Empirical Analysis of NFL Ticket Price Determinants
Kenny Page
Abstract:
This research paper examines the determinants of revenue for National Football League
(NFL) franchises with respect to ticket prices. Using a dataset of ticket prices and various factors
such as team performance, stadium capacity, market size, and team revenue, we analyze the
relationship between ticket prices and a franchise’s revenue. The results align with past studies in
this area of research as they indicate that past team performance and average attendance have the
strongest effect on ticket prices. Additionally, the results reveal that stadium capacity and market
size most significantly affect team revenue. We find that ticket prices are less sensitive to
stadium capacity and market size than team performance. Again, the results align with previous
literature as the findings suggest that teams price their tickets corresponding to the inelastic
portion of demand. Overall, this study provides insights into the economics of NFL ticket pricing
and highlights the importance of strategic decision-making in maximizing revenue for teams.
JEL Classifications: Z23,
Keywords: Sports Economics, Profit Maximization, Price Elasticity of Demand
Department of Economics, Bryant University, 1150 Douglas Pike, Smithfield, RI 02917
Phone (203) 999-9985. Email: [email protected]
1.0 Introduction
Prior to the 2022 season, Forbes.com evaluated every NFL team as being worth at least
over $3 billion. This past year, the valuation for the average NFL team increased 28% to $4.47
billion. A major aspect of the NFL’s revenue stream is ticket sales, which contribute significantly
to a team’s financial success. There are several economic factors that contribute to the complex
process of NFL ticket pricing.
From the Forbes valuations stated previously, the NFL has been consistently found as the
most profitable league in the United States. Despite popular criticism laid out on owners for
charging so much out of pure greed, sports economists generally find that fans actually pay a
lower price than that which would maximize profits for the team. In sports economics, ticket
prices are commonly assumed to correspond to the inelastic portion of demand, which is below
the profit-maximizing price of an NFL team. Understandably, the reoccurring findings of
inelastic ticket prices below the profit-maximizing level raise questions for researchers, and there
are few explanations for this behavior.
This research paper aims to analyze NFL ticket price determinants by focusing on the
factors which impact prices the greatest. Using a dataset of average ticket prices and numerous
economic factors such as regional population, stadium capacity, team revenue, and average
attendance, this study attempts to examine the factors that most affect NFL franchise revenue
and their corresponding ticket prices. These factors are also complemented by “variables of
prestige” such as win percentage, number of pro bowlers, and number of Super Bowl wins as of
the 2022 season. By testing these variables against the dependent variables (average ticket prices
and team total revenue) the study can identify possible relationships between the listed factors
and NFL ticket prices.
The paper is organized so we first provide an overview of the relevant literature on the
economics of professional sports as well as the factors that influence ticket prices. Then, we will
describe the strategy and methodology for gathering and testing the data, including the variables
and statistical methods. To conclude, we will next present the results of the analysis and discuss
the implications of the findings for teams and their fans.
The findings in this study contribute to the understanding of the economics of the NFL
while providing insights into the factors determining ticket prices. By identifying the key
determinants, this study may help NFL teams make strategic decisions about ticket pricing and
optimize their revenue streams. The research has valuable implications for fans just as much for
teams, as fans may use the findings in this study to shed light on the factors that influence the
cost of attending their favorite football team’s game.
2.0 Price Elasticity of Demand for NFL Ticket Pricing with Respect to Revenue
The accepted reasoning for this inelastic ticket model is related to profit maximization
when other non-ticket revenues are included in the team’s objective plan. In this theory, owners
follow additional multiproduct to their pricing strategy. Ticket prices in this theory are set in the
inelastic region of demand to maximize total stadium revenues which includes parking,
concessions, merchandise, etc. (Krautmann and Berri, 2007). Similarly, other researchers with
this theory often include local and shared television revenues as trade-offs to inelastic ticket
pricing (Brunkhorst and Fenn, 2010). Figure 1, it is listed as the top twelve most expensive NFL
games to attend.
Figure 1: Inelastic Pricing of NFL Tickets (Top 12 most expensive games to attend)
Rank
Team
Average
Ticket
16oz.
Beer
Hot Dog
Parking
Total
1.
Las Vegas Raiders
$153.47
$12
$8
$100
$273.47
2.
San Francisco 49ers
$139.71
$11.50
$5.50
$85
$241.71
3.
New England Patriots
$131.45
$8.40
$4.50
$80
$224.35
4.
Dallas Cowboys
$99.50
$9.50
$6
$95
$210
5.
Carolina Panthers
$114.67
$10.50
$3
$81
$209.17
6.
Los Angeles Rams
$103.62
$13.75
$8
$80
$205.37
7.
Washington Commanders
$110.07
$11
$5
$65
$191.07
8.
Philadelphia Eagles
$127.06
$14.67
$6
$40
$187.73
9.
Green Bay Packers
$128.93
$9.50
$6
$40
$184.43
10.
Los Angeles Chargers
$80.38
$13.75
$8
$80
$182.13
11.
Baltimore Ravens
$110.38
$8.13
$3
$55
$176.51
12.
New York Giants
$115.31
$13
$7
$40
$175.31
Source: James Brinsford; Newsw eek
There is an abundance of literature that covers studies on a professional team’s
attendance, revenue, and individual ticket pricing. Many of them focus on gate receipts alone,
while others focus primarily on secondary ticket transactions from third-party sellers (Salaga and
Winfree, 2013; Diehl et al., 2015). As well, a few (Marburger, 1997) model sports teams as
multi-product monopolists that sell both admission and concessions. There are other arms of this
type of literature that focus on the relationship between ticket prices and home-field advantages.
This study, however, will be focusing on the average ticket price (average of both gate receipt
and secondary market) for a specific team throughout the duration of a given season concerning
prestige variables such as the number of star players and Super Bowls won by a given franchise.
In this area of study, it is a challenge to obtain price and quantity data for variables that
pertain to individual teams such as quantity demanded, seat quality, and ticket complements,
especially since all stadiums do have capacity restrictions. As a result, researchers have heavily
relied on aggregate demand attendance figures and average ticket prices which are impossible to
capture the true variation in demand that exists for specific seating locations, and accordingly the
consumer types (Marburger, 1997; Fort, 2004; Diehl et al., 2015). Furthermore, total attendance
measurements do not provide information on the number of spectators in a particular section, or
a particular type of ticket. To clarify, it is difficult to distinguish whether a certain ticket holder
owns season passes or is simply a single-game ticket holder. Given the countless amounts of
seats and sections for the stadiums examined, such price averages are likely to be biased
estimators of the prices paid by ticket holders. Therefore, it is also likely that the average price
given in this field of study understates the price of the most desirable tickets (lower-level and
box seats), and consequently overstates the average price of the least desirable tickets (upper-
level seating). It is also important to note that more desirable tickets are more likely to be sold
out compared to less desirable tickets (Krautmann and Berri, 2007; Diehl et al., 2015). Figure 2
displays the top 24 teams based on average home attendance for the 2022 season. Through
Figure 2, one may receive a better understanding of the team’s market size, it is no surprise that
Dallas and the two New York teams are the top three by average attendance. However, being that
Green Bay is a relatively smaller market size, it is surprising to see the Packers rank fourth, yet
their fan base is famously known for being one of the most loyal in sports.
Figure 2: Average Home Attendance (Top 24 franchises based on home attendance)
Games
Percentage
9
93.5
8
94.6
9
92.7
9
96.5
8
99.8
8
100.8
9
101.7
9
104.6
9
96.7
8
99.8
8
97.8
9
100.0
9
92.8
9
103.2
9
96.1
9
100.1
8
99.2
8
95.5
8
94.3
8
100.0
9
100.3
8
97.9
8
96.9
7
101.1
Source: ESPN.com
Another variable in this field of study that is considered valuable, yet hard to obtain, is
fan loyalty. The most loyal customers of a sports franchise are their season ticket holders, who
are generally considered to be less sensitive to ticket price year-over-year changes. In previous
research, it has also been consistently found that single-game buyers have a greater price
elasticity than season ticket holders, accordingly, single-game buyers are classified as casual fans
(Scully, 1989: Simmons, 1996; Fort, 2004; Diehl et al., 2015). For this study, the geographic
population for each team has been obtained in the hope of finding a correlation between ticket
prices and the population of the region in which a team is located. In this sense, this element is
brought into the study for teams such as the Green Bay Packers and the Buffalo Bills. The
regional population must be utilized in this field of research so that the fan loyalty variable can
be potentially tested. It is known that Buffalo and Green Bay have some of the most passionate
fans in the NFL while being in lesser populated regions compared to markets like Los Angeles or
New York.
3.0 Literature Review for NFL Ticket Pricing
NFL teams face a variety of costs associated with operating their franchises. These costs
contain player salaries, stadium expenses, marketing costs, and other operating costs that are
necessary for the franchise. To combat these costs, owners use a variety of strategies to generate
profits including pricing strategies, revenue sharing, and licensing agreements. Understanding
these strategies and their impact on profit maximization is important for both team executives
and fans (Brunkhorst and Fenn, 2010). In Brunkhorst and Fenn’s study, the two men conducted a
study examining the factors affecting profit maximization in the NFL. In their study, the men
were able to gather data on revenue, costs, and profits for 32 NFL teams during the 2007 NFL
season. The analysis focused on two main strategies: ticket pricing and revenue sharing. Ticket
pricing was found to be a significant factor in profit maximization. Brunkhorst and Fenn (2010)
found that teams that charged higher prices for tickets tended to achieve higher profits.
Accordingly, the two men also found that the teams who charged the highest prices also tended
to have lower attendance rates, suggesting a possible trade-off between ticket prices and
attendance. The other main strategy analyzed, revenue sharing, was also found to be a significant
factor in profit maximization. In the NFL, there is a revenue-sharing system in which all teams
share a portion of their revenue, including revenue from ticket sales, merchandise, and
broadcasting rights. Brunkhorst and Fenn (2010), find that revenue sharing has a positive impact
on profits for smaller-market teams, but a negative impact on profits for larger-market teams.
Their conclusion suggests that revenue sharing may help level the playing field for small-market
teams yet have unintended consequences for larger-market teams. The two men also call for the
exploration of further research on how these two main strategies may vary across different
markets over time.
While there is a substantial amount of research and data on the topic of professional
sporting event ticket prices, there is a lack of literature on the NFL specifically. Salaga and
Winfree (2013) looked to find the determinants of NFL ticket prices. In their study, they were
cleverly able to obtain a gauge of these determinants by testing the resale price of individual
teams’ Personal Seat Licenses (PSLs) or Season Ticket Rights (STRs) in the secondary resale
market. In Salaga and Winfree’s article, the two researchers view PSLs and STRs as an excellent
way for NFL franchises to generate revenue. However, when fans buy these PSLs and STRs,
they are very easily able to sell single-game tickets on the secondary market. Salaga and Winfree
viewed the secondary market as a better determinant of ticket sales as there is more of an
element of pure supply and demand. The data examined was on PSL and STR sales prices for all
32 NFL teams during the 2011 season. A variety of other factors such as stadium characteristics,
team performance, and market demographic were also tested. Salaga and Winfree concluded that
all three of these factors are statistically significant to secondary market prices. Tickets for teams
with higher winning percentages, teams with newer and more comfortable stadiums (and higher
capacity stadiums), and teams with fans of greater population size and income levels are found to
have significantly more expensive secondary market ticket prices.
Pricing strategies in professional sports are complex with many factors influencing the
price of tickets and concessions. While there are many studies on the factors determining ticket
prices of professional franchises, Krautmann and Berri’s (2007) study aimed to investigate the
price elasticity of demand in the context of concession pricing in professional sports. To measure
the price sensitivity of fans, Krautmann and Berri gathered data on concession prices and sales
volume for Major League Baseball, the National Football League, the National Basketball
Association, and the National Hockey League during the 2004-2005 seasons. The study found
that the price elasticity of demand for concessions varied depending on the league and the type of
concession. For example, beer and hot dog sales were found to be relatively inelastic in the NBA
and MLB, while being relatively elastic in the NFL. Over the big four professional leagues, the
study found that the concessions with higher prices tend to have lower sales volumes, indicating
that fans are generally price-sensitive when it comes to concessions. The study is also consistent
with future findings on ticket prices as the teams with the highest quality rosters and opponents
positively influence the price elasticity of demand (Krautmann and Berri, 2007).
The secondary market for sports tickets has become a crucial source of revenue for many
professional sports teams. For franchises to maximize profits, NFL team executives must
understand the price elasticity of demand for these tickets. Diehl et al. (2015), performed a study
on this topic to investigate the sensitivity of consumers to changes in ticket prices. In this study,
the three researchers use data from the NFL to shed light on this NFL fan price sensitivity. In the
context of sports tickets, understanding the price elasticity of demand allows franchises to
maximize revenues by setting prices that will maximize profits while also attracting the
maximum number of fans. Diehl et al. (2015) focused their analysis on the price elasticity of
demand for regular season games, playoff games, and the Super Bowl. The results from their
study suggest that NFL fans are relatively price-sensitive to game tickets in the secondary
market, particularly for higher-demand events such as the Super Bowl. Other factors such as
team performance, stadium capacity, and market demographic were found to be significant
predictors of demand in the secondary market. The findings by Diehl et al. (2015) align with
previous studies (Krautmann and Berri, 2007) as team performance, stadium capacity, and
market size were all positively correlated with higher demand for tickets in the secondary
market.
In another attempt to capture evidence of the price elasticity of demand in professional
sports, Coates and Humphreys (2007) examined the relationship between ticket prices,
concessions, and attendance at all major four professional sporting events in their study. Like the
other study performed by Krautmann and Berri (2007), Coates and Humphrey also collected data
from the MLB, NBA, NFL, and NHL, but during the 2002-2003 seasons. However, unlike
Krautmann and Berri, Coates and Humphery focused their study on the effects of ticket price
changes rather than concession stands. Coates and Humphrey (2007) found that higher ticket
prices were typically associated with lower attendance, especially for weekday games. As well,
the two men found that the effect of ticket prices on attendance varied depending on the quality
of the team and the importance of the game. To clarify, higher ticket prices were found to have a
smaller negative impact on attendance when the game means more, such as a playoff game or a
game between two teams with winning records.
Professional sports franchises are often accepted as drivers of local economic growth and
development. Yet, just as often city councils reject the idea of hosting professional teams,
therefore making it a subject of debate of just how beneficial it is to host a professional sports
team in one’s local community. Kuznitz (2011) examines the local economic impact of the four
major professional sports leagues and their teams in the United States in his study. In the study,
Kuznitz classifies the economic impact of professional teams into two categories: direct and
indirect. The direct impact includes the spending by teams on goods and services within the local
economy, while the indirect impact includes the economic activity generated by fans attending
the games, such as spending on hotels and community restaurants. Kuznitz (2011) found that the
economic impact of sports teams on their local economy is often overstated. The study finds that
the direct impact of teams on the local economy can be substantial, however, the indirect impact
is often minimal. These findings are especially apparent with teams that have low-performance
records and poor-quality rosters. Furthermore, many of the economic benefits of sports teams,
such as job creation and new infrastructure, are generally temporary and often do not lead to
long-term economic growth (Kuznitz, 2011).
4.0 Data and Empirical Methodology
4.1 Data
The study utilizes 2021-2022 cross-sectional data for the variables potentially affecting
NFL team revenue and ticket prices. The data used in this study are derived from multiple
sources. 2022 NFL team revenue was found from Forbes valuations, 2021 average price per
ticket, amount of Super Bowl wins, and the number of teams per region was found from
Statista.com. The study also used data on the 2021-win percentage, 2022 average attendance,
2022 returning Pro Bowlers per team, and team stadium capacity from ESPN.com. Finally, the
last variable, region population (market size), was attained from census.gov. Summary statistics
for all variables are listed in Tables 1 and 2.
Table 1: Summary Statistics
Table 2: Summary Statistics
4.2 Empirical Methodology
The model that is adopted and modified in this study is acquired from Brunkhorst and
Fenn’s (2010) study. From their study, this study can embrace specific variables such as home
attendance, winning percentage, the population of the home city, Pro Bowl selections, stadium
capacity, and dummy variables for divisions. This study differs from Brunkhorst and Fenn’s, as
this tests for revenue concerning ticket prices, rather than profit concerning ticket prices.
Moreover, here tests for revenue rather than profit due to the lack of sufficient 2022 data on
player, stadium, and team operational expenses. In these models, we have added the 2022
average ticket per home team, total revenue for each NFL team, the number of Super Bowl wins
a franchise has achieved, and the number of pro teams within the NFL team’s region. We
introduced Super Bowl wins and the number of pro teams within region variables as this study
hypothesizes that these factors play a significant role in an NFL team’s revenue and ticket prices.
In this study, three separate models were used to display changes in an NFL team’s
revenue and average ticket price. The models can be written as follows:
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(Model II)
The first two models that are tested in the study pertain to the determinants of an NFL
team’s total revenue. This is the first dependent variable that is examined as it is viewed that
team revenue is a better measure of fans actual choices (Krautmann and Berri, 2003; Salaga and
Winfree, 2013; Diehl et al., 2015) than simply average ticket price. TR
t
represents the total
revenue of a specific NFL team t, at the beginning of the 2022 regular season. Data on all 32
teams’ total revenue was found from Forbes’ 2022 NFL valuation rankings. From the rankings,
notably, the Dallas Cowboys experience the highest revenue with $1.087 billion, while the
Detroit Lions experience the lowest NFL revenue at $452 million. In this study, Diehl et al.
(2015) definition of revenue is used as this study also assumes that tickets are priced in the
inelastic portion of demand, meaning that franchises price tickets at a level lower than the profit-
maximizing price. Therefore, for this study, we view the NFL team’s revenue as the total gate
receipts divided by the total attendance, plus non-ticket stadium revenues such as parking,
merchandising, and concession sales (Diehl et al., 2015; Depken, 2001; Scully, 1989).
There are seven independent variables used to investigate the determinants of total team
revenue. Appendix A provides the acronyms, descriptions, and data sources for each variable
used in the study. First, Pop
t
(the regional population of the home city t) represents the market
size of a specific team. Variable #2 is Att
t
, which simply represents the average attendance
throughout the 2022 season for NFL team t. Followed by Cap
t
, the total stadium capacity of team
t. The study also uses PTR
t
, representing the amount of other professional sports teams located
within the same state or region as team t, whether the team is in the NFL or any of the other three
major North American professional leagues. This study also introduces “variables of prestige”.
The first prestige variable is W%
t
, which is the specific 2021 season winning percentage of team
t. The next variable of prestige utilized was ProB
1
, which is the number of Pro Bowlers expected
to play on team t’s roster in the 2022 season, this variable displays the number of all-stars a team
has on its roster. The final prestige variable is SB
t
, this is considered the main variable of prestige
as it represents Super Bowl championship titles, which is something not every NFL team has
attained and is a variable that is often neglected in this area of research.
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(Model III)
The last model that is tested in the study pertains directly to a specific NFL team’s
average ticket price. ATP
t
represents the average ticket price of a specific team, t. Unlike TR
t
,
ATP
t
does not have a definition to it as it is simply the average of all gate receipt and secondary
market prices taken at the end of the 2021 season. The data on average ticket prices derive from
Alan Snel’s, from LVSportsBiz.com, “fan cost index” which ranks Las Vegas Raider tickets
most expensive at $153.47 and ranks Las Angeles Chargers tickets the least expensive at $80.38.
Because this study does not consider other costs for fans associated with ticket prices such as
costs for parking, concessions, and merchandising, this study only includes the average ticket
price index rather than the combination of average ticket price and fan cost index that is found in
Coates and Humphreys (2013) study.
Like the models that we ran to investigate total team revenue, we use the same blueprints
to test average ticket prices. Utilizing the same six independent variables as in models I and II,
team total revenue is added into the regression for average ticket prices.
5.0 Empirical Results
Presented below are the empirical results from regression models I, II, and III. Model II
has the best fit out of all the regressions run, accounting for 73% of the variation in the
dependent variable Team Revenue. Model I also fit well explaining 67% of the variation in the
dependent Team Revenue variable. In both models, I and II, the independent variables Average
Attendance and Stadium Capacity are significant at a 1% level. However, these models do differ
somewhat. In Model I, Average Ticket Price is statistically significant at the 5% level.
Furthermore, model II displays that Regional Population and Super Bowl titles are both
statistically significant, Super Bowl wins being more significant to Team Revenue than Regional
Population. Average Ticket Price is no longer statistically significant when the variables Pro
Bowlers, Super Bowl titles, and Pro Teams within Region are added to model II. The fit of
model III is relatively low explaining just 41% of the variation in the independent variable
Average Ticket Price. The only variable shown to be statistically significant in this model is
Super Bowl titles at a 5% level.
Table 3: Regression Results for Models I, II, and III
Note: ***, **, and * denotes significance on a 1%, 5%, and 10% respectfully.
P-Values are shown under the coefficient estimate.
The study found Average Ticket Price (ATP) to have a positive and statistically
significant impact on a Team’s Total Revenue at a 5% level in model I, yet is statistically
insignificant in model II with the addition of the prestige variables. Average Ticket Price
positively correlating with Team Total Revenue aligns with Brunkhorst and Fenn’s (2010) study
on profit maximization in the NFL as they found that teams with higher ticket prices tend to
achieve the highest profit margins. In economic theory, this finding is in accordance as when the
average price of tickets increases, ceteris paribus, the team’s total revenue should therefore
increase greatly over the span of a full regular season.
Unlike Average Ticket Prices, Regional Population (Pop) is positive and statistically
significant in model II at a significance level of 10%, yet is statistically insignificant when the
variables of prestige are omitted in model I. This positive correlation is as expected, however, is
a surprise that it is not as strongly correlated as hypothesized. For an NFL franchise, those
franchises that are home to larger Regional Populations, which is a proxy for fanbase size, are
expected to gain higher revenues due to a greater demand for tickets. From the study, we find
that one unit change in Regional Population leads to a $14.83 increase in a Teams Total
Revenue. It has not been tested but is assumed that Regional Population and Average Attendance
are also positively correlated. This study on Regional Population (fanbase size) aligns with
Salaga and Winfree’s (2013) findings that a larger population size of a fanbase correlates with
higher revenues for franchises. However, it was found that the size of the team’s Regional
Population has a small positive, but no significant effect on the Average Ticket Prices of their
team.
Average Attendance (Att) and Stadium Capacity (Cap) are both statistically significant at
the 1% level to a Team’s Total Revenue in both models I and II, as expected, as these variables
are viewed to be related. The variables do differ in their findings, however. Average Attendance
positively influences a Teams Total Revenue. A one-unit change in the Average Attendance over
the course of a regular season increases Teams Total Revenue by $20,585.86 in model I, and by
$19,687.92 in model II when the variables of prestige are added to the regression model. This
finding goes against the trade-off of attendance and profits as mentioned in Brunkhorst and
Fenn’s (2010) study. Although in this study, this finding is not surprising as additional
attendance corresponds to more tickets being sold and therefore higher revenues, all else
remaining equal. This finding is also not a surprise due to the lack of sufficient data found on the
operation costs of an NFL stadium. It is assumed that Average Attendance is not as statistically
significant with the addition of stadium cost variables. Also, it is assumed that smaller-market
franchises benefit more from additional attendance than more established, larger-market
franchises. Average Attendance did not play a role in the Average Price of Tickets for an NFL
franchise.
Franchises are restricted to the number of tickets they may sell due to the capacity size of
their stadiums. The variable Stadium Capacity (Cap) is statistically significant at the 1% level
and harms a franchise’s revenue. This finding does not align with previous research (Salaga and
Winfree, 2013; Diehl et al., 2015) which states teams with higher capacity stadiums do
experience higher profits, however, this study slightly differs. In theory, and as it is assumed, a
franchise with a higher capacity stadium should experience higher profits. Although, this study is
testing the effects of additional capacity on a team’s revenue, strictly. Therefore, it is clearer why
the model displays a unit increase in Stadium Capacity has a -$12,731.28 effect on Team’s Total
Revenue in model I, and a -$14.091.47 effect in model II. To produce more capacity a team must
spend a high percentage of their revenue, which is why a negative correlation between Stadium
Capacity and Team Total Revenue is not unexpected. Again, Stadium Capacity was not found to
correlate with Average Ticket Prices.
The only variable found to correlate both Team Total Revenue and Average Ticket
Prices, and the only variable that is statistically significant to ticket prices, was Super Bowl
Titles (SB). In models I and II respectfully, the Super Bowl Title variable is statistically
significant at the 5% level and displays a positive force on both revenue and ticket prices. This is
not at all shocking, as this variable is a proxy for the pinnacle achievement in the NFL and is the
ultimate measure of prestige. This finding presents the notion that a past team’s performance
does indeed impact the revenue and ticket pricing of an NFL franchise. And it is assumed that
the number of Super Bowl Titles marginally increases NFL team revenue and ticket prices. Also,
whether it be one or multiple Super Bowl wins, the demand for an NFL team’s tickets does
skyrocket with a Super Bowl win. From model II, it is indicated that a Super Bowl win translates
to an $18,652,076.60 increase in a Team’s Total Revenue. And from model III, fans should
expect an increase in the price of tickets of at least $5.14 on average. This finding does align
with other studies (Brunkhorst and Fenn, 2010; Salaga and Winfree, 2013; Diehl et al., 2015) as
team performance, revenue, and average ticket prices are all positively correlated. Team owners
should take note of this variable, as a Super Bowl win not only places an NFL team on a pedestal
for league prestige but can also be used as a financial cushion for future seasons. Furthermore, it
is assumed that a Super Bowl title is essentially the only trade-off that a fan would accept for
higher ticket prices.
Shockingly, Super Bowl Titles were the only variable of prestige to have a statistically
significant effect on either Team’s Total Revenue or Average Ticket Prices. Team Winning
Percentage (W%), was the most surprising conclusion as it directly goes against the findings of
Brunkhorst and Fenn (2013) who tested that previous season winning percentage does play a
small, yet significant role on average ticket prices. This study’s result of the winning percentage
being statistically insignificant is not concerning, however. As it is cited in Brunkhorst and
Fenn’s (2013) findings, previous seasons’ winning percentage is quietly significant due to the
franchise owner’s lack of knowledge of how the team will be performing on the field in the
upcoming season. This is why it is not concerning that the winning percentage has no correlation
to Team’s Total Revenue and Average Ticket Price in this study’s models.
The number of Pro Bowlers (ProB), as a proxy for the number of all-stars on the roster,
was found to be negatively correlated, yet statistically insignificant to both Total Team Revenue
and Average Ticket Price. This is an unusual finding for this study as it goes against the findings
cited in Krautmann and Berri’s (2007) study which established that teams with quality rosters,
and teams that play quality rosters more frequently, tend to have higher Average Ticket Prices.
Through this study, it is now assumed that teams with higher-quality rosters may have increased
ticket prices, although, these teams suffer greater hits to their revenue because of the costs
associated with retaining these high-quality players. The findings in this study again align with
Brunkhorst and Fenn (2010) as they also found that star players do not play a significant role in a
franchise’s total revenue or average ticket price.
As for Pro Teams Within Region (PTR) and the dummy variable for specific divisions,
there should be more research and focus done on these topics to ensure that there is no
relationship between these variables and Total Team Revenue and Average Ticket Prices. For
Pro Teams Within Region, however, could be slightly statistically significant as it is somewhat
negatively correlated with Total Team Revenue. More research needs to be done, as there is a
possibility that the number of pro teams within an NFL franchise’s region may hinder the
demand for NFL game tickets. This is especially a possibility due to the NHL and NBA seasons
overlapping with the NFL, and it is assumed that teams from other leagues act as substitutes
rather than complements. The dummy variable for specific divisions was omitted from the study
as there were no correlations found between specific divisions and Total Team Revenue or
Average Ticket Prices.
Unfortunately for this study, there was a lack of sufficient data on other aspects that
affect a franchise’s profitability, hence the reason why this study focuses the attention on purely
revenue rather than profit. Unlike Krautmann and Berri’s (2007) study, this study was unable to
attain sufficient data pertaining to the costs and corresponding revenues of other franchise
aspects such as concessions, merchandising, parking, personnel salaries, and marketing.
Although, from this limitation, franchise owners can gather a clearer message. For owners, their
number one priority should be acquiring the top talent within their team’s financial means, and
ultimately, reaching the pinnacle of the NFL by winning a Super Bowl title. From the study,
owners may gather and assume that fans’ willingness to pay for tickets, concessions, parking,
and merchandising drastically increases when a franchise reaches its ultimate goal of winning the
Super Bowl.
6.0 Conclusion
Overall, this study has attempted to gain insights into the determinants of NFL ticket
prices as well as the factors that play into an NFL team’s revenue. From this study, it has been
found that Regional Population (fanbase size), Average Attendance, Stadium Capacity, and the
number of Super Bowl titles all play a factor in an NFL franchise’s revenue. Also, we can
conclude that many outside factors that could play a role in a franchise’s revenue often do not
also play a role in the determinants of ticket prices. The main factor found in this study to
correlate both an NFL franchise’s revenue and ticket prices is Super Bowl titles. It is now
undoubtedly a fact that winning a Super Bowl does not only put a franchise on a pedestal in
terms of NFL prestige, but it also places a franchise on a financial cushion as well. For NFL
owners, their number one priority should be the talent that they are responsible for signing to
play on the field, as it is the only factor tested in this study affecting revenue that owners
essentially have control over. Moreover, a Super Bowl win is the only trade-off a fan would
accept for more expensive tickets. More research on this topic is recommended as there are
factors in this study that need more examination. For example, it is assumed that the number of
professional teams within an NFL team’s region is detrimental to a franchise’s revenue, as teams
from other leagues could be considered substitutes. However, in this study, there were no
correlations found between the number of professional teams within the region and team total
revenue or average ticket prices.
Appendix A: Variable Description and Data Source
Acronym
Description
Data Source
ATP
Average Ticket Price
Statista.com
TR
Total Team Revenue (2022)
Forbes
Pop
Regional population for a specific NFL team
Census.gov
Att
Average home stadium attendance (2022)
ESPN.com
Cap
Home field stadium capacity
ESPN.com
PTR
The number of professional teams in the
same region as a particular NFL team
Statista.com
W%
Teams 2021 winning percentage
ESPN.com
ProB
Number of Pro Bowlers (all-stars) on the team
roster at the beginning of the 2022 season
ESPN.com
SB
The number of Super Bowl titles that a
franchise has won in their team history
Statista.com
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