Major League Baseball Reporter: Ben, your thoughts on A-Rod joining the Yankees? Ben Affleck: You know, George Steinbrenner is the center of evil in the Universe there s no question about that. Ben Affleck: Eventually, they might be able to just buy everybody why not? Academy Award winning actor Ben Affleck and an avid Boston Red Sox fan asked about the New York Yankees acquiring shortstop Alex Rodriguez and his $252 million contract in a trade, in Feb. of 2004 at the Daytona 500. Nicholas Pritzakis State University of New York at Albany Economics Department Master s Essay Advised by Michael Sattinger November 2004

2 - 2 - Acknowledgements I would like to thank Professor Michael Sattinger for his input and guidance throughout this project. I would also like to acknowledge Professor Craig A. Depken, II for his insight and advice on constructing my econometric model. Lastly, I would like to thank the late Doug Pappas for making his free agent data readily available to the public.

3 - 3 - Section I: Introduction The aforementioned comments made by actor Ben Affleck mirror those beliefs that some fans and sportswriters had about free agency in Major League Baseball. Through free agency a team owned a player for the first six years of his career. After that a player became his own owner or free agent, and was then able to sell his services to the highest bidder. The six years, however, did not include the player s minor league career. For example, if a player served seven years on a minor league club and then later was promoted to a Major League Baseball club he would still have to give the Major League Baseball club six years of service before he could be eligible for free agency. Major League Baseball also has a draft that determines an amateur player s initial professional contract assignment. The reverse-order amateur draft allows teams to select amateur prospects in reverse order of standings. The free agency system and the draft both represent a reassignment of property rights to the player s labor services. So why does an overwhelming perception of free agency cause a lack of competitive balance? Would an open player s market generate the most efficient results? One element that is unmistakable to fans and sportswriters is that there are no team payroll restrictions in Major League Baseball. The

4 New York Yankees had a team payroll exceeding $150 million and they competed in the same division as the Tampa Bay Devil Rays whose team s payroll was just under $32 million. 1 If teams bid for the same players in the free agent market, it is apparent that the clubs with a higher payroll have a distinct advantage over clubs with a lower payroll. Prior to the 1970s, players were largely bound, for their entire baseball career, to the original team that drafted them. The only way a player could switch teams was if the team that owned that player s rights either sold him or traded him to another team. This previous system was known as the Reserve Clause. In 1976, The Basic Agreement, an agreement between the player s union and the owners, introduced free agency to Major League Baseball. This shifted the team s monopolistic rights of a player s services to the player, could now own his own rights. In the late 1990s the New York Yankees won four World Series championships in a five-year period ( ) while having a payroll that dwarfed most of its opponents. 2 With the on-the-field success of such large market clubs as the New York Yankees and the Atlanta Braves the issue of competitive balance became a focal point in Major League Baseball team payroll was obtained from 2 World Series results and team records obtained from

5 - 5 - In July of 2000, The Independent Members of the Commissioner s Blue Ribbon Panel on Baseball Economics issued its report recommending broad changes to Major League Baseball s economic structure. This report intended to close the gaping disparity between what the member s called the game s haves and have-nots. After an 18-month investigation, these problems were stated in the report: A. Large and growing revenue disparities exist and are causing problems of chronic competitive imbalance. B. These problems have become substantially worse during the five complete seasons since the strike shortened season of 1994, and seem likely to remain severe unless Major League Baseball undertakes remedial actions proportional to the problem. C. The limited revenue sharing and payroll tax that were approved as part of Major League Baseball s 1996 Collective Bargaining Agreement with the Major League Baseball Players Association have produced neither the intended moderating of payroll disparities nor improved competitive balance. Some low-revenue clubs, believing the amount of their proceeds from revenue sharing insufficient to enable them to become competitive, used those proceeds to become modestly profitable.

6 - 6 - D. A majority of Major League Baseball markets; the cost of clubs trying to be competitive is causing escalation of ticket and concession price, jeopardizing Major League Baseball s traditional position as the affordable family spectator sport. 3 According to economic theory, free agency would not have a detrimental effect on the competitive balance in Major League Baseball. I intend to test the claims of the economic theory by putting them through an econometric model. Section II of my paper will detail an overview of the economic theory on competitive balance relating to Major League Baseball. Section III will provide an overview of some of previous empirical literature on the subject. Section IV will propose an econometric model to test economic theory of competitive balance. Section V will provide my hypothesis on the model. Section VI will explain my results from the econometric model. Section VII will propose an alternative model and explain why it is preferred to the first. Section VIII will offer my conclusions based on the results from the econometric model. Section VIIII proposes material that should be considered for future researchers studying competitive balance in Major League Baseball. 3 Source:

7 - 7 - Section II: Relevant Theoretical Literature The theory of competitive balance in Major League Baseball was first introduced by Rotenberg (1956). According to Rotenberg, the concerns many owners and fans had about the competitive balance in Major League Baseball would be checked by the law of diminishing returns, which operated concurrently with each team s strategic avoidance of diseconomies of scale. Rotenberg pointed out that no team could be successful unless its competitors also survive and prosper sufficiently such that the differences in quality of play among teams are not that far off. Rotenberg (1956,p.255) provides the following assertion of this argument Beyond some point-say, when a team already has three.350 hitters-it will not pay to employ another.350 hitter. If a team goes on increasing the quantity of the factor, players, by hiring additional stars, it will find that the total output-that is, admission receipts-of the combined firms will rise at a less rapid rate and finally fall absolutely. At some point, therefore, a first star player is worth more to poor Team B then say, a third star to rich Team A. The key element to this argument is that teams need each other to be successful. For the sake of simplicity, they work together to form one single product, entertainment for their fans. The closer the contests are between teams the higher the fan interest will be, increasing attendance at baseball games. Acquiring too much talent would be detrimental to a team because this would create lopsided games, and consequently fan interest will be lost and team

8 - 8 - revenues will decrease. According to Rotenberg, all teams will strike a balance between revenue and cost of talent no matter if the rules entail free agency or the reserve clause; teams will limit themselves from becoming dominant. Rotenberg offered an interesting remedy for those who feared eliminating the reserve clause would threaten the competitive balance of Major League Baseball; let a franchise(s) be distributed so that the size of the product market is equal for all teams. If teams move to areas where the marginal revenue per win is greater than that of their initial location, the competition among teams in the same league within the same metropolitan area will reduce revenues earned by the original teams in that area. This could potentially reduce the financial discrepancies amongst teams, assuming that attendance is a unique function of the size of the market. For example, adding a team in the New York metropolitan area would make it more difficult for the Yankees and the Mets to attract free agents. Evidence of this is found when you drive down the street and see a Burger King located right next door to a McDonald s fastfood chain. Many economists have also found the work of Ronald Coase in, The Problems of Social Cost (1960) to be applicable to Major League Baseball. The Coase Theorem requires two assumptions. If there are well-defined property rights for production of externalities or for protection

9 - 9 - from their effects, and if transaction costs are zero, private negotiations among producers of the externalities and the victims or beneficiaries lead to efficient allocations. In regards to these assumptions, Coase claims that a monopoly with zero transaction costs producing a durable good with consumers able to substitute consumption for consumption in the future, behaves like a perfect competitor, since it will seek to maximize profits. The Coase Theorem also states that a change in property ownership should have no effect on mobility of players between teams. In a competitive market teams will bid for a player s service up to the point where the salary offer equals the value of the player s worth to the team, and as a result the player will capture the rents. For example, suppose Team A has a player who they value at six million dollars, but Team B values him at eight million dollars. There are gains to be made from trading, so Team B will offer Team A more then six million dollars but less than eight million dollars in resources to acquire this player, the distribution will be efficient and the rewards will go to the player. The free agent market simply provides a more direct way of player movement amongst teams. Most academic study on competitive balance in Major League Baseball is based on defending or refuting the theories of Rotenberg and Coase. Competitive balance has been measured in two ways in the past. The first method

10 focuses on the effects of fan interest and revenue from the competitive balance within a game. The second method emphasizes policy changes made throughout the history of Major League Baseball, most importantly changes in the role of free agency. This paper focuses on the latter. Section III: Relevant Empirical Literature An enormous amount of research has been done on free agency and its effect on competitive balance in Major League Baseball. Although most previous research tends to coincide with the Coase Theorem, which states that free agency has had a non-detrimental effect on competitive balance in Major League Baseball, there have been studies refuting its validity. The first part of this section will focus on those studies that feel that free agency has a detrimental effect on the competitive nature in Major League Baseball. The second part of this section will concentrate on studies that feel that free agency does not have an impact on the competitive balance in Major League Baseball. The last part of this section will describe studies that fell in the middle, not fully agreeing or disagreeing with the Coase Theorem. Daly and Moore (1981) investigated player movement before and after free agency and after the amateur draft. They used the Spearman s rank correlation coefficient to compare league standings from one year to the next. They

11 argued that the change in migration property rights did affect the final allocation of players, as well as affecting their relative team s performances. They discovered that players were more likely to move toward large market cities as free agents than when bound by the reserve clause. They stated that if earnings for ineligibles were proportionate to productivity, eligible players would be able to take better advantage of migration opportunities. This supported their belief that market structure did influence the outcome of the market. Cymrot and Dunlevy (1987) used an earnings equation, which related a player s earnings to his personal characteristics, the characteristics of the team for which he played, and the factors, that represented interplay between the player s ability and that of his teammates. They also included The Gain from Migration equation, which was a salary function used to calculate the gain from moving. They supplemented that equation with The Migration Equation, which was used to determine whether the probability of moving was affected by the magnitude of Gain. They concluded that players who were eligible for free agency tended to migrate to a different team when it was to their monetary advantage. Cymrot (1983) developed a similar conclusion in that quality free agents had the tendency to move from successful teams to teams in large cities.

12 Vrooman (1996), throughout his studies, believed that superior small-market teams were dismantled in the pursuit of maximum profit through the inefficient free agent acquisitions of large-market clubs. He concluded that the free agency was a zero sum game. Jewell & Molina (2001) analyzed the effect of salary dispersion on team winning percentage in Major League Baseball, by measuring how payroll inequality affected a team s ability to reach its production potential. According to their results, the distribution of salaries within Major League Baseball teams had a significantly negative effect on team success as measured by the team s winning percentage. Fishman (2002) measured competitive balance by using the standard deviation of team winning percentages as the model s dependent variable. Contrary to previous work, he included a variable to count the number of free agents, which would measure free agency s effect on competitive balance. Previous studies used a dummy variable equal to one for years where free agency was implemented. The estimated coefficient of the free agent variable was positive and highly significant, which implied that free agency did indeed have an effect on competitive balance, although his free agent variable did not offer a quality measurement of the players, nor did it involve if the free agent transferred clubs or stayed with his initial club.

13 Basanko and Simon (1985) measured the competitive balance by using the standard deviation of team winning percentages. They compared the seven years before, , and after free agency, Although the standard deviation decreased, which indicated a more competitive balance; their findings were not statistically significant, which therefore made them inconclusive. Scully (1989) used two measures to test free agency s impact on competitive balance. One measure was the annual standard deviation of league win percents, and lower standard deviations indicated there was less variation between team win percents and greater competitive balance. His second measure was the Spearman rank correlation between team cumulative wins percent rank and population rank within leagues. He found that there were some indications of improved competitive balance in the National League for the ten years, mean standard deviation fell by a statistically significant amount from to and the American League showed no change. Vrooman (1995) used the ratio of annual league win percent standard deviation to an idealized standard deviation that would occur if each team had a 50 percent of winning every game. After observing the annual ratios from , he concluded that both the American League and the National League became increasingly competitive, though he did not support this with significance tests.

14 Fort and Quirk (1995) expanded on Basanko & Simon s study, and also reached equivalent results. Empirically, they showed there were no significant changes in the standard deviation of winning percentages in the period of versus They also examined changes in the Gini coefficients of concentration in league pennant winners between the same periods. In this study a lower value would indicate less concentration and more competitive balance. Their findings concluded that the coefficients were slightly lower in both leagues and there was no significant change in either league. Horowitz (1997) used a relative entropy measure of balance derived from the annual distribution of a team s wins within leagues. His multiple regression analysis covered the period of He concluded that free agency did not cause an imbalance in winning when compared to the pre-free agency period. Lee and Fort (2002) calculated a time series analysis of structural change from the early 20th century to the 21st century. In their results they concluded that competitive balance in Major League Balance had improved over time, both in trend and in episodes that altered the structure of competitive balance itself. Depken (2002) calculated the concentration of wins using the Herfindahl-Hirschman Index (HHI) with market share defined as a team s percentage of total wins,

15 measured relative to a hypothetical HHI corresponding to an equal distribution of wins; a nonlinear transformation of win percent standard deviation. He used home runs and strikeouts as variables to measure the HHI. His evidence suggested that free agency has been beneficial in reducing the overall concentration of home runs in the individual leagues and the overall major leagues. Eckard (2001) examined competitive balance from season to season rather than within one season as was done in previous studies. The logic being that league standings fluctuate from year to year and they cannot be conveyed by the conventional single-season standard deviation of team win percents. Empirically, he showed that competitive balance improved after free agency. He agreed with Rotenberg by explaining that there were diminishing marginal returns in each additional year s production of contenders, reducing the incentive to continually bid for top players. This allowed non-contenders to obtain players in the free agent market that would help their chances of improving. Barra (2002) proposed two complementary competitive imbalance measures with regard to post-season play: the number of different measures with regard to post-season play for a championship. He compared Major League Baseball to the National Football League and the National Basketball Association. Although Major League Baseball had available playoff spots, over the last two decades, 20 franchises have

16 appeared in the World Series, compared with 19 teams in the Super Bowl and 15 in the NBA Finals. Krautman and Oppenheimer (1994) ran a logit model testing the probability that a player will move with the assumption that free agents view migration as an investment allowing them to maximize utility. They found that migration decisions of free agents were affected by the preference of the player, suggesting that the allocation of labor is likely to be different under free agency than under the reserve clause. Also, large market teams are more attractive to potential migrants than small-market teams. They concluded that big-city teams have not dominated the sport, despite the magnitude of their attractiveness, because of the existence of the draft and the relatively small impact free agents have on team wins. Hylan, Lage and Treglia (1996) refuted the Coase Theorem. They examined the mobility of all Major League Baseball pitchers during through panel data; using the beginning of free-agency in as the midpoint of their interval. They found that before 1976 total player service time was positively significant with mobility, other things equal; after 1976, this was still true, but the effect of longer service on mobility was much smaller than before free-agency. Dunlevy, Even and Cymrot (2000) tested the Coase Theorem by examining whether the effect of gain on player

17 migration is independent of the player s eligibility for free-agency. Their results indicated that the effect of an increase in a player s marginal revenue product from moving on player movement is independent of free agent status. They later noted that free agency had created a wealth affect due to increased salaries of players, shifting rents from the owners to the players. They felt that the wealth affect might threaten teams located in economically small markets. They concluded that movement of teams would result from free agency as the gains of movement to the owners rose to exceed the transaction costs of team movement. Maxcy (2002) ran a logit model to examine the marginal effects of the factors that determine the likelihood that an individual player between clubs. He also used the Spearman s rank correlation coefficient to compare league standings from one year to the next as well as using a dispersion of win percentage which measured the ratio of actual standard deviation, were each club to be equal strength. He showed from his empirical analysis that the increased player mobility appeared to have improved competitive balance when measured by a club s ability to improve their standing year to year. There seemed to be less evidence of improvement when competitive balance was measured by the distribution of talent across teams within a given season. He concluded by stating that competitive balance has not declined since the inception of free agency.

18 Chatterjee and Wiseman (2003) examined the relationship between team salary and team performance by focusing on three variables, team win/loss percentage, team payroll and allocation of payroll among players. Their results suggested that the large disparity in team payrolls does have an effect on the competitive balance of Major League Baseball. In conclusion they noted that owners with a fixed payroll who built an evenly balanced team as measured by individual salaries of its players do better than owners who spend a large percentage of its payroll on only a few highly paid superstar players. The majority of studies in this field have found little to refute the theory of Rotenberg and Coase that free agency does not affect competitive balance in Major League Baseball. Over the years the econometric models have improved, adding dummy variables for league expansion, free agent years and work stoppages. Fishman used the number of free agents in his free agent variable; previous work used a dummy variable for free agent years. Although this was a new take on the competitive balance model, his free agent variable tells us nothing about the quality of free agents. In the next section I will try to improve on the competitive balance model done by previous researchers. Section IV: Empirical Model

19 The standard deviation of team winning percentages will be my model s dependent variable and will be used to measure competitive balance in Major League Baseball. We take the winning percentage of each team in the league in year i and then calculate the standard deviation of each teams winning percentage in year i to get the standard deviation of team winning percentages, looking at competitive balance within a season. The standard deviation of team winning percentages is used instead of team winning percentages because it shows diversity in the winning percentages of each team, making it a better measure of competitive balance than simply using team-winning percentages. The data starts in the 1950 season and concludes in the 2003 season. The model also includes eight independent variables. The first independent variable is the average number of GAMES played. This variable is used for two specific reasons. The first reason is that there was an increase of games played in Prior to 1962, the average number of games played by a team was 154 games, after the 1961 season the average number of games played increased to 162. This variable is also used to control for work stoppages and strikes that might have occurred in Major League Baseball. In 1972, there was a strike concerning pensions, which resulted in a loss of 86 total games for the league. In 1981, there was a strike concerning compensation for losing a free agent, which resulted in a loss of 712 total games

20 for the league. In 1994, there was a strike concerning salary arbitration and a salary cap, which resulted in a loss of 920 total games for the league. The 1994 strike extended into the 1995 season, which resulted in a total loss of 504 games for the league in the 1995 season. The second independent variable is TEAMS. This variable is added to account for league expansion. The standard deviations are dependent on the size of the population; therefore the standard deviation will get smaller when more teams are added to the league. The TEAMS variable is added to control this problem. In 1950 there were only 16 teams, versus the 30 teams there are now. The league expansion years are as follows: 1961, 1962,1969,1977,1993 and The third independent variable is DRAFT. This is a dummy variable used to control for the reverse-order amateur draft, which was instituted in The reverse-order amateur draft allowed teams to select amateur prospects in reverse order of standings, for example the team with the worst record would have the top pick in the amateur draft. The amateur draft was instituted to give weaker teams an opportunity to improve by getting the chance to select the top-level prospects. The fourth variable is FREEAG_ALL_STAR. Most of the relevant research uses a dummy variable for the effect of free agency on competitive balance. Fishman used the number

21 of free agents in his model, but his variable explained nothing about the quality of the free agent, nor did it account for the free agent transferring clubs. This model tries to improve on previous research by adding a quality measure for six-year free agents and if the free agent transferred or stayed with his team. The quality measure used for free agents is future all-star appearances. For example, in 1976 Reggie Jackson played for the Baltimore Orioles but was signed by the New York Yankees when he became eligible for free agency that season. At the time Jackson was a 6-time all-star, and he turned out to be a 12- time career all-star. Only free agents who became future all-stars and transferred clubs are included. This variable also confronts the accusations made by fans and sportswriters. We can assume that all-star players are amongst the highest paid players in the league, and if only high revenue teams can bid for the best players, the results of this variable in the model will explain if this is true or not. The variable FREEAG_ALL_STAR also includes players that made the 2004 All-Star game. The starting position players of the all-star game are selected by the fans, the remainder of the roster is filled by the manager of the game as well as the selection of the starting pitcher. From , there were 33 future all-star free agents that transferred clubs. From there were 112 future allstar free agents that transferred clubs. The graph below

22 depicts how well teams have faired in gaining, or losing future all-star free agents from AMERICAN LEAGUE FUTURE ALL-STAR FREE AGENTS # OF PLAYERS LOST STAYED GAINED 0 ANA BAL BOS CWS CLE DET KC MIN NYY OAK SEA TAM TEX TOR TEAM NATIONAL LEAGUE FUTURE ALL-STAR FREE AGENTS # OF PLAYERS ARI ATL CHC CIN COL FLA HOU LA MIL T EA M MON NYM PHI PIT STL SD SF LOST STA Y ED GA INED The fifth independent variable is EXPANSION. This is a dummy variable used to control the shocks associated with league expansion. New teams had to acquire the players for their roster from existing teams in the league. As noted earlier, league expansion occurred in 1961, 1962, 1963, 1969, 1977, 1993 and All-Star information obtained from

23 The sixth independent variable is REENTRY. This is a dummy variable used to control the effects of the free agent reentry draft. The first free agents in 1977 were placed in a reentry draft and could only negotiate with those teams that chose them. The reentry draft allowed a team only five draft choices, which put restrictions on free agency. The reentry draft only affected the seasons. The seventh independent variable is COMPENSATION. This is a dummy variable used to control the effect of the compensation rule for signing a free agent. The 1981 Basic Agreement stated that a team signing one of the top nine free agents (Type A) could protect 24 players on their roster; a non-signing team could protect 26 players. A team losing a Type A free agent could choose from a pool of unprotected players. No team could lose more than one player in the compensation pool. A non-signing team that lost a player in the pool would receive $150,000 from the industry fund, while lesser free agents were compensated for with draft picks. According to the rating system established by Elias Sports Bureau, a Type A free agent is ranked among the top 30 percent of major leaguers at his position, a Type B free agent is ranked among the top half (but not the top 30 percent) of major leaguers at his position, and a Type C free agent is ranked among the next 10 percent of major free agent player mobility obtained from Doug Pappas of SABR.

24 leaguers at his position. 5 This affected the seasons. The eighth independent variable is COLLUSION. This is a dummy variable used to control the effect of collusion of the owners. The owners colluded to not sign free agents in order to keep salaries low. This affected the seasons. During this period only three future all-star free agents transferred teams. The econometric model to be estimated is as follows using the ordinary least squares method: STDWPi = Bo + B1COLLUSIONi + B2COMPENSATIONi + B3DRAFTi + B4EXPANSIONi + B5FREEAG_ALL_STARi + B6GAMESi + B7REENTRYi + B8TEAMSi + ei STDWPi= Standard Deviation of team winning percentage in ith year. COLLUSION= A dummy variable used to control for years of collusion. (Coded 1 for years 1986,1987,1988; all other years equal 0). COMPENSATION= A dummy variable used to control for the years of compensation for the loss of a free agent. (Coded 1 for years ; all other years coded 0). 5 Classification of free agents and definitions were obtained from

25 DRAFT= A dummy variable to control for the amateur draft. (Coded 0 for ; Coded 1 for ). EXPANSION= A dummy variable used to control for the years of league expansion. (Coded 1 for the years of 1961,1962,1963,1969,1977,1993 and 1998; all other years coded 0). FREEAG_ALL_STAR= Future free agent all-stars that transferred clubs in the ith year. (Year:FREEAG_ALL_STAR) (1977:9),(1978:7),(1979:4),(1980:1),(1981:1),(1982:1),(1983:1),(1984:2),(1985:1),(1986:0), (1987:2),(1988:1),(1989:3),(1990:4),(1991:6),(1992:2),(1993:15),(1994:6), (1995:6),(1996:12),(1997:9),(1998:9),(1999:12),(2000:9),(2001:9),(2002:6),(2003:6). GAMES= The average number of games played by a team in the ith year. Variable added to control for the increase of games played as well as shocks caused by strikes and work stoppages. ( = 154 games played.1972 = 158 games played.1981= 134 games played. 1994= 129 games played. 1995= 144 games played. All other seasons not mentioned averaged 162 games played.) REENTRY= A dummy variable used to control for the effect on the free agent entry draft. (Coded 1 for ; all other years coded 0.) TEAMS= The number of teams in the league in ith year. Variable added to account for league expansion. ( = 16 teams. 1961= 18 teams = 20 teams = 24 teams = 26 teams = 28 teams = 30 teams.) Section V: Hypothesis A smaller standard deviation of team winning percentages would represent a greater degree of competitive balance. A negative coefficient would lower the standard

26 deviation and therefore have a positive effect on competitive balance. A positive coefficient would increase the standard deviation and therefore be a detriment to competitive balance. We would expect COLLUSION, REENTRY, DRAFT, FREEAG_ALL_STAR and COMPENSATION to be zero, or have no effect on competitive balance according to the Coase Theorem. These variables are just a reassignment of property rights. We should expect TEAMS to have a negative coefficient. The more teams in the league, the harder it will be for a single team to affect the overall distribution of team winning percentages. The GAMES variable should also have a negative coefficient because the more games played by teams, the lower the standard deviation should be. The EXPANSION variable should have a positive coefficient because it would take a while for a new team to become competitive, therefore it would cause a higher standard deviation of winning percentages. Variable Freeag_all_star Expansion Draft Games Teams Collusion Compensation Predicted Sign Negative Positive Negative Negative Negative Negative Negative

27 Reentry Negative Section VI: Results When the model was run the first time, the Durbin- Watson statistic showed that there was serial correlation. In order to correct this problem a first-order autoregressive process, AR (1), was added to alleviate the serial correlation. Variable Coefficient Std. Error t-statistic C COLLUSION COMPENSATION DRAFT EXPANSION FREEAG_ALL_STAR GAMES REENTRY TEAMS AR (1) The estimated coefficient for COLLUSION is negative meaning that it decreased the standard deviation of team winning percentages and improved competitive balance. This was consistent with the hypothesis although this variable was not statistically significant (p-value=.75). The estimated coefficient for COMPENSATION was negative, meaning that it decreased the standard deviation

28 of team winning percentages and improved competitive balance. This was consistent with the hypothesis although the variable was not statistically significant (P-value=. 54). The estimated coefficient DRAFT had a negative coefficient implying that it decreased the standard deviation of team winning percentages and therefore improved the state of competitive balance. This was equivalent to the hypothesis that drafting top-level prospects can help weaker teams become more competitive. This statistic was not statistically significant (P-value=. 28). The estimated coefficient EXPANSION had a positive coefficient meaning that it increased the standard deviation of team winning percentages and therefore had a detrimental impact on competitive balance. This matched the hypothesis that an expansion team would not be competitive in its initial first season; this statistic was highly significant (p-value=. 00). The estimated coefficient FREEAG_ALL_STAR had a negative coefficient meaning that it improved competitive balance. This result was equal to the hypothesis. The Coase Theorem states that free agency is just a reassignment of property rights, and because this variable was not significant (p-value=. 52), we accept the Coase Theorem that

29 free agency would have zero or no effect on competitive balance. The estimated coefficient GAMES had a negative coefficient and therefore improved competitive balance. This followed the hypothesis that the more games played the lower the standard deviation of team winning percentage will be. This variable was not statistically significant (P-value=. 22). The estimated coefficient REENTRY had a positive coefficient and therefore increased the standard deviation of team winning percentages and hurt the competitive balance of major league baseball. The variable was not consistent with the hypothesis that the reentry draft is just a reassignment of property right. This variable was not statistically significant (p-value=. 38). The TEAMS coefficient was positive implying that it decreased the standard deviation of team winning percentages and therefore was detrimental to competitive balance. This went against the hypothesis that the more teams added, the less one single team can affect the outcome of overall standard deviation team winning percentage. This statistic was not statistically significant (p-value=. 90). Lastly, many of the variables were statistically insignificant. This should not be that discouraging because theory suggests that only certain activities should have a direct impact on competitive balance. The coefficients

30 REENTRY and TEAMS were positive, making them different from their hypothesized sign. The results of the regression tend to agree with previous literature and therefore accept the theories of Coase and Rotenberg. There seemed to be a problem with the model, which will be examined in the next section. Section VII: Alternate Model Although the previous model agreed with the Coase Theorem, all of the variables were statistically insignificant except for the EXPANSION variable. A correlation matrix was built to see how correlated the variables are amongst each other. The correlation matrix below shows that the TEAMS variable is correlated with both the FREEAG_ALL_STAR and DRAFT variables. Correlation Matrix STDWP COLLUSION COMPENSATION DRAFT EXPANSION FREEAG_ALL_ST AR GAM ES REENTRY TEAMS STDWP COLLUSION COMPENSATION DRAFT EXPANSION FREEAG_ALL_STAR GAMES REENTRY TEAMS

31 When the Teams variable was eliminated from the equation, all the variables except for EXPANSION were statistically insignificant. After the elimination of the TEAMS variable the dependent variable becomes unstable. The standard deviations are dependent upon the size of the population. As you add more teams, the standard deviation gets smaller, deceptively making the league appear more competitive as time goes on. The use of standard deviation may have also caused some other problems with the model. First, the outliers influence the standard deviation; one value may have contributed largely to the results of the standard deviation. In regards to the model, the independent variables selected may not have been picking up on any of the limited variation of the dependent variable. With the elimination of the TEAMS variable a new dependent variable must be selected. An alternative to the standard deviation of team winning percentage is the normalized standard deviation (standard error of the mean) of team winning percentage. The normalized standard deviation of team winning percentage is the standard deviation of team winning percentage divided by the square root of the number of teams in the ith year. The normalized standard deviation of team winning percentage tells us how much a sample mean differs from the sampling distribution of sample means. It gives the deviations of the sample mean around the mean of the sampling distribution.

32 The alternative model will use the dependent variable NSTWP; this should correct the problem of eliminating the TEAMS variable because the formula takes into account the number of teams in the league. The alternate model to be estimated is as follows: NSTDWPi = Bo + B1COLLUSIONi + B2COMPENSATIONi + B3DRAFTi + B4EXPANSIONi + B5FREEAG_ALL_STARi + B6GAMESi + B7REENTRYi + ei After running the regression the adjusted R squared of the alternate model was close to 69 percent compared to 49 percent for the first model. There were still several variables that were statistically insignificant. Variable Coefficient Std. Error t-statistic C FREEAG_ALL_STA R EXPANSION DRAFT COLLUSION COMPENSATION REENTRY GAMES -6.14E E AR (1) It appeared that more variables could be eliminated to make the model tighter. The variables COLLUSION, COMPENSATION and REENTRY may be insignificant because they might already be reflected in the variable FREEAG_ALL_STAR.

33 From , there were 33 future all-star free agents that transferred clubs. From there were 112 future all-star free agents that transferred clubs. We can eliminate the GAMES variable as well, due to its awkward coefficient results. The revised econometric model is as follows: NSTDWPi = Bo + B1FREEAG_ALL_STARi + B2EXPANSIONi + B3DRAFTi + ei Results: Like the first model, serial correlation was present and a first-order autoregressive process; AR (1) was added to alleviate the serial correlation problem. Variable Coefficient Std. Error t-statistic C DRAFT FREEAG_ALL_STAR EXPANSION AR (1) The estimated coefficient DRAFT is negative. It decreased the normalized standard deviation of team winning percentage and improved competitive balance. The DRAFT variable was highly significant (p-value =.00). The estimated coefficient FREEAG_ALL_STAR is negative. It decreased the normalized standard deviation of the team

34 winning percentage and improved the competitive balance. The statistic was not significant (p-value =.23). The estimated coefficient EXPANSION was positive. It increased the normalized standard deviation of team winning percentage and decreased competitive balance. The EXPANSION variable was highly significant (p-value =.00). The results of the regression tend to agree with the theories of Coase and Rotenberg. According to the model, the DRAFT variable improved the competitive balance of Major League Baseball. The EXPANSION variable harmed the competitive balance of Major League Baseball. The model results displayed that the FREEAG_ALL_STAR variable improved competitive balance although it was not statistically significant. The FREEAG_ALL_STAR tested the argument that big market teams have an advantage over small market teams because it included only future all-star players who transferred clubs, and are most likely the highest paid players in the league. Section VIII: Conclusion The July 2000, the Blue Ribbon Report stressed that there was a growing disparity amongst teams and competitive balance was indeed a dilemma in Major League Baseball. In my paper I have not only measured competitive balance by viewing the policy changes in baseball, but also most

35 importantly I have measured the role of free agency. According to economic theory from Rotenberg and Coase, free agency should not have affected the competitive balance of major league baseball. The econometric model used a variable counting the number of future all-stars who were free agents as a measure of player quality, rather than using a dummy variable for free agency which was popular in previous literature. The free agent variable used in this model showed that free agency has improved the competitive balance of major league baseball, although the variable was not significant. The model accepted the Coase Theorem that free agency would not have an effect on competitive balance. After examining the correlation matrix of the first model, there was a problem with the number of teams variable. With the elimination of that variable the standard deviation of team winning percentage became an unsuitable dependent variable. The new dependent variable was the normalized standard deviation of team winning percentage. This variable took into account the number of teams in the formula making it an acceptable dependent variable. The results of the second model were better than the first model. The reverse order draft variable displayed that it improved competitive balance. The dummy variable for expansion displayed that it was harmful to competitive balance. The free agent future all-star players who

36 transferred clubs variable displayed that it improved the competitive balance. In respect to improving the model, there are some interesting variables that may be used for future works. A better variable than FREEAG_ALL_STAR may be a variable that include Type A free agents that transferred clubs. This variable would include more quality players; it would be naïve to think that only all-star players can improve the competitiveness of a team. The Blue Ribbon Report showcased some of the problems they saw in Major League Baseball, but according to the results of this model, team payroll disparities do not harm the competitive balance of Major League Baseball. Section VIIII: Related issues possibly affecting future research Future researchers on this subject matter will have to deal with new policy changes that were instituted in the Collective Bargaining Agreement, most importantly the luxury tax and revenue sharing. The luxury tax was first implemented in Major League Baseball in the seasons and was reinstated in the Basic Agreement. The luxury tax states that teams must pay a 17.5 percent penalty on payroll exceeding $117 million for the 2003 season. Teams must pay a 22.5 percent

37 penalty on payroll exceeding $120.5 million for the 2004 season. Teams must pay a 22.5 percent penalty on payroll exceeding $128 million and no tax in Second time offenders must pay a 30 percent penalty and third and fourth time offenders must pay a 40 percent penalty. The money from the luxury tax is said to be used for player benefits, including a player benefit plan. Sanderson and Siegfried (2003) believe that the implementation of the luxury tax can be beneficial in improving competitive balance if the tax rate is set at the proper payroll level and the rate is fixed so that it internalizes the externality. The theory behind the luxury tax is that acquiring a highly paid team is a luxury for one owner that imposes negative externalities on other franchises. The luxury tax should be effective to the point where incremental talent on the high revenue team creates a league-wide net negative impact that might be ignored by the owner of the high revenue team because under league revenue sharing rules they bear little of the cost of an overaccumulation of talent. If the tax rate accurately reflects this internal externality, it creates an incentive for the high revenue team owner to balance their gain against the cost of third parties. The revenue sharing base plan states that each team contributes 34 percent of its net local revenue, after deductions for ballpark expenses, to pool. This is

38 redistributed equally to all 30 teams, plus a central fund to be distributed to low revenue teams. The central fund component entails that $72 million be taken from those teams that are net players in the base plan and redistributed to teams that are net receivers in the base plan. The central fund component phases in at 60 percent in 2003, 80 percent in 2004, and 100 percent in 2005 and It is collected by taking a figure in which the numerator is $72.2 million and the denominator is total net local revenue after ballpark expenses of all player clubs, and multiplying the figure by a payer club s total net local revenue after ballpark expenses. It is redistributed on a split-pool to be shared equally each season and the rest be split up by the Commissioner out of the central and discretionary funds. According to Sanderson & Siegfried (2003) the implementation of revenue sharing could either benefit or hurt competitive balance. They indicated if revenue sharing blunts the incentive for all teams to bid aggressively for talented players, thereby muting salary differentials between more and less talented players, non-pecuniary considerations will loom larger in some free agents decision between competing offers. If players valued the opportunity to play on championship contenders for reasons beyond financial rewards, increased revenue sharing could lead to a greater competitive imbalance.

39 At this time it is too early to tell what the implications of the luxury tax and revenue sharing will be. They will be important factors in coming years when researchers try to model competitive balance. Model I Dependent Variable: STDWP Method: Least Squares Eviews Sample (adjusted): Included observations: 53 after adjusting endpoints Convergence achieved after 11 iterations

40 Variable Coefficient Std. Error t-statistic Prob. C EXPANSION FREEAG_ALL_STA R DRAFT COMPENSATION COLLUSION GAMES REENTRY TEAMS AR (1) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) Inverted AR Roots.53 Model II Dependent Variable: NSTDWP Method: Least Squares Eviews Sample (adjusted): Included observations: 53 after adjusting endpoints Convergence achieved after 10 iterations Variable Coefficient Std. Error t-statistic Prob. C DRAFT FREEAG_ALL_STAR