The Effect of Nba Superstar Players on Game Attendance

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THE EFFECT OF NBA SUPERSTAR PLAYERS ON GAME ATTENDANCE

A Project

Presented to the

Faculty of

California State Polytechnic University, Pomona

In Partial Fulfllment

Of the Requirements for the Degree

Master of Science

Economics

Xinpeng Wei

2019 SIGNATURE PAGE

PROJECT: THE EFFECT OF NBA SUPERSTAR PLAYERS ON GAME ATTENDANCE

AUTHOR: Xinpeng Wei

DATE SUBMITTED: Spring 2019

Department of Economics

Dr. Craig Kerr Thesis Committee Chair Economics

Dr. Carsten Lange Economics

Dr. Greg W. Hunter Economics

ii ACKNOWLEDGMENTS

I would thank my professor Dr. Craig Kerr for his inspiring and advisory guidance throughout the process of my paper. What I have accomplished in this paper would not have been possible without his help and comments.

iii ABSTRACT

With the progress and development of our society, the sports industry has attracted more attention than ever before. When it comes to the National Basketball Association (NBA), the value of superstar athletes has also become more prominent as they are able to generate externalities that increase fans’ passion to attend live games, other revenue sources and potentially creates business opportunities. This paper concentrates on how NBA superstar players impact the level of game attendance at both home and away games. I collect data and investigate the impact on attendance for two particular NBA teams; Los

Angeles Lakers and Los Angeles Clippers from the past nine NBA seasons. The reason for picking these two particular teams is because they are the only teams in the league that share the same stadium. They are both based in the city of Los Angeles, which has a relatively higher income population than other cities. This comparison will allow for a better measure in calculating the difference in the types of value that a superstar has to offer because it holds constant any effect that is specifc to that city and stadium.

iv Contents

Signature Page ii

Acknowledgments Page iii

Abstract iv

1 Introduction 1

2 Literature Review 3

3 Data and Empirical Model 5

4 Empirical Results 7

5 Conclusion 12

v Bibiliography 14

vi Chapter 1

Introduction

The National Basketball Association (NBA) is the men’s professional basketball league in North America, which consists of twenty nine teams in the United States and one team in Canada. It is publicly deemed as the most competitive and favorable professional basketball league around the world. Undoubtedly, “NBA athletes are one of the world’s best paid population by annual salary on average” (Gaines, 2015a). Rosen (1981) defnes superstars as “a small numbers of people who are able to earn enormous amounts of money and dominate their feld of activities in which they engage.” In order to maximize profts, television broadcast ratings and the demand of fans, the NBA’s organizations, broadcasting companies, and social media have enhanced the signifcance of the superstar effect more than ever before.

Chung and Cox (1994) studied the consumption theory of superstars. According to Chung and Cox (1994) “the consumer’s choice of artistic products would be more demanded and artistic outputs will be concentrated among a few lucky individuals.” Su- perstars have become the object of consumption nowadays as they promote an increase of consumption to their markets and can drive demand in other markets, which is more

1 appealing to sport business. It can be seen that the star may penetrate many aspects of society and provide inspiration to fans of their performance. The superstar effect may also stimulate the local economy through consumption. The stars entertain the public, guide consumption and have the ability to spread a sense of community. In line with Gaines

(2015b), LeBron James was not only bringing a championship to the city of Cleveland in his return in 2014, but also other contributions in terms of revenue sources to the city and the franchise. “Bar and restaurants owners near the arena have seen a 30-200% increase in revenue on games nights. In addition, demand for local hotel rooms are increasing too in comparison with the same period before LeBron James returned last year” (Gaines,

2015b).

Superstar athletes changed the outlook on the team, especially those starting lineup players of the NBA All-Star Games. Technically, players who have been selected to the NBA All-Star Games starting lineup are considered as superstar athletes, as the voting is done by fans based on their preference. Fans and supporters are likely to decide their votes by taking players’ individual performance and their teams’ records both into consideration. This paper mainly focuses on the two NBA professional basketball franchises; Los Angeles Lakers and Los Angeles Clippers as they are based in one of the most populous cities in the North America. By taking the presence of superstar players into account, I collected their individual game attendance numbers to test if superstar players have a positive effect enhancing match attendance numbers.

2 Chapter 2

Literature Review

The defnition of superstars put forward by Rosen in 1981 was followed by Hausman and Leonard (1997), who state that superstar athletes are more infuential and productive positively in terms of raising tickets revenue. Also, the television ratings for NBA games are substantially higher when superstar athletes are involved. The raising of broadcast ratings are dramatic, not only for their own organizations but also for other organizations if they play road games. Not surprisingly, “broadcast contractors end up contributing an estimated 2.6 billion dollars to the NBA per season”(Mcfarlane, 2018).

For the relative articles that published recently, Berri, Schmidt and Brook (2004) focus on two-sided relationships; the relationships between match attendance and team performance, and the team’s employment of star athletes in the league. The authors also mention that the team performance, franchise characteristics and players’ popularity are other important factors in determining audience’s preference.

Moreover, Jane (2016) investigates the relationship between star effect and match attendance in the NBA based on player’s salary, popularity and performance. He fnds the evidence that superstar athletes are able to generate more attendance externalities

3 through the appearance on both home and road games.

In fact, the superstar effect is not only limited to basketball. According to Franck and

Nuesch¨ (2012), the controversial theory that a star’s popularity may not be the only factor that is able to directly increase gate revenue and match attendance, while the team performance is considered more important in terms of making people attend games. Franck and Nuesch¨ (2012) fnd evidence from their empirical model that national superstars and local superstars attract audience in different ways, as national superstars are more ver- satile and attractive in both home and road games, where local superstars are popular in home games only. Additionally, superstars attract fans by their sensational performance, whereas local heroes touch audience by popularity and emotion. Likewise, Deutscher and Schneemann (2017) fnd positive attendance effects for newly-signed star athletes, especially if the superstar is on the visitor roster.

4 Chapter 3

Data and Empirical Model

Attendanceit = α + Superstar ∗ (α1LastRecordt−1 + α2CurrentRecordt2)+

+ α3Homet3 + α4Weekdayst4 + α5Weekendst5 + υi (3.1)

Attendanceit = α + α1Superstart1 + α2LastRecordt−1 + α3CurrentRecordt2+

+ α4WinStreakt3 + α5Di finPointst4 + α5Homet5

+ α6Mondayt6 + α7Wednesdayt7 + α8VisitingStarst8 + υi (3.2)

The data used in this paper represents the most comprehensive NBA game attendance numbers and outcomes, and are collected from the database of basketball-reference.com.The data contains each individual regular season game of Los Angeles Lakers and Los Ange- les Clippers from 2009/2010 to 2017/2018 season. The information specifcally indicates the time, day, locations, opponents, points and attendance numbers of each game. Game attendance data are collected from the twentieth game in December of each season for both teams as they have an obvious winning or losing streaks, and current records then.

With the exception of 2011-2012 season, attendance data are collected until January of

5 2012 because that season was a NBA lockout season, the regular season games did not

start until the Christmas. The data of winning and losing streaks are manually calcu-

lated in order to see if teams’ performance and momentum would have any impact on

the attendance level. Likewise, difference in points of each game is collected to see au-

dience’s game-attending preference. The data of NBA All-Star Games starting lineup

players each year was collected manually from the basketball-reference.com. Players

who have been selected to the NBA All-Star Games starting lineup last year are counted

as superstars in the current season. Players who are selected to the starting lineup, but

are not able to participate due to injuries are still counted. Since the voting is done by

fans, it can still show that those injured players are irreplaceable in terms of popularity

and infuence.

As seen in Equation 3.1, the dependent variable is the level of attendance. Indepen-

dent variables are last season’s record, current record and home court advantage as more

fans may be likely to show up. Likewise, weekdays and weekends are also independent

variables in order to fnd the difference in days, where people are more willing to attend

games.

When it comes to Equation 3.2, the dependent variable remains as the level of atten-

dance. Independent variables are added with winning streaks, difference in points and

visiting teams’ superstars. As star’s performance and popularity may not be the only

factors that impact the game attendance level. Teams’ performance and franchises’ characteristics are also supposed to be taken into consideration. It allows a better measure in calculating different types of value.

6 Chapter 4

Empirical Results

Estimated coeffcients in the frst row, as seen in Table 4.1, the most important days are applied, which are Mondays and Wednesdays. They end up being statistically insignif- cant. Estimated coeffcients in the second row lumped up weekends and the home effect as a dummy variable because local fans may be more fexible to attend games on weekends. It is signifcant as well as the home effect. As far as Column 3, everyday is treated as dummy variables with the exclusion of Sundays. Fridays and Saturdays are the only signifcant days.

On average, there is about 380 less people in attendance on Mondays and Wednes- days as people may be more likely to watch games on TV with such national broadcasting days. Respectively, 383 less people in attendance on Mondays and 373 on Wednesdays.

For all of the other days, they are not signifcant except for Fridays and Saturdays because fans and their families are more fexible to attend games on weekends. There are also many big rivalry games taking place during the weekends. Also, the home effect is positive meaning whenever they play home court, more people are willing to show up to support them. However, all of the R-squared numbers are low indicating that this is not

7 a perfect model, where the model is not overall ft perfectly.

In Table 4.2, the superstar effect is signifcant in both speculations. On average,

the team is likely to sell about additional 360 tickets if there are superstars involved,

especially before Kobe Bryant retired and Chris Paul joined the Houston Rockets. Addi-

tionally, superstars from the visiting teams are able to increase attendance by twenty two.

As Staples Center is almost sold out each game night, the extra tickets that visiting superstars create may not be as big as expected. The current record variable is signifcant. For every 1% increase in the current season record, it will increase attendance by nineteen tickets. As for Column 1, the home effect is signifcant and increases attendance by 228.

On average, the attendance number is 18,358 if all variables are zero, and the capacity of

Staples Center is 18,997.

Mondays and Wednesdays are added to Column 2 and both days were found to have a negative relationship on attendance by 352 and 327 respectively. Likewise, this phe- nomenon is because fans are more fexible to attend games on weekends, there are also big games taking place on weekends.

When teams are on a winning streak, about twenty four less people in attendance in both speculations, but the effect is not signifcant. The franchise and resellers are likely to raise the tickets price when teams have been performing extremely well, as more people desire to witness amazing moments in the fesh. This adjustment may result in people not willing to pay the higher tickets price, and would rather not attend games any more.

On all of the regressions, one surprising variable that came out to have an unexpected coeffcient sign is last year’s regular season record. There is a negative relationship of attendance with the performance of last year’s season record. This does not make sense since an increase in last year’s performance should be positively correlated by attendance.

However, fans may still like to show up and support their teams no matter what teams’

8 Table 4.1: Regression Results

Dependent variable:

(1) (2) (3)

Mon −382.098∗∗ −374.461∗

(180.218) (209.965)

Tues −234.528

(159.435)

Wed −373.589∗∗∗ −368.462∗∗

(133.513) (171.087)

Thur −30.523

(201.903)

Fri 37.535

(151.936)

Sat 29.413

(238.969)

Home 173.923∗ 153.422

(99.605) (108.604)

Weekend 282.954∗∗∗

(99.366)

SuperStar*Last Season Record −373.053 −301.305 −327.816

(438.260) (438.204) (440.743)

SuperStar*Current Record 6.863 6.166 6.480

(4.523) (4.522) (4.547)

Constant 18,733.750∗∗∗ 18,413.030∗∗∗ 18,685.560∗∗∗

(86.809) (103.751) (151.347)

Observations 569 569 569

R2 0.043 0.047 0.052

Adjusted R2 0.036 0.040 0.037

Residual Std. Error 1,172.091 (df = 564) 1,169.985 (df = 564) 1,171.825 (df = 559)

F Statistic 6.370∗∗∗ (df = 4; 564) 6.901∗∗∗ (df = 4; 564) 3.416∗∗∗ (df = 9; 559)

Note: ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

9 records are, as Staples Center is almost sold out each game night.

10 Table 4.2: Regression Results

Dependent variable:

(1) (2)

SuperStar 358.709∗∗ 359.382∗∗

(145.516) (145.004)

Dif 0.751 0.494

(4.912) (4.905)

Visiting Stars 22.492

(129.215)

Last Season Record −1,961.198∗∗ −1,985.169∗∗

(826.618) (823.506)

Streak −23.285 −24.441

(22.787) (22.713)

Current Record 19.076∗∗∗ 19.319∗∗∗

(5.899) (5.880)

Home 228.349∗∗ 138.055

(101.398) (112.614)

Mon −352.989∗

(184.026)

Wed −327.176∗∗

(137.814)

Constant 18,358.910∗∗∗ 18,480.680∗∗∗

(168.944) (173.688)

Observations 569 569

R2 0.041 0.055

Adjusted R2 0.031 0.039

Residual Std. Error 1,175.438 (df = 562) 1,170.241 (df = 559)

F Statistic 4.021∗∗∗ (df = 6; 562) 3.594∗∗∗ (df = 9; 559)

Note: ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

11 Chapter 5

Conclusion

Superstar athletes are the treasury and core of NBA organizations, as they are able to generate externalities that increases fans’ passion to attend live games, gate revenues and potentially creates business opportunities. By their dominating performance, they may be able to change the entire blueprint and playing styles of teams and the league. More importantly, stars may be the inspirational role models and entertaining attention catchers for fans. With the game attendance data of Los Angeles Lakers and Los Angeles Clip- pers over the past nine NBA seasons, this paper investigates the impact of NBA superstar athletes on game attendance level at both home and away games. The data here covers more than nine season, which allows a comprehensive empirical results. There is an evidence that superstar effect persists through almost a decade. Superstar athletes literally have an impact on game attendance level, as superstars end up being statistically signif- icant. When each additional superstar player is involved in the games of Los Angeles

Lakers, there are almost 400 additional tickets being sold for the franchise with sixteen championships. Not to mention, the constant attendance number for the team is around

18,500 with the maximum capacity of 18,997. Likewise, team’s performance also has

12 important impact on attendance level as the current record is statistically signifcant. For every 1% increase in the current record, the team is likely to attract more audience the following game. Last but not least, fans prefer attending games more on weekends than weekdays. There are an additional 282 spectators in the audience in comparison with weekdays. Fans and their families prefer attending games on weekends as they might be physically more fexible. Also, there are big rivalry games generally taking place over the weekends.

13 Bibliography

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