Empirical Analysis of NBA Ticket Prices Correlation to NBA All-Stars

Empirical Analysis of NBA Ticket Prices Correlation to

NBA All-Stars

Michael McNeil

Abstract:

This paper investigates the possibility of a correlation between NBA ticket prices and if

NBA All-Stars increase this cost. The model will incorporate the points, rebounds,

assists, and minutes an NBA team has, the number of audience members, and how many

All-Stars are at the game.

JEL Classification: L83, Z2

Keywords: NBA Allstar, Ticket Prices.

Department of Economics, Bryant University, 1150 Douglas Pike, Smithfield, RI

02917. Email: [email protected]

1.0 INTRODUCTION

NBA ticket prices have increased a large amount over the last couple of years

making it expensive for a fan to go to the game. Some reasons for this could be because

of the inflation that has taken place and because of Covid. One question that I had while

looking at these ticket price increases, is if this price increase could increase further if an

All-Star is playing in the game. This thought came into mind because one of the features

of the NBA that draw fans is the celebrity of many of the most famous players in the

league.

This study aims to enhance understanding of why NBA tickets are so expensive

and if one of the reasons is because of All-Star NBA players. To better understand this

study, it will include many factors that go into why people choose to see an NBA game.

This will include the team points, team wins, assists, NBA all-stars, and attendance. All

these factors go into the regression because of the impact they potentially have on the

overall price of NBA tickets. The largest impact on how enjoyable a game is for fans is

the statistics that teams can put up during a game. When teams score many points in a

game and keep it close for both teams, it will result in a more interesting game for the

fans, leading to higher ticket prices. This also goes for the attendance of teams. If the

game is more interesting, there is a higher demand for tickets, which would also result in

higher prices. Lastly\, the reason for this study on NBA all-stars, there is a large lure to

NBA all-stars that make a lot of people want to attend a game in person to watch these

players play.

The research objective that guides this study is if NBA all-stars have a correlation

to NBA tickets. While doing this study I will also be doing three regression analyses. The

first is on NBA team statistics in the 2022 season with ticket prices, the second will be on

team 2022 attendance with ticket prices. The last regression will be on NBA all-stars and

ticket prices. These will all be evaluated and try to find if any of the variables will be

correlated to ticket prices.

The paper will be outlined as follows: Section 2 will be brief literature about 3

different studies on sports economics. Section 3 will be an outline of the empirical model

that I used. Section 4 will be on the data estimation methodology. The discussion of the

empirical results will be in section 5 and lastly, the conclusion will be in section 6.

2.0 NBA STATISTICS

Table 1: In the 2022 NBA season, each team played 82 regular-season games.

The points earned by each team during these games were a crucial factor in determining

their position in the standings leading to a team’s playoff seeding. NBA teams scored

points through various methods such as field goals, three-pointers, and free throws. The

top-scoring team in the 2022 season was the Minnesota Timberwolves, who averaged

115.9 points per game. The Memphis Grizzlies closely followed them, averaging 115.6

points per game. Other high-scoring teams included the Charlotte Hornets, Atlanta

Hawks, and the Phoenix Suns. The points scored by each team contributed to the exciting

competition and drama of the 2022 NBA season.

Table 1: 2022 NBA Team Statistics (Points)

Source: ESPN

Table 2: In the 2022 NBA season teams were able to play a total of 82 games in

the season, this includes 41 of them being home games and another 41 being away

games. Throughout that season, the team with the highest total attendance was the

Chicago Bulls with 856,148 people, and the second was the Philadelphia 76ers with

846,867. Some other notable teams that totalled many fans were the Dallas Mavericks,

Miami Heat, and the Boston Celtics. Other statistics that also went into attendance were

the team’s average for home and away games as well as the team’s total for home and

away games.

Table 2: NBA Team Attendance 2022

Source: ESPN

Table 3: The NBA All-Star Weekend is an annual event that highlights the

league's top players from both the Eastern and Western Conferences. The All-Star Game

provides an opportunity for fans to see their favourite players compete against each other

in a highly competitive and entertaining setting. The players selected for the All-Star

teams are chosen through a combination of fan, player, and media voting, with the most

popular and skilled players earning spots on the rosters. Along with the game itself, the

All-Star Weekend includes other events such as the Slam Dunk Contest, Three-Point

Shootout, and Skills Challenge, which add to the excitement and entertainment value of

the event. The NBA All-Star Game is considered one of the premier events of the

basketball season, drawing fans from around the world and providing a platform for the

league's top players to display their talents.

Number of NBA All-Stars Per Team 2022

Source: ESPN

3.0 LITERATURE REVIEW

The study (Berri et al. 2004) examines the impact of star players on National

Basketball Association gate revenues. The authors begin by introducing the concept of

star power and its potential impact on ticket sales. They argue that star players have a

significant effect on team performance, which in turn affects attendance and revenue. The

authors conducted a study of the NBA from the 2002-2003 season through the 2006-2007

season, analyzing data on player salaries, team revenues, and ticket prices. They found

that star players have a positive and statistically significant impact on gate revenues. The

authors also found that teams with higher payrolls and more star players tend to have

higher ticket prices. In addition to analyzing the impact of individual star players, the

authors also considered the impact of team-level star power. They found that teams with

more star players, as measured by All-Star selections, tend to have higher gate revenues.

Furthermore, they found that the impact of team-level star power is greater than the

impact of individual star players. The authors conclude by discussing the implications of

their findings for team management and player salaries. They argue that teams should

invest in star players to increase gate revenues and that players should be compensated

based on their impact on team performance and revenue (Berri et al. 2004). The authors

also suggest that future research should explore the impact of star power on other aspects

of team performance, such as merchandise sales and television ratings. Overall, Berri et

al. provides compelling evidence of the impact of star power on NBA gate revenues. The

study's findings have important implications for team management, player compensation,

and future research in the field of sports economics.

Another study that investigates the relationship between ticket prices, concession

prices, and attendance at professional sporting events is Coates and Humphreys (2007)

study. The authors argue that understanding the impact of these factors on attendance is

important for team management and policymakers’ decisions. The authors conducted a

study of Major League Baseball (MLB) and National Football League (NFL) games from

the 2006-2007 season. They analyzed data on ticket prices, concession prices, attendance,

and other variables such as team winning percentage, market size, and stadium age. The

authors found that higher ticket prices are associated with lower attendance, which is

consistent with economic theory. However, the authors also found that higher concession

prices are associated with higher attendance, which is counterintuitive. The authors

suggest that this may be due to the "sunk cost" effect, where fans who have already paid

for expensive tickets are more likely to spend more money on concessions while

attending the game. The authors also found that winning percentage and stadium age are

significant factors affecting attendance, with winning percentage having a larger impact.

Market size, however, did not have a significant effect on attendance. The authors

conclude by discussing the implications of their findings for team management and

policymakers. Coates and Humphrey suggest that teams should consider lowering ticket

prices to increase attendance, but also be careful not to lower concession prices too much,

as this may not have a significant impact on attendance but could lead to a decrease in

revenue. They also suggest that policymakers should be aware of the impact of stadium

age on attendance and consider investing in stadium renovations to increase attendance.

Overall, (Coates and Humphreys 2007) provide valuable insights into the factors that

affect attendance at professional sporting events. The study's findings have important

implications for team management, policymakers, and future research in the field of

sports economics.

In this research paper written by Scott D. Grimshaw and Jeffrey S. Larson (2021)

that examines the impact of star players on television ratings for the NBA All-Star Game.

The authors argue that star players have a significant impact on the popularity of the All-

Star Game and therefore on the ratings for the game's broadcast. The authors conducted a

study of the NBA All-Star Game from 1991 to 2012, analyzing data on player salaries,

player statistics, and television ratings. They found that the presence of star players, as

measured by All-Star selections, and player statistics, has a positive and statistically

significant impact on television ratings for the All-Star Game. The authors also found that

the impact of star power on ratings is greater for games that are more competitive. In

addition to analyzing the impact of individual star players, the authors also considered the

impact of team-level star power. They found that teams with more star players, as

measured by All-Star selections, tend to have higher television ratings for the All-Star

Game. Furthermore, they found that the impact of team-level star power is greater than

the impact of individual star players. The authors conclude by discussing the implications

of their findings for the NBA and its players. They argue that the NBA should continue to

promote its star players and their participation in the All-Star Game to increase the

game's popularity and television ratings. They also suggest that players should be

compensated based on their impact on the league's revenue, including television ratings.

Overall, Star Power on NBA All-Star Game TV provides compelling evidence of the

impact of star power on television ratings for the NBA All-Star Game. The study's

findings have important implications for the NBA, its players, and future sports

economics research.

In the study (Megia-Cayuela 2023) it explores the pricing strategy for tickets to

first division teams in the Spanish soccer league during the 2018/2019 season. The way

that they find their findings in this paper is by using a dual hybrid model with supply and

demand and its relationship to the pricing of the tickets. In the paper they talk about

multiple reasons that fans decide to attend these games. Some reasons include the game's

atmosphere, level of the opposing team and the facility. All these things are put into

thought when a fan decides to attend a game, they want to see a game that is good so they

will look at the team's schedule and decide to attend a game against a good team. At this

game fans will also be able to use the facility provided with concessions and clothing to

buy while they are there. In the conclusion of the paper the findings were that the

difference in pricing for each club in the Spanish league was 300%. A second thing that

was found as well was that only five out of the twenty clubs use the optimal pricing

strategy when making up the ticket prices for the games.

The study (Steven Salaga and Jason Winfree 2015) explores the secondary market

that has emerged in the National Football League with personal seat licenses (PSL) and

season ticket rights (STR) sold electronically. With this data they were able to able to

find out reasons for NFL ticket prices for games. In this study they were able to find that

there was a correlation between both the price of tickets and the area in the stadium that

you are sitting. If someone is sitting in the top of the stadium the ticket price for them

will be cheaper than someone who is sitting much closer to the field. They also

mentioned that there was a clear difference in STR and PSL markets with both having

interest in NFL games and how a team is doing during a season having an effect on the

market price for the tickets. Lastly, they were able to find that higher prices for an NFL

ticket are associated with lower secondary market STR and PSL sales prices.

4.0 DATA AND EMPIRICAL METHODOLOGY

4.1 Data

The study uses cross-sectional data from the 2022 season of the NBA. Data were

obtained from the NBA website to get the team averages in attendance, points, assists,

rebounds, and much more. Other data was collected from ESPN to get the all-star

information for that season. I was also able to collect data for team attendance during the

2022 season. I was able to get the average for home and away as well as the total for the

season. Summary statistics for the data are provided in Table 1.

NBA Team Statistics 2022

4.2 Empirical Model

Following Grimshaw and Larson (2021) this study adapted and modified from

previous studies, they focused on how the NBA gains most of their viewers for the NBA

All-Star game by getting celebrities to attend to make it more memorable for viewers. In

their model, they focused more on television standpoints with viewers but had many of

the same variables that I wanted to use in my module. One of the main differences was

that they had player efficiency in their module, however, for this model I focused on team

statistics instead.

I added team statistics, team attendance, and the number of all-stars per team to

better understand what variable affected ticket prices.

The model could be written as follow:

Model - Ticket Prices = B0 + B1 AllStar + B2 PTS + B3 TO + B4 Team + B5

AST + B6 REB + B7 FTM + B8 FTA + B9 FT% + B10 HATT + B11 HAVG + B12

AATT + B13 AAVG Error Term

The first variable in my model is AllStar. This represents if each team has an NBA

All-Stars on their team or not. This will be shown in the regression if a team has an All-

Star on their team, it will count as a 1 and if they do not have any, it will be a 0. Within

the model, there are the team statistics for the 2022 season, and these variables should

allow teams to see if there was any correlation between team statistics and ticket prices.

These variables include Points (PTS), Turnovers (TO), Assists (AST), Rebounds (REB),

Field Throws Made (FTM), Field Throws Attempted (FTA), and Field Throw Percentage

(FT%). The last variable is team attendance. This set of variables can influence ticket

prices because if a team has more people attending games, then there is a higher demand

for tickets which can result in a higher price. These variables include HATT (Home total

attendance), HAVG (Home average attendance), AATT (Away average attendance), and

AAVG (Away average attendance). The independent variable for this empirical module

is the ticket price average for the 2022 season. These ticket prices are for each of the 30

teams in the NBA where they took the ticket prices for every game during the 2022

season and averaged them out.

5.0 EMPIRICAL RESULTS

The empirical estimation results are presented in Tables 2, 3, and 4. The empirical

estimation shows a negative to no relationship for the first two regressions of Team

Attendance as well as NBA All-Star. For the last regression though, it did show a positive

relationship for some of the independent variables and NBA ticket prices. These variables

were FTM, FTA, FT%, OR, DR, REB, AST, and TO.

Table 2: Regression results for Team Attendance 2022

Table 3: Regression results for NBA All Star

Table 4: Regression results for NBA Team Statistics 2022

The team statistics, as stated before, are shown to have a positive relationship

because of the p-value that each of them has. This can be seen in Table 4 with the

highlighted section for each independent variable. When looking at the table it is clear to

see that each of the variable’s p-values are less than .05 meaning that they all have a

positive relationship with ticket prices for NBA games. This finding can have a

significant impact on teams that are trying to earn the most revenue during the season.

Teams now can change the ways that they format their team in the future by adding larger

centers and power forwards as well as more playmakers on teams to increase the statistics

that have a positive relationship. When a team adds more larger players to their roster, it

helps teams get more rebounds on the offense and defense side of the ball. The reason for

this is that the height and size of these players will allow the team to get more rebounds

much more easily. If a team also adds more playmakers, it will allow them to facilitate

the ball which will allow for more shot attempts and assists. Moving the ball will also

increase the chances of turnovers since it is less controlled. For tables 2 and three there

was no correlation between All Stars and Attendance to ticket prices for NBA games. I

was surprised to find that attendance for a team did not affect the ticket prices. The

reason for this is because if a team has more fans attending a game, then it would not be

surprising to see the ticket prices increase. If a team has more of a demand for tickets

than to increase revenue, they would just increase the price of the tickets. Since there is a

large demand, they will be bought no matter the price. I was also surprised to find that

All Star players playing in the game had no relationship to the ticket price. I felt that

people would be willing to pay more money to see these players in person, which would

result in higher ticket prices.

5.0 CONCLUSION

In summary, NBA All-Star players do not have an impact on ticket prices for a

team, but some team statistics do have an impact. These include FTM, FTA, FT%, OR,

DR, REB, AST, and TO. Some of the limitations that come with my study include the

inflation caused by the coronavirus. Since the virus did not allow people to be in close

contact with each other this caused all teams to not allow fans to attend games. This

could have affected how teams priced their tickets since they were trying to make up for

the profits lost during the virus. If an NBA team wants to increase its revenue throughout

its season, then it will want to increase these statistics. If a team wants to do this, they

will need to build their rosters in a way that will allow them to be a winning team and

increase these statistics. Teams will need to add more centers and power forwards to their

teams to increase rebounds and playmaking. Adding more centers and power forwards

will allow teams to increase the number of rebounds they can get on the offense and

defense side of the ball. Centers and power forwards are larger players in NBA and focus

their attention on the court at the paint which is the closest to the basket. Since they play

around there for their teams it means that they have a higher chance of getting rebounds.

Playmakers in the NBA play either the point guard or shooting guard position, their main

objective while playing is to create space and shot attempts for teammates by passing the

ball and getting by on their defender. Getting their teammates open and shooting the ball

themselves it will allow for more FTM, FTA, and FT%. Passing the ball is also

sometimes dangerous to teams because it could lead to turnovers if a player is not paying

attention or makes a risky pass. These findings I found from my empirical analysis do not

line up with the findings I found from (Coates and Humphreyes 2007), (Berri et al 2004)

and (Grimshaw and Larson 2021). In those findings they were able to find that revenue

for teams were based off if a celebrity was at the game and the concession stands had an

effect. One of the ways I could have had my results line up with previous studies is by

adding a section of how many celebrities attended games for teams. This would allow for

me to see if adding celebrities to games has any effect on the price of tickets to a game.

If an NBA team wants to increase ticket prices for games, they will need to increase

FTM, FTA, FT%, OR, DR, REB, AST, and TO make the most revenue within a season.

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