NFL ATTENDANCE FIGURES: AN ECONOMETRIC MODEL
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
Joseph Pietro Jannetty
Graduation May 2014
NFL ATTENDANCE FIGURES: AN ECONOMETRIC MODEL
Joseph Pietro Jannetty
May 2014
Economics
Abstract
This paper examines the demand for attendance at National Football League (NFL) games by taking a previously developed model that attempted to explain game-day attendance at NFL games using variables that may exhibit a relationship with game-day attendance and testing it within the context of the modern NFL. This model is then expanded to include additional explanatory variables and is once again tested using a pooled dataset that was collected by gathering game-by-game data from every NFL regular season game in the 2010, 2011, and 2012 NFL seasons. Attendance is quantified as a ratio of the actual attendance at a game compared to the maximum stated capacity of the host stadium. The expanded model explained more of the variance than the replicated model when applied to the modern NFL. In particular, the attendance at the previous home game, average ticket price, home team win percentage to date in the current season, whether or not the away team made the playoffs last year, and whether or not the game was held between members of the same conference were found to hold a significant relationship with NFL game attendance figures. From these findings, it is concluded that the NFL is suffering from correctable inefficiencies related to their scheduling practices and that moving a struggling franchise is not a viable solution for generating fan interest.
KEYWORDS: NFL, Attendance, Model
ACKNOWLEDGMENTS
I would like to thank all of the people who assisted me in the completion of this thesis. I have to begin with my family, who always encouraged me to go out on a limb to follow my dreams and be who I want to be. I know this wasn’t easy, because it meant letting me go as I moved to Colorado. At the end of the day, their love and support are what made all of my accomplishments at Colorado College possible.
I would also like to thank my thesis advisor Jim Parco for setting high expectations and holding me to them. Jim is a creative motivator who always knows how to bring out the best in me, both while I was working on my thesis and while I was a student in his classes.
I should also thank Aju Fenn, the sports economics expert in the Colorado
College Department of Economics and Business. Although we only spoke a few times, his pointers got my project headed in the right direction and led to several reliable data sources.
Finally, I would like to thank my friend Phoenix Van Wagoner for the technical support. His problem solving skills made doing my thesis as painless as possible. I enjoyed working with him both as a thesis student and as classmates.
ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED AID ON THIS THESIS
Joseph Pietro Jannetty
TABLE OF CONTENTS
ABSTRACT ii ACKNOWLEGEMENTS iii 1 INTRODUCTION 1
2 LITERATURE REVIEW 2
3 MODEL & DATA 5 3.1 Dependent Variable 5 3.2 Independent Variables 5
4 RESULTS & ANALYSIS 15
5 CONCLUSION 23
6 FUTURE RESEARCH 25 APPENDIX REFERENCES
LIST OF TABLES
4.1 Welki and Zlatopper (1999) Replicated Model Tobit Regression Results …….… 16
4.2 Expanded Model Tobit Regression Results ……………………………………… 19
A.1 Independent Variables and Descriptions ………………………………… Appendix
A.2 Summary of Statistics ………………………………………………… … Appendix
A.3 Welki and Zlatopper (1999) Original Tobit Regression Results ………… Appendix
A.4 Coefficient and Significance Comparison Table ………………………... Appendix
LIST OF FIGURES
3.1 Welki and Zlatopper Replicated Model (1999) …………………………………… 5
3.2 Expanded Model …………………………………………………………………... 6
1. INTRODUCTION
In 2013, the National Football League (NFL) was the most profitable sports league in the United States, with a total revenue of $9.5 billion (Isidore, 2013). The next closest sports league was the MLB, which raked in $7.5 billion in revenue. Even though
Football is primarily an American sport, the NFL dominates every other national and international professional sports league in terms of profitability. Thirty of the Thirty-two
NFL franchises were on Forbes’ 2013 list of the 50 most valuable sports franchises in the world (Badenhausen, 2013).
Although a large chunk of the NFL’s revenue comes from licensing and television advertising, the returns from actual game day attendance shouldn’t be understated. With millions of people attending football games every year, sports Illustrated estimated that as much as $2 billion of the NFL’s 2010 revenue came from ticket sales, concessions, parking, and other costs associated with attending a football game (SportsIllustrated.com,
2014). There is still room for growth, however, as NFL game attendance figures dropped significantly from the 2007 to the 2012 seasons (Florio, 2012).
Roger Goodell, Comissioner of the NFL, has stated that his goal is for the league is to generate $25 billion annually by 2027 (Schrotenboer, 2014). If this goal is to be achieved, one of the issues that needs to be addressed is the downward trend of game attendance since 2007.
This paper will analyze the relationships between the circumstances surrounding an NFL game and fan interest quantified by attendance figures. The benefits of such an understanding would be numerous. One benefit would be that the NFL could optimize their marketing dollar by being able to predict levels of fan engagement, thus knowing
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when promotions, advertisements, and other special events would be most impactful.
Another benefit would be that the NFL would know exactly which types of games fans like to see, allowing them to alter rules in the post season in order to create more of these desirable games. One final benefit could be that the NFL would know which cities would be most favorable for a franchise, should they choose to expand or relocate an existing franchise. The ultimate goal of this research is to help the NFL address the downward trend in attendance figures since 2007, thereby maximizing game day profits.
The paper will proceed as follows; the following section will discuss relevant literature. The next section will discuss the models being tested, data collection, and the dependent and independent variables used in the models. Results from the tobit regressions and their implications will follow in the fourth section. After that, the paper will move on to the conclusion section, where the findings of the study and their practical applications will be summarized. The sixth and final section will outline potential areas for future research.
2. LITERATURE REVIEW
Many economic studies have focused on the demand for sports games. Despite prevalent interest in the subject, relatively few of these studies have been conducted within the context of the NFL. Fewer still are those that examine demand for NFL games using data broken down game-by-game, as opposed to season aggregate data.
One of the most important hypotheses in sports economics is the Uncertainty of
Outcome Hypothesis (hereafter UOH), which was developed by Simon Rottenberg
(1956). The UOH considers the effect of competitive balance on demand for sports
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games, and it states that fans prefer games where the outcome is uncertain due to even competitive balance.
Studies have been conducted to test the relevancy of this hypothesis within different sports markets and have reached varied conclusions. One such study examined the demand for MLB games for the 2007 season (Lemke, Leonard, & Tlhokwane, 2010).
Attendance was used to quantify demand, and betting lines were used to determine a team’s probability of winning. It was found that attendance would increase as the home team’s probability of winning increased beyond 54%, refuting the applicability of the
UOH to patterns in demand for the 2007 MLB season.
Others have supported the UOH, even within the same sport. A similar study using data from the 1996 MLB season found that attendance was optimized at the point where the home team’s probability of winning was 67%, after which it would decline
(McDonald & Rascher, 1999). This supports the UOH, suggesting that while baseball fans would prefer to attend a game where their local team is favored, fans will lose interest if the competitive balance becomes too skewed in favor of the home team.
These varied findings demonstrate that the UOH cannot be deemed universally valid or invalid. This could be attributed to fundamental shifts in fan preferences, an example of which may have occurred in the eleven-year period between 1996 and 2007.
Very few people have studied the UOH within the context of the NFL. One study used the actual game outcomes from the 2009-2010 NFL season to quantify competitive balance, and the corresponding post-game fan rankings from NFL.com to quantify fan satisfaction with the games (Paul, Wachsman, & Weinbach, 2011). It was found that the
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margin of victory was highly significant, as each additional point added reduced the fan rating by 0.28 points out of the possible 100.
Considering competitive balance’s unpredictable impact upon fan engagement, there must be other variables affecting the demand for sporting events as well. In fact, it has been found that both current season performance and previous season performance affected attendance at NCAA Division II football games, for example (DeSchriver &
Jensen, 2002). As one might expect, it was also found that in-season performance became more important relative to a team’s previous season performance as the season progressed. Numerous other variables have been deemed to hold a relationship with fan engagement, both within the context of the NFL and within the context of other sports leagues, as this paper will discuss in the theory section.
There has been one similar study to this one, one that analyzed game-by-game data from the 1986 and 1987 NFL seasons using a tobit regression (Welki & Zlatopper,
1999). Evidence was produced that suggests that the quality of the opposing teams is a major factor for attracting fans to a game, especially the quality of the home team. It was also found that fans preferred games that were expected to be more competitive, supporting the UOH. An inverse relationship between ticket price and attendance was also reported. This study was conducted fifteen years ago, using only about 350 data points from over ten years prior to that.
With so much at stake for the NFL in terms of fan engagement and profitability, it is logical to re-test and expand upon this model using a larger data set from some of the most recent seasons. That is exactly what this research aims to do.
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3. MODEL & DATA
This research project uses a dataset that includes information about every NFL game for the 2010 through 2012 regular seasons. There are 32 teams in the NFL, and each participates in sixteen games per season in addition to a single bye week in which they do not play. As a result, there are a total of 256 games per season. Over the course of three seasons, that amounts to 768 observations. A number of these games were omitted, however, due to the occasional game being hosted in England or Toronto, as well as the collapse of Mall of America Field’s dome late in the 2010 season, leading to the relocation of several Vikings home games. Eight games were omitted for these reasons, resulting in a data set comprised of 760 total observations. Due to the inherent nature of certain variables, some observations were omitted while testing different models resulting in the usage of either 712 or 664 observations. For example, games during week one had no previous history, and therefore had no win/loss percentages for the season up to date.
A Summary of Statistics Table is provided in the Appendix.
Two empirical models were tested, one replicating Welki and Zlatopper (1999) and one expanded model that included new variables that more recent research suggests may be important. The model for the Welki and Zlatopper replication is:
Attendance = (Price, Income, Home Team Record, Away Team Record, Betting Line, Betting Line ^ 2, Week, Temperature, Precipitation, Temperature x Precipitation, Dome, (3.1) Division Rival, Conference Rival, Non-Sunday)
Three variables were omitted from the original model, and they were whether or not a game was blacked out (BLACKOUT), a year dummy variable (YEAR), and the city name (CITY). Blackouts occur when a team has failed to sell enough tickets, so the franchise blocks local television broadcasts of the game so that people in the region must
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attend the game to see it. Welki and Zlatopper (1999) found that blackouts had a negative impact on game day attendance. Because blacking out games has been proven to be counterproductive, it has become an uncommon event in recent years. With only a handful of blacked out games in the seasons examined in this study, this variable has lost relevance and has not been included. The “YEAR” dummy variable was included in due to a work stoppage during the 1987 season on attendance figures. It was suspected that this may have led to a shift in consumer preferences. This variable was not-statistically significant and no such work stoppage was observed during the time period examined in this study. So, this variable was also omitted. Finally, CITY was omitted as Welki and
Zlatopper (1999) found it not to be significant. The model expanding on that of Welki and Zlatopper is:
Attendance = (Week, Dome, Metro Population, Income, Substitutes, Previous Attendance, Temperature, Precipitation, Temperature x Precipitation, Early 1-3, (3.2) Mid 3-6, Holiday, Price, Stadium Age, Home Team Record, Home Team Playoffs, Away Team Record, Away Team Playoffs, Non-Sunday, Division Rival, Conference Rival, Betting Line, Vegas Over/Under, Betting Line ^ 2)
3.1 DEPENDENT VARIABLE
Attendance. The dependent variable in the empirical model is attendance divided by the stated maximum capacity of a stadium measured for each individual game for the
2010 through 2012 NFL seasons (Drinen, 2014).
3.2 INDEPENDENT VARIABLES
Week. The week of the season variable in which the game is being played should reveal any changes in demand related to the progression of the season. The impact of
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independent variables on attendance figures for NCAA Division II football games is not static and can change as the season progressed (Jensen & DeSchriver, 2002). If the same effect occurs within the context of the NFL, it is possible that a variable would have a positive effect on attendance figures at one point in the season, while having a negative effect later on in the season. This may serve to neutralize the predictive strength of a variable within the model, but its impact may instead be reflected in the week of the season variable. Because of worsening weather as the season progresses, the loss of the novelty of football for fans of poorly performing teams, and previously observed negative relationships between the week in which a game is played and attendance figures (Welki
& Zlatopper, 1999), a negative relationship is expected.
Game Time. Dummy variables indicate the official starting time of each game in local time. Start times are broken down into three categories: (1) early games defined by having a starting time between 12:00pm and 2:59pm; (2) games that start between
3:00pm and 5:59pm; and, (3) games that start at 6:pm or later. Previous research that used ticket sales to quantify fan interest in sporting events found that night games were positively correlated with ticket sales (Dubin, 2001). Because of this, it is expected that later game times will have a positive impact on game attendance.
Day. Another explanatory variable included in this model is the day on which each game is played. Within the context of the 2007 MLB season, the day on which a game was played had an impact on attendance (Lemke, Leonard, & Tlhokwane, 2010).
Games were played on six different days during the period examined in my dataset. The only days on which no games were played were Fridays, with Sunday games being the most common (Drinen, 2014).
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Holiday. Whether or not a game was played on a major holiday or an adjacent weekend is also considered in this model. It has been found that holidays had a positive impact on attendance figures in other sports leagues, such as the MLB (Lemke, Leonard,
& Tlhokwane, 2010). This is an even more important consideration within the context of the NFL, as football games have become synonymous with holidays like Thanksgiving for many Americans. Thanksgiving has a particularly strong tradition of football that dates back as far as 1876, a mere thirteen years after Abraham Lincoln declared
Thanksgiving a national holiday (“History.com”, 2013). Christmas, Christmas Eve, and
New Years day games have also been played during the seasons examined in the dataset.
Population. A number of demographic variables are also used in this model. One of these variables is the population of the metropolitan statistical area, the data from which was gathered from the United States Census. Within the context of the 2007 MLB season, variables affecting attendance figures presented themselves differently for small market teams and large market teams (Lemke, Leonard, & Tlhokwane, 2010).
Specifically, it was found that many of the competitive and time variables had a more pronounced effect on attendance figures for games played in small markets than they did on those played in large markets. This could simply be due to the fact that if a certain percentage of the population is always going to be willing to sit in the cold to watch a football game, for example, larger populations will result in a larger turnout than would occur in a smaller market, as the turnout will be proportionate to it’s size. For this reason, a positive relationship is expected between population and attendance.
Income. The median income of the metropolitan statistical area in which a franchise is located is also considered in this model (Engebreth, 2014) with all figures
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adjusted for inflation. Between 1985 and 2002, the median income had no significant impact on NFL attendance figures (Spenner, Fenn, & Crooker, 2004). Though this suggests little-to-no impact will be observed, median income is worthy of inclusion in this model as fundamental shifts in how consumers view the NFL may have occurred in the years since that study. If consumers view an NFL game as a different type of good than they did in the past, income figures stand to have a dramatic impact on the demand for these goods. It also warrants inclusion on the grounds of replicating previously tested models (Welki & Zlatopper, 1999).
Average Ticket Price. The next explanatory variable is average ticket price, which is defined as the average ticket price for games played by the home team over the course of the current season (Fort, 2012). The Law of Demand would lead one to expect a negative relationship between ticket prices and attendance figures, the strength of which would be dictated by the price elasticity of demand. Contrary to this line of thought, it has been found that ticket prices to have no significant impact on attendance in the NFL
(Spenner, Fenn, & Crooker, 2004). It can be assumed that there will be no relationship between average ticket price and attendance figures.
Substitutes. The number of other professional sports franchises within the metropolitan area of the home team is another economic variable incorporated in this model. Only MLB, NFL, NHL, NBA, and MLS teams were considered, as they are the leagues with the largest attendance figures in the United States. This variable is worthy of consideration because it may create resource scarcity, as consumers have limited monetary and time resources to dedicate to going to see professional sports games. It is also possible that the number of sports franchises in a metropolitan area is correlated with
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the region’s population, in which case the amount of available resources will be relatively similar to NFL franchises in markets of different size. It has been found that the number of major professional sports teams in the same metropolitan area as an NFL franchise had an insignificant impact on attendance figures for the 1985 season through the 2002 season (Spenner, Fenn, & Crooker, 2004). As is the case with median income, the gap in time between the two studies leaves room for fundamental changes in how consumers view NFL games, making this variable worthy of inclusion in the model. Even so, little impact is expected.
Temperature. Game temperatures are also considered in this model (Drinen,
2014) (“NFLWeather.com”, 2014) (“Wunderground.com”, 2014). Game temperature, in it of itself, has not had a statistically significant relationship with NFL attendance in the past (Welki & Zlatopper, 1999), but was important when considered in conjunction with precipitation where cold weather and precipitation had a significant negative effect on attendance figures. Game temperature may have a positive effect on attendance, as people will likely avoid games with extreme cold, but is not expected to have a significant effect on attendance. It will be included in order to replicate the original model (Welki &
Zlatopper, 1999).
Precipitation. Whether or not there was precipitation during the game is another variable used in the model (“NFLWeather.com”, 2014) (“Wunderground.com”, 2014).
Precipitation was represented with a “1”, and a lack of precipitation was denoted with a
“0”. Previous research has found precipitation to be of significance to NFL game attendance figures in the past (Welki & Zlatoper, 1999). Because of this, it is expected that the expanded model will reveal a statistically significant negative relationship
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between precipitation and attendance, as it would create less desirable conditions for the fan both while they’re watching the game and while they are traveling to or returning from the stadium.
Temperature x Precipitation. A variable will be created by multiplying the binomial representation of whether or not there was precipitation in the area code of the stadium on game day by the temperature from the same area during the time of the game’s kickoff. This variable is used in order to explore the relationship between temperature and precipitation, and has proven to be statistically significant to the .01 level in the past (Welki & Zlatopper, 1999). For the purpose of replicating previous research, and the fact that it was found to be statistically significant, it is important to include this variable in the model, and it is expected to be significant for the 2010 through 2012 NFL season as it was in the past.
Dome. The game environment is further described in whether the host stadium has an open roof, or a retractable roof/permanent dome (“ESPN.com”, 2014). Despite finding temperature and rain to be statistically significant and to have a negative relationship with game attendance figures, whether or not a game was played in a dome was found to be insignificant within the same study (Welki & Zlatoper, 1999). These findings were based on fewer than 400 data points from games that occurred nearly 30 years ago, so their reliability and applicability to recent consumer demand are questionable. Is expected that this variable will have a significant, positive impact on attendance figures as domes and retractable roofs serve to negate any negative effects that harsh temperatures and precipitation may have on the in-game fan experience, while open stadiums will leave their attendance figures vulnerable to these variables.
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Age of Stadium. Age of the stadium was quantified by the number of years since the stadium’s construction or last major renovation. The goal of this variable is to encompass the comfort and technological compatibility of the stadium, which make for a more enjoyable fan experience. In the NFL, the age of a stadium has been found to have a negative impact on fan attendance (Spenner, Fenn, & Crooker, 2004). With this in mind, a negative relationship between the age of the stadium and attendance figures is expected.
Vegas Point Spread. This model also considers competitive variables, including the perceived competitive balance of each game. This goes back to the UOH (Rottenberg,
1957). Previous research has investigated the validity of this hypothesis within different sports markets and time periods with mixed results. One study that focused on MLB games found that attendance figures were greater during the 2007 season as the home team was more heavily favored (Lemke, Leonard, & Tlhokwane, 2010). Others focused on the NFL using point spreads to quantify the competitive balance of games during the
1986 and 1987 seasons that revealed that a more even competitive balance had a positive effect on attendance figures (Welki & Zlatoper, 1999). This variation may be due to the fact that the values of the consumers have shifted, and that they now prefer to see their home team win instead of a close competition. The home team being favored had a positive effect on attendance figures in more recent seasons (Coates & Humphreys,
2010). This study will similarly quantify competitive balance using the Vegas point spreads for each home team for each game (Drinen, 2014). These point spreads indicate which team is favored and by how many points. It is expected that the home team being heavily favored will have a positive impact on attendance figures as more recent research has indicated. Past models have also included the squared value of the point spread to test
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for this effect, and it will be included for the sake of replication (Welki & Zlatopper,
1999).
Home Team Playoffs Last Year. Whether the home team made the playoffs in the previous season and whether the away team made the playoffs in the previous season.
In NCAA Division II football games, a team’s previous season’s performance did have an impact on attendance figures, but that this effect was greater at the beginning of the season than it was at the end (Jensen & DeSchriver, 2002). A positive effect is expected, both because fans would rather see games with higher quality participants and because they would rather see their team win, which is more likely if their team was competitive enough to make the playoffs last year.
Vegas Over/Under. The Vegas over/under value for expected total points scored in a game was also considered (Drinen, 2014). Alterations in the rules of the NFL have led to prolific offensive production in recent years, and NFL fans preferred the higher scoring games this has produced during the 2009 and 2010 NFL seasons (Paul,
Wachsman, & Weinbach, 2011). For that reason, the over/under is hypothesized to have a positive effect on attendance figures, as people would prefer to attend higher scoring games.
Participant’s Winning Percentage. The home team’s winning percentage and the away team’s winning percentage in the current season were also included in the model. A team’s winning percentage had a positive and significant impact on attendance in research focusing on season aggregate data (Spenner, Fenn, & Crooker, 2004). Both the home team’s winning percentage and the away team’s winning percentage had a significant positive impact on ticket sales figures in game-by-game analyses as well
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(Dubin, 2001). Both are expected to have a positive impact on attendance figures within this model, as fans would be more interested in attending games with higher quality competitors.
Attendance at Previous Home Game. This variable is a ratio of the attendance of the previous home game (Drinen, 2014) over the stated maximum capacity of the stadium (“ESPN.com”, 2014). Attendance of the previous game hosted in a stadium could have a major impact on attendance figures as full stadiums make for a more exciting environment and an overall more enjoyable experience (Dubin, 2001). There has also been evidence of the bandwagon effect on attendance figures within the context of the NFL, which means that demand was impacted by aggregate consumption (Dubin,
2001). Past attendance was found to be statistically significant in a rational addiction model of the NFL, suggesting that NFL games are a habit forming good (Spenner, Fenn,
& Crooker, 2004). This discovery was made within the context of the NFL, but focused on season aggregate data, leaving previous game’s attendance yet to be examined as an explanatory variable in a game-by-game study focused on the NFL. For these reasons, this variable is expected to have a significant positive relationship with attendance figures.
Division Rival. Whether or not a game was played by teams within the same division is also accounted for in this model using a binomial dummy variable. Games between division rivals typically involve cities that are relatively close geographically and have serious playoff implications, as winning the division earns an automatic playoff berth. Past studies have found division rivalry games to have a positive and significant
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relationship with attendance figures, and it is expected that this model will produce similar results (Welki & Zlatopper, 1999).
Conference Rival. While games between teams in the same conference have less impact on who makes the playoffs than those between division rivals, it is possible that teams that met in the playoffs in previous years will once again face each other. The potential for a rubber match and the extra excitement surrounding it should have a slight, positive effect on attendance figures, although previous research suggests otherwise
(Welki & Zlatopper, 1999).
4. RESULTS & ANALYSIS
Tobit regressions were used because the dependent variable assumed values between 0 and 1, which would lead to the possibility of biased results if OLS were used
(Welki and Zlatopper, 1999). In many cases, the actual attendance exceeded the stated capacity of the stadium as standing spaces and luxury boxes were filled beyond the available seating. Because of this the upper limit was set to 1 and the lower limit was set to .5, as there was no game with less than 50% attendance. The use of binomial dummy variables, like those for game time, would also necessitate something other than an OLS regression.
The replicated model returned a ^2 value of 145 with a p-value of zero, passing all standards for strength and validity. Because this is a tobit regression, there is no need to test for errors like heterosketasticity that may occur during an OLS regression.
The replication of Welki and Zlatopper’s (1999) model found the square of the
Vegas point spread to be statistically significant at the 0.01 level with a p-value of 0.008
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and a coefficient of 0.0002. This provides some evidence against the UOH, as fans would rather go to games with uneven competitive balance.
Table 4.1
Welki and Zlatopper (1999) Replicated Model Tobit Regression Results
Standard Independent Variable Coefficient Error t-statistic p-value
Betting Line -0.0007 0.0009 -0.77 0.439 Betting Line ^ 2 0.0002 0.0001 2.68 0.008 Conference Rival -0.0109 0.0097 -1.12 0.261 Division Rival 0.0087 0.0099 0.88 0.378 Dome 0.0084 0.0054 1.55 0.121 Week -0.0023 0.0010 -2.2 0.028 Home Team Record 0.0582 0.0166 3.51 0 Income 0.0000 0.0000 2.96 0.003 Non-Sunday 0.0213 0.0117 1.83 0.068 Price 0.0014 0.0003 5.16 0 Precipitation -0.0260 0.0367 -0.71 0.479 Temperature -0.0007 0.0003 -2.16 0.031 Temperature x Precipitation 0.0008 0.0007 1.15 0.249 Away Team Record 0.0605 0.0168 3.61 0 Constant 0.7728 0.0395 19.54 0
It was also found that the week in which the game was played had a slightly negative impact on fan attendance, as it had a negative coefficient of -0.0023 and a p- value of 0.028. One explanation for this phenomenon could be that all teams will see high attendance figures early on due to the novelty effect of football returning. It is also possible that the fan base of franchises that had success the previous year would still be excited about their previous success while fan bases from unsuccessful franchises would be excited to see their high draft pick, highly touted collegiate prospects perform in the hopes that they can turn the franchise around. As the season progresses, teams that find success will sustain that level of excitement while unsuccessful franchises will lose the
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interest of some of their fan base. As a result, the overall attendance figures will drop as the season progresses.
The tobit regression supported the hypothesized relationship between home team winning percentage and attendance figures, as home team winning percentage had a p- value of zero and a relatively large, positive coefficient of 0.0582. This indicates that the performance of the home team is one of the most important variables affecting the fan experience and perceived quality of the game.
The winning percentage of the visiting team was found to be slightly more important than home team winning percentage, contrary to previous findings (Welki &
Zlatopper, 1999). With a p-value of zero and a coefficient of 0.0605, it proved to have one of the most significant relationships with attendance figures of all the independent variables included in the model. This clearly indicates that a shift in consumer preferences has occurred, placing more value on the quality of the teams participating in a game instead of on other competitive variables. This is re-enforced by the fact that games within the division and conference were determined not to have an impact on attendance figures, contrary to the hypothesized relationships and previous findings
(Welki & Zlatopper, 1999).
A metropolitan statistical area’s median income was statistically significant as well, with a p-value of 0.003. Although the regression returned a positive coefficient for this variable, it’s value was extremely small at 1.27e^-6, indicating that median income is not the most important economic variable in this model.
A strange phenomenon is observed regarding ticket prices, as they proved to be both statistically significant, with a p-value of zero, and to have a large positive
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coefficient of 0.0014. One possible explanation for this phenomenon could be that the average ticket prices used were those posted by the franchises and not those of the secondary market. During the time period examined in this study, no franchise used a dynamic pricing strategy to adjust for demand in-season (“TiqIQ Blog”, 2014) . In order to encourage maximum fan engagement to capitalize on television revenue (as games are blacked out on television if enough tickets aren’t sold), NFL franchises typically try to set ticket prices below market equilibrium, which is demonstrated by the higher ticket prices on the secondary market (Courty, 2000). With that being said, franchises whose games are more desirable will still charge more than a franchise with less desirable games, while struggling franchises are unable to set their prices below equilibrium price and still cover the costs associated with maintaining the stadium. The result is that higher prices indicate more desirable games, but since the stated ticket values for these desirable games are still below market equilibrium, the higher stated price would actually lead to higher attendance.
Games hosted on days other than Sunday were found to have higher attendance figures, with the “Non-Sunday” variable being statistically significant at the 0.10 level with a p-value of 0.068 and a coefficient of 0.0213. This supports the hypothesized relationship between attendance figures and non-Sunday games, as a non-Sunday game is typically the only game being hosted on that day and receives greater media attention on a national level.
Out of the weather variables, only the game temperature proved to be significant with a p-value of 0.031 and a coefficient of -0.0007, which goes against previous findings
(Welki & Zlatopper, 1999). The inclusion of domed stadiums may have negated the
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impact of precipitation, although the domed stadium dummy variable fell just shy of significance at the 0.10 level with a p-value of 0.121. The other weather variables and the dome variable did not exhibit a statistically significant relationship with NFL game attendance figures, counter to the hypothesized relationship. The negative effect of temperature on attendance is unusual because that would indicate that games played at higher temperatures are attended less than cold games. It may be that a shift in consumer preferences has occurred and that fans accept winter weather as a part of the game, while they still dislike games played on uncomfortably hot days.
The tobit regression of the expanded model revealed a ^2 value of over 542, which is massive even compared to the 145 observed in the replicated model. This indicates that the model is significantly more sound than the previous model.
Median income, average ticket price, and home team win percentage all maintained a similar relationship to that observed in the previous model. Median income, however, only met the 0.10 significance threshold with a p-value of 0.075, as opposed to the previous model where it was significant on the 0.01 level. Week, game temperature, away team win percentage, and the square of the Vegas point spread all fell out of significance, even at the 0.10 level.
Table 4.2
Expanded Model Tobit Regression Results
Independent Variable Coefficient Standard Error t-statistic p-value
Week -0.0003 0.0008 -0.39 0.695 Dome 0.0006 0.0042 0.13 0.894 Metro Population 0.0000 0.0000 -3.12 0.002 Income 0.0000 0.0000 1.78 0.075 Substitutes 0.0067 0.0041 1.66 0.098 Previous Attendance 0.7708 0.0388 19.86 0
19
Table 4.1 Continued
Temperature 0.0000 0.0002 0.1 0.919 Precipitation -0.0134 0.0275 -0.49 0.627 Temperature x Precipitation 0.0005 0.0005 0.93 0.351 Early 1-3 -0.0072 0.0113 -0.64 0.522 Mid 3-6 0.0073 0.0123 0.6 0.552 Holiday 0.0027 0.0066 0.41 0.684 Price 0.0013 0.0003 3.89 0 Stadium Age 0.0005 0.0003 1.75 0.081 Home Team Record 0.0559 0.0139 4.01 0 Home Team Playoffs 0.0132 0.0073 1.8 0.072 Away Team Record 0.0098 0.0142 0.69 0.492 Away Team Playoffs 0.0260 0.0069 3.76 0 Non-Sunday 0.0074 0.0109 0.68 0.498 Division Rival 0.0043 0.0074 0.57 0.568 Conference Rival -0.0152 0.0074 -2.06 0.039 Betting Line 0.0014 0.0008 1.87 0.062 Vegas Over/Under 0.0009 0.0007 1.2 0.229 Betting Line ^ 2 0.0001 0.0001 1.55 0.122 Constant 0.0302 0.0558 0.54 0.589
Of the additional variables, Previous Attendance had the strongest relationship with attendance. With a p-value of zero and a coefficient of 0.7708, this variable explains a lot of the variance that was left unexplained in the constant of the previous model and defines the base line attendance level at any given NFL game. The constant of this updated model was 0.0301 with a statistically insignificant p-value of 0.589, while it had a strong 0.7728 coefficient with a p-value of zero in the replicated regression. This effect supports the hypothesized relationship between previous attendance figures and current attendance figures. This continued pattern of consumption based on past consumption supports the applicability of rational addiction theory to the NFL sports market, supporting findings in previous research (Spenner, Fenn, & Crooker, 2004). Another possible explanation for this finding could be that the number of fans present at a game
20
can add or detract from the atmosphere, as stadiums that are only partially filled have less energy and enthusiasm than those that are sold out. This atmosphere is important in determining the quality of the overall product that is a football game and can play a role in the expected future consumption because fans will be more likely to attend future games if the atmosphere at the game is better and they expect that same atmosphere to be present at the next game. Conversely, if attendance is low and the fan base is unenthusiastic, the consumer will be less likely to purchase attend another game in the future as they have come to expect lower turnouts and a worse atmosphere.
The population of the metropolitan statistical area was significant, with a p-value of 0.002 and a coefficient of -6.33e^-9. This variable demonstrated a negative effect on attendance figures, contrary to the hypothesized relationship between the two. This is somewhat difficult to explain but may be due to larger markets being oversaturated with sports franchises, and the small coefficient suggests that its impact is very small and should not be overstated.
Another variable with a significant relationship with attendance figures was the number of substitute sports franchises in the metropolitan statistical area, which cleared the 0.10 confidence level with a 0.098 p-value and a coefficient of 0.0067. The positive coefficient suggests that people do not see other sports leagues as substitutes for NFL games, so the other sports franchises in the area do not compete for resources. Instead, it is possible that having other sports franchises in the area is indicative of a local culture that places a higher value on professional sports games than other areas, leading to a larger pool of resources available for the NFL franchise to draw from.
21
The age of the stadium was observed to have a positive effect on attendance figures, contrary to the findings of previous research and the hypothesized relationship between the two variables. This variable had a p-value of 0.081, clearing the 0.10 confidence interval, but falling short of the preferred 0.05 level. This, combined with a small coefficient of 0.0005, indicates that other competitive variables like the win to loss ratio of a home team likely skewed this finding. Perhaps teams playing in older stadiums performed better than those playing in newer stadiums during the time period being observed purely by chance, and the increased attendance due to other favorable conditions overcompensated for the negative effects of a stadium’s age on the fan experience. Another possible explanation could be that a fundamental change occurred in consumer preferences and that football fans favored old stadiums for their historical and nostalgic traits rather than the comforts and conveniences offered by newer stadiums.
Whether or not the home team made the playoffs the previous season was found to be statistically significant at the 0.10 level, with a p-value of 0.072 and a coefficient of
0.0132. Whether or not the visiting team made the playoffs last year displayed an even stronger relationship with attendance figures, easily clearing the 0.01 confidence level with a p-value of zero and having a major impact with a coefficient of 0.0260. The positive coefficients observed support the hypothesized positive relationship between these variables and attendance figures. Another interesting occurrence was the loss of significance of the visiting team’s in season performance, and that there previous season’s performance was more important. One possible explanation is that making the playoffs helps with a team’s branding, and could lead to higher ticket sales for their away games when fans are purchasing tickets before the new season begins. Whether or not the
22
team performed to the same level that season would be irrelevant because fans across the league would have already purchased tickets to their away games. In order for a consumer to be a potential attendee of the game, they must first purchase the ticket, and while every ticket holder may not attend, the increased ticket sales creates favorable conditions for high attendance games. Fans may not even be paying that close attention to the performance of franchises other than their favorites, so their evaluation of the quality of the visiting team could weigh more heavily on the increased awareness of a visiting team based on the heightened media coverage of the previous year’s playoffs.
In this model the Vegas point spread was significant at the 0.10 level with a p- value of 0.062, as opposed to the previous model, which placed significance on its squared value. The coefficient of this variable was 0.0014. This too refutes the applicability of the UOH within the context of the modern NFL.
5. CONCLUSION
In the new expanded model, variables affecting the quality of the competition in a game, specifically competitive variables like the home team’s winning percentage and whether or not the two teams participated in the playoffs the previous year, are far more important in determining fan interest than the economic, demographic, or weather variables surrounding it. From this it can be deduced that moving a franchise that struggles to generate fan interest may not be a viable solution, as their competitive shortcomings would still be driving down fan interest while the change in circumstances generated by moving to a new city would have minimal impact.
The findings in this study further suggest that the NFL could maximize fan interest by manipulating their scheduling. Stronger franchises should have little issue
23
generating fan interest regardless of their opponent, as the quality of the home team quantified by current season win percentage had a strong, positive relationship with attendance figures. Struggling franchises, however, will have a harder time filling their stadiums, as the quality of the team won’t be enough to entice fans to come out to the games. Instead, the quality of the visiting team quantified by whether or not they made the playoffs in the previous season could be relied upon to draw fans in. These findings suggest that the NFL could maximize fan interest by structuring their schedule so that games are always played in the weaker team’s stadium whenever a non-playoff contender and a playoff contender from the previous year face off.
Furthermore, there is some evidence to suggest that fans become more interested in games as one team is more heavily favored, calling into question whether or not the
UOH is applicable within the context of the modern NFL.
Overall, the most important explanatory variable is the ratio of attendance to maximum capacity at the previous home game, which provides further evidence of behavior in line with rational addition, as well as the importance of attendance figures on the overall atmosphere and quality of the fan experience. This variable essentially set a baseline attendance level for any given game. In other words, it behaved as a defined constant in the expanded model, as it mirrored the significance and relationship that the constant had in the replication.
The replication of Welki and Zlatopper’s (1999) model revealed a number of shifts in consumer preferences, the first of which concerns the weather in which the game was played. The new model asserted that consumers no longer care about precipitation and prefer lower temperatures, accepting poor weather as a part of the game. The median
24
income was found to be significant, where the income of the region was not important in the 1986 and 1987 NFL seasons, suggesting that the price elasticity of demand for football games may have changed as demand for football rises as prices rise and shrinks when income drops. Divison and Conference opponents have lost consumer interest in favor of those with stronger in season performances. Two variables that maintained a similar significant relationship with attendance figures were the week in which the game was played, the Vegas point spread, and games not held on Sunday.
6. FUTURE RESEARCH
In the future, there are a number of additional variables that could be tested for relevancy in an expanded model. One such variable could be the effect of game day promotions on NFL attendance, as these events were found to increase attendance within the MLB (Barilla, Gruben, & Levernier, 2009). Along the same line, it may be valuable to incorporate advertizing budgets into a future model. Another change that could be made would be to quantify fan interest with ticket sales as a ratio with the maximum number of tickets sold. From perspective of an NFL executive, this type of information would be more important as sales and not attendance are what directly affect the bottom line. These sales figures are extremely difficult to gain access to, however, which is why this study used attendance as a ratio with the stated maximum capacity of a stadium.
Geographic variables ranging from the distance between the stadiums of the two teams playing, public transportation available to the stadium, and others may also be worth of inclusion in a future model (Lemke, Leonard, & Tlhokwane, 2010).
25
Appendix
Table A.1
Independent Variables and Descriptions
Variable Name Description
Week The week of the season
Dome Dummy variable equals 1 if game was held in a dome or on a field with a retractable roof, 0 if outdoors
Metro Population The population of the metropolitan statistical area in which the game was held
Income Median income of the metropolitan statistical area in which the game was held
Substitutes Number of other NFL, NBA, MLB, and MLS franchises based in the metropolitan statistical area in which the game was played
Previous Attendance The attendance of the last home game played by a team in that stadium divided by the official maximum capacity of the stadium
Temperature Temperature at the start of the game in the zip code of the stadium where the game was played
Precipitation Dummy variable equal to 1 if there was precipitation in the ZIP code of the stadium on the day of the game, 0 if no precipitation
Temperature x Precipitation Temperature variable multiplied by Precipitation variable
Table A.1 Continued
Early 12-3 Dummy variable equal to 1 if the game began between 12:00 pm and 2:59pm local time, 0 if not
Mid 3-6 Dummy variable equal to 1 if the game began between 3:00pm and 5:59pm local time, 0 if not
Holiday Dummy variable equal to 1 if the game was held directly on a federally recognized holiday or on a weekend directly adjacent to that holiday, 0 if not Price Average ticket price for the home team’s games during that season
Stadium Age Number of years since the stadium’s construction or last major renovation resetting to 1
Home Team Record Winning percentage of the home team up to that point in the season
Home Team Playoffs Binary variable equivalent to 1 if the home team participated in the previous year’s playoffs, 0 if not
Away Team Record Winning percentage of the away team up to that point in the season
Away Team Playoffs Binary variable equivalent to 1 if the away team participated in the previous year’s playoffs, 0 if not
Non-Sunday Binary variable equivalent to 0 if the game was played on a Sunday, 1 if the game was held any other day
Division Rival Binary variable equivalent to 1 if the game was between two teams in the same division, 0 if it was not
Table A.1 Continued
Conference Rival Binary variable equivalent to 1 if the game was between two teams in the same conference but not in the same division, 0 if it was either a divisional game or an inter-conference game
Betting Line The Vegas point spread for the game
Vegas Over/Under The Vegas over/under prediction for total points scored
Betting Line ^ 2 The Vegas point spread for the game squared
Constant The constant
Table A.2
Summary of Statistics
Independent Standard Variable Observations Mean Deviation Minimum Maximum
Week 760 9.0842 5.0249 1 17 Dome 760 0.4342 0.7445 0 2 Metro Pop. 760 4577192 4381668 306775 1.98E+07 Median Income 760 57940.72 9848.2 44379 89010 Substitutes 760 2.7684 2.069 0 9 Attendance 760 0.9481 0.0795 0.51 1.13 Prev. Attend. 664 0.9483 0.0793 0.51 1.13 Temperature 760 58.9632 16.3998 0 101 Precipitation 760 0.1421 0.3494 0 1 Temp. x Precip. 760 7.5974 19.7685 0 85 Early 1-3 760 0.7026 0.4574 0 1 Mid 3-6 760 0.1474 0.3547 0 1 Holiday 760 0.2342 0.4238 0 1 Price 760 77.5080 16.9502 54.2 120.85 Stadium Age 760 11.5618 10.3583 1 50 Home Record 712 0.4820 0.2601 0 1 Home Playoffs 760 0.3763 0.4848 0 1 Away Record 712 0.5152 0.2615 0 1 Away Playoffs 760 0.3763 0.4848 0 1 Non-Sunday 760 0.1329 0.3397 0 1 Division Rival 759 0.3768 0.4849 0 1 Conference Rival 760 0.3763 0.4848 0 1 Betting Line 760 -2.4138 5.9049 -20.5 14.5 Over/Under 760 43.823 4.3442 33 56 Betting Line ^ 2 760 40.649 51.3837 0 420.25
Table A.3
Welki and Zlatopper (1999) Original Tobit Regression Results
Standard Significant at Significant at Independent Variable Coefficient Error .05 .01
Price -0.0072 0.0034 Yes No Income 0.0001 0.0001 No No Home Team Record 0.0519 0.0134 Yes Yes Visiting Team Record 0.0107 0.0114 No No Betting Line 0.0020 0.0008 Yes No Betting Line^2 2.78E-06 0.0001 No No Week of Season -0.0042 0.0009 Yes Yes Temperature 0.0003 0.0004 No No Rain -0.1913 0.0451 Yes Yes Temperature x Rain 0.0028 0.0007 Yes Yes Dome -0.0009 0.0406 No No Division Rival 0.0146 0.0067 Yes No Conference Rival -0.0056 0.0080 No No Non-Sunday 0.0240 0.0101 Yes Yes
Note. Welki and Zlatopper (1999) didn’t list their P-values or t-Statistic values for their tobit regression, instead indicating whether or not a given variable was significant at the .05 or .01 level. Significance at the .05 level requires a t-Statistic value of at least 1.645, and significance at the .01 level requires a t-Statistic value of at least 2.575.
Table A.4
Coefficient and Significance Comparison Table
Welki & Expanded Replicated Zlatopper's Predicted Model Model Independent Variable Findings Coefficient Findings Findings
Price -** - +*** +*** Income + + +* +*** Home Team Record +*** + +*** +*** Visiting Team Record + + + +*** Betting Line +** + +* - Betting Line^2 + + + +*** Week -*** - - -** Temperature + - + -* Precipitation -*** - - - Temp x Precip +*** + + + Dome - + + + Division Rival +** + + + Conference Rival - + -** - Non-Sunday +*** + + +* Metro Population N/A + -*** N/A Substitutes N/A - +* N/A Previous Attendance N/A + +*** N/A Early 12-3 N/A - - N/A Mid 3-6 N/A + + N/A Holiday N/A + + N/A Stadium Age N/A - +* N/A Home Team Playoffs N/A + +* N/A Away Team Playoffs N/A + +*** N/A Vegas Over/Under N/A + + N/A
Note. “–” indicates a negative coefficient, while “+” indicates a positive coefficient, * indicates p < .1, ** indicates p < .05, *** indicates p < .01
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