NFL Demand

NFL Demand: On-Field Determinants of Off-Field Success | Ben Forster

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NFL Demand: The On-Field Determinants of

Off-Field Success

Benjamin G. Forster

Attn: Professor Thomas Downes December 2014


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Introduction

The Most Valuable League in the World

The NFL is the highest-grossing sports league in the world, generating revenues far

beyond other leagues’ financial aspirations.1 The league does not show signs of slowing down,

as they increased their revenue by over 5% from the 2012 to the 2013 season. The

Commissioner of the NFL, Roger Goodell, claimed in 2010 that he expected the NFL to reach an

annual income of over $25 billion by the year 2027. With the current value of the league sitting

near $8 billion, Goodell looks to more than triple its value in less than 20 years.2 This is a bold

and optimistic goal, but one that may be achievable, as the NFL continues to grow in value each

year. From lucrative TV Deals with broadcasters (the most recent being a $4 billion dollar, four-

year deal signed in 2010 with Direct TV) to partnerships and sponsorships with large companies,

the NFL has plenty of ways to increase its revenue. Most recently, the NFL signed a $400 million

deal with Microsoft for the exclusive use of Microsoft Tablets on the sidelines for all teams

during the 2014 season. All this aside, the NFL also is responsible for the most watched

television program in history, several times over.

The Super Bowl in 2013, between the Denver Broncos and the Seattle Seahawks, was

the most watched television program in US history, bringing in over 110 million viewers.3 This,

however, is not a new trend. The annual Super Bowl game often breaks this record, year after

year. In fact, the top three most watched programs in the US before the most recent Super

Bowl were all previous Super Bowls, played within the last decade. More and more Americans

1 “How the NFL Makes the Most Money of Any pro Sport.” CNBC.

2 “NFL Takes Aim at $25 Billion, but at What Price?” USA Today

3 “Super Bowl XLVIII Most-Watched TV Program in U.S. History.” NFL.com.


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are watching football, and the NFL’s growing net-worth reflects this. If Goodell’s goal is to be

achieved by 2027 then, it is critical to understand the driving factors behind demand for NFL

football. Previous research suggests that there are many important factors that contribute to

demand, from ticket pricing and stadium capacity to population of a specific team’s city. These

authors and their research (discussed below) seem to suggest that a large amount of what

determines demand for the NFL are off-field factors. However, concessions are made that while

on-field performance is not the driving factor behind NFL demand, it does contribute to and

influence changes in demand. With this in mind, this paper seeks to answer the question of

what aspects of on-field play are the most important in determining demand for the NFL. This is

an important question to ask because teams and owners may begin to change how the draft

and value players depending on how the player is expected to influence demand. For example,

do prolific offenses have a bigger effect on demand than prolific defenses? What is more

important when trying to increase demand? If Goodell and the rest of the NFL are determined

to continually increase the value of the league, the effect of on-field factors must be taken into

account.

Football of the Future

A lot of recent regulations and rule changes in the NFL have been put in place in order to

limit injuries and the possibility of concussions during play. However recent amendments to

these rules have some wondering whether the league is framing its regulations in order to favor

offensive success. After the most recent rule changes, coming in the 2014 offseason, many find

that “the league has made several rule changes hoping to protect players from sustaining


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concussions, most notably the “defenseless” player and helmet-to-helmet tackle rules. Now it

has been speculated the league is adjusting rules to help offenses score more. Most notably are

the changes to defensive holding penalties which now include a defender grabbing the jersey of

an offensive player. Also of note are illegal contact penalties, which now state that the

defender cannot initiate contact after 5 yards off the line of scrimmage while the quarterback is

in the pocket or the ball is in the air. These changes limit the ways in which a defender can

interfere with an offensive play, and therefore as a result, offenses will have more success in

the seasons to come. This seems to indicate that the league recognizes that there is an innate

importance to offense and offensive production of a team. This is incorporated into the

following research, exploring the effect of total points scored on demand in the NFL. In

summation, recent rule changes seem to suggest that the NFL is trying to increase average

offensive production. I seek to answer the question of if this behavior is explained in part by the

NFL’s commitment to increase revenue. I theorize that the NFL believes that higher scoring

games are more exciting, will draw a larger TV audience, which will in turn lead to more

lucrative sponsorships and more valuable broadcasting deals. Additionally, by increasing game

excitement the NFL seeks to widen their fan base.

Previous research concerning demand in the NFL has traditionally used game-day

attendance as a measure of demand in the NFL. However, this measurement is less likely to

return accurate results in the NFL. For leagues like the MLB, a strong indicator of demand is the

attendance of a given game in a given stadium. However, for leagues like the NFL, measuring

demand is not as straightforward. Even with stadiums capable of holding upwards of 60,000

fans, almost every single NFL game is sold out, no matter the teams playing or the city in which


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it is being played. With this in mind, and as technology progressed, researchers began

employing other ways of measuring NFL demand. There are several papers which choose to

measure demand by observing trends in television broadcast demand, or similarly, television

rating for weekly games. However in 2014, data availability and evolving viewing habits make

the use of TV viewership problematic. With these concerns in mind, this paper explores

potential determinants of demand, using Google Trends to generate a new measure of

demand. This measure indicates how many people are searching for news, tickets,

merchandise, and other information with respect to each team. These data can be collected

from GoogleTrends.com from 2004 to present day.

The Question At Hand

All of the above introductions serve to explain what this paper seeks to accomplish. The

NFL has a large net worth that only seems to increase year after year. The commissioner of the

league has publicly announced his plans to see the NFL’s value increase three-fold over the next

two decades. While much past research has found that on-field performance has an effect on

demand, most papers focus on off-field factors. The question that not many authors seem to

address is what on-field aspects of football have the most effect on demand.

The recent debunking of the adage “Defense wins championships” tends to indicate that

offense may be the most important aspect in regular and postseason success for NFL teams.

This, coupled with the fact that recent rule changes have benefitted the offensive side of the

ball suggests that elite offenses may also have a positive effect on demand. The NFL wants to

create more exciting, high-scoring games in order to, in part, increase revenue from


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sponsorships, merchandising and other off-field factors. Will all of this in mind, I seek to

discover the most important on-field factors of football with respect to impact on demand.

Before analysis is undertaken, I looked at further previous research in order to form

expectations of what results my research will yield.

Literature/Previous Research

Defense Wins Championships?

Defense wins Championships. This tried and true adage of the National Football League

has been stated and restated over and over again since the merger over 40 years ago. A team

sees better and more sustained postseason success if they can field a top-tier defense. Coach

Bear Bryant is credited with the original quote, saying “Offense wins games, but defense wins

championships.” But is this really the case? Does a highly ranked and prolific defense really lead

to on-field success in the NFL? Many recent attempts to answer this question have yielded

interesting results.

Stephen J. Dubner, co-author of the best-selling book Freakonomics, decided to

evaluate this statement critically, and his findings are as follows: “Contrary to conventional

wisdom…Advanced NFL Stats found that elite offenses historically out-perform elite defenses”.4

Additionally, a second analysis by Moskowitz et. al support the findings after an analysis of

10,000 regular season games, and found that “Defense is no more important than offense. It’s

not defense that wins championships. In virtually every sport, you need either a stellar defense

4 “Football Freakonomics >> Does Defense Win Championships?”


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or a stellar offense, and having both is even better.”5 These two independent analyses

demonstrate a growing trend towards a debunking of the belief that defense is more important

for a team’s on-field success. These recent trends suggest that not only is offense as important

as defense, but it may be the case that it is more important to the on-field regular season and

post-season success of an NFL team. Therefore, it seems that offense is more important to on-

field success. However, does this trend translate to off-field success? Does fielding an elite

offense increase demand more than fielding and elite defense? The findings by Dubner et. al

suggest that the answer is yes. With past results and conclusions like these, I would look to

expect offensive statistics to be more important in determining demand in the NFL.

There have been many other previous research articles that address measuring demand

in the NFL. As a valuable league, there is plenty of reason to spend time researching which

aspects of play have the largest effect on demand in order to continue to increase the net-

worth of the NFL.

NFL Gameday Attendance Research, a paper by Welki and Zlatoper in 1994 was one of

the first papers to truly try and explain determinants of NFL demand. The paper uses a Tobit

analysis and divides the determinants of attendance into several categories. These are

economic variables (price, income), demographic variables (population), and quality of game

variables (winning percentage, difference in records). The analyses looked only at the 1991

season, and the findings were consistent with the authors’ expectations. They found that

“higher ticket prices reduce attendance with the demand appearing to be inelastic, and a

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“Freakonomics » Does Defense Really Win Championships?”


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winning home team spur game-day attendance.”6 These findings suggest that higher ticket

prices have a negative influence on demand, which tends to be inelastic. This, rightly, suggests

that stadiums seem to be consistently selling-out or coming close to selling-out their stadiums

every given game day. This is reflected in the inelasticity of demand, being consistent at the

stadium capacity. However, the second finding of their research does seem to suggest that on-

field performance does have an impact on their measurement of demand (attendance). This is

consistent with my expectations of the results of the regression analyses conducted in this

paper. This paper is an ideal illustration of why using attendance as a measure of demand may

lead to misleading results. Because demand for attendance is inelastic, an increase in the

average price of tickets would seem to reflect an increase in demand over the previous year for

a given team. This is because, if the team continues to sell-out each game at a higher ticket

price than they were able to last year, this indicates an increase in demand, with more

consumers willing to pay a higher price for the same experience as the previous year. By using

Google trends over a 10-year period, this paper will be able to illustrate demand as more

elastic, allowing for increases and decreases due to both on- and off-field factors. However, this

paper done in the early 1990’s did suggest that team on-field performance did have a positive

effect on attendance and demand, which is a trend I believe continued into the 21st century.

A chapter of Jeffrey Dubin’s 2002 book Empirical Studies in Applied Economics also

attempts to measure the determinants of NFL demand, again using ticket sales and attendance.

Dubin looks at games from the 1995-1999 seasons and his results are very interesting. By using

a model created by DeSerpa in 1994 which dictates that an individual’s demand is influenced by

6Welki, Andrew M., and Thomas J. Zlatoper. “US Professional Football: The Demand for Game-Day Attendance in 1991.”


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the demand of the greater population, Dubin finds that ticket pricing has a significant positive

effect on demand: “The ticket price effect is statistically significant and positive; this effect

confirms…and supports a positive association between [ticket] price and sales.”7 This result

seems to be a more accurate description of how ticket prices affect demand. In my regressions,

I will be using ticket prices as a control when looking at on-field variables, and I expect the

coefficient to be positive, reflecting the findings in Dubin’s analysis. A second and more direct

finding from his paper is the fact that “…a team’s performance, while helpful in generating

ticket sales, is not by any means the only contributing factor.”7 This assertion reinforces the

earlier conclusion that a large amount of past research shows that many off-field factors have

an effect on NFL demand, yet on-field factors also contribute in a statistically significant

manner. This conclusion has yet to be explored in a thorough and exhaustive manner, which is

what this paper aims to do. Many other papers have confirmed that on-field performance

effects demand, so which factors of on-field performance have the most effect on demand?

A third paper, by Spenner, Fenn and Crooker (2010) examine both attendance and

rational addiction to explain the consistent increase in demand for NFL games over the past

decades. They argue that an individual is more likely to attend a football game in the future if

they have attended a game in the past. The analysis uses a Two-Stage Least Squares estimate

model, and their findings are important to how this paper’s regressions were formed. The

authors argue that attending NFL games can be considered a habit-forming good, where

consumers are making decisions based on their current utility-maximization plan, which is

influenced by past and expected future behavior. This is an interesting model which

7 Dubner, Stephen J. “Empirical Studies in Applied Economics”


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incorporates past and present data. Their findings were as follows: “It is found that past and

future attendance, winning percentage, the age of the stadium in which a team plays…influence

attendance.”8 This conclusion, in part, confirms the hypothesis that winning percentage and

therefore on-field performance influence demand for the NFL. Additionally, the model used,

which incorporates past and present attendance was the driving factor behind the decision to

include lagged variable in the regressions run in this paper. With this in mind, demand may also

be driven by the on-field performance of the team in the previous year. Did the team make the

playoffs the previous year, does that lead to an increase in demand in the following year? By

using lagged variables, I will be able to address this question.

All of the previous research suggests that although on-field performance may not be the

only factor influencing demand in the NFL, it does consistently seem to have a statistically

significant effect on demand. Again, this begs the question as to which aspect of on-field play is

the most important to determining demand, and that is the question this paper seeks to

answer. Although based on previous research done by others, this paper distinguishes itself in

several ways. First, it addresses aggregate seasonal demand for each team using a variable

(Google Trends) that has not yet been used by others when considering a representation of

demand. Second, it looks at 10 consecutive seasons (2004-2013) instead of focusing on 1-4

years of data, it encompasses a more expansive time period.

The importance of the findings in this paper cannot be understated. If it is found that an

elite offense is more important to winning championships and increasing demand it may affect

how teams draft and where they build depth on the roster. It will also influence how the league

8 Spenner, Erin: Fenn, Aju: Crooker, John. “The Demand For NFL Attendance: A Rational Addiction Model”


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continues to evolve, putting more emphasis on high-scoring games and teams and less on elite

defenses. Before analysis is undertaken, the variables used will be presented and explained in

order to better facilitate an understanding of the upcoming results.

Data and Models Used

Data was collected from several different databases in order to compile a complete and

relevant dataset. All on-field team statistics were collected from Pro-Football-Reference.com9, a

statistical database that has complete seasonal statistics from 2004 to the present. By collecting

all on-field statistics from one place, it controls for any variation in measurement tactics that

are sometimes found in sports statistics (i.e. different systems rank offensive and defensive

production in different ways). In addition to these statistics, each NFL team’s value each year

was included, collected from Rodney Fort’s Sports Business Data collection.10 The Google

Trends Data was collected from the Google analytics website. Google Trends are measured on a

weekly basis, and they have values between 0 and 100. All values for a team are relative to the

other 31 teams’ popularity on Google over the past week. For this paper, weekly Trends data

was collected for the regular season of each year from 2004 to 2013. Finally, each team’s

weekly trends were summed in order to capture aggregate demand for an entire season.

11Other city-specific data was collected from the American Community Survey 1-year

estimates.12 All of these data were collected and organized in an excel document, which was

9 “NFL Season By Season Team Offense.” Pro-Football-Reference.com.

10 Fort, Rodney. “Rodney Fort’s Sports Business Data - Rod’s Sports Economics.” 11

This resulted in collecting weekly data from the first week in September to the last week in December 12

The city-specific variables collected were “Per-capita income” and “total population”. When included in the regressions, their effect was negligible and are therefore not featured in the regressions presented in this paper


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then imported into STATA for further analysis. Presented below are summary statistics for

every variable used in regressions.13

Summary Statistics

Variable Label Variable name Count µ σ Min Max

Google Trends lnTrends 320 4.2318 0.6781 2.7726 5.9712

Win % winpct 320 0.5001 0.1954 0 1

Points Scored pts 320 349.3188 73.3376 168 606

Points Allowed ptsAllowed 320 349.3188 59.6809 201 517

Offensive Rank offrank 320 16.5 9.2476 1 32

Defensive Rank DefRank 320 16.5 9.2476 1 32

Avg. Pass

Yds/Game

avg_passyds-game 320 218.0998 40.2601 118.625 340.25

Avg. Rush

Yds/Game

avg_rushyds_game 320 115.0354 21.5623 70.5625 183.6875

Team Value teamValue 320 982.0156 241.1213 552 2300

Passing TDs passTDs 320 22.3656 7.6051 7 55

Rushing TDs rushTDs 320 13.0375 5.1221 2 32

Ticket Price tixprice 320 70.2350 16.3395 37.13 120.85

Postseason playoffTeam 320 0.3765 0.0024 0 1

N 320

For all on-field statistics (aside from average pass and rush yards per game) the numbers

presented here are end of regular season totals. The Team’s Value is measured in thousands of

dollars. From looking at these summary statistics, we can see a few things that stand out. First,

the average NFL team scores roughly nine more touchdowns by passing than by rushing.

Additionally although Points Scored and Points allowed both have the same mean, the standard

deviation of the two are markedly different, indicating that there is larger spread of team’s

scoring points than teams allowing points. The win percentage variable has an average of 0.500

which makes sense when taking into account for all games played by all teams over ten years.

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Regressions also include lagged values of variables presented in table


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All of these variables were included because through past research they were suggested to

have the biggest impact on demand. Variables such as Team Value and Ticket price are included

to account for team-specific effects.

With respect to the models used in regression analyses, there were three separate

regressions run, each with specific determinants. First, all models were fixed-effects models

with standard errors robust to heteroskedasticity and autocorrelation. The basic model, with

simple regressions followed the regression equation presented here:

Yit = β0 + β1X1,it + β2X2,it + … + βrXr,it + εit

This fixed-effect regression analysis of panel data for 10 seasons in the NFL is the model

followed throughout the paper. After initial regressions, a second series of regressions were

undertaken, including lagged values of certain variables. As mentioned above, this set of

regressions was influenced by previous literature suggesting that present performance was

influenced by past performance. Finally, in the lagged regression set, the variable for playoff

team is included, a dummy variable that indicates whether a team qualified for the playoffs of a

given season.

In order to facilitate clear interpretation of results, the seasonal sum of each team’s

Google Trends data was transformed into its natural log (ln (sum of seasonal Google Trends)).

This transformation gives more meaning to the coefficients, as they now indicate percent

change in Google Trends. This percent change, for the sake of this analysis, is also considered

the change in demand for an NFL team.


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As mentioned earlier, it appears that offense is more important to playoff success than

defense, contrary to a long-standing myth that defense wins championships. Does this

offensive importance hold true when influencing demand? Early results from the analyses

suggested yes:

Referring to the tables above, it can be seen that over the past ten years, teams that qualified

for the playoffs had scored nearly 100 more points than those teams not playoff bound. This

trend holds when comparing Google Trends to teams that did and did not make the playoffs.

Teams that made the playoffs had an aggregate trend sum roughly 30 points higher than non-

payoff teams. Just with these initial glances at the data presented here, it appears that offense

is more important than defense in reaching the playoffs, and reaching the playoffs is very

important to NFL demand. It is now time to take a look at the regression performed.

Empirical Results and Interpretation


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All regressions were run through STATA, using fixed effect log-linear model robust to

heteroskedasticity and clustered standard errors:


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Simple Regressions

(1) (2) (3) (4) (5) (6) (7)

VARIABLES lnTrends lnTrends lnTrends lnTrends lnTrends lnTrends lnTrends

pts 0.00138*** 0.00225*** 0.00320*** 0.00352***

(0.000501) (0.000639) (0.000931) (0.000860)

ptsAllowed -0.00104** -0.000853** 0.00200 0.00198

(0.000391) (0.000410) (0.00127) (0.00127)

avg_passyds_game 0.00132 0.00238**

(0.000957) (0.00112)

avg_rushyds_game 0.00190 0.00324**

(0.00134) (0.00135)

teamValue 0.000722** 0.000673** 0.000698** 0.000716*** 0.000676**

(0.000277) (0.000271) (0.000281) (0.000256) (0.000260)

tixprice 0.0150*** 0.0138*** 0.0155*** 0.0250*** 0.0238*** 0.0115*** 0.0122***

(0.00405) (0.00410) (0.00376) (0.00231) (0.00219) (0.00346) (0.00358)

offrank -0.00651** 0.00550 0.0160** 0.0174***

(0.00265) (0.00570) (0.00649) (0.00626)

DefRank -0.00461 -0.00665** -0.0187** -0.0182**

(0.00326) (0.00273) (0.00748) (0.00767)

winpct 0.422** 0.280** 0.271** 0.273**

(0.158) (0.121) (0.125) (0.124)

passTDs 0.0171*** 0.00354

(0.00592) (0.00603)

rushTDs 0.0115* -0.00291

(0.00659) (0.00657)

Constant 1.841*** 1.796*** 2.229*** 2.446*** 1.910*** 0.769* 0.666*

(0.267) (0.249) (0.188) (0.211) (0.345) (0.379) (0.381)

Observations 320 320 320 320 320 320 320

R-squared 0.610 0.592 0.607 0.568 0.590 0.644 0.640

Number of teamid 32 32 32 32 32 32 32

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1


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This first table yields very interesting results that support findings found in previous

papers. First and more importantly, it appears that the number of points a team scores is

consistently statistically significant at the 1% level. Although it may at first seem as if the

amount of points scored does not have real-world significance on impacting demand, but this is

not necessarily the case. Considering each touchdown (and extra point) nets a team 7 total

points, the effects are magnified. With this in mind, each touchdown increases demand by

1.81%.14 This initial results seems to suggest that offensive production is very important to

determining demand. Looking at its counterpoint, it seems as if the amount of points a defense

allows is less important. If a defense allows a touchdown, the demand, on average, will

decrease by 1.03%. Also important to note is that while the all four coefficients for Points

Scored are significant at the 1% level, only two of the four coefficients for Points Allowed are

statistically significant, and only at the 5% level. What this seems to suggest is that not only

does allowing a touchdown affect demand less than scoring a touchdown, it is also less

statistically significant. This finding is in line with the theory that offensive production is more

important to increasing demand than defensive production.

Moving down the table, it appears as if average pass yards per game and average rush yards

per game do not have a large impact on demand, and are only statistically significant at the 5%

confidence level in one regression.15 However in the regression where they are significant, their

impact is large. In regression (2), the coefficients suggest that an extra 10 yard run will increase

demand by 3.24%. The logic behind this seemingly large number can be found when looking at

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This was calculated by averaging the “percent change in demand when scoring a touchdown” in each of the four regressions that include the Points Scored variable 15

Other regressions were run with these two variables with results suggesting statistical insignificance and were therefore not included in the results presented here


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the other variables included in regression (2). The pass yards and rush yards per game are the

only two variables that reflect offensive production, and therefore absorb explanatory power

usually found in variables such as Points Scored and Points Allowed.

Other interesting results are found in the coefficient values for both Offensive and Defensive

Rank. Looking at regressions (4), (5), (6), and (7), Defensive rank is consistently negative and

statistically significant at the 5% level in regressions (5), (6), and (7). This suggests that the

lower ranked a team’s defense the more adverse effect the ranking has on demand. This makes

sense intuitively, as one would imagine that teams with poor defensive units would have a

lower demand. Conversely, the coefficients for offensive rank are very inconsistent, with one

being negative (regression (4)) and another being statistically insignificant (regression (5)). In

regressions (6) and (7), offensive rank is positive and statistically significant which does not

seem to make much sense. However, looking at the other variables included in those

regressions makes these coefficients more understandable. If offensive rank changes but a

team does not score more points or allow less points, demand should not decrease and may

increase because they are performing at the same level while being ranked lower. These

suggest that a poorly ranked defense will lessen demand noticeably, but a poorly ranked

offense does not seem to have a consistent negative or positive effect on demand. This is an

interesting result that seems to suggest teams make sure to field skilled defenses even though

offense seems to be more important for both on-field and off-field success.

Win percentage has a consistently statistically significant effect on demand, which also

suggests that success on the field translates to success off the field in increasing demand. One

final important note about this set of regressions is that when Passing touchdowns and rushing


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touchdowns take the place of Points Scored and Points Allowed in a regression, they are both

statistically significant. Looking at regression (5), one can see that every passing touchdown

increases demand by 1.71% and every rushing touchdown increases demand by 1.15%.

Additionally, the coefficient for passing touchdowns is significant at the 1% level, while the

coefficient for rushing touchdowns is significant at the 5% level. This seems to suggest that

passing touchdowns increase demand more than rushing touchdowns, and the passing

touchdown coefficient is also statistically significant at a higher level.

What the results from this first regression analysis seems to suggest is that scoring points on

offense is more important than not allowing points on defense. Additionally, a poorly ranked

defense will decrease demand much more than a poorly ranked offense. Finally, passing

touchdowns lead to a bigger increase in demand than rushing touchdowns.

After these regressions were complete, a second analysis was undertaken. This time, the

variable indicating whether a team qualified for the playoff was included, as well as lagged

versions of variables presented above. These results are presented in the table below:

Lagged/Playoff Regressions


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(1) (2) (3) (4)

VARIABLES lnTrends lnTrends lnTrends lnTrends

offrank -0.00901*** -0.00510*

(0.00241) (0.00275)

l1offrank -0.00465*

(0.00256)

DefRank -0.00516* -0.00353

(0.00264) (0.00298)

l1DefRank -0.00321

(0.00238)

playoffTeam 0.137** 0.117 0.184*** 0.0949**

(0.0566) (0.0694) (0.0563) (0.0459)

tixprice 0.0167*** 0.0141*** 0.0161***

(0.00404) (0.00378) (0.00363)

teamValue 0.000706** 0.00155*** 0.000746** 0.000749***

(0.000275) (0.000255) (0.000276) (0.000252)

winpct 0.340* 0.343**

(0.187) (0.158)

l1winpct 0.124

(0.161)

l1playoffTeam 0.0630 0.0766*

(0.0500) (0.0420)

passTDs 0.0138*** 0.0153***

(0.00397) (0.00334)

rushTDs 0.000567 0.00589*

(0.00384) (0.00324)

Constant 2.680*** 1.976*** 1.863*** 2.304***

(0.230) (0.265) (0.223) (0.205)

Observations 288 288 288 320

R-squared 0.553 0.549 0.583 0.599

Number of teamid 32 32 32 32

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1


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These regressions included the variable indicating a team’s qualifying for the playoffs, as

well as lagged values of other variables. The results found in these regression also seem to

indicate that offense is more important than defense.

First, looking at the first four variables in the table (Offense and Defensive ranks and their

lags), we can see findings from the previous set of regressions differ slightly in these

regressions. It appears as if offensive rank is more important, as it is consistently more

statistically significant than defensive rank, and the coefficients also hold a larger negative

value, indicating a more adverse effect on demand. In the previous set, it appeared that having

a poor defense was more detrimental to total demand than having a poorly ranked offense.

Here, both seem to have a real adverse effect. An explanation can be found by looking at the

other variables included in the regressions above. Because these regressions include lagged

values and do not include other variables such as Points Scored, the Offensive and Defensive

ranking are the main variables to explain on-field performance with relation to demand. These

results, once again seem to suggest an elite offense is more important and beneficial to

demand than an elite defense.

The addition of the lagged values demonstrated that the present season statistics have

more effect on demand than past performance. For all lags included it appears that the lagged

coefficients are both less statistically significant and have a lesser absolute value than their

present day counterparts. This suggests that while some lagged coefficients hold statistical

significance, they have a much smaller effect on demand.


22

This observation holds true when including the lagged value of “playoffTeam”, which does

not hold any statistical significance in the regression in which it is included (2)16 However,

whether or not a team makes the playoff after the end of the current season seems to have a

large effect on demand. Using coefficients found in these results, demand goes up by an

average of 13.86% (only statistically significant coefficients used to estimate effect). This is a

very important finding, as it truly ties on-field success to increasing demand for the NFL.

Finally, the coefficients associated with passing and rushing touchdowns yield similar

results to those found in the previous set of regressions. Passing touchdowns are statistically

significant at a higher level than rushing touchdowns, and also have a larger positive effect on

demand, which is consistent with the results found in the previous set of regressions. All of

these findings have the potential to influence changes in the NFL if they continue to strive to

achieve their $25 billion goal.

Summary & Implications

The findings of this paper confirm several conclusions made by others in previous

research. First, it appears that a potent offense is more important to overall demand than a

potent defense. This suggests that offensive excitement and scoring increases demand more

than a stout defense unwilling to surrender points. This works in tandem with the finding that

the total number of points a team scores is much more important to increasing demand than

having a defense that surrenders very little points. Essentially, demand is tied to offensive

production much more than defensive prowess. With this in mind, it appears that passing

16

Other regressions were run including the lagged value of “playoffTeam” and results proved similar to regression (2) and were therefore not included in the final regression table


23

touchdowns increase demand much more than rushing touchdowns. This can be attributed to

the fact that rushing touchdowns are more likely to be rushes of 1-5 yards, while passing

touchdowns can typically be plays of 20+ yards. This may increase excitement levels more than

rushing touchdown which increases interest in the sport and increases demand. Also important

to note, winning percentage and playoffs also have a very large impact on demand. Teams that

consistently go to the playoffs will consistently have a higher demand. This ties the importance

on-field success to off-field success (demand).

What does this mean for the NFL and their future? As mentioned earlier, it appears as

though the NFL is altering its rules and regulations in order to facilitate a higher level of

offensive production throughout the league. This behavior, when coupled with the implications

of the findings in this paper, indicate the NFL is aware of these connections and they are

altering regulations in order to increase demand and achieve Goodell’s goal of increasing net

worth. Additionally, the finding that passing touchdowns and offensive scoring are more

important to increasing demand, these findings may alter how certain teams draft and build

their roster. It seems as if the quarterback and receiving corps is more important than running

backs when considering their effect on demand. A team may focus on finding better offensive

players and be willing to sacrifice the skills of their defensive corps in order to field an elite

offense.

A second point of interest in this analysis is the large effect of playoff eligibility on

demand. While demand for an individual team increases when they make the playoffs, there is

always the same number of teams each season that will make the playoffs. This means that

unless the NFL decides to expand the postseason structure, there will be no aggregate change


24

in league demand on a year to year basis. However, this is exactly what Roger Goodell plans to

do. There had been rumors of a playoff expansion, allowing more than the current twelve

teams to qualify. Many people speculate that change could come sooner rather than later, as

soon as the upcoming 2015 season. This push to increase playoff teams seems to suggest that

the NFL recognizes the large impact that playoff have on demand. If they increase the number

of teams in the postseason they will also be affecting demand in a largely positive way. In fact,

in the most recent spring meetings, Goodell was quoted as saying “I do believe it (expanded

playoffs) will be approved for the 2015 (season).”17

Conclusions and Further Research

This paper sought to find the most important aspects of on-field play with respect to

affecting the demand for both individual teams and the NFL as a league. Through multiple

regression analysis both with and without lagged variables, this paper has found several

important and interesting conclusions.

Contrary to the old adage “Defense Wins Championships,” it appears that not only has

recent research shown that offense is more important to on-field success, it is also more

important to off-field success and increasing demand. This conclusion seems to be reflected in

the behavior of the NFL and their recent rule and regulation changes that benefit offense and

make defending offensive players more difficult to do without being penalized.

The finding that playoff teams have a consistently higher demand means that the NFL

17

“Roger Goodell Expects Playoff Expansion in 2015.” NFL.com.


25

would be attempting to increase the number of playoff teams each year in order to increase

aggregate league demand. It is possible that when more teams enter the playoffs, the effects

found here will become diluted and less significant. This is a suggestion for further research. If

the playoffs are expanded starting next season, it would be interesting to compare the effects

of playoff success on demand before and after the expansion.

This paper finds that offense is more important to both on-field and off-field success,

and even more specifically the higher the frequency of passing touchdowns the highest impact

on team demand. This is a relevant finding because it may affect how teams build their rosters

and what organizations strive for when referring to elite offenses or defenses. There is plenty of

room for further research, most importantly after the expansion of the playoffs is put in place.

Does defense win championships? No, Offense wins championships and offense is more

important to off-field success.


26

Works Cited

“The Demand For NFL Attendance: A Rational Addiction Model” Spenner, Erin: Fenn, Aju: Crooker, John. Journal of Business and Ecnomics Research, December 2010. Accessed Dec. 17th, 2014

“Empirical Studies in Applied Economics” Dubner, Stephen J. Chapter 2, Accessed December 17th,

2014. “Freakonomics » Does Defense Really Win Championships?” Accessed December 18, 2014.

http://freakonomics.com/2012/01/20/does-defense-really-win-championships/. “Football Freakonomic >> Does Defense Win Championships?” Accessed December 18, 2014. http://www.nfl.com/features/freakonomics/episode-15 “How the NFL Makes the Most Money of Any pro Sport.” CNBC. Accessed December 18, 2014.

http://www.cnbc.com/id/101884818. “NFL Season By Season Team Offense.” Pro-Football-Reference.com. Accessed December 18, 2014.

http://www.pro-football-reference.com/years/NFL/team_stats.htm. “NFL Takes Aim at $25 Billion, but at What Price?” Accessed December 18, 2014.

http://www.usatoday.com/story/sports/nfl/super/2014/01/30/super-bowl-nfl-revenue-denver-broncos-seattle-seahawks/5061197/.

“Rodney Fort’s Sports Business Data - Rod’s Sports Economics.” Accessed December 18, 2014.

https://sites.google.com/site/rodswebpages/codes. “Roger Goodell Expects Playoff Expansion in 2015.” NFL.com. Accessed December 17, 2014.

http://www.nfl.com/news/story/0ap2000000352241/article/roger-goodell-expects-playoff-expansion-in-2015.

“Super Bowl XLVIII Most-Watched TV Program in U.S. History.” NFL.com. Accessed December 18,

2014. http://www.nfl.com/superbowl/story/0ap2000000323430/article/super-bowl-xlviii-mostwatched-tv-program-in-us-history.

Welki, Andrew M., and Thomas J. Zlatoper. “US Professional Football: The Demand for Game-Day

Attendance in 1991.” Managerial and Decision Economics 15, no. 5 (September 1994): 489–95. doi:10.1002/mde.4090150510.

http://freakonomics.com/2012/01/20/does-defense-really-win-championships/

http://www.nfl.com/features/freakonomics/episode-15

http://www.cnbc.com/id/101884818

http://www.pro-football-reference.com/years/NFL/team_stats.htm

http://www.usatoday.com/story/sports/nfl/super/2014/01/30/super-bowl-nfl-revenue-denver-broncos-seattle-seahawks/5061197/

http://www.usatoday.com/story/sports/nfl/super/2014/01/30/super-bowl-nfl-revenue-denver-broncos-seattle-seahawks/5061197/

https://sites.google.com/site/rodswebpages/codes

http://www.nfl.com/news/story/0ap2000000352241/article/roger-goodell-expects-playoff-expansion-in-2015

http://www.nfl.com/news/story/0ap2000000352241/article/roger-goodell-expects-playoff-expansion-in-2015

http://www.nfl.com/superbowl/story/0ap2000000323430/article/super-bowl-xlviii-mostwatched-tv-program-in-us-history

http://www.nfl.com/superbowl/story/0ap2000000323430/article/super-bowl-xlviii-mostwatched-tv-program-in-us-history

Documents

NFL Demand