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
5
“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
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Welki, 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
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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
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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.
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Other 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.”
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This resulted in collecting weekly data from the first week in September to the last week in December
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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.
13
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
14
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.

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