Comparing the Revenue and Profit Effects of Winning and Having a Star Player for a Major League Baseball Team

Transcription

1 Comparing the Revenue and Profit Effects of Winning and Having a Star Player for a Major League Baseball Team Haverford College Economics Department Thesis Advisor: Anne Preston 2006 By Jon Kelman 1

2 Abstract This thesis studies the revenue and profit effects of winning and having a star player for Major League Baseball (MLB) teams over the period of Regression analysis is used to determine the revenue and expenditure effects of having a star player and winning; the two are then compared to gauge profits. The analysis also attempts to find the value of stars and winning for teams from different sized cities, as well as the marginal revenue product of star players as the number of stars on a team increases. The findings are used to determine the best financial strategies for MLB teams. 2

3 Table of Contents Introduction...5 Previous Research...7 Dependent Variables...14 Independent Variables..17 Revenue Findings..24 Effect of City Size on Revenue.32 Marginal Revenue Product of a Star.36 Expenditures and Profits from Star Players and Winning...41 City Population Effects on Expenditures for Star Players and Winning..45 Marginal Expenditures for Star Players 51 Summary of Findings 54 Team Strategy Implications..56 Bibliography

4 I firstly want to thank my parents for their amazing support in all of life s endeavors. I also would like to acknowledge the kind people at Baseball Prospectus who helped me obtain most of my data, and my thesis advisor Anne Preston, who is always helpful and graciously dealt with my numerous last-minute meetings. 4

5 Introduction It is often forgotten that all major professional sports teams are businesses. Although there are owners who use their team for leisure purposes, perquisites, political power, and tax sheltering, the goal of most professional sports teams is to maximize profits. One would think that the most obvious way to maximize revenues is to field a winning team, but winning teams can be very costly, and thus do not necessarily maximize profits despite maximizing revenues. A team can be unsuccessful in winning, but very successful as a business. An example is the Los Angeles Clippers of the National Basketball Association (NBA); the Clippers are known as the least successful franchise in major professional sports. The team has never won an NBA championship, and is almost always near the bottom of the league in winning percentage. The team s lack of success is no accident, however, as the owner strives to make profits at the expense of winning. By playing in a large market, the Clippers are able to attract fans despite their lack of success. The owner of the team, Donald Sterling, recognizes the situation and thus seeks to minimize payroll to reduce costs. The team is known to earn over $10 million in profits annually, a substantial figure for an NBA franchise. The financial success of the Clippers indicates that factors other than winning can drive revenues. The primary sources of revenue for a professional sports team are game attendance, media (television and radio) contracts, sponsorships, concessions (all purchases within a stadium, including food, beverage, and parking), and merchandise (team memorabilia purchased outside of the stadium). Concessions and attendance are inexorably linked, as are sponsorships, media contracts, and attendance. These revenue sources depend on several factors, including team success, geographic location, and team facilities such as a new stadium. For example, a large part of the Clippers financial success stems from their location in Los Angeles. Teams from 5

6 larger cities have a larger fan base to draw attendance from, and also have larger television audiences. Interestingly, the Clippers are not the only NBA team in Los Angeles, yet are still able to attract more fans than a smaller market team that wins more games. Teams with new stadiums have also shown increased attendance for a few years after the stadium is built (approximately five years in Major League Baseball). Teams from larger cities are more able to field winning teams and build new stadiums, however, so it is difficult for teams from smaller cities to compete. Another source of revenue for a professional sports team may be having a star player (a star player being one with great skill or popularity). A star player can result in improved competitive success; a winning team can market itself as successful, whereas a losing team has much more limited marketing opportunities. A star player can also give the team a marketing platform regardless of team success; that player will become the face of the team and be featured on every team publication and printed item, as well as all advertisements. Not only will a star player draw more attention to a team, and thus cause higher attendance, greater concession sales, larger media contracts, more sponsorships, and will significantly boost merchandise sales. The value of a star that extends beyond his contribution to winning must be measurable, but what is the value of a star player? The central problem with this specific question is that star players are generally on winning teams. Not only do winning teams create star players by giving the player more fan and media exposure, but star players are also better players, and thus help their teams win. In Major League Baseball (MLB), there has been research performed within the past decade that has tried to measure the exact athletic value of each player. Whereas the exact value of a player in a dynamic team sport such as the NBA is seemingly impossible to measure, the value of an MLB 6

7 player is estimable as is the degree of responsibility for the team s success. The potential for star players to have value beyond their contribution to team success leads to the question of what is the value of a star player beyond contributions to team success in relation to the value of winning? The study will have great implications for MLB, as it can be used to judge player personnel decisions and overall front-office philosophies. Previous Research Previous research has generally neglected to value the revenue impacts of individual players. The only author performing any work on the specific topic is Nate Silver, a writer for Baseball Prospectus. Silver has written multiple articles trying to evaluate an individual player s revenue value by finding how many games he causes a team to win, and then translating those wins into revenue gains. In his series of articles for Baseball Prosepectus Lies Damned Lies, Silver (2005b) details the positive revenue effects of the Florida Marlins trading away most of their highest paid players in an effort to increase profits during this past/current season. Although fans are frustrated that their team is essentially committed to being a losing team next season, Silver finds that selling talent was in the best interest of the Marlins organization. His basic premise is that the marginal value of a few more wins that are brought by a star player are not cost effective to a team that will not make have a significant chance of making the playoffs or winning a championship. The Marlins situation is very similar to that of the Los Angeles Clippers, who also do not stand to gain from building a more competitive team. Another piece by Silver (2005a) from his series Lies Damned Lies studies the value of MLB free agents from the 2005 off-season in relation to the contracts they received. Silver uses two measures developed by Baseball Prospectus (2006), Value Over Replacement Player (VORP) ratings, 7

8 and a system called PECOTA, which projects future player performance. Baseball Prospectus (2006) defines VORP as a statistical measure of the number of runs contributed by a player beyond what a replacement-level player at the same position would contribute if given the same percentage of team plate appearances; VORP scores do not consider the quality of a player's defense. VORP ratings are similar to Wins Above Replacement Player (WARP), which translates the runs into wins. The PECOTA rating system uses a number of factors to predict player performance on a yearly basis, including VORP. Simply, player performance is predicted by comparison to past players who had similar profiles. Silver s article takes the first step toward evaluating an individual player s total value by determining the number of a wins a player is worth and how much money a win is worth. Silver, however, does not discuss the fact that wins can be worth different amounts to different teams, nor the marketing value of players beyond contributions to team success. He notes at the end of the article that teams are paying $2.14 million per win, which may mean that all players are being overpaid in that free agent class. Silver s (2006) most recent article, Is Alex Rodriguez Overpaid is the most interesting. Silver uses the revenue figures obtained from the Cleveland Indians 1997 season, when the team went public for a year and thus released detailed financial records, as well as financial data from all MLB teams during Silver uses the data to find the revenue from marginal wins, and in his Linear Model finds one win to be worth $1.196 million. By using WARP, he is able to find the revenue added by the wins created by the player. Silver also creates a Market-Price Model and Two-Tiered Model. The Market Price Model finds the value of one win by using the salary paid in the free agent market, and the Two-Tiered Model improves upon the Linear Model by accounting for each additional win s value in increasing the likelihood of a playoff 8

9 appearance. As Silver points out, however, each win is not worth the same amount in reality. A win is worth far less to the best and worst teams in the league; each win is far more valuable to teams that are on the cusp of a playoff appearance. For example, the seventieth or one-hundredth win for a team is worth about $600,000, whereas the ninetieth win is worth $3.5 million. One area missed by Silver, however, is that star players can have economic value in more than just creating wins, in terms of star quality that attracts fan interest above and beyond competitive success. Although the Los Angeles Clippers games nearly sell-out and the team easily receives media attention, the Florida Marlins have very small attendance figures and media exposure and thus may benefit from a star player. Silver s analysis and regressions are, however, almost identical to those used in this thesis. His work has been particularly helpful in deciding which variables to include in the model. There have also been articles indicating that Value-Over Replacement-Player (VORP), the measure of how valuable a player is towards a team s on-field success, is undervalued. In an article from 2004 titled You Get What You Pay For: Are Major League Teams Overpaying for Power?, Ben Murphy and Jared Weiss (2004b) found that teams are paying more money for players with high power statistics (high amounts of homeruns, triples, and doubles). Teams are undervaluing players who single and walk more frequently, and are actually more valuable to a team s success. The authors, however, do not discuss the possibility that powerful hitters are more popular with fans and thus more valuable in a revenue sense than their VORP would imply. Weiss and Murphy (2004a) have also written an article titled Predicting Future Salaries: A Simple Model. In the article, the authors find the best model for predicting future salaries is to take 94% of the previous year s salary, and then modify that figure depending on last year s 9

10 VORP. The striking finding of the article is that once a player is paid a certain salary, he will continue to receive that amount, as his on-field performance will only affect his salary slightly. Although the economic value of specific players has not been well researched, there have been many articles written on the revenue effects of winning in MLB. Most notably, recent Haverford graduate Aaron Rabinowitz (2003) wrote about the value of an additional win to each MLB team. Aaron s findings suggest that wins are worth more to larger market teams than to smaller market teams. The effect of a new stadium on attendance has also been researched by several people; the findings have been relatively consistent in that new stadiums produce an attendance boost for a three-to-ten year period. Recent research by Baseball Prospectus, however, has indicated that the attendance boosts are declining, and that the length of the attendance increase is five years or less in the newest MLB stadiums; the attendance also decreases every year during that period, until falling back to previous levels. As discussed, many revenue sources are interrelated in professional sports. Thus, a University of Texas at Arlington professor, Craig Depken (2004), has examined the effects of a new stadium on concession sales. His findings indicate that game attendees at a new stadium spend $2.86 more per person than at the old stadium. Depken, however, does not examine how concession sales vary as attendance in the new stadium declines. Merchandise sales have been under researched. Rankings are given annually of the bestselling players jerseys, but these rankings come from a select few locations, and are not representative of all retailers in the US. The actual number of jerseys sold for each player are not given publicly, thus making it difficult to gauge how popular each player is in relation to others. Although merchandise sales comprise a relatively small portion of total revenue for an MLB 10

11 team, the lack of data and research proves problematic in creating a model that accounts for player marketability. Purpose The goal of my thesis is to bridge the gaps between these previous research articles and extend their analyses to compare the effects of winning and having a star player on total team revenue. Most of my work will come from regression analysis. I will study all thirty MLB teams over a five year period. I plan to use different dependent variables, all related to revenue, including total revenue, gate revenue, local media revenue, and operating income. My independent variables will include many of the factors mentioned in previous research: winning percentage in the previous season, city population, city median per capita income, playoff appearances in the past ten seasons, new stadium, and stadium quality. I will also account for team history by including past championships won by the team. The more novel part of my research is to discover the effects of the star player. I have used two star player variables 1 ; the first identifies the twenty position players and ten pitchers with the highest VORPs in each season. Each team is then assigned a variable based on how many stars they employ in a given season. The second measure of stars is All-Star game appearances. All-Star game starters are voted by fans, and the rest of the roster is filled by the manager s choices of the best players in his league. All-Star game participants should thus be viewed as the best and most popular players in MLB. Unfortunately, I have been unable to use merchandise sales as a measure of stardom due to lack of data. Not all stars are great players; the best example being players who were once stars, and although they are still popular, have 1 A third measure of a star player was also attempted by using the Highest VORP of a player on each team for each season. The variable yielded statistically insignificant coefficients in all regressions, however, and thus has been excluded from the analysis. 11

12 suffered dramatic declines in their VORP. These players can still be used as marketing platforms, and thus should be included as stars. There are also players who are popular in specific regions, for example a Latino player in a predominantly Hispanic market, who are not otherwise counted as stars. Another method considered to evaluate a star player was salary, as the best and most popular players are generally given the highest salaries. Because lucrative contracts are given in professional sports for varying reasons, salary may give a poor definition of a star player, and thus is not used. A measure using total endorsement revenue for each player was also considered, but not used due to lack of data. I have used the star player variables as independent variables, trying to determine their effects on revenue holding constant for all of the other aforementioned variables. Most importantly, I can separate the revenue effects of increased team winning percentage from all other revenue effects of a star. Understanding which aspects of a franchise affect which aspects of revenue has great implications for MLB teams and players. As there has been much research on the effects of winning on attendance, the goal of my research will be to specifically investigate the overall effects of a star player. I will not only attempt to find which revenue inputs are affected by star players, but also how much these inputs are affected. The analysis has been extended to a star s effect on operating income by comparing revenue to expenditure. The marginal value of a star may be different as the number of stars changes, thus the analysis has also sought the marginal value of a star as number of stars increases. Different teams are also likely to have different responses to star players, as was the case with winning in Rabinowitz s (2003) thesis, thus the analysis will be extended to compare teams from different market sizes in terms of revenue and expenditures. 12

13 The revenue effects of star players will be compared to the revenue effects of winning. The revenue effect of winning is manifested in several ways. As Nate Silver (2006) notes, each extra game won brings a team closer to the playoffs, which he estimates to have a $30 million impact on current revenues; therefore, the relationship between winning and revenue is not necessarily linear. As with current and lagged winning, a current playoff appearance also helps future revenues. Similar to the analysis of star players, the effects of winning on revenue and expenditures will be estimated and compared to gauge operating income and finally used to compare revenue and expenditures for teams from different sized markets. A comparison of the results between star players and winning will show what type of strategy different teams should pursue. I will first determine the revenue effects of winning and having a star player. I will then explain whether the revenue effects change for different sized markets. Then, I will attempt to determine the marginal value of a star as the number of stars increases. I will then perform the same analysis on expenditures by firstly determining the expenditures associated with winning and having a star player, then examine the expenditures for different sized markets, and finally determining the marginal expenditure associated with a star as number of stars increases. The revenue effects will be compared to the expenditure effects in the expenditure section to gauge profitability. 13

14 Table 1: Summary of all Dependent Variables Examined (all in millions of dollars) Observations Mean Standard Deviation Minimum Maximum Total Revenue Gate Revenue Other Revenue Local Media Revenue Total Expenditures Player Expenditures Other Expenditures Operating Income Dependent Variables Total Revenue 1 The most basic dependent variable measured is total revenue for each team. Total revenue includes all forms of revenue, including gate receipts, concession sales, sponsorship revenue, merchandise sales, and local media revenue. The values for the variable, as well as all the other revenue related variables, have been derived before taxes, interest, depreciation, and amortization. One of these taxes is MLB revenue sharing, which essentially takes money from wealthier teams and gives money to the lower revenue teams. The revenue sharing is accounted for in later analysis, but not in the regressions. As seen in table 1, the mean revenue figure is $ million, but varies greatly from $53.9 million to $264 million. Although teams do not generally release financial data, the total revenue figures were obtained from Forbes via Rodney Fort s Sports Business Data Pages website. The MLB commissioner s office did release financial figures in 2001 as part of the Blue Ribbon Panel, but these figures are generally viewed as incorrect in comparison with the Forbes estimates. 1 All revenue, expenditure, and winning data from Rodney Fort s Sports Business Pages (Fort 2006) 14

15 Gate Revenue Gate revenue includes all revenue received from game attendance. The gate revenue thus includes not only game day ticket sales, but also season ticket sales and luxury box/suite sales. As seen in table 1, the mean is $ million, but has a large standard deviation of $ million. Unlike many other revenue sources, gate revenue is not strongly related to contracts and can thus change very quickly in response to changing environment around the team. In other words, if the team suddenly starts winning more games, gate revenue should immediately increase. Other Revenue Other revenue is all revenue that is not gate revenue; the figures for the measure were obtained by subtracting gate revenue from total revenue. Other revenue thus includes sponsorship revenue, concession sales, local media revenue and merchandise sales. The measure is somewhat vague, as it covers several types of revenue, but is useful when used in comparison with local media revenue figures. The measure includes forms of revenue that are able to respond quickly to changes in the team, but also includes revenue sources that are unable to respond quickly. Concession and merchandise sales are able to quickly respond, but sponsorship and local media revenue are derived from multi-year contracts, and thus do not generally respond to sudden or brief changes to the team. The measure should thus show slight responses to sudden team changes, but will not react as dramatically as gate revenue. 15

16 Local Media Revenue 1 Media revenue includes all revenue from local media, namely television and radio contracts. In lower revenue professional sports leagues, such as the NHL, television and radio contracts are such that the teams are given a certain amount of airtime and receive revenue by selling advertisements (while giving some of the money back to the network). MLB teams, however, are popular enough that local media outlets pay for the rights to broadcast their games. The networks then maintain the rights to all revenue received through advertisements. The local media revenue figures were obtained from Broadcast and Cable Magazine. The figures were given as a sum of television and radio contracts, and thus cannot be separated for more in-depth analysis. The figures were not released for 2002, and were unavailable for 2004, leaving only 3 years of observations 2. As the figures were also obtained from a different source than the other revenue variables, they differed slightly from Forbes estimates (Forbes estimates were generally lower). The data was thus not subtracted from other revenue, as the figures could be misleading. As seen in table 1, the mean value is $20.99 million, but local media revenue varies greatly from $0 to $63 million 3. Similar to gate revenue, the local media revenue data indicate how a specific revenue source is affected by the independent variables. The local media data can also be compared with other revenue, as an indicator of which portion of other revenue is affected by the independent variables (the local media revenue portion, or the sponsorship sales, merchandise sales, and concession sales portion). Unlike gate revenue, local media revenue is derived from contracts and is thus slow to react to changes in a franchise; therefore, should a team suddenly start winning more games, the local media revenue will not increase unless the 1 Local media revenue data received from Nate Silver, who compiled the data from Broadcast & Cable Magazine 2 The Montreal Expos did not report their local media revenue in 2001, thus leaving 89 total observations over the five year period. 3 The Montreal Expos did not have any local media contracts in 2003, and thus did not have any local media revenue for the year. 16

17 current season is the last in a given contract, then the contract value may increase for the following season. Operating Income The revenue measures do not account for the costs to achieve such revenue levels. A variable has thus been included that measures operating income. Operating income is simply the measure of a team s total revenue minus total expenditures, or more simply, profit. As seen in table 1, the mean operating profit is $2.012 million, indicating that MLB teams were profitable as a whole over the five year period examined. Independent Variables Table 2: Summary of all Independent Variables Examined Observations Mean Standard Deviation Minimum Maximum Stars All-Stars (past two seasons) City Population Median Income Past Championships New Stadium Stadium Quality Lagged Winning Percentage Playoffs (past 10 seasons) Market Characteristics City Population 1 1 City population and median income data is from US census reports in The figures for 2000 are repeated for each year in the five year period, thus there is no variation in city population for each city 17

18 An important factor in determining individual team revenue is the market/city in which the individual team plays. The New York Yankees and Mets play in a vastly different market than the Kansas City Royals of Kansas City, Missouri. The variable chosen to account for market differences is city population. As shown in table 2, the mean city population (measured in millions of people) is 1.578, but the market sizes vary from 303,000 to 8.08 million. Previous research indicates that teams from larger cities maintain a greater revenue flow than teams from smaller markets. Income A variable has also been included for median per capita income; the variable is similar to the one used by Nate Silver (2006) in his regressions. The values given are in thousands of dollars. Table 2 shows a mean value of , and a standard deviation of A wealthier market may not only buy more tickets, but may also buy more expensive tickets and more merchandise. A higher median per capita income should thus lead to higher revenues. Team Characteristics Championships 1 Another factor that plays into team revenues is team history and previous success. For example, the Boston Red Sox play in a market that is not relatively large, but are team with a rich history and a strong following because of their history. To account for team history, a variable for past championships is included. The championships variable includes all World Series won in team history, with equal weight given to all championships. There are several teams that won championships and then relocated. The championship variable only counts 1 Championship and playoff data was obtained from HickokSports.com (Hickok 2005) 18

19 championships won by the current franchise in the current city, since team success in another city should not affect the fan base in a new city. Table 2 reveals the mean number of championships to be 2.733, and a standard deviation of The large variance is likely due to the New York Yankees having won twenty-six championships, seventeen more than any other team. Playoff Appearances in the past ten years 1 Team success is another important factor in determining revenue. On-field success can be manifested in several ways in terms of revenue, but potentially the most lucrative is achieved by a playoff berth. According to estimates by Nate Silver (2006), a playoff appearance is worth approximately $25 million. The eight teams that reach the playoffs in a given year should thus be expected to earn at least $25 million more than the other twenty-two teams. Consistent with Nate Silver s (2006) findings, a variable for number of playoff appearances in the past ten seasons has also been included. The variable is intended to measure recent team success. As seen in table 2, the number of playoff appearances in the past ten seasons varies between zero and nine 2. The variable can be compared to the winning percentage and past championships won variables to indicate how current performance compares with recent past and distant past performance. Unlike the variable for playoff appearance in the current season, this variable should increase all of the revenue inputs. Lagged Winning Percentage 1 Variables for playoff appearance in the current season and winning percentage in the current season were also examined. Neither of the variables provided statistically significant coefficients, however, thus lagged winning percentage and past playoff appearances were used instead. 2 The MLB strike in 1994 caused the season to end before the playoffs, thus, the highest number of playoff appearances possible in the ten year period is nine. 19

20 Team success also has the effect of increasing team popularity, which, in turn, lures more fans to the ballpark, as well as increases merchandise, concession, and sponsorship sales. To measure team success, a variable for lagged winning percentage has been included, which measures the winning percentage from the previous season. Table 2 indicates lagged winning percentage varies between 26.5 and MLB teams play a 162 game schedule, thus a one percent increase in winning percentage is the equivalent of about 1.5 more games won. New Stadium Another tool used by teams to increase revenues is having a new stadium. Many teams in recent years have lobbied extensively for public funding to help build a new stadium. There have been numerous studies on the revenue effects of a new stadium, with the prevailing idea being that new stadiums have a honeymoon effect of temporarily increasing revenues. The effect wears-off gradually, and is generally negligible after five years. The profitability of such short-term revenue gains is thus highly debatable. A 0,1 variable has been included to indicate whether the given team has built a new stadium within the past five years 1. As seen in table 2, the mean value of the variable is 0.24, and the standard deviation A new stadium should have the effect of increasing attendance, as well as concession, merchandise, and sponsorship sales. Stadium Quality A variable has also been included that measures stadium quality. The stadium quality ratings were created in 2003 by several columnists for espn.com (2004). This variable is the same as the one used by Nate Silver (2006) in his regressions. Five data points are missing 1 Stadium opening dates were obtained from MLB Baseball Stadium (MLB Teams 2006) 20

21 because espn.com compiled the survey in 2003, and ratings are not available for the old stadiums of teams that built new stadiums between 2001 and The rating system is on a one-hundred point scale, but table 2 indicates a high mean value of High stadium quality should have a similar effect on attendance as having a new stadium, in increasing attendance and concession, sponsorship and merchandise sales. Stars 1 The central focus of this thesis is to analyze the effects of a star player on revenue. Star players can effect revenue by several means. Not only do star players boost a team s winning percentage, but holding constant for these winning percentage effects, they also increase the team s marketability. A star player should thus increase all forms of revenue above what would be expected from the win shares added by the player. There are several ways to define a star player, but only two have been used for this analysis. The first and more prominent method is using the twenty best position players and ten best pitchers in a given season. Best players is defined by highest VORP. The total number of thirty stars was chosen to allow for all teams to have one star (although considering the competitive landscape of MLB teams with zero stars were expected). The division of twenty field players and ten pitchers was chosen due to there generally being nine significant position players on each team and five significant pitchers (approximately a 2:1 ratio). The variable used in the regressions is the number of star players on the given team in the given year. As seen in table 2, the number of stars on a team varies between zero and four. 1 VORP data for Stars and Best VORP was obtained from Baseball Prospectus website 21

22 The biggest problem with the measure is the inability to account for popular players who do not necessarily perform well, or players who perform well and are not necessarily as marketable. VORP also favors players who play more games, and most notably, the best relief pitchers in baseball (of whom about three per season could be considered stars) are not included as stars due to their limited number of innings pitched. VORP is also based on position, thus certain positions are easier to obtain a higher VORP. Corner outfielders are generally the best hitters in baseball, while second basemen and catchers are generally weak-hitting positions. The result is that a catcher or second baseman who is a worse hitter than a particular right fielder could receive a higher VORP. The variable in the regressions is essentially assuming that a player s relative, not absolute, value is recognized. All Stars 1 Variables have been included that represent All-Star game participants from the current and previous seasons combined 2. The variable only counts players who played in the All-Star in the current or previous season; a player who played in an All-Star game anytime before 1999 is not counted. As mentioned previously, All-Stars are determined by a fan vote and manager selection. Every team has at least one All-Star representative each season. All-Star selections who were unable to play in the game, as well as their injury replacements have both been counted. Thus, there are about sixty to seventy All-Stars in each season. As seen in table 2, the mean number of All-Stars is 4.353, with a standard deviation of All-Star data was obtained from MLB.com (2006) 2 Variables for All-Stars in the current season and All-Stars in the previous season were also examined separately, but were not as statistically significant as combined number of All-Stars over the two-year period. The lagged All- Star variable was statistically significant, and indicates an over $3.4 million increase in total revenue for each All- Star, with $2.4 million manifested in gate revenue. 22

23 The reasoning for not using career All-Star appearances is that many of the players who are commonly considered stars are young and have not been selected to many All-Star games. Similarly, there are many players who accumulated All-Star game appearances in their prime, but fade from stardom in the last few years of their careers. A variable using career All-Star appearances would not have counted Derek Jeter as an All-Star, despite the fact that he is arguably the biggest star in baseball. The variable used is nonetheless flawed. Despite hardly being considered stars, there are always several players who have one spectacular season and are selected. Baseball s biggest stars are also prone to injury, and thus occasionally miss an All-Star game. Generally, however, most of the biggest stars receive All-Star recognition based on reputation and past performance (usually by fan balloting). The lagged All-Star idea is also be troublesome as the variable does not account for All-Stars who change teams after the season. The reasoning behind using All- Star appearances from the previous and current seasons combined is an attempt to find players who are truly stars. If a team has two All-Stars, those two players should be on the All-Star team every season. Problems arise if one other player on the team suddenly has a great season and becomes the team s third selection to the All-Star game. By combining two seasons of All-Stars, those abnormal All-Star appearances are given less weight than players who are All-Stars every season. Another problem with this variable (as well as the other two All-Star variables) is that a team with one star player will appear similar in the data as a team with no star players, as both teams will receive one All-Star selection per season. 23

24 Revenue Findings 1 Table 3: The Effect of Stars and Winning on Total Revenue, Gate Revenue, Local Media Revenue, and Total Expenditure Total Revenue Gate Revenue Local Media Revenue Stars 4.314*** (2.60) 5.213*** (3.38) (0.92) Lagged Winning Percentage (1.02) 0.568** (2.57) ** (-2.41) Playoffs (past 10 seasons) 5.234*** (5.76) 1.984** (2.35) 4.034*** (5.33) City Population 6.310*** (7.32) 2.736*** (3.42) 1.948*** (2.90) Median Income 1.177*** (3.09) 0.701* (1.98) (0.89) Past Championships 1.321*** (3.40) 1.284*** (3.55) (0.45) New Stadium 6.049* (1.67) 7.696** (2.29) (-1.57) Stadium Quality 0.976*** (7.10) 0.721*** (5.64) 0.334*** (3.10) Year ** (2.17) (-0.04) (0.17) Year ** n/a (2.51) (-0.90) Year *** (3.97) (-0.42) (0.59) Year *** n/a (6.15) (0.69) n Notes: T-Values are in parentheses * indicates significant at the 90% level ** indicates significant at the 95% level *** indicates significant at the 99% level 1 All revenue findings do not account for MLB revenue sharing payments unless otherwise noted. MLB revenue sharing is a payment of 34% of all locally generated revenues from each team into a league-wide pool, which is then divided equally amongst all thirty teams. There is also a second form of revenue sharing as described by Silver (2006) called a split pool system, which varies the revenue sharing payments such that in 2005, the highest grossing team, the New York Yankees, paid 39% of their local revenues and the lowest grossing team, the Kansas City Royals, paid 47%. In the rest of the analysis, revenue sharing payments of 47% and 43% are used, which account for the highest payment and the average of the Yankees and Royals figures. 24

25 Market Characteristics City Population Table 3 reveals the total revenue, gate revenue, and local media revenue effects of the independent variables. All of the variables generally agree with their individual hypotheses. As assumed, higher city populations lead to higher revenues. Table 3 indicates that a one million person increase in city population leads to a $6.31 million increase in total revenue. The revenue gain is manifested almost equally in gate revenue and other revenue streams. Income Table 3 shows that per capita income effects also support the hypothesis that cities with higher median per capita income have greater revenue flows. A one unit increase in median per capita income leads to a $1.177 million increase in total revenue. The revenue gains are realized relatively equally between gate revenue and other revenue. The data gives slightly different results, however, than Nate Silver s (2006) findings that indicated only a $7 million revenue difference between the highest and lowest per capita income cities. This data shows an over $40 million revenue difference between the highest and lowest per capita income markets per season. Team Characteristics Championships As hypothesized, table 3 indicates winning a championship has a positive and statistically significant effect on revenue. The data estimates a $1.321 million increase in total revenue for every championship won, with almost all of the revenue coming from gate receipts. 25

26 New Stadium Table 3 shows a $6.049 million increase in revenues from having built a stadium in the past five years. Although the regressions do not match up well (showing a new stadium as having a negative effect on other revenue), the data does show that almost all of the revenue gain is realized in the form of gate receipts. The results generally agree with the hypothesis, although one would expect sponsorship, concession, and merchandise sales to increase from the presence of a new stadium in addition to gate revenue. Stadium Quality In accordance with Nate Silver s (2006) findings, the stadium quality variable in table 3 was highly statistically significant. The data indicates a $976,000 revenue gain from a one point increase on the rating scale. As expected, the revenue gain is mostly realized in gate receipts. Oddly, however, table 3 indicates that the other revenue gains appear to mostly be attained from local media contracts. Such a finding is perplexing, as logically the revenue input that should be least affected by stadium quality is local media contracts. The variable is the only one obtained through an arbitrary measure, which could be the cause of confusing findings. The high statistical significance, however, should remove some of the doubt. Lagged Winning Percentage Table 3 shows lagged winning percentage to only be statistically significant and logically correct for gate revenue. A one percent increase in lagged winning percentage leads to about a $568,000 increase in gate revenue. The lack of statistical significance limits the analysis in finding the effects of lagged winning percentage on total revenue and other revenue sources. A 26

27 simple solution to the problem was achieved by running a regression similar to those in tables 3 and 4, but excluding past playoff appearances. The data shows an over $700,000 revenue gain from each percent increase in winning percentage the previous season, with almost all of the money coming from gate revenue. The revenue value is being overestimated, but the more important portion of the analysis is that almost all of the revenue comes from gate receipts. Playoff Appearances in the past ten seasons In agreement with Nate Silver s (2006) findings, table 3 shows the variable for playoff appearances in the past ten seasons to be highly statistically significant. The data indicates a $5.234 million revenue increase for each playoff appearance in the past ten seasons. Interestingly, less than $2 million comes from gate revenue. The data is thus affirming the logic that although past success will lead to revenue gains from all inputs, the most affected mediums are sponsorship and local media revenue. Stars As hypothesized, table 3 indicates the presence of a Star player boosts revenues after holding constant for all other variables (most notably winning percentage). Over the five year period, each Star player adds $4.314 million to total revenue per year. Contrary to the hypothesis, however, the revenue gains appear to mostly be realized in the form of gate revenue. One would think that star players could have a very noticeable effect on merchandise sales in particular. 27

28 Table 4: The Effect of All-Stars and Winning on Total Revenue, Gate Revenue, Local Media Revenue, and Total Expenditure Total Revenue Gate Revenue Local Media Revenue All-Stars 3.275*** (3.57) 2.374*** (2.69) (0.99) Lagged Winning Percentage (0.08) 0.477* (1.99) ** (-2.44) Playoffs (past 10 seasons) 4.438*** (4.76) 1.535* (1.71) 3.892*** (4.93) City Population 6.429*** (7.61) 2.740*** (3.37) 2.073*** (3.07) Median Income 1.181*** (3.20) 0.794** (2.23) (1.11) Past Championships 0.891** (2.20) 1.007** (2.58) (0.06) New Stadium 6.210* (1.75) 7.575** (2.22) (-1.61) Stadium Quality 1.000*** (7.47) 0.755*** (5.85) 0.345*** (3.23) Year ** (2.26) (-0.01) (0.19) Year *** n/a (2.67) (-0.82) Year *** (4.01) (-0.49) (0.58) Year *** n/a (6.24) (0.60) n Notes: T-Values are in parentheses * indicates significant at the 90% level ** indicates significant at the 95% level *** indicates significant at the 99% level Table 4 displays a regression identical to that in table 3, except the variable for Stars has been replaced with All-Stars (combined from the past and current season). The values for the other independent variables are similar to those found in table One should note that the revenue values for lagged winning percentage and past playoff appearances are slightly lower, albeit similar, in table 4 compared to table 3. The decreased effect is likely due to the fact that there are almost twice as many All-Stars as Stars, and the cumulative effect on revenue of All-Stars is thus higher. 28

29 All Stars The variable for combined All-Star appearances from the previous and current season is has a positive and statistically significant effect on total revenue. Table 4 indicates a $3.275 million revenue increase from each additional All-Star over the two season period. A true star will appear in the All-Star game every year, and is thus worth $6.55 million in revenue per year. Table 4 indicates that $4.748 of the $6.55 million will come from gate revenue. The results from tables 3 and 4 agree with the hypothesis that star players have a positive effect on revenue beyond helping a team win. The results vary between definitions of a star player, but the most conservative figure from table 4 indicates a $4.314 million revenue boost from the presence of each Star. After accounting for revenue sharing by using the same methodology as Nate Silver (2006) by averaging the highest and lowest revenue sharing payments made in 2005, which is 43% of all local revenue, the conservative estimate of a star s value becomes $2.459 million. Revenue sharing was only 20% prior to 2003, leaving the most conservative star valuation at $3.451 million for the first three years analyzed. The values should be much higher, however, considering the definitions used for star players are flawed. Although the measure using VORP to determine the twenty best position players and ten best pitchers includes baseball s best players as stars, there are also several players included each year who would not be considered stars, as well as several other players who are considered stars, but fail to make the cut-off. For example, Derek Jeter is only counted as a star by the VORP measure in two of the five years analyzed. The VORP measure is not actually indicating the thirty biggest stars in baseball, and is thus under-estimating the effect of a star. The same logic applies to the measure using All-Star appearances; the value of each star player is being under-estimated in all regressions run for this analysis. 29

30 Table 5: The Effects of Lagged Winning Percentage and Past Playoff Appearances on Total Revenue and Gate Revenue 1. Total Revenue Gate Revenue Total Revenue Gate Revenue Stars 5.386*** (2.93) 5.619*** (3.61) 4.714*** (2.92) 6.148*** (4.02) Lagged Winning Percentage 0.797*** (3.29) 0.779*** (3.79) Playoffs (past 10 seasons) 5.609*** (6.75) 2.861*** (3.63) City Population 5.989*** (6.25) 2.614*** (3.22) 6.346*** (7.37) 2.820*** (3.45) Median Income 1.544*** (3.70) 0.840** (2.37) 1.264*** (3.41) 0.905** (2.58) Past Championships 2.029*** (4.93) 1.552*** (4.45) 1.339*** (3.44) 1.325*** (3.60) New Stadium (0.47) 6.100* (1.82) (1.64) 7.383** (2.16) Stadium Quality 1.080*** (7.11) 0.760*** (5.90) 0.963*** (7.04) 0.690*** (5.32) Year ** (2.13) (0.04) 9.878** (2.17) (-0.04) Year ** (2.50) (-0.78) ** (2.52) (-0.86) Year *** (3.90) (-0.27) *** (3.96) (-0.44) Year *** (6.07) (0.91) *** (6.13) (0.63) n Notes: T-Values are in parentheses * indicates significant at the 90% level ** indicates significant at the 95% level *** indicates significant at the 99% level To further examine the revenue effects of winning, regressions were run in table 5 that are similar to the basic regressions for total revenue, but instead use only one of the winning variables. The regression using lagged winning percentage as the only winning variable reveals 1 For sake of simplicity, regressions that were run to find the value of winning, all use Stars instead of All-Stars as an independent variable. The findings for the team success regressions using All-Stars as an independent variable will be noted in footnotes. 30

31 an almost $800,000 revenue increase from a one percent increase in winning percentage the previous season, with almost all of the revenue increase realized through gate revenue. The gate revenue figure is about $200,000 different from the regression that includes past playoff appearances in tables 3 and 4. The regression for past playoff appearances yields a $5.6 million revenue increase from each playoff appearance in the past ten years, with almost $2.9 coming from gate revenue. The past playoff appearance figure for total revenue is about $400,000 less than in tables 3 and 4, with almost all of the revenue difference manifested in gate revenue. The variation in coefficients in the different regressions indicates that the best estimate of the revenue effects of winning should be gleaned from the regressions in tables 3 and 4 that feature both winning variables. The only safe conclusion is thus that each one percent increase in lagged winning percentage is worth at least $500,000 to total revenue, with most of the money realized through gate revenue, and that each playoff appearance in the past ten seasons is worth over $5.2 million in total revenue, with almost $2 million coming from gate revenue. What is the total value of a one percent increase in winning percentage? The exact value of a one percent increase in current winning percentage is difficult to decipher. The lagged winning percentage variable shows that a one percent increase in current winning percentage will increase revenues in the following season by at least $500,000. The figure should rise dramatically given that current team success should also fuel greater gate revenue in the current season, and when considering Silver s (2006) Two Tiered Model. As Silver discusses, each win is not worth the same amount; more importantly, the additional value of each win towards a playoff appearance is not realized by teams that do not make the playoffs. The value of winning can thus be segmented into teams that know before a season that they are not going to contend for the playoffs, and teams that know they are playoff contenders. If each playoff appearance in 31