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Determinants of Sports Franchise Values DETERMINANTS OF SPORTS FRANCHISE VALUES: HOW DOES MLS HOLD UP AGAINST THE BIG FOUR? A Project Presented to the Faculty of California State Polytechnic University, Pomona In Partial Fulfillment Of the Requirements for the Degree Master of Science In Economics By Carlos Pina 2018 SIGNATURE PAGE PROJECT: DETERMINANTS OF SPORTS FRANCHISE VALUES: HOW DOES MLS HOLD UP AGAINST THE BIG FOUR? AUTHOR: Carlos Pina DATE SUBMITTED: Spring 2018 Economics Department Dr. Craig Kerr Project Committee Chair Economics Dr. Bruce Brown Economics Dr. Carsten Lange Economics ii ACKNOWLEDGMENTS I would like to thank Dr. Craig Kerr for the tremendous guidance provided throughout process of writing this paper. I also would like to thank my friends and family for the constant support during my time in the program. Lastly, I would like to thank Lindsey Garcia for keeping me sane and believing in me. iii ABSTRACT This paper examines the effect population, fan income, player value, market size, game attendance, stadium age, historical performance, and local competition have on profes­ sional sports franchises amongst teams in Major League Soccer (MLS), the National Bas­ ketball Association (NBA), the National Football League (NFL), the National Hockey League (NHL), and Major League Baseball (MLB). The results indicate that multiple variables have statistically significant relationships depending on the sports league. It appears that each league has league-specific logistic and financial rules that affect how certain determinants change team value. Moreover, I find that using additional league specific variables for the MLS model prove to provide more accurate results. iv Contents 1 Introduction 1 1.1 League Structure of the Big Four . 3 1.2 League Structure of MLS . 5 1.3 Literature Review . 6 2 Methodology and Data 9 2.1 Methodology . 9 2.2 Dependent and Independent Variables . 10 2.3 Data Description . 12 3 Results 15 4 Conclusion 19 Bibiliography 21 v Chapter 1 Introduction Over the past few decades, determinants of professional sports franchises have been studied extensively either examining one specific sports league or the four major North American Leagues: The National Basketball Association (NBA), the National Football League (NFL), Major League Baseball (MLB), and the National Hockey League (NHL). The literature compares North American sports franchises to European sports firms, more specifically soccer clubs. What has not been attempted is to compare the franchises of these four major leagues to a relatively newcomer: Major League Soccer (MLS). I examine data between 2012 and 2017 using franchise values provided by Forbes’ annual team valuations. The research question I address is: To what extent is the varia­ tion in MLS franchises values determined by differences in economic variables, such as media market rank, historical performance, game attendance, population, player value, and GDP per capita? Secondly, how statistically significant are those are relationships? Furthermore, how do those results compare with those franchise values of the other four major sports leagues in the United States? Previous research is quite extensive for the four major sports leagues, but nonexistent 1 for MLS-related models. Due to this fact, my research is innovative and can pave a way for Major League Soccer team values to be further researched in the future. Previous papers show that individual income, metropolitan population, and player value are sta­ tistically significant in their relation to franchise value. One particular source proved to be the most helpful amongst the rest, which helped me build my model based on the variables used in their study. Scelles (2016) examined European soccer club values with models previously used for the major North American sports leagues previously men­ tioned. I test the hypotheses using data I have gathered estimating an ordinary least squares model and also generalized least squares (GLS) due to the presence of serial correlation and heteroskedasticity. Franchise value is the dependent variable and I used numerous explanatory variables to examine my research question. Since all sports league included in this paper have their own individual sets of financial and logistical league rules, each league has their own model. These models are illustrated in the next chapter. Figure 1.1 examines the average franchise values of teams within all five leagues over the six-year span. Due to data limitations, MLS franchises do not have observations for the years 2012 and 2014. The NFL teams clearly have the highest average annual values, whereas MLB and the NBA are similar in average values. The NHL trails well behind the previous three league and MLS is well below all other leagues. Now, to be able to answer my research question accurately, there needs to be a better understanding of how the league structure of MLS differs from the other major leagues. This comparison will be addressed in the next two sections. Due to the different league structures, my models are not completely identical, but they do examine the same end goal of how economic variables affect franchise values. 2 Figure 1.1 1.1 League Structure of the Big Four The big four are structured as traditional sports leagues in the sense that teams are owned individually. Therefore, they are free to run their business in any way they choose so long as they abide by league specific rules. Teams are free to sign endorsements with any companies, sign local television deals, and make other financial decisions they want as a way to generate income. Revenue sharing exists for these leagues, but rules differ amongst each sport. The NBA implements that all teams contribute an annual fixed percentage, not to exceed fifty percent, of revenue to a pool and the pool is then divided to teams based on the league’s average payroll for that season (NBA, 2011). The NFL splits ticket revenue where the 3 home team receives sixty percent of gate revenue and the away team the remaining forty percent. Also, the league sets aside revenue in a stadium fund, which will be used to match team’s investment in their facilities (NFL, 2011). MLB teams pay thirty one per­ cent of their local revenue into a shared fund, which is then divided amongst all teams. On top of that, national revenue is further split amongst smaller market teams to help balance the competitiveness of the league (MLB, 2011). National revenue meaning tele­ vision and radio revenue a team earns from nationally broadcasted games. The NHL has a system in place where the top ten highest earning teams contribute to a pot from which the bottom fifteen earning teams to collect. There are some rules for the lower earning teams to gain eligibility to collect from the pot, but the details are out of scope for this paper (NHL, 2012). Aside from these revenue sharing stipulations, it is important to remember that for the most part, any income teams generate belongs to said team rather than the league. Payroll works slightly different in a way that is supposed to level the playing field by virtue of a salary cap. In all leagues, except Major League Baseball, the salary cap exists to deter bigger market teams from greater spending than smaller market teams. There are ways of spending above that cap with fees, such as luxury cap that adds additional tax onto teams above the salary cap. MLB, on the other hand, does not implement a salary cap, so long as their business is profitable. These systems leads to large amounts of payroll inequality amongst teams. The league does implements a luxury tax as a way of softly capping teams, but that tax implementation is not always enough to deter teams from spending beyond the salary cap. 4 1.2 League Structure of MLS Major League Soccer is considered a single entity league where the league owns every team and team owners are partners within the league with rights to operate a given fran­ chise. Team owners, also called investor operators, can be thought of as collaborators with ties to a single organization. With this system, the structure is set up to put the league before the teams. Essentially, the league is the first thought ahead of any team within the league on any financial matters. Revenue Sharing within a single entity league is quite unique when dealing with distribution. With MLS, all revenue belongs to the league instead of individual clubs. Essentially, revenue collected through local TV and radio broadcasting rights, sponsor­ ships, and player sales all belong to the league with the clubs receiving a portion of the revenue. The portion the league keeps goes into the league’s bank account and in turn is used to pay player salaries, cost of travel, and referee fees (Taylor, 2015). In turn profits are given to owners to do with as they will. However, operating income data, provided by Forbes, suggests that this system is not always profitable. The salary cap structure for MLS is restrictive compared to the big four in terms of quantity and rules. Currently, the salary cap is $3.845 million, which is minuscule in comparison to other leagues that have salary caps in the tens and hundreds of millions of dollars (MLS, 2018). To put this in perspective, the 2017 average salary of MLS players was roughly $300,000 whereas other leagues in the US having average salaries in the millions. MLS adopted the Designated Player (DP) rule back in 2007 to lure David Beckham from Europe to become the league’s first major international superstar. With this new rule created, the rules of the salary cap were altered in a way for teams to be able to attract top, expensive talent without the issue of spending more than the salary cap allows.
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