DOES LOSING MATTER? AN ANALYSIS OF NBA FRANCHISE REVENUES

A THESIS

Presented to

The Faculty of the Department of Economics and Business

The Colorado College

In Partial Fulfillment of the Requirements for the Degree

Bachelor of Arts

By

Lance Nicholas Jacobs

May 2009 111

DOES LOSING MATTER') AN ANALYSIS OF NBA FRANCHISE REVENUES

Lance Nicholas Jacobs

May, 2009

Economics

Abstract

The National Association (NBA) is one of the four largest professional sports organizations in the United States, There are currently 23 teams in the NBA that gathered over $100 million in revenue during the 2007-08 season alone. This study examines the components of total NBA franchise revenues and investigates the effect that multiple losing seasons has on total revenue performance. A fixed-ellects regression analysis is used to examine the effect of multiple losing seasons on total NBA franchise revenue. All the statistics and data observed in this study are from the 10 year period of 1999 to 2008. The findings in this study provide valuable infom1ation to NBA teams as to whether losing consecutive seasons affects total revenue performance.

KEYWORDS: (National Basketball Association, Revenues, Consecutive Losing Seasons) ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED AID ON THIS THESIS

Signature TABLE OF CONTENTS

ABSTRACT ...... '" ...... 11l ACKNOWLEDGEMENTS...... IV I. INTRODUCTION ......

II. LITERATURE REVIEW...... 6 Superstar Impact...... 7 New Stadiums...... 8 Ticket Prices...... 11 Player Productivity ...... "...... 14 Sponsorships...... 15 Conclusion ...... " ...... 16 lII. THEORy...... 18 Dependent Variable...... 19 Independent Variables...... 19 Dummy Variables...... 24

IV. DATA AND METHODOLOGy...... 29 Variables Summary. '" ...... 30 Dependent Variable...... 33 Independent Variables...... 34 Method...... 41

V. RESULTS...... 43 Modell...... 45 ModeI2...... 47 Model 3...... 49

VI. CONCLUSION...... 52 Significant Variables ...... ". 53 Limitations...... 55 Further Research...... 56

SOURCES CONSULTED...... 58 LIST OF TABLES

4.1 Variables Summary ...... '" ...... 30

4.2 NBA Teams and Conferences...... 32

5.1 Fixed Effects Regression Results...... 44 LIST OF MODELS

4.1 Simple Regression Equation"."" " ... "",,.,,"""""" """".""""".,,",,",,. 28 LIST OF FORMULAS

4.1 Win Percentage...... 36

4.2 Fixed Effects Regression Model...... 42 IV

ACKNOWLEDGEMENT

I would like to recognize Professor Andrew Nelson for assisting me in creating this project and for his advisement throughout the process of senior thesis. His positive criticisms and insightful suggestions proved valuable in each and every meeting. Finally, I would like to thank my mother Molly and my father Scott for always giving constant support and guiding me through the strenuous process of writing this thesis. I cannot thank them enough for there unwavering support in all of my endeavors and for providing me with the opportunity to attend this wonderful institution. I dedicate this thesis to them. CHAPTER [

INTRODUCTION

The National Basketball Association (NBA) is one of the four largest professional team sports organizations in terms of popularity in the United States. The other three are

Major League Baseball (MLB), The (NHL), and The National

Football League (NFL). There are currently 23 teams in the NBA that gathered over 100 miIlion dollars in revenue during the 2007-2008 season. [n recent years most professional sports, including the NBA, have undergone dramatic changes in order to generate greater revenues and growth in specific regional economies. I These changes include the sponsorship and naming rights of stadiums, and the strategy of building new stadiums to generate a "honeymoon effect"· therefore greatly increasing revenues for a short period of time2 However, there has been an absence of research about how two or more consecutive losing seasons effects the amount of revenue that NBA franchises can generate. This research paper will focus on examining the effect that two or more consccutive losing seasons has on NBA franchise revenues. One would assume that winning has a positive effect on revenues and losing has a negative effect, but this paper

j The Economic Determinants of Professional Spons Franchise Values. Donald L Alexander and \',\"ilham Kern. Journal of Sports Economics 2004; 5; 51.

\Vhen is the Honeymoon Over? National Basketball Association Attendance 1971-2000. John C. Lcadley and Zenon X. Zygmont. Journal of Sports Economics 2005; 6; 203.

*The honeymoon effect represenL"> the relationship bct"\veen spectator attendance at a professionai sporting event and the age of the sports facility at which the event is held. 2 will detennine if there is a secondary effect on franchise revenues if a team loses consecutive seasons. The hypothesis is that the total revenue that an NBA franchise accumulates will not be significantly affected by losing consecutive seasons.

As previously mentioned, 23 teams in the NBA arc making over 100 million dollars in franchise revenue a year. Where are these teams making this enonnous amount of money and in which sectors are revenues the most significant? Some people may wonder how sports entertainment is generating this money and it is indeed an interesting phenomenon. For example, in the 2007·2008 season the New York Knieks generated the most revenue of any team in the NBA, accumulating 196 million dollars in total revenue.

One might assume that the Knicks had a very high win percentage, thus generating higher revenue from attendance due to people wanting to see a winning team. This is definitely not the case. The ' finished the 2007·2008 season with a record of23 wins and 59 losses which translates to a winning percentage of 28%3 This brings up the question: where and how are the Knicks generating so much revenue if they are losing so many games? The answer, I believe, lies in their market size. The Knicks play their home games at Madison Square Garden which is located in the largest market in tenns of population4 in the United States. From a business standpoint this is a very interesting situation because, in sports, losing usually translates to bad attendance resulting in less revenue from ticket sales and attendance. However, this research paper will detennine the exact cffect that multiple losing seasons has on franchise revenues.

4 Population is defined by the metropolitan ~tatistical area of each city. 3

Now let's compare New York to a much smaller market such as San Antonio, where the San Antonio Spurs play their home games. The San Antonio Spurs ranked 10th in the league in total franchise revenue, generating 138 million dollars in the 2007-2008 season. This market is much smaller in population than the enonnous market New York

City has, yet somehow the Spurs are earning tremendous amounts of revenue. In comparison, San Antonio has a population of 1,990,675 and New York City has a population of 18,815,988 5 Although San Antonio is collecting $70 million less in revenues, it is interesting to wonder how they are in loth place among 30 NBA teams in tenns of total revenues. In fact they are bringing in more revenue than larger market teams such as the and the . Since the 1998-99 season the Spurs have won 4 championships and have had a range of win percentages from .646 to .768. They are one of the most suecessful teams on the court during this era in tenns of winning. This eould be an explanation of why they bring in so much revenue as compared to teams who play in larger markets. People may enjoy watching winning teams more than losing teams, which would affect revenues in tenns of ticket sales, concessions, and merchandising. Is the tradition of winning the reason the Spurs are so successful in tenns of total revenues accumulated? A simple regression model will tell whether losing consecutive seasons has any sif,'Tlificant effect on NBA franchise revenues.

This research is important for a variety of reasons. The first involves general managers of NBA teams. General Managers must be able to make informed decisions on how to put together teams that are able to win games and hopefully win an NBA championship. They are paid huge sums of money to do this seemingly easy work.

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However, a large percentage of the time these general managers fail to hire players who can help produce wins for teams as well as possibly generating significant revenue growth for their specific franchise. The regression model used in this research will tell whether or not failure on the court, in tenns of losing, has a negative effect on revenue perfonnancc. lflosing does not have any significant influence on revenues than General

Managers may want to look into simply hiring high profile players who will bring fans to the arena. However, iflosing does in fact negatively affect revenues, then General

Managers will surely want to put together a team of highly productive players who are capable of bringing success to the team.

Owners of NBA teams may find this research appealing as well. The main focus ofthis research is to see whether or not two consecutive losing seasons significantly affects franchise revenues. However, there are a variety of other factors that affect the total revenue that a specific team generates. All-star players, ticket prices, market size,

6 fan cost index , winning percentage, attendance, and player expenses may affect revenues and this can be very useful infonnation for owners of NBA franchises. The results shown from the regression analysis will be beneficial because it will allow people to see which factors arc most significant in affecting franchises revenue.

The general public, for the most part, may be unaware of how professional basketball teams are garnering millions of dollars in revenues. Whether someone is a fan or not, it can be hard to decipher exactly how these NBA teams are acquiring so much revenue. For instance. the general public may assume that gate revenues and advertisement space generates quite a bit of revenue for these teams. but the public may

6 The Fan Cost Index comprises the: prices of 4- average price tickets, ::: small draft ooers, 4 .small soft drink", 4 regular-size hot dogs. parking for I caL :2 game programs and two least expensIve, aduh-sizc adjustable caps. 5 not know how two or more consecutive losing seasons affects total revenues and overall team values. Another question that fans may ponder about is whether or not an all-star player enhances the value of the franchise they follow. This paper allows fans to see, first and foremost, the effect that two or more consecutive losing seasons has on franchise revenues, and what other factors are important to professional basketball team values.

Now, fans should know how these teams gather so much money, and iflosing consecutive seasons affects total franchise revenues. CHAPTER II

LITERATURE REVIEW

The purpose of this literature review is to examine the current research on the determinants of National Basketball Association franchise revenues and give thorough iusight on the research done in related topics. There are many scholarly articles and relevant literature related to this topic which will help determine how NBA franchises are generating the most revenue. These studies have included, but are not limited to, how team win percentage, superstar players, and market size affect team revenues. I wish to take this topic a bit further and observe other areas that affect how much total revenue these NBA teams generate, such as teams with two or more consecutive losing seasons.

Teams with two or more consecutive losing seasons may significantly affect how much revenue their franchise is able to generate. But do fans really care about winning or losing?

Or do they just want to support their local team and watch their favorite players? Further research of literature and a simple regression analysis will provide answers to these questions. The economics of basketball indicate that the sport is in the midst of an era of unprecedented growth and prosperityl. During an era such as this, it is important for teams to know what factors cause significant changes in revenue performance in order to maximize profit.

J Staudnhar, Paul D. Plavinf! for DoUars : Labor Relations and the_~Sp(lns BUSIneSs, ;";ew York: Comell UP < 1996.

6 7

Superstar Impact

A study done by Hausman and Leonard (1997) explored the value of superstar players to teams in tenns oftelevision revenue. Certainly eaeh team's finaneial success is heavily detennined on its specific television contract and the share of the lea!,rues television

contract2 The authors found that the mere presence of stars had a substantial positive

impact on club revenues. By analyzing all NBA local and national television ratings as

well as match attendances, they singled out that in 1993 the estimated value of Michael

Jordan for the NBA was $53 million. This being said, superstar players have a huge effect on team's television revenues because obviously they draw people to watch the game and thus increase the television ratings for a specific game. Teams may find it profitable to lure superstar players to their organization in order to generate revenue through television ratings. Not only are the teams with superstar players seeing a superstar externality but the opponents are also generating revenue when people watch these teams with superstar star players.

Superstars also have a large affect on NBA gate revenues. Berry, Schmidt and

Brook (2004) investigated the impact of star power on gate revenues and found that stars had a significant positive effect although the ability of a team to consistently win was found to be morc important and statistically significant3 However, in a later study perfonned by Berri and Schmidt (2006) they found that star power matters more for a team's opponent and that when superstars come to an opposing team's city it generates

2 Hausman, 1, A., & Leonard. G. K. (1997). Superstars in the national basketball association: Economic value and policy. Journal o.fLabor Economics, 15(4),586-624.

'; David 1. BeITI, Martin B. Schmidt & Stacey L Brook. Stars at the Gate: The Impact of Star Power on NBA Gate Revenues. Journal of Sports Economics 2004; 5: 33. 8 greater attendance of fans. 4 This is espeeially true when a player like Kobe Bryant comes to town. People from cities other than Los Angeles rarely get to see him play live, so it is easy to see why superstars can, in this study, generate more attendance and gate revenues on the road. Thus, superstars matter more for the revenue of a team's opponent because the superstar generates a greater marginal increase in attendance for a host, non-superstar team.

Another study performed by Brandes, Franck, and Nuesch (2008) examined star player's in German soccer and how they effect attendance and enhance gate revenues.

The authors analyze star attraction' of national superstars and of so called "local heroes" defined as the most valuable players of teams without national superstars. In this case, the composition of a team played a fundamental role in enhancing fan support. In a survey the authors conducted, 69% of European soccer fans said that their identification with and affiliation with a team is by and large determined by the particular players the team hires5 This makes sense because in the case of "local heroes" fans will feel a connection, and sense they should show support to the athlete who was produced by their city. The empirical model showed that superstars increased attendance at home and during road games. However, the "local heroes" effect on gate revenues was limited to home games.

New Stadiums

When new stadiums are built for professional sports franchises it generates a substantial amount of interest in terms of entertainment for conswners. Donald L. Alexander and

4 David 1. Berri & Martin B. Schmidt. On the Road With the National Basketball Association's Superstar Externality. Journal OfSPOflS Economics 2006; 7; 347 .

• Star attraction describes superstar players that Me so well-known they increase attendance levels.

5 LeiI' Brandes, Egon Franck. and Stephan Nuesch. "Local Heroes and Superstars: An Empirical Analysis of Star Attraction in Gennan SOCCCL "JournaI2J' Sports Economics 9 (2008): 266-86. 9

William Kern (2004) perfonned a study in which they looked at the detenninants of professional sports franchise values and specitically looked at the building of a new playing facility. Teams who recently moved to a new stadium tended to have higher franchise values because the new stadium generated higher amounts of revenue in sales of corporate boxes and naming rights.6 For example, in Madison Square Garden corporate luxury suites can cost anywhere within the range of$225,000 to $500,000 per year. 7 From this infonnation it is easy to see that luxury boxes, and the revenue generated from them, are almost a requirement for cities trying to lure or keep professional teams.

Recently the Charlotte Hornets lost their franchise because there weren't a sufficient number of lUXUry boxes in their arena, The Charlotte Coliseum. The city of Charlotte, who owned the stadium, was unwilling to pay for more luxury boxes so they were forced to move. They have since moved to New Orleans and have 56 privately owned lUXUry suites that corporate owners spend generous amounts of money on each year to enjoy games and market their company. The money from luxury suites, which typically flows directly to the team, is the number 2 revenue source for most franchises aftcr media rights. 8 In another example, 's United Center, where the Chicago Bull's play, has almost 200 more luxury boxes than the old Chicago Stadium. That translates into about

$20 million more a year in income for the Bull's, which is crucial to the franchise's success and growth in revenue streams. These two examples show just how important luxury boxes are to professional sports teams in tenns of revenue, and also in the ability

(; Donald L Alexander & \Villiam Kern. The Economic Determinants of Professional Sports Franchise Values. Journal of Sports Economics 2004; 5; 5L

Rhec\ Joseph, and Richard E:::,posito. "AIO Still Paying For LtLxury Suite at Madison Square Garden." 16 Oct. lOOR. 21 Det: 200R. g Din!'morc, Christopher. "Luxury Boxes CriticaI in Luring Major Teams, ,. ThuimJillilLrJ.Q! 28 Dec. ::UO L 10

of the franchise to endure in any particular place. This leads the discussion into the

honeymoon effect, which will bc briefly examined in the following paragraph.

The building oflarge, state of the art sports facilities produces an excitement

among sports tans. John C. Leadley and Zenon X. Zygmont (2005) performed a study on

the honeymoon effect on National Basketball Association attendance. The honeymoon

effect is the relationship between spectator attendance at a professional sporting event

and the age of the sports facility at which the event is held. They found that when a new

facility is built the demand by consumers to attend games increases by 15% to 20% for

about five years, but begins to diminish in year five and after9 Ticket sales and stadium

related revenues, which are two of the four principal sources of franchise revenue

(stadium related revenues, ticket sales, local and national broadcasting rights, licensing

sales), sce a considerable increase during the honeymoon period oftime. 1o This is

interesting because new stadiums and old ones differ in a couple of important ways:

technology and history. New stadiums often have retractable roofs, swimming pools, and

enormous television screens. However, older stadiums such as Fenway Park have so mueh history within the stadium that fans attend games just to be a part of it, and experience it first-hand. Why wouldn't consumers prefer older stadiums? Now, baseball stadiums are usually designed to be modem but look old.

In another study done by Brown, Nagel, McEvoy and Raseher (2004) the impact of new facilities on revenue and wealth maximization for the NFL is analyzed. Although the study uses data from the National Football League, it is relevant to this study because

i) Jolm C Leadley & Zenon X, Zygmont. \Vhen is the Honeymoon Over? National Ba.;;ketball Association Attendance 1971~2000. Journal of Sports E-:conomics 2005; 6; 203. j(j Lcc&,,< Michael. and Peter Von Allmen. The Economics o($.1?~~:I:t--,-"-' "\ie\-v York: Addison \Yesley. 2004. I I

the two leagues are very similar exeept for that faet that the NFL uses a revenue sharing

system. The findings suggest that the movement of a team into a new stadium causes

franchise value and revenue to increase siguificantly. Ifbuilding a new arena is profitable, professional sports owners may increase the rate of stadium turnover to keep gate

revenues up and their wealth maximized. This means that owners may decide to build

new stadiums based on wanting to increase revenues rather than because of unsafe or out of date conditions of facilities. II

Ticket Prices

As noted above, ticket prices are one of the four principal sources of franchise revenue and, consequently, are extremely important to owners of teams in order to generate revenue. Professional sports teams have very specific and strategic ticket pricing strategies to maximize gate revenue. James T. Reese and Robin Mittelstaedt (2001) perfonned a study about NFL ticket prices and investigated the strategies used by the league. This is important because ticket sales are the one of the most important ways of generating revenue. If ticket priees are too high, fans may decide to go elsewhere for entertainment or watch the game on television. In the study, team perfonnance turned out to be the most important aspect in detennining ticket prices. 12 As a result, teams who accomplish more wins are blessed with more discretion in detennining ticket prices. On the contrary, teams who have a lower winning percentage have less pricing discretion due to the fact that fans want to see winning teams. So, teams with lower winning percentages must have reasonable prices in order to attract fans and thus earn a higher revenue margin.

11 :Nlatthew BrO\:vn, Mark Nagel, Chad McEvoy, & Daniel Raschcr. Revenue and \Vealth Maximization in the National Football League: The Impact of Stadia. Sport Marketing Quarterly: 13;4. 2004. i2 James T. Reese & Robin D. Mittelstaedt. An Explanatory Study of the enterla Used to Establish NFL Ticket Prices. Sport Marketing Quarterly; 10:4,2001. 12

Large amounts of revenue dollars could be forever lost for franchises if they have a poor

win-loss record. As an owner, it is important to take this into consideration in order to

maximize revenue.

A study by Patrick James Rishe and Michael J. Mondello (2003) used a

quantitative approach to examine the determinants of NFL ticket pricing. Specifically, the

paper identifies factors that cause cross-sectional differences in average ticket prices

across teams and, separately, factors that cause seasonal changes in average ticket prices

over time, I J Some interesting results were found. The two teams in the study with the

largest price increase in tickets were the Denver Broncos and the Pittsburgh Steelers. This

was because they were both playing their first year in a new stadium which gave each

team morc freedom in setting prices due to increased demand. Consumers were willing to

pay a higher price for tickets to be the first to watch the teams play in their new stadiums.

Also, the Super Bowl finalists from the previous season raised their ticket prices

considerably. Conversely, teams that had a poor season in the previous year reduced their

ticket prices, Fans generally do not want to see losing teams, so it makes sense that they

had to reduce their ticket prices to keep attendance rates steady.

Patrick James Rishe and Michael J. Mondello (2003) completed another very

important study regarding the detenninants of ticket prices in the NFL, NHL, NBA, and

MLB. This time they not only included the NFL in their study, they expanded their work

to include 3 of the other major sports organizations in the United States, The article

speeiflcally analyzes why ticket prices differ among cities and presents additional

intonnation concerning the fiercely debated topic of whether ticket price increases are

;} Rishe. Patrick Land ;v1ichael J. i'v1onddlo. "Ticket Pri(c Iktcrminatl{.lfj in the ;-...i3tlonal Football

A qu;:mtitative approZlci-L" ~')ort Marketing Qwtrterlv 12 '! 2~ 79. 13 tied to changes in team payrolls. Playing in a new facility was the most important detenuinant of differences in ticket prices among participating cities in the 4 major sports

(NBA, NFL, MLB, NHL).14 Population size was tenued positive and significant for each of the 3 of the 4 sports studied, leaving out the NFL. Attendance as a percentage of capacity was significant in describing differences in ticket prices in every league except the NFL. Team payroll atfected ticket price differences in baseball and basketball, but not in hockey or football. Overall, this article is important to the literature involving sports economics and raises ncw insights into ticket price detcnuination in the NBA, NFL,

MLB, and NHL.

It is typically known in sports economics literature that sports teams are setting admissions prices in the inelastic range of demand. This means that at any given price range a customer will still purchase the same amount of tickets because they are non responsive to the changes in price. Thus ticket prices are unit inelastic. However, there has been constant criticism over the recent years that owners are eharging too much for tickets and causing fans to search for other fonus of entertainment. In spite of the popular criticism laid on owners for charging so much, sports economists regularly find that fans pay a lower price than that which would maximize team profits. 15 Why would the owners of professional sports teams sell tickcts at a price less than what they could receive to maximize profits? In a study carried out by Krautmann and Berri (2007), they attempted to discover why owners were pricing tickets in the inelastic range of demand. They found

l4 Rishe, Patrlck and Michael Mondello, "Ticket Price Dctt:mlinarirm in Professional Sports: An empirical analysis of the NBA, NFL, NHL and Major League BascbaH." Sport Marketing Quarterlv 13 (2004): I04- 12.

15 Krautmanl1, c.. and David J Beni "Can \Ve Find It at the Conce%ions'! Understanding Price Elasli;.;ity in Profe~sional Spnrts." Jotll11al of Sports h::onomic.~, R 183-91. 14

that franchise owners were using lower ticket prices to enlarge their non-ticket revenues.

So, in effect, they are using efficient ticket prices to attract more fans to games in order to

maximize their concession revenues inside the arena. In today's day and age fans may

pay as much as $5 for a hot dog and $8 tor a beer. If 10,000 fans purchase 1 beer and 1

hot dog a piece, that generates $130,000 per game in revenue for the club. Thus far in the

2008-2009 NBA basketball season the lowest average home attendance for any team was

12,025, accomplished by the . The highest average attendance of any

team was the who, on average, attract 22,076 people to every home game.

Based on these numbers, concession revenues could be much higher than the $]30,000

mentioned before. These complimentary concession revenues may make up or exceed the

lost gate revenues caused by inelastic ticket pricing.

Player Produetivity

There have been numerous studies focusing on the measurement of the productivity of an

individual participating in a team sport. Some studies have shown that team win

percentage is one of the most important factors in generating greater revenues for franchises. Thus, having highly productive players who can help generate wins may have a positive affect on overall revenue. A study by David J. Berri (1999) showed that Karl

Malone, by himself, produced a total of 18.8 wins for the during the 1997-1998

NBA season. 16 As stated before, tearn win percentage may playa huge roll in generating franchise revenue through ticket sales. Productive players who help create wins such as

Karl Malone are seen to have a positive affect on revenues and are key determinants in how much revenue is generated by sped fie teams. The revenues that Karl Malone creates

David 1. BeITi. Who is "Most Valuable'?" Measuring the Player's Production of\vlns in the National Basketball Ass-cciation. Managerial and Decision Economics. 1999.20: 7, 15

for the Utah Jazz are larger than the revenues generated by teams hosting the Utah Jazz

and Karl Malone a few times a year.

Specific statistical categories such as field goals, free throws, rebounds, and

turnovers were studied by Chatterjee, Campbell and Wiseman (1994). They showed that

these statistics determine 90% of a team's win-loss percentage. So, in essence, the

analysis suggests that building a team around these four significant statistics should be a

good strategy to perform well and win games. 17 If certain players do well in these specitic

categories a franchise owner may want to invest in these players because they may create

more wins. If wins are positively correlated to franchise revenues, then hiring players that

do well in those four categories may be a profit maximizing decision for a franchise

o\vner,

Sponsorships

Sponsorships and licensing agreements have an enormous impact on generating revenues.

This is because professional sports teams have an invaluable ability to promote products due to the popularity and amount of fans these organizations have. A study done by

Daniel S. Mason (1999) showed that professional sports are an economically efficient

way to market a company's product. And as the market continucs to grow, companies

will generate huge benefits from sponsorships and advertisements at professional sporting events. 18 Sports organizations should take advantage of this due to the fact that sponsorships and advertisements arc key sources of revenue. For example, Starbucks spent $3.7 million on sponsorships with the Supersonics from the years 2002 to

17 Sangit Chatterjee, 1\'1al1i11 K Campbell, & Frederick \Viscman. Take that Jam! )"\n analysis of Winning Percentage for NBA. Teams. Mana£crral and Decision Economics, 15 (1994): 521-35. j?, Daniel S. Masou. \Vhat is the sports product and who buys it? The marketing of professional sporl"> leagues. European Journal of Marketing. 33: %. 16

2006. 19 This may seem like a miniscule amount of revenue for the Supersonics, but if you

combine this with the six other major corporate sponsors the team endorses, a

considerable amount of revenue is produced. Although sponsorship statistics were not

available for the Supersonics, a comparable team such as the New Orleans Hornets can

be analyzed. They earned total revenue of$95 million in 2007-2008 and produced

approximately 15% of this revenue through corporate sponsorships2o This 15% seems

small, but for a $95 million corporation this translates into $14 million for the New

Orleans Hornets. It is easy to see how much sponsorships can affect team revenues in a

very positive way.

Conclusion

The revenues generated by professional sports organizations are unbelievably lucrative

and are influenced by a number of variables. Superstars, new stadiums, ticket prices,

player productivity, and sponsorships are only a few of the aspects that affect franchise

revenue. It is interesting to examine what proportions ofrevenue come from each

contribution and which are the most significant because, in any given season, they can determine how much revenue an organization is able to generate. It is highly possible that a team will need to rebuild every few years and in these years may lose more games than usual. Thus, it will be important for teams in this situation to know how to maximize revenues in hard times such as losing seasons. This paper will expand the research done on this topic to see what effect two or more losing seasons has on a club's revenue.

Depending on what the results reveal, owners of professional sports franchises will bc

Harris, Craig, "Starbucks ends Sonies, Mariners Sponsorships:' Seattlepi.com. 26 OCL 2007. 18 Dec. 2008 .

"Forbes NBA Valuation". Forbes.com, 03 Dec. 2008, 18 Dec. 2008 . 17 able to observe how much a losing record is actually affecting his/her pockets. If it has no effect the owner may look to excel elsewhere in the franchise, such as adding more star players. Further research in this paper will allow for a better understanding of where exactly revenues are coming from and how a losing record for mUltiple seasons actually affects these multi-million dollar franchises. CHAPTER III

THEORY

This chapter will present the theory used to study factors that impact NBA franchise revenues. Most importantly, teams with two or morc consecutive losing seasons will be examined to see what effect this has on total revenue. The hypothesis in this research paper is that two or more consecutive losing seasons will have a nonlinear negative effect on NBA franchise revenues. However, it is also hypothesized that teams with higher numbers of consecutive losing seasons will have an increasingly higher negative correlation to total revenues. This is because as teams fall further into a tradition oflosing it is expected that fans will lose interest and thus will look for other forms of entertainment at an increasingly faster rate. Some loyal fans may still attend games and support the team, but for the everyday citizen who enjoys watching NBA basketball, this may be a reason to defer to something else more enjoyable. The following pages in this chapter will explain each dependent, independent, and dummy variable in detail. A regression equation will be presented to sbow how each of the independent variables and dummy variables are expected to affect NBA franchise revenues.

18 19

Dependent Variable

NBA Franchise Revenues

The dependent variable in this research question is total NBA franchise revenues. In the

previous research reviewed there has been an absence in studies observing total revenues.

This research will show what affect certain variables have on total revenues and will add

a new understanding to the significance of certain components in total revenues. Data from 1999 to 2008 is gathered and used in this model which will give this study a solid amount of validity.

Independent Variables

A variety of independent variables including gate revenues, win percentage, ticket prices,

attendance, population, all-star players, fan cost index and player expenses will be examined to see which ones affect revenue and in what manner. A strong examination will be placed on win percentage because the focal part of this paper is to determine whether or not two or more consecutive losing seasons affects NBA team franchise revenues.

Gate Revenue

Gate revenue is expected to have a positive and significant correlation to total franchise revenues. This prediction is based off the tact that professional sports teams generate revenue from four main sources: gate revenue, local and national broadcasting lights, licensing income, and other stadium related revenues, including luxury boxes, 20

concessions, and stadium-naming rights. 1 Simply put, gate revenues should be positive

and significantly correlated to total franchise revenues.

Win Percentage

Previous research in the world of professional sports has shown winning percentage to

positively affect attendance levels in professional sports (Forest, Beaumont, Goddard,

and Simmons 2005, Jennett 2004, Peel and Thomas 1982, Whitney 2007). If winning percentage increases attendance rates then it will also increase gate revenues. Thus, it is

expected that win percentage will be positively correlated but not significant to affecting

franchise revenues. However, while examining NBA team values on Forbes. com a suecessful NBA team in terms of winning percentage does not always translate into higher revenues and team values. One example explains this perfectly. The New York

Knicks are the most valuable team in the NBA but have had a losing record in 7 out of the last 10 years. Of course there are probably some fans that refuse to watch the Knicks play when they have bad years, but a lot of fans simply go to Madison Square Garden

(MSG) because they want to be entertained. That, and the fact that MSG has been tabbed

"the world's most famous arena" by its own website and by people across the world.

Even though the Knicks have a high probability oflosing, fans are still entertained by some of the best athletes in the world night in and night out, and are able to watch basketball from the most famous arena in the world. Once again, win percentage is expected to be positively correlated but not significant to franchise revenues.

Ticket Prices

Ticket Prices are expected to be positively correlated and significant to total franchise revenues. A majority of people who attend NBA games are not season ticket holders, so a

I Ibid 21

lot of people who attend these games are simply looking for entertainment. Ticket prices

need to be in a reasonable price range for the general publie to be interested in attending

games. The most pressing issue facing the league is how to make bargain-hunting fans

open their wallets during the worst eeonomic crisis since the NBA was born in the mid-

1940s2 Last year (2008), the NBA had 1.7 million tickets priced at $10 or less. Bill

Sutton, a former NBA marketing executive and current consultant to several NBA teams,

estimates that most franchises are now selling between 1,000 and 2,000 seats for $10 or

less for each game3 Based on the evidence that teams place a huge amount of importance

on preserving fan interest and affordability, ticket prices are expected to be positively

correlated and significant to team revenues.

Attendance

Attendance in professional sports was previously researched and results showed that

attendance is positively related to total revenues of professional sports teams. Thus, it can be expected that attendance will be positively correlated and significant with franchise revenues. Gate revenues are one of the four major components to the total amount of revenues gathered by teams.4 Also, the leagues consistent focus on preserving average attendance figures and attracting new ticket buyers sends a message of importance for attendance5 The NBA mandates that all teams offer at least 500 tickets for $10 per game,

4 Leeds, Michael An and Peter V nn AHmen. 11K' Economics ,,)f Sport:'L New Y nrk: Addison-\V 6ley Longm'cn. IUCOI),oralod. 200".

:, Lombardo, john. "~BA team:' Sk'p up ticket discounting,'· Sneer &_Smith's Sport, Butint5?)oumal 11 I 22

so it is clear that the league considers attendanee to be signifieantly important to every

team.

Population

Population in this study is measured using the Metropolitan Statistical Area (MSA) of the

location of a specific NBA franchise. A Metropolitan Statistical Area is a major city

together with its suburbs and nearby cities, towns, and environs over which the major city

exercises a commanding economic and social influence6 Previous research suggested

that cities with larger populations generate higher attendance levels (Walker 1975). Thus,

it is anticipated to have a positively correlated and significant affect on franchise

revenues. The New York Knicks will be used as an example once again. They have not

won many games over the past 10 years and have a wins to player cost ratio 7 of only 35.

The only explanation of how the Knicks can be 1$I in the league in value and total

revenues lies in their population. New York City (MSA) has 8,274,527 people living in it,

and this is the largest market of any team in the league. It is quite possible that this is why

they generate so much revenue as a franchise and this research will show whether or not

market size affects franchise revenues.

Fan Cost Index

Fan Cost Index (FCl)s is an important calculation that few people have studied Of, for that

matter, have been educated about. However, it is a very important measure of the actual

Compares the number of wins to player payToll relative to the rest of the NBA. Post season wins count twice as much as regular season wins. A score of 120 means that the team achieved 20% more victories per dollar of payToll compared with the league average. R It comprises the prices of 4 average priced tickets, 2 small draft beers, 4 small soft drinks, 4 regular sized hot dogs, parking for 1 car, 2 game programs and 2. least expensive, adulksized adjustable caps, Costs were dctennined by telephone calls with the representatives of the teams, venues and concessionaires, along \vith information provided on the teams' official web site, Identical questions were asked in ail interviews. 23 cost of attending an NBA basketball game. It comprises the prices of 4 average priced tickets, 2 small draft beers, 4 small soft drinks, 4 regular sized hot dogs, parking for 1 car,

2 game programs and 2 least expensive, adult-sized adjustable caps. The fan cost index is anticipated to be positively correlated but not signitlcant with franchise revenues. The price of concessions and necessities at NBA games follows the state of the economy at that particular moment. Although prices of these items at sporting events are much higher than in normal retail stores, fans will not be driven away because of these high prices. If someone is willing to pay for tickets to a game, it is probably meaningless to them to spend a few extra dollars on a drink, food or a cheap ball cap at the game. At the same time, higher prices may mean better profit margins for teams who have a high FCl's. This will surely have a positive affect on total revenues. If fans are unhappy about these priees they should be able to make a conscious cost-benefit decision not to purchase these items, and instead satisfy their consumption needs prior to game time. However, in most cases it is assumed the fan will probably purchase items at the game, which is cause for the FC] to have an expected positive relation to total revenues.

Player Expenses

Player expenses are defined as the total combined player salaries on each specific team including all bcnefits and bonuses for thc players. It is expected that player expenses will be negatively correlated and significant to franchise revenues. Some players get paid upwards of $18,000,000 dollars a year in salary which is definitely a considerable amount. It is almost unbelievable how much some players are paid to entertain the general public and die-hard sports fans. Kevin Garnett, tor example, collects $21,000,000 annually hom the Boston Ccltics for his entertainment services. There are 14 other 24

players on the roster who need to be paid as well, and even though they do not get paid as

much as Kevin Garnett, the sum of their payroll is $74,060,475. This number is

staggering and should be significant with affecting franchise revenues in a negative

manner. Since there is an absence ofresearch on how player expenses effects total

franchise revenue the author's intuition is used to make the prediction.

Dummv Variables

Along with the previous independent variables, three dummy variables will be tested.

Dummy variables have no numerical value within the study but will serve as an indicator

of the absence or presence of some categorical effect that may be expected to shift the

outcome. Dummy variables are either given a value of one or a value of zero depending

on its relationship with the modeL The three dummy variables used in this model are

consecutive losing seasons, all star players and new stadiums.

Two or More Consecutive Losing Seasons

There has been an absence in research about how revenues are affected when

professional sports teams have consecutive losing seasons. When teams have multiple

losing seasons in a row it causes fans to become less interested because of the lack of

competitive balance and knowing that the team is very likely to lose. This makes sports much less interesting. The uncertainty of outcome hypothesis says if fans were certain that their tcam would lose or win a majority of the games it plays, it would take away a major source of excitement from the game.s Thus, fans will become less excited to watch a game and probably find alternative forms of entertainment It is behind this theory that

') L~eds. Michael A", Jnd Pctt;~r Von /\llrnen. fhe Economics ofSQ()rts. Ntw \\)rk: Addison-\Vesley Longman. Incorporated. 2007. 25 consecutive losing seasons will produce a negatively correlated and significant effect on franchise revenues. In this study the number of consecutive losing seasons will be corresponded with the specific variable it applies to in the regression model. These variables are (CLS 1), (CLS2), (CLS3), and (CLS4). All of the consecutive losing season variables will be described in detail in the following chapter. Teams with higher numbers of consecutive losing seasons will have an increasingly higher negative correlation with total franchise revenues.

All Star Players

All star players wiU be used as a dummy variable and each team will be assigned a "I" if they had an all-star player in a specific year or a "0" ifthcy did not have an all­ star player. The NBA selects the best players from the Eastern conference and the

Western conference (based on fan votes) to play against each other in an aU-star game at the middle of the season. Each year fans vote for the players they believe are the best at their individual position, and the players who receive the most votes are chosen to start the game. In theory, since the fans vote for the players they enjoy watching perform, teams with all-star players should see higher revenue via attendance. There have been numerous studies in professional sports research that have revealed a positive relationship between all-star players and club revenues (Hausman and Leonard 1997, Berri and

Schmidt 2006, Brandes, Franck, and Nuesch 2008). Based on these previous studies all star players arc expected to have a positively correlated and si!,'11ificant affect on franchise revenues. Although having m1 all-star player on the roster may not translate into higher numbers in the win column, it may bring about a considerable increase in the amount of fans attending games. 26

New Stadiums

Based on previous research the construction of a new stadium causes a substantial

amount of interest in terms of entertainment for consumers. Teams who recently moved

to a new stadium tend to have higher franchise values because of revenue from corporate

boxes and stadium naming rights (Alexander and Kern 2004). Also, teams with new

stadiums were found to have an advantage in attendance figures because of the positive

relationship between spectator attendance at a professional sporting event and the age of

the sports facility at which the event is held (Leadley and Zygmont 2005). A new stadium

in this thesis is defined in two separate ways as there are two dummy variables for a new stadium. In the first variable (ST AD) a stadium that was built within 4 years of the specific year of data being used receives a "2" in the regression. A stadium built within 5

to 10 years of a specific season receives a "1 ". Finally, a team with a stadium that is equal to or greater than 10 years old receives a "0",

In the second dummy variable used to describe new stadiums the label ST AD2

will be used. If a specific team plays in a stadium that is 4 years old or younger a "1" will be used in the regression model. If a team is playing in a stadium older than the 4 year maximum, a "0" will be used in the regression model. Based on previous theory, new stadiums are expected to have a positively correlated and significant affect on franchise revenues. Both measures of new stadiums are expected to produce positive an significant results with regard to total franchise revenues.

The method used in this specific model to determine the significance of the independent variables will be a rel,'Tession model. The regressions will be examined to see whether or not the initial hypotheses are proved or disproved. All the data used in this 27 experiment will be quantitative because statistical data is the only type of data being used. No human observation or questioning is needed to prove of disprove the initial hypotheses. This analysis will include a Jarque-Bera Test to account tor any possible non-normal errors. A White Test for heteroskedasticity and a Durban-Watson Test for autocorrelation will both be performed as well. Finally, a Hausman test will be carried out to see if a fixed effects regression model or a random effects regression model is better suited for this study.

All of the data used in this research paper is readily available and are sufficient enough to answer the question of: Does two or more consecutive losing seasons have a significant affect on NBA franchise revenues? The dataset being used in this research will be gathered through the years 1998-2008. The 10 years being examined will be a sufficient amount of time to provide for the most accurate results in this research. The data compiled for Attendance, player expenses, all star players and ticket prices is available through Rodney Fort's Sports Economics Sports Business data.!O Data gathered for the Fan Cost Index is found at teammarketing.com. Winning percentages and total franchise revenues are found through Forbes NBA Team Valuations. liThe data for market size is found through The Us. Census Bureau. 12

Based on previous research. a model has been developed in order to analyze the variables presented in this thesis. Below is the model that will be the base for any tests performed in this study.

j () \vww. rodneyfort com

!,' http:i'\V\\'w.forbes.cofrr'llsts/200S/3L'nba08 NBA~ Team-Valuations Rank. html i2 \1.;,\V\v,census,gov 28

MODEL 3.1

NBA FRANCHISE REVENUES = ~O + ~ 1 TMC + ~2 WIN + ~3TPRICE +

~4MARKET + ~5A TTEND + ~6FCI - ~7PEXPENSE + ~8STAR + ~9STAD + error

This chapter introduced the dependent, independent and dummy variables that will be used in the final analysis. Each of the variables were explained in their relation to importance for the study. The predicted outcome of all the variables was presented, with most variables having previous research supporting their significance for examination. A model was given as a starting point for the analysis. The next chapter will display the test results and analyze each specific variable's si!,'llificance with regards to the dependent variable (NBA Franchise Revenues). CHAPTER IV

DATA AND METHODOLOGY

While the last chapter presented the theory used to study the factors that impact

NBA franchise revenues, this chapter will present the data and methodology used. It will provide a solid background of how the data was collected and an explanation of it as well. This study is concerned with 32 NBA teams and exactly 11 major variables 1. As noted in the previous chapter there is one dependent variable, NBA franchise revenues, and 10 independent variables that are expected to have certain effects. This chapter is broken into 11 parts plus a last section dedicated to describing the methods used in this study. Each of the II parts represents one of the variables tested in the model and provides detailed observations about where the data was found and specific reasons for using the data. The variables are defined on the following page in Table 4.1. The table discusses all of the variables used in the analysis. The table presents the abbreviations that are used in the testing of each variable, the variable name, a brief definition that aids in the understanding of the factors, and the sources used to find the needed data.

: Table 4.1 has more than 11 variables. However, on should combine all of the (CLS) variables and the variables (STAD) and (STAD2).

29 30

TABLE 4.1

Variables Summary

Variable Variable Definition Sources Abbreviation Name trcvdef Total revenue Total revenue deflated to 1999 dollars Forbes.com clsl Consecutive The end of one losing season dat

Stad2 New How new a stadium is for the specific armchairgm.wi Stadium team. Measured differently than stad kia.com attend Attendance The total home attendance in a given .com season for a specific team 31

Before examining the relevant data associated with each variable, it is important

to look at the number of teams included in this study. A total of 49 locations have been

oecupied by NBA teams since the league was founded in 19462 This means the NBA has essentially encompassed 49 teams that have participated in the league; however multiple

teams have relocated or been renamed during the 63 years the NBA has existed. This paper includes all 32 teams that have been a part of the league during the 10 years of data that was collected from 1999 to 2008. The reason 10 years of data is being used is because three of the most important variables in the study were only available for 10 years. GREVDEF, TREVDEF and PEXPENSEDEF are only available dating back to year

1999. The data was retrieved using rodneyfort.com but was originally found through

Forbes. The Forbes website does NBA team valuations for every franchise, and it is

important to note that it started examining NBA teams 10 years ago, which is what hindered the data set to this amount of time. A table will be utilized to highlight the teams who are accounted for in this study and it is shown below in Table 4.2. One may notice that only thirty teams are included in the chart, but this is because ofteam relocations.

The Charlotte Hornets who are not shown in the chart relocated to New Orleans because of a decrease in attendance and refusal to fund a new arena by the city of Charlotte. Also, the Grizzlies relocated to Memphis due to a lack in attendance levels. The two situations are very similar but nonetheless both teams are accounted for in this study in the years that the data applies. Finally, the chart shows the specific conference and division that each team competes within tor further understanding of all the teams and locations being utilized. 32

TABLE 4.2

NBA Teams and Conferences

Teams

Boston Celtics Dallas Mavericks New Jersey Nets New York Knicks Memphis Grizzlies Philadelphia 76ers New Orleans Hornets San Antonio Spurs

Chicago Bulls Denver Nuggets Detroit Pistons Indiana Pacers Milwaukee Bucks Utah Jazz

Atlanta Hawks Charlotte Bobcats Miami Heat Phoenix Suns

*Note: This chart excludes the Seattle Supersonics and the Charlotte Hornets because they no longer exist 33

Total Revenue

The dependent variable in this study is total revenue (TREVDEF). TREVDEF is an

important variable in this study because it is a good measure of how successful NBA

teams are in a business aspect. It is important to note that the numbers for TREVD EF are

deflated using the chained consumer price index (C-CPI-U) for all urban consumers. The

base year used in deflation is 1999, so all numbers for this variable are in 1999 dollars.

TREVDEF is generally described in equation form as TR= RG + RB + RL + Rs, where Rc;

is gate revenues, R/J is broadcast revenue, RI. is licensing revenue, and Rs is other stadium

related revenues (including luxury boxes, concessions, and stadium naming rightS)3

Higher revenue can sometimes translate into higher profits for a given franchise if costs

stay relatively the same or decrease.

The data on total revenue comes from Forbes.com and was very readily

accessible. According to Forbes, total revenue is defined as the total amount of revenue

collected net of arena revenues used for debt paymcnts4 Every year Forbes performs

valuations of every NBA team to examine how valuable each team is, and how they have

changed since the previous season. The data collected for TREVDEF stretches from 1999

to 2008 because 1999 was the tirst year that Forbes began to put values on NBA

franchises. All 10 years are used because a desire to use data that is relevant to present

times is very important to this study, and 10 years is a considerably large amount as compared to past research in this field. Data is available for every year and every team so there are no incomplete data points in the collection. The summary statistics for

) Leeds, Michael. [eoDomics of sports. Boston: Pearson! Addison Wesley, 2007. 34

TREVDEF demonstrate a large distribution among the sample. The variety ofTREVDEF

among the NBA's participating cities emphasizes the important of illuminating the

components ofNBA franchise revenues.

Consecutive Losing Seasons

Consecutive losing seasons is defined as how many losing seasons a speeific franchise

has in a row. For example, if a team has 1 losing season the term CLS] is used, for 2

consecutive losing seasons CLS2 is used, for 3 consecutive losing seasons CLS3 is used,

and for 4 consecutive losing seasons the term CLS4 is used. To be more particular, this is

a measure of how losing affects a team's revenue in the specific year they are losing, so it

is not a measure of what effect it has the following year. The data for consecutive losing

seasons is split up into four important dummy variables. CLSl, CLS2, CLS3, and CLS4,

as noted before, and are used to describe how many losing seasons a specific franchise

has compiled in consecutive years. Win percentage is used to determine whether or not a team had a losing or winning season. In mathematical terms, a team with a record below

a .500 average is considered to be a losing team. A team with a record above .500 is considered to be a winning team in that specific year. It is true that a team with a record of .499 is not very different from a team with a record of .510, however it is traditionally recognized that a team above .500 is considered a winning team and a team below .500 is a losing one. Thus, this study uses this widely known method of detelmining whether or not a team is a losing or a winning franchise. All of the data for CLSl, CLS2, CLS3, and

CLS4 is collected from rodneyfort.com. This website has data tor winning percentages lor every team dating back to the inaugural season of the NBA. However, data on win percentage was only collected back to the year J 999 to correspond with the number of 35

years of data compiled for TREVDEF. It also lists the team winning percentages in

alphabetical order which helped in organizing data in the correct format. For every year

analyzed in this study the data for winning percentages was available, which made it very

helpful in determining how many consecutive losing seasons a specific team

encompassed. Four consecutive losing seasons is the maximum amount of consecutive

losing seasons examined in this study. This is because it is concluded that a team who has

lost 4 consecutive years in a row has established itself as a losing franchise. Therefore, higher amounts of consecutive losing seasons are thought to have a diminished effect on

TREVDEF. This is because, from a fan's perspective, an uncertain outcome is much more

5 interesting than a foregone conelusion By the 4th consecutive losing season it is

assumed that fans have already found other fonns of entertainment so the effect on

TREVDEF is diminished. It is also important to note that no other study, to my knowledge, has specifically included consecutive losing seasons which make these assumptions the first in any study.

Win Percentage

The independent variable for win percentage (WINPCT) is defined in Table 4.1 as an individual team's win percentage. The fonnula for win percentage in this study is total number of wins divided by total number of games and is depicted as follows in formula

4.1 on the following page.

5 Leeds. Michael. Economics of sports. Boston: Pearson! Addison Wesley, 2007. 36

Formula 4.1

Total # of Wins = Win Percentage

Total # of Games

All of the data for win percentage, as briefly mentioned in the previous paragraph, was taken from rodneyfort.com. The data is readily available for all 10 years this study involves, and there are no gaps whatsoever for this variable.

Ticket Price

The independent variable ticket price (TKTPRICEDEF), described in Table 4.1, is briefly defined as average ticket price deflated to 1999 dollars. The values were deflated using the C-CPI-U and the base year used for this particular variable is 1999. This means that all average ticket prices are in terms of 1999 dollars to provide for a more efficient analysis. The C-CPI-U is a time series measure of the prices of goods and services which makes in a fundamental tool for deflating prices. All the data used for TKTPRICELJEF was found through teammarketing.com. This website has average ticket price data going baek to the 1990-1991 season, however only data from 1999 to 2008 was used in order to be relevant to this study. TKTPRICEDEF is more specifically defined as a weighted average of season ticket prices for general seating categories, determined by factoring the tickets in cach price range as a percentage of the total number of seats in each stadium.

Premium seating (tickets that come with at least one added amenity) is not included in the ticket average. Luxury suites are also excluded from the survey. Season ticket pricing is used for any team that otfers some or all tickets at lower prices lor customers who buy 37

season tickets. 6 There are no gaps in the data which improves the validity of the

regression model.

Population

The independent variable for population (POP), described in Table 4.1, is defined as the

Metropolitan Statistical Area of a given city. The U.S. Office of Management and Budget

defines a Metropolitan Statistical Area as a standardized county or equivalent-based area

having at least one urbanized area of 50,000 or more population, plus adjacent territory

that has a high degree of social and economic integration with the core, as measured by

community ties. 7 This is a very specific definition that allows for a better view of the

majority ofpeoplc who are within a realistie range to actually attend a game. It is much

more specific than the typical population measurement of a city which is better for the

validity of this model. Like most of the other data, POP has 10 years of data gathered

from 1999 to 2008. The Census Bureau has data for all of these years, thus there are no

gaps or missing blanks for data in these years.

Fan Cost Index

The Fan Cost Index (FCIDEF) is the actual cost of attending an NBA basketball game.

Teammarketing.com says the FCI "comprises the prices of four average price tickets,

two small draft beers, four small soft drinks, four regular sized hot dogs, parking for one

car, two game programs and two least expensive, adult sized adjustable caps. ,,8 This only measures the prices of games for a family of four which may be a t1aw in this data;

(,05 Mar, 2009 :;):.?Ofcio'o2 007 -08 _pdt>,

05 tv'1ar. ~009

6 OS ;\'far. 2009 . 38

however it is a very unique measurement of the cost of attending a game that is crucial to

this study. It makes sense to use this specific measurement of cost because so many teams

are marketing their ticket sales towards "Family Nights".9 Teammarketing.com has

calculated the fan cost index for 16 years so there was a sufficient amount of data to

compile for this study. Because FCIDEF is measured in dollars it was deflated to 1999

dollars using the chained CPI for all urban consumers. The chained CPI for all urban

consumers was used for two reasons. First, because it is a great measure of the changes in

prices paid by urban consumers for a representative basket of goods and services, and secondly because it is the common deflator used throughout this study. Bls.gov was used

to find the CPI for all urban consumers in order to deflate to 1999 dollars values.

Player Expenses

The independent variable for player expenses (PEXPENSEDEF) is defined as total player salaries for eaeh team. This measure, provided by Forbes, com also includes all of the benefits and bonuses given to players who fulfill certain objectives described in their contracts. Ph"XPENSEDEF is measured in dollars and it was deflated using the chained

CPI for urban consumers. The base year used was 1999, similar to all the other variables in this study that are measured in dollar terms. Forbes collected data on player expenses dating all the way back to the 1999 season which is eftlcient for my 10 year data set The original data provided by Forbes measures player expenses by using rounded numbers instead of the exaet amount oftotal player expenses on each team.

() Street & Smith's SPol1sBusines:~Jgumal 11 39

Gate Revenue

Gate Revenue (GREVDEF), described in Table 4.1, is defined as all the revenue gathered

by a specific franchise through gate receipts. All data collected for GREVDEF was found

at rodney/art. com through Rodney Fort's sports economics business data. According to

the website, gate revenue also includes club seats at the game which are special high class

seats in the arena. The data is partially incomplete because gate revenues were not made

public by NBA teams during the 1999, 2000, and 2001 seasons. Therefore the data loses

some validity within the model since it is not complete over the 10 years (1999-2008)

collected in this study. Similar to the independent variables TREVDEF, TKTPIUCEDEF,

FCIDEF, and PEXPENSEDEF, GREVDEF is deflated to show the numbers in real

dollars instead of nominal. The chained CPl for urban consumers is used to deflate the

dataset numbers back to the base year 1999. This allows the study to examine the effects

of these independent variables on TREVDEF without the effect of inflation, which causes bias in the model.

All Stars

Thc dummy variable all star (ALLSTAR) is briefly described in Table 4.1. ALLSTAR is

defined as a team who has a player that made the all star game in a given year. The data

for ALLSTAR is found through databasebasketball.com. This website has a multitude of data on the NBA, including data from every all star game since 1950. Specifically, it lists the all star players tor each conference in every NBA all star game since 1950. There are no missing pieces in this data set which gives it strong validity in the model. 40

New Stadium

The independent variable for new stadiums (STAD), deseribed in Table 4.1, is briefly

defined as how new a specific stadium is with regard to the team that perfonn in the

stadium. However, this study measures new stadiums in two different manners. The

variable STAD measures how new a stadium is with two different dummy variables. A

"2" is used if the stadium is 1-4 years old, a "I" is used if the stadium is 5-10 years old,

and a "0" is used if the stadium is 10 years or older in age. The variable STAD2 measures

the age of a stadium in a different manner. A "I" is used if the stadium being examined is

1-4 years of age, and a "0" is used if that stadium is older than 4 years. This study uses

both measures of a new stadium to see the difference in how much each variable affects

the model, and to see which measuring practice is better suited for this model. All of the

data gathered on STAD and STAD2 was found on annchair.wikia.com which gives detailed infonnation about when every NBA stadium was constructed and opened, and provides notes on the naming rights of each stadium and how they have changed over the

years. It also provides infonnation on past stadiums that are now demolished or used for public entertainment other than basketball. All of the data for stadiums in this study is available and there are no missing pieces of data in the ten years being examined in this study. The first year the stadium is opened is used as the base measure of the true age of the stadium, rather than using the first year a specific team is playing in that particular stadium. This approach is uscd because it is assumed in this study that some spectators will come to games simply because of the new technology and amenities associated with the new stadium. 41

Attendance

The independent variable for attendance (ATTEND), described in Table 4.1, is defined as

the total home attendance in a given season for a specific team. The data for ATTEND

was collected from two different websites. First, Espn.com was used to find total home

attendance for every team in each year from 2002 to 2008. Second, rodneyfort.com was

used for this data from 1999 to 2001. All of this data is complete which means there is no

missing data for this specific variable.

Method

This study uses panel data to complete the examination of total revenue for NBA teams.

In pane! data, a cross section of firms (NBA teams) is observed over a decided time period which, in this case, is from 1999 to 2008. 10 Panel data enables a correction for the

problem of having unmeasured explanatory variables that affect the behavior ofthc firms being analyzed in this study. Omitting these variables causes bias in estimation, but panel

data is able to deal with this omitted variable problem. II A second advantage of panel

data is that they often allow us to study the importance of lags in behavior or the result of decision making. This information is significant because many of these variables are

expected to have an impact only after some time has passed. Panel data create more variability which alleviates multicollinearity problems in the model. Finally, perhaps the most important attribute of panel data is that it examines issues that time series and cross sectional data cannot analyze. 12 The regression model \vill be examined next. 42

The regression model in this study is a fixed effects model. This model allows one

to view the unobserved faetors affecting the dependent variable as consisting of two

types: those that are time invariant and those that fluctuate over time. 13 Letting i denote

the cross sectional unit and t the time period, we can write a model with a single observed

explanatory variable as follows in Formula 4.2.

Formula 4.2

Fixed Effects Model

In the notation Yib i denotes the specific NBA team and t denotes the time period. The variable d2, is a dummy variable that equals zero when t = I and one when t = 2; it does not change across i. whieh is why it has no i subscript. Therefore, the intercept for t = I is

~o. and the intercept for t = 2 is ~O + bo. The variable ai catches all unobserved. time- constant factors that affect Yit. In this model ai is the fixed effect, whieh means that ai is fixed over time. The !lit represents the unobserved factors that change over time and affect

Yi', This model is essential to the success and validity of this research. There are two main reasons why a fixed effect model is used in this project. First, because it is ideal to f()cus on specific NBA tcams in this study, and secondly becausc the study imposes time independent effects for each variable.

1! \Voolrjdgc, Jeffrey M. IntroduclOrv r:conomectrics: A Modem Approach. !vlason, OB: Thomson South­ 'vVestem, 2006. CHAPTER V

RESULTS

This chapter will present the results found through the fixed effects regression analyses. Three separate regressions models were run to see which variables had a statistically significant effect on NBA franchise revenues, with a specific focus on the variables (CLS]), (CLS2), (CLS3), and (CLS4). Each model will be described and analyzed to help better understand how each of the variables differs between the models.

Table 5.1, on the following page, shows the regression results for each of the three different models. The tlrst number in each row corresponding with the variables is the variables coefficient. The number displayed below the coefficient, in the parentheses, is the individual variables t-statistic. The R-squared value, F-statistic, and observations are displayed at the bottom of Table 5.1. The p-values are denoted with asterisks next to each individual variables t-statistic.

43 44

Table 5.1: FE Regression Results

Variables Model 1 I Model 2 Model 3 allstar 0.031 -0.001 0.293 (0.02) . (-.11) (0.18) grevdef 1.195 0.409 (11.00)*** (10.4)*** winpct -5.819 -0.079 16.51 (-0.89) (-109) (2 79)*** tktpricedef -0.075 -0.03 0.247 (-0.64) ( -0.62) (292)*** attend 0.00004 0.0000003 0.00005 (404)*** (3.44)*** (6.42)*** fcidef 0.059 0.Q78 (1.89)* (0.97) cls1 0.246 0.001 (0.13) (-.07) cls2 -0.532 0.003 -1.44 (0.28) {0.13J . (-0.66) cls3 1.704 0.02 0.255 (0.68) (0.70) (0.09) cls4 -3.526 -0.047 -3.51 (-1.36) (-1.58) (-1.27) pop 0.00001 0.0000001 0.00003 (5.55)*** (5.04)*** 9.58*** pexpensedef -0.033 -0.006 -0.023 (-1.52) • (-1.99)** (-.79) stad -0.159 0.017 (-0.09) (0.88) stad2 1.463 0.008 2.17

I (0.56) {0.28} (1.11)J R2 0.351 0.30 0.36 I Fstatistic 38.64 35.67 48.62 I I Observations 207 207. 207 I

• significant at the 90% level ** significant at the 95% level ... significant at the 99% level 45

Modell

The first model examined the results of all 14 independent and dummy variables against the dependent variable and did not omit anything. Four ofthe variables in this model proved to be statistically significant in affecting NBA team revenues. The significant variables were gate revenues (GREVDEF), attendance (ATTbfYD), fan cost index (FCIDEF), and the metropolitan statistical area of a team's city (POP). Some variables that proved to be interesting, although they weren't significant, were player expenses (PEXPENSEDEF) and four consecutive losing seasons (CLS4). This model has an r-squared of 0.351. This means that 35% of the dependent variable NBA franchise revenues (TREVDEF) is explained by all of the independent and dummy variables. This model included 207 observations.

Gate revenues (GREVDEF) are hypothesized to have a positive and significant affect on NBA franchise revenues and in this model it turned out that the hypotheses failed to be rejected. (GREVDEF) produced a t-statistic of 11.00 and a coefficient of

1.195. This means that if there is $1 generated in gate revenues, total revenues will increase by $1.195. This variable is significant at the 99% level which is not surprising.

Gate revenues are one of the four main sources of revenues for NBA franchises so it is not surprising that this variable had such a high t-statistic.! NBA teams should have a large interest placed on having a supply of scats in varying price ranges to accommodate for consumers demand.

The second significant variable attendance (ATTEND) was hypothesized to positively correlated and signitlcant and the model showed that this was true. In model J,

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(ATTEND) produced a t-statistic of 4.04 and a coefficient of .00004. (ATTEND) had a slight correlation with (GREVDEF) so it is expected that it would also be significant.

Since there was correlation between the variables and (GREVDEF) is a component of the dependent variable total franchise revenues, (GREVDEF) was omitted in model 3.

Fan Cost Index (FClDEF) is the next variable that was found to be significant in model 1. It was not as significant as the past two variables (GREVDEF) and (ATTEND) but it proved to be significant at the 90% level. It produced a t-statistic of 1.89 which was just enough to make it slightly significant in this study. Its p-value of .060 also indicated that it was slightly significant in predicting NBA franchise revenues. The coefficient corresponding with (FClDEF) is .059 which indicates that with a $1 increase in the Fan

Cost Index, total revenues will increase by $.059. The higher that (FClDEF) goes, the more revenues can be generated because people are forced to spend more money at the games due to higher prices and no other substitutes.

The last significant variable ill model I is the metropolitan statistical area of the city (POP). It proved to be extremely significant in the model, having a t-statistic of 5.55 and a coefficient of .00001. The population of a city obviously should have a positive effect on revenues because with more people living in a team's city, the more fans should attend games. Population is very important to the ability of franchises to generate revenue because a higher population translates into a larger consumer base for a specific team.

Some variables that proved not significant in the fixed effects regression model I are player exp"'l1ses (PEXPENSEDEF) and four consecutive losing seasons (CLS4).

Player expenscs had a I-statistic of -1.52 and a coeftieient of -.033. Although it was not significant it was very close to being so, and since player expenses are a large payroll it 47 should be noted that it was almost significantly affecting total revenues in a negative manner. Four consecutive losing seasons was also very close to significantly affecting a team's total revenue in a negative manner.lt had a t-statistic of -1.36 and a coefficient of

-3.53. It was hypothesized that four or more consecutive losing seasons would negatively affect total revenues and this shows that losing does not matter very much in terms of affecting a team's total revenue. Losing has no significant economic impact on NBA franchise revenues.

Model 2

The second model included all 14 independent and dummy variables. However, the log was taken from some of the variables to correct for any skewed behavior. The variables that were logged in model 2 are as follows: Gate Revenues (GREVDEF), Ticket

Prices (TKTPRICEDEF), Fan Cost Index (FCIDEF), and Player Expenses

(PEXPENSEDEF). Four of the variables in this model produced a significant t-statistic and have a p-value that showed significance in at least the 90% leveL The significant variables included Gate Revenues (GREVDEF), Attendance (ATTEND), Population

(POP), and Player Expenses (PEXPENSEDEF). The variable 4 consecutive losing seasons was very close to being significant but did not quite have a sil,'llificant impact

However, it is important to this study because a particular importance is placed on the variable for consecutive losing seasons. This model has an r-squared of.3 which means

30% ofthe dependent variable (TREVDEF) is explained by all of the independent variables. The model included 207 observations.

The first sil,'llificant variable gate revenues (GREVDEF) produced a t-statistic of lOA and a coefficient of A09. This variable has a highly significant positive atTect on 48

total revenues. In fact, it was significant at the 99% level meaning it is highly correlated with predicting total revenues. Gate Revenues are one of the four main sources of revenues for NBA teams so it is to no surprise that it is significant.

The regression results proved that attendance (ATTEND) also has a positively

significant affect on NBA franchise revenues. The predicted hypothesis that (ATTEND) would have a positive and si!,'I1ificant affect on total revenues was confirmed. In this model, (ATTEND) produced at-statistic of 3.44 and a very small coefficient of

.0000003. Based on the t-statistic (ATTEND) has a highly significant and positive impact on total revenues for NBA franchises. The small coefficient indicates that when attendance increases by 1 person, total revenues increases by .0000003. (ATTEND) is si!,rnificant to the 990/0 level.

In model 2, population (POP) is significant as well. (POP) was predicted to be a positive and significant indicator oflotal revenues. The t-statistic tor (POP) in model 2 is

5.04 and the coefficient .0000001. (POP) is a highly correlated determinant of total revenues and is significant at the 99% level. The coefficient .0000001 says that with a 1 person increase in population that total revenues will increase by .0000001.

This model showed that player expenses (PEXPENSEDEF) was statistically significant. The t-statistic corresponding with (PEXEPENSEDEF) is 1.99 and the coefficient is -.006. It was expected that it would have a negative affect on total revenues and this is exactly what it showed. (PEXPENSEDEF) is significant at the 95% level.

The model showed that 4 consecutive losing seasons (CLS4) was very close to being significant. It produced a t-statistic of -1.58 and a coefficient of -.047. It was hypothesized that consecutive losing seasons would have a significant negative affect on 49

franchise revenues but, in fact, it is not significant enough to say it bas any affect on total

revenues. Thus, it can be concluded that total revenues are not significantly affected by

having 4 consecutive losing seasons. This allows one to say that losing does not have a

negative economic impact on NBA franchises.

Model 3

The third model included only 10 of the 14 variables in order to eliminate some of

the collinearity within the model. The variables that were excluded are the following:

gate revenues (GREVDEF), tan cost index (FClDEF), 1 consecutive losing season

(CLSl), and new stadium (STAD). (GREVDEF) was highly correlated with ticket price

(TKTPRICEDEF) and (FClDEF) so it was concluded that it should be taken out for the

third model regression. (FCIDEF) was extremely correlated with (TKTPRICEDEF) so it

was excluded from the study because it is not thought of as being as significant as

(TKTPRICEDEF). (CLS I) was omitted because 1 losing season is not a long enough time period to tell if losing has any affect on total revenues. Finally, (STAD) was omitted because the other measure used for stadiums in this study (ST AD2) produced better results in the model and is highly correlated to (STAD). Model 3, like models 1 and 2, had 4 significant variables in the results. This model has an r-squared of .36 and an F­ statistic of 48.62. This means that 36% of the dependent variable (TREVDEF) is explained by all of the independent variables in this study. The model included 207 observations.

The first significant variable in model 3 is win percentage (WINPCT). It produced a t-statistic of 2.79 and has a coefficient of 16.51. The coefficient (16.51) means that a

1% increase in (WINPCT) produces an increase in total revenues of $16. 51. This is very 50

intriguing because in the first two models (WINPCT) was not significant. In this model

(WINPCT) proved to be statistically significant in positively affecting NBA franchise revenues. This means that teams should place a focus on winning in order to increase

revenues, and that winning actually matters for financial success. (WINPCT) is

significant at the 99% level.

Ticket price (TKTPRICEDEF) also had a positive and significant affect on NBA franchise revenues. It had a t-statistic of 2.92 and a coefficient of .247. Within this model

it is important to note that (TKTPRICEDEF) was significant at the 99% level. This

variable was predicted to be positively correlated and significant to total revenues and it confirmed the hypothesis.

The next variable attendance (ATTEND) is extremely significant in determining total revenues. The results showed that (A TIEND) has a t-statistic of 6.42 and a coefficient of .00005. The results for (A TIEND) confirmed the hypothesis that it would be positively correlated and significant to determining total revenues. (ATTEND) is also significant at the 99% level which means it is highly correlated to total revenues.

Population (POP) is the final vmiable that proved to be significant in Model 3. It is highly signifieant for determining total revenues and produced at-statistic of9.58 and a coefficient of .00003. Similar to the other models (POP) is one of the most significant variables in this study. It was hypothesized that population would significantly and positively efteet total revenues and the hypothesis was contlrmed. It was significant at the

99% level. Because it was signitlcant in all 3 models, (POP) is considered a very important determinant of total revenues for an NBA franchise. 51

Four consecutive losing seasons (CLS4) did not prove to be significant in Model

3. In all 3 models consecutive losing seasons did not significantly affect NBA franchise

revenues. This is interesting because, in effect, this means that losing does not have much

of an economic impact on an NBA team's ability to generate revenues.

The goal of this study was to examine the determinants ofNBA franehise

revenues and to specifically observe whether losing has any significant affect on total

revenues. The 3 models used in this study all produced fairly high r-squared numbers,

whieh means the independent variables included in the study did a good job explaining

the dependent variable. In the upcoming chapter the final conclusions and possible suggestions for further research will be discussed in regards to factors that influence

NBA franchise revenues, CHAPTER VI

CONCLUSION

The goal of this study was to identify the detenninants of National Basketball

Association (NBA) franchise rcvenues and to specifically analyze whether consecutive losing seasons affects total franchise revenues. The motivation behind this study was derived from observing that some of the worst teams in the NBA, in tenns of winning percentage, were generating as much revenue as teams with great winning success. It would therefore seem that success, in tenns of winning, is not significant in determining the amount of revenue that NBA franchises can generate. However, an important question that needed to be answered using this study was how much losing consecutive seasons affected total franchise revenues? The most recent example of this is the New

York Knicks. In the 2007-08 season the New York Knicks were first in the league with

$208 million in total revenues. However, their record for the season was 23-59 which translates into a winning percentage of 28%, and they had 6 losing seasons prior to the

2007-08 season. The Knicks are obviously generating $208 million in some other way than winning games. This study identified the detenninants ofNBA franchise revennes to figure out how the Knicks and similar teams are generating high revenue totals.

Attendance and Population were found to be positive and significant in all thrce models

52 53

used so it is safe to say that both of these variables are the main causes behind generating

franchise revenues.

All of the variables used in this study were crucial to answer two questions: \Vhat

are the determinants ofNBA franchise revenues? And does losing consecutive seasons

negatively affect NBA franchise revenues? This study found that losing up to 4

consecutive seasons (CLSI, CLS2, CLS3, and CLS4) does not have any significant affect

on NBA franchise revenues, and this confirms the hypothesis. Thus, losing consecutive

seasons should not be significantly important to the owner's ofNBA teams if they are

concerned about total revenue performance.

Population (POP) and attendance (ATTEND) were the two most significant

variables in this study. Thus, it is concluded that the population of the city and the

amount of people who attend a specific team's games are the two most important factors

that affect NBA franchise revenues. IfNBA team owners are concerned about total

revenue performance they should consider relocation, adding more seats and luxury

boxes to their stadium, or building a new stadium with the ability to seat more people and

accommodate fans who wish to purchase expensive luxury boxes. Since (POP) and

(ATTEND) were the two most significant variables in this study based on their t­ statistics, an importance should be placed on them if owners wish to increase revenue

performance.

Win percentage (WINPCT) was significant in Model 3 and this is interesting because losing consecutive seasons (CLS 1, CLS2, CLS3, and CLS4) has no affect on total revenues and (WINPCT) has a significant positive affect on total revenues. Thus, evm though (CLS 1, CLS2, CLS3, and CLS4) docs not cause a significant shift in total 54

revenues, (WINPCT) should be considered by general managers and owners as a way to

increase revenues. One reason (WINPCT) had a statistically significant and positive affect on total revenues could be that fans generate more excitement around winning teams than they do disappointment in losing teams. This is because of media bias. The sports media rarely highlight losing teams but frequently highlight winning teams, so this

could be why (WINPCT) had a statistically significant affect on total franchise revenues.

This means an importance should be placed on drafting and developing the right players who will help produce a winning team.

The average price of a ticket (TKTPRICEDEF) proved to have a significant affect on revenues in Model 3. NBA executives have placed a strong consideration on ticket prices in recent years because ofthc harsh economic times. l (TKTPRICEDEF) is very important to NBA franchises because it can affect the demand to attend NBA games.

Ticket prices are fairly inelastic because people are willing to pay premiums to see the entertainment ofNBA basketball and some of the best athletes in the world. However, in the painful economic times in recent years the importance of ticket pricing has increased.

This study proved that ticket pricing should be very important to owners and general managers because it can significantly affect revenue performance.

Fan Cost Index (FCIDEF) was significant and positive in Model I. It was significant at the 90% level which means it was barely significant enough to affect total franchise revenues. However, since it is significant, an importance should be placed on the prices tickets as well as the prices of concessions and amenities at NBA games. A lot of fans spend a considerable amount of money at NBA games and sometimes a greater

Lombardo. John. "l'\BA teams step up ticket discounting." ~~!&2!lli!n:i~Qllilil!iL~~,Js2lliJQillll !-2R 55 amount than the ticket they paid for to attend the game. This shows that not only should ticket prices be important to owners and general managers but the Fan Cost Index should as well.

In model 2, player expenses (PEXPENSEDEF) proved to be negative and significant in affecting NBA franchise revenues. It was significant at the 95% level. If owners are going to spend a considerable amount on players that negatively affects total revenues, than the players better cause an increase in attendance or winning percentage to nullify the affect.

Gate revenues (GREVDEF) is significant in Models 1 and 2, however

(GREVDEF) is a portion of total revenues so it is no surprise that this is the case. The variable was excluded in !"v10del 3 because it seemed irrelevant to the study and created bias in the models due to being a component of total franchise revenues.

The data collected for consecutive losing seasons (CLS) was used to determine how losing affects franchise revenue in that particular season. However, another way of analyzing this data could be to examine how losing consecutive seasons affects total revenues in the following season. This would allow people to see whether losing has any affect on the following season in terms of generating revenue. It is quite possible that after a dismal season that team's could see a negative effect on attendance, thereforc causing total revennes to decrease. If another similar study is performed this would be an interesting way to further this research.

This study had some limitations in terms of the amount of observations used in the analysis process. Ten years was a sufficient enough time period to examine how losing consecutive season's affects NBA franchise revenue, however, some data were 56 incomplete due to being unavailable. Gate revenue (GREVDEF) and player expenses

(PEXPENSEDEF) eould not be found for the 1999,2001, and 2002 seasons. If a similar examination is performed it would produce more accurate results if all the variables are found for every year analyzed.

This researeh did prove to be valuable in discovering whether consecutive losing season's affects NBA franchise revenues. However, this study is not perfect by any means. There could have been more variables used in all 3 fixed effects regression models. Part of this study was to find the significant determinants ofNBA franchise revenue and quite a few variables that affect total revenues were left out. For instance, local and national broadcasting rights make up a large portion ofNBA franchise revenues and it was excluded from the study due to a largely incomplete dataset. Since local and national broadcasting rights are one of the four principal sources of how teams generate revenue it would be interesting to see exactly how significant they are if another similar study is performed2

It would also be interesting to see if consecutive winning seasons have a positive effect on revenue performance for NBA teams. Losing consecutive seasons proved to have no significant affect on revenucs, but it is possible that winning could have a positive affect on total revenues due to the fact that winning teams generate more interest from fans because of media bias. Winning teams are shown on television more often which may, in fact, positively affect revenue performance in a multitude of areas. Further research on consecutive winning seasons would be an important addition to this study and sports economics research.

2()07 _ 57

Further research examining the affect of consecutive losing seasons on revenue performance would be useful for all other professional sports. The four largest professional sports leagues in the United States in terms of sales are the National Football

League (NFL), the National Basketball Association (NBA), Major League Baseball

(MLB, and the National Hockey League (NHL). It would be very interesting to see whether losing consecutive seasons has different affects on franchise revenues across di fferent sports.

The conclusions drawn from this study are indeed significant and do partially explain the determinants of NBA franchise revenues and iflosing consecutive seasons affects revenue performance. The study expanded upon previous research on the NBA and proved to be useful for NBA teams to understand how losing consecutive seasons affects their revenue performance. As it turns out, it has no significant affect by any means. With the conclusions drawn from this study, there are hopes that it will inspire further explorations so that NBA teams can have a better understanding of how to improve revenue performance thus creating better opportunity for economic success. SOURCES CONSULTED

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