MANAGERIAL AND DECISION ECONOMICS
Manage. Decis. Econ. 31: 517–529 (2010)
Published online 16 April 2010 in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/mde.1506
NCAA Conference Realignment and
Football Game Day Attendance
Mark D. Groza
University of Massachusetts, Amherst, MA, USA
Between the 2004 and 2005 football seasons, 17% of the college football programs competing in
the NCAA’s Football Bowl Subdivision (FBS) changed conference affiliation. Football
represents nearly half of the revenue generated by athletic departments competing at the FBS
level and is thus critical to their financial success. The objective of this study is to estimate the
impact a change in conference affiliation has on football game day attendance. The results
indicate teams that changed conferences enjoyed an increase in attendance even after controlling
for the increase in quality of competition. Copyright r 2010 John Wiley & Sons, Ltd.
INTRODUCTION
Collegiate athletics have long played an integral
role in higher education in the United States. The
National Collegiate Athletic Association (NCAA)
was created over a century ago to create a uniform
set of rules in college football and to protect the
integrity of college sports. Today, the NCAA is
comprised of a number of divisions based on
the size and the competitiveness of its member institutions. The NCAA’s Football Bowl
Subdivision (FBS) consists of the nation’s most
competitive and profitable football teams. As of
the 2007 football season, 119 universities were
among the ranks of the NCAA’s FBS. All but four
of these 119 universities belong to 11 conferences
across the country. Between the 2003 and 2007
seasons, 19 football programs in the FBS changed
conference affiliation. Over that same period of
time, two programs joined the FBS from the
Football Championship Subdivision (a lowered
tiered division). By 2007, 18.5% of the football
*Correspondence to: Isenberg School of Management,
University of Massachusetts, 121 Presidents Drive, Amherst,
MA 01003, USA. E-mail: [email protected]
Copyright r 2010 John Wiley & Sons, Ltd.
teams in the FBS were playing in a different
conference than in 2003.
College football is a multi-million dollar
industry. According to US Department of
Education figures, in 2006 the top 34 FBS
football programs each netted over $10 million in
profits. During that same season another 30
football programs each netted over $1 million in
profits. In total, the 119 FBS football programs
netted over $804 million during the 2006 football
season. Due to football’s earning power, it is often
used to fund non-revenue sports in athletic
departments. In response to federal legislation,
universities across the country are required to
sponsor a certain number of women’s sport
programs. This, among other factors, has placed a
tremendous amount of pressure on the budgets of
athletic departments.1 Athletic directors continually
look to their football programs to carry this
financial burden. During the 2006 fiscal year, the
median revenue of football programs in the FBS
accounted for 43% of all athletic department
generated revenues. At the same time, football
accounted for only 24% of athletic department
expenses (Fulks, 2008). The money generated by
football programs is vital to the financial well-being
of member institutions of the FBS.
518
M. D. GROZA
Football programs are able to generate a great
deal of revenue through gate receipts. Football
game day attendance is also an excellent proxy for
other revenues. Teams with a large fan base are
able to generate more apparel sales, get invited to
bigger bowl games, and are able to negotiate larger
sponsorship deals. With all of this said, one can
assume athletic departments want to maximize
football attendance. The goal of this paper is to
examine whether the decision to change conference
affiliation was successful in terms of football game
day attendance.
The paper is organized as follows. A brief
review of the pertinent literature is contained in
the next section. A detailed account of the
realignment is contained in the ‘Conference
Realignment’ section. The empirical model, data
description, and results are contained in the
‘Empirical Model’ section. A second model
further examining the factors that change when
teams switch conferences is contained in the
‘Dissecting Change’ section. A brief discussion is
contained in the ‘Discussion’ section. Finally, the
last section includes the limitations of this analysis
as well as suggestions for future research.
LITERATURE REVIEW
A large body of research has confirmed the
importance of intercollegiate athletics to
universities across the United States. Research
has indicated universities can attract better
academic students by sponsoring a successful
football program. For example, McCormick and
Tinsley (1987) concluded football programs that
are successful for a 10- to 15-year period strongly
increase SAT scores of incoming freshmen to
their respective universities. This conclusion was
affirmed by Tucker (2004) and Tucker (2005), who
concluded that a successful football program has
the ability to attract ‘more and better’ applicants
in as little as 5 years.
Equally important to advertising and attracting
potential students, college football programs
generate revenue for their given universities. One
source of this revenue is financial donations from
alumni and non-alumni fans. Frank (2004)
reviewed the literature concerning athletic success
and donations to athletic departments and found a
positive relationship. While Turner et al. (2001)
Copyright r 2010 John Wiley & Sons, Ltd.
and Humphreys and Mondello (2007) found no
relationship between athletic success and
unrestricted donations to the university, Rhoads
and Gerking (2000) concluded there is a positive
correlation between a bowl appearance and
alumni contributions to athletic departments.
Other research has indicated increased spending
by athletic departments result in increased
donations by alumni to athletic departments
(Litan et al., 2003). While most of the previous
literature has focused on athletic success and
donations, the single most important source of
revenue for athletic departments continues to be
ticket sales (Fulks, 2008).
Fulks (2008) reported ticket sales accounted for
28% of the revenue generated by members of the
FBS during the 2006 fiscal year, the largest single
source. In recent years, not only has it been
financially imperative for FBS programs to fill
their football stadiums but also an NCAA
requirement. Suggs (2004) described how some
university presidents whose football teams draw
poorly are concerned with losing their prestigious
FBS ranking. Bylaw 20.9.7.3 of the NCAA’s
Division 1 manual requires all FBS football
programs to maintain an attendance minimum:
‘Once every two years on a rolling basis, the
institution shall average at least 15,000 in actual or
paid attendance for all home football games
(NCAA, 2009).’ This requirement has made it
imperative for certain athletic departments to
increase attendance at home football games.
Seeking to increase attendance at home football
games requires athletic departments to look closely
at the factors that affect game day attendance.
A relatively small, yet substantial body of research
has examined attendance at collegiate sporting
events. Armstrong (2002) studied the consumption
of spectator sports by African Americans at
historically black colleges and universities and
Fizel and Bennett (1989) examined the impact
telecasts plays on college football attendance.
DeSchriver and Jensen (2002) estimated a
demand model for college football attendance at
the NCAA’s Division II level of competition. In
their econometric model, they included four sets of
independent variables: economic determinants,
demographic determinants, game attractive
determinants, and a set of variables they termed
‘residual preference determents.’ This final set of
variables included factors specific to when the
game was played (e.g. the year the game was
Manage. Decis. Econ. 31: 517–529 (2010)
DOI: 10.1002/mde
NCAA CONFERENCE REALIGNMENT
played and when within the season the game was
played). They concluded recent on-field success
(i.e. win percentage) and tradition are the two
most important factors in attracting fans to
football games; both of these variables were
positively associated with game day attendance.
Price and Sen (2003) performed a crosssectional analysis of the 1997 Division 1-A
(currently known as FBS) football season. They
included three sets of independent variables: game
specific variables, team specific variables, and
university specific variables. The game specific
variables included the success of each team in the
current season, the difference in quality of each
team at the time the game was played, and other
factors specific to when each game was played (e.g.
time of day and day of week). It was hypothesized
that the quality of each team would be positively
associated with attendance while the difference in
quality between teams would be negatively
associated with attendance. The team specific
variables included factors specific to the home
football team including the number of bowl games
they have appeared in (a proxy for tradition) and
the athletic conference affiliation of the home
team. Finally, the university specific variables
included factors unique to the home university
including the size of its stadium. Price and Sen
(2003) found that of the broad range of factors
influencing football attendance at the Division 1-A
level, conference affiliation and the quality of both
teams had the strongest influence. Leonard (2005)
evaluated visitor attendance at Mid-American
Conference football games. He hypothesized that
the distance between universities competing in a
Mid-American Conference football game would
be negatively associated with visitor ticket sales.
Leonard (2005) empirically supported this main
hypothesis and in addition found that visitor ticket
sales were positively associated with the quality of
both teams involved in the contest. Collectively,
DeSchriver and Jensen (2002), Price and Sen
(2003) and Leonard (2005) all confirmed the
importance of winning and tradition on college
football game day attendance.
Besides Price and Sen (2003) however, little
research has been done on the importance of
conference affiliation on a university and its
football program. Maxcy (2004) argued the
NCAA’s ‘power conferences’ act as a monopoly
and as a result enjoy incredible revenues; the six
conferences that comprise the Bowl Championship
Copyright r 2010 John Wiley & Sons, Ltd.
519
Series (BCS) are quite effective at consolidating
money and power. Other works have dealt with
competitive balance across conference lines. For
example, Sutter and Winkler (2003) concluded
that despite attempts to create parity in college
football, great competitive imbalances still exist.
These works have dealt with the apparent
monopolistic status of large conferences. The
monopolistic nature of large collegiate athletic
conferences provides financial incentives for
individual athletic programs and universities to
join these conferences. While these incentives
include shared revenues in television, bowl
games, and attendance, this paper will focus on
the impact a change in conference affiliation has
on football game day attendance.
CONFERENCE REALIGNMENT
Despite much effort by representatives of lower
profiled FBS programs, great financial and
competitive imbalances still exist throughout
college football. One of the enablers of this
imbalance is The BCS, which was created a
decade ago. According to the BCS official
website: ‘The BCS was implemented beginning
with the 1998 season to determine the national
champion for college football while maintaining
and enhancing the bowl system that’s nearly 100
years old.’ The formation of this alliance
consolidated a great deal of money amongst the
top six FBS football conferences. While the BCS
may be relatively new, alliances created to play
common opponents and consolidate revenues have
been around for over a century. The Big Ten
Conference, the nation’s oldest Division 1
conference, dates back to late 19th century.
Conferences have generally been made up of
institutions that are similar in size, academic
pursuits, and have similar athletic aspirations.
Over the years, conferences have developed
athletic programs of varying quality. Today,
certain conferences are made up of football
powerhouses while other conferences have
weaker football programs.
Three criteria can be used to judge the quality
of an FBS football conference: Bowl appearances
(a proxy for tradition), average attendance (a
proxy for fan base), and Sagarin Computer
Ratings2 (a proxy for recent on-field success).
Manage. Decis. Econ. 31: 517–529 (2010)
DOI: 10.1002/mde
520
Table 1.
M. D. GROZA
Conference Average Bowl Appearances, Attendance, and Sagarin Ratings 2002–2003 Seasons
Conference
SEC
Big 12
ACC
Pac-10
Big Ten
Big East
Mt. West
C-USA
WAC
MAC
Sun Belt
Independents
Bowl games (as of 03 )
Attendance (02 and 03)
Sagarin rating (02 and 03 )
Ave (per team)
Rank
Ave (per team)
Rank
Ave (per team)
Rank
27
23
20
19
18
14
10
8.3
7.5
2.7
1.6
8.5
1
2
3
4
5
6
7
8
9
10
11
73 668
56 025
51 308
50 717
70 487
45 619
34 174
28 331
24 668
17 320
13 995
37 507
1
3
4
5
2
6
7
8
9
10
11
79.16
77.63
78.92
77.61
77.55
75.51
69.12
65.33
62.26
63.70
56.28
67.76
1
3
2
4
5
6
7
8
10
9
11
Table 1 shows the average number of bowl games
participated in by members of each FBS
conference as of 2003, the season prior to the
conference realignment. Table 1 also provides
average attendance figures and average Sagarin
ratings for the 2002 and 2003 football seasons for
members of each conference. From this table, it
appears that a positive relationship exists between
bowl appearances, average attendance, and
Sagarin Computer Ratings. These three criteria
enable a cursory ranking of the conferences before
any movement took place.
The quality of competition as well as the aura of
playing in a traditional power conference is very
attractive to teams playing in traditionally weaker
conferences. Unfortunately, for universities in
weaker conferences, the decision to admit a new
member into a conference must be made by the
current members of the conference. A university can
only enter a conference after the conference agrees to
admit the university. For a team to enter a new
conference two things must happen: (1) the
conference must approve a given university for
admittance and (2) the university must agree to be
admitted. Assuming the conference and the
individual program are making a decision based on
the well-being of each institution, the admittance of a
new university to a conference would have to be
beneficial to both the conference and the university.
If this was not the case the conference would not
accept the university or the university would not
accept the conference’s invitation to join.
There are many different goals for a university
or a collegiate athletic conference. Unlike
professional sport, student athletes’ academic
pursuits as well as the academic reputation of the
Copyright r 2010 John Wiley & Sons, Ltd.
university are important in college athletics. With
that said, however, financial obligations are
making the business side of collegiate athletics of
growing importance. Athletic departments are
forced to increase revenue. This is often
accomplished by increasing the profile of their
money making sports, namely football and men’s
basketball. The pursuit of financial security was
undoubtedly a major impetus, which led to the
dramatic realignment among the nation’s FBS
conferences. To better understand the reason for
the realignment, one must carefully examine the
intricacies of the team movements.
There appears to be a systematic pattern
associated with the conference movement that
took place between 2004 and 2006. The
realignment was initiated by the ACC admitting
Miami (Fla.), Virginia Tech, and Boston College in
2004 and 2005. In the two seasons prior to the
movement, the 2002 and 2003 seasons, Miami,
Virginia Tech, and Boston College had the first,
second, and third ranked football teams in the Big
East. The ACC was able to acquire three programs
from the Big East with the highest Sagarin
Computer Ratings in the two seasons leading up
to the realignment. As the ACC was acquiring
Miami (Fla.), Virginia Tech, and Boston College,
the Big East admitted into their conference four new
universities. Included were three teams from
Conference USA: South Florida, Louisville, and
Cincinnati: the second, third, and fourth ranked
teams from the C-USA. The Big East also picked up
Connecticut; an independent with a high Sagarin
rating. Meanwhile, Conference USA also lost its top
ranked team to the Mountain West Conference,
Texas Christian University. The Big East and
Manage. Decis. Econ. 31: 517–529 (2010)
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521
NCAA CONFERENCE REALIGNMENT
the FBS in Florida Atlantic, Florida International,
and Troy St. In 2005, Temple and Army both
became independents. While Army remains an
independent, Temple joined the MAC prior to the
2007 football season. A list of these conference
movements can be found in Table 2. Table 2 also
contains the Sagarin Computer Ratings and the
pre-movement conference rank of all programs
involved in the realignment.
The chain of conference movements that took
place between 2004 and 2006 was initiated by the
ACC, arguably the strongest conference involved
in the realignment. The actions of the ACC in
acquiring three teams from the Big East set off a
chain reaction that resulted in 21 teams ultimately
changing conference affiliation. As was previously
stated, prior to a conference realignment both the
conference and individual athletic program must
agree to the change. It is likely that the ACC saw
an opportunity to enhance the reputation of its
conference by admitting the football powers
Miami (Fla.), Virginia Tech, and Boston College.
A conference is incentivized to enhance their
reputation because strong conferences are able to
negotiate better bowl deals and better television
contracts. The actions of the ACC forced the Big
East and other conferences to find new members.3
While the specific motivation of the realignment
from the conferences’ perspectives is beyond the
Mountain West decimated Conference USA with
their poaching of the conference’s four highest
ranked football programs. The movement between
C-USA, The Big East, and The ACC all appears to
be quite systematic. The best teams from C-USA
moved to The Big East and Mountain West, and the
best teams from The Big East moved to The ACC.
The next series of movement that took place
does not appear to be as systematically hierarchal.
Conference USA picked up two teams from the
Mid-American Conference (MAC) and four teams
from the Western Athletic Conference (WAC).
Marshall and Central Florida from the MAC were
ranked during the 2002 and 2003 seasons as the
fourth and eighth strongest of fourteen MAC
teams. On the other hand, Rice, Southern
Methodist, Tulsa, and Texas El Paso were
among the weakest programs from the WAC.
These four teams were ranked sixth, eighth, ninth,
and tenth out of the ten teams in the WAC during
the 2002 and 2003 seasons. Unlike the movement
between the ACC and Big East, the programs
C-USA acquired were not the strongest WAC or
MAC teams. The WAC looked to the Sun Belt
Conference to replenish its ranks by picking up
Idaho, New Mexico St., and Utah St. These teams
were ranked second, third, and fifth out of eight
Sun-Belt teams during the 2002 and 2003 seasons.
Finally, the Sun Belt picked up programs new to
Table 2.
Team Movement and Rank in Pre-movement Conference
Team
Miami (Fla.)
Virginia Tech
Boston College
Texas Christian
South Florida
Louisville
Cincinnati
Connecticut
Rice
Tulsa
Southern Methodist
Texas El Paso
Marshall
Central Florida
New Mexico State
Utah State
Idaho
Troy State
Temple
Florida International
Florida Atlantic
Army
Moved from
Big East
Big East
Big East
C-USA
C-USA
C-USA
C-USA
Independent
WAC
WAC
WAC
WAC
MAC
MAC
Sun Belt
Sun Belt
Sun Belt
Independent
Big East
FCS
FCS
C-USA
Copyright r 2010 John Wiley & Sons, Ltd.
2002 and 2003 Sagarin average
Rating
Conference rank
92.04
79.80
78.86
75.07
72.25
71.07
68.25
69.97
59.28
56.82
47.80
46.53
73.36
61.94
58.82
57.55
54.65
57.93
61.18
38.82
50.76
43.65
1/8
2/8
3/8
1/10
2/10
3/10
4/10
2/4
6/10
8/10
9/10
10/10
4/14
8/14
2/8
3/8
5/8
4/4
7/8
10/10
Moved to:
ACC
ACC
ACC
Mountain West
Big East
Big East
Big East
Big East
C-USA
C-USA
C-USA
C-USA
C-USA
C-USA
WAC
WAC
WAC
Sun Belt
MAC
Sun Belt
Sun Belt
Independent
Manage. Decis. Econ. 31: 517–529 (2010)
DOI: 10.1002/mde
522
M. D. GROZA
scope of this paper, one factor remains constant:
all of the programs that voluntarily changed
conference affiliation moved into a seemingly
better football conference. Each team moved to a
conference whose teams appeared in more bowl
games, had larger average attendance, and had a
higher average Sagarin Rating compared to the
conference they came from. In general, the
systematic realignment resulted in teams playing
in better conferences.
Better conferences offer new programs the
opportunity to play better in-conference
opponents. Teams that move into better
conferences will, on average, play more
opponents with better on-field success and a
richer tradition. The prospect of playing stronger
opponents with more tradition may appeal to fans
and increase game day attendance. Previous
collegiate game day demand models often cite
the importance of tradition on attendance. The
following empirical model is intended to estimate
the relationship between a change in conference
affiliation and football game day attendance.
EMPIRICAL MODEL
The majority of movement during this conference
realignment took place just prior to the 2005
season. To analyse a significant sample,
attendance figures were gathered for the 2002
through 2007 seasons; three seasons before the
majority of movement and three seasons after the
majority of movement. Included in this analysis
are the home games played by the 21 teams that
changed conference affiliation over this time
period.4 These teams are listed in Table 2.
Between 2002 and 2007, the 21 football programs
involved in the conference realignment played 686
home games. Seventy-four of those games were
played against opponents from the Football
Championship Subdivision. To protect against
anomalies these, 74 games are excluded, thus
leaving 612 games included for analysis.
To estimate the relationship between a change
in conference affiliation and football game day
attendance the following model is proposed5:
PCTCAPi;t ¼ b0 1b1 CHANGEi;t 1b2 TRENDt
1b3 HTEAMi;t 1b4 GAMEDAYi;t
1b5 UNIVERSITYi 1ei;t
Copyright r 2010 John Wiley & Sons, Ltd.
ð1Þ
where PCTCAP represents the percentage of the
home team’s stadium that was filled on game
day. PCTCAP is calculated by dividing the
observed in stadium attendance for each game by
the reported stadium capacity.6 CHANGE is a
dummy variable that represents if the game was
played after the team changed conference
affiliation; if the game was played after the team
changed conferences, the variable is coded with
a 1. Considering teams decided to make the
voluntary change in conference affiliation, it is
predicted that CHANGE will have a positive
influence on game day attendance. TREND is
the annual average percent capacity for those
teams that did not change conference affiliation.
The purpose of this variable is to control for any
macro attendance trend that may have occurred
across college football. During the six seasons
included, college football as a whole may have
experienced an increase or decrease in attendance;
this variable is intended to control for this.
HTEAM is a set of variables that are unique to
the home football team for the given season,
GAMEDAY is a set of variables that are unique
to each game, and UNIVERSITY is a set of
dummy variables that are included to encompass characteristics of the home football program
and university that are constant across each
season.
HTEAM includes variables that represent the
tradition and current success of the home football
team. Included in this set of variables are the
number of seasons the home team has played
football (YEARSFOOT), the number of bowl
games the home team has participated in over
the previous 5 years (HTBOWL), and the final
Sagarin Computer Rating of the home team for
the current season (SAGARIN). It is predicted
that the HTEAM variables will all positively
influence game day attendance. (YEARSFOOT)
is designed to be a proxy for tradition. Consumers
of sport are generally more apt to support their
team if there is tradition behind that team. Sport
fans also generally prefer when their team has had
recent on field success and is currently ranked high
compared to other teams. (HTBOWL) and
(SAGARIN) are intended to represent the recent
success and current rating of the home football
team.
GAMEDAY includes variables that are
expected to influence game day demand and are
unique to each individual game. Included are the
Manage. Decis. Econ. 31: 517–529 (2010)
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NCAA CONFERENCE REALIGNMENT
winning percentage of the home team over the
previous eleven games (WINPCT)7, the squared
difference in the home team’s and visiting team’s
Sagarin computer rating (DIFFSAGSQ), a
dummy variable that takes the value of 1 if the
game was played on Saturday and 0 otherwise
(SATURDAY), a dummy variable that takes the
value of 1 if the game was played at night and 0
otherwise (NIGHT), and a dummy variable that
takes the value of 1 for the first game that pits new
conference rivals that have not played each other
in the previous 5 years and 0 otherwise
(NEWCONFOPP). (WINPCT) is expected to
positively influence game day attendance. Teams
that have recently won a high percentage of their
games appeal to fans. (DIFFSAGSQ) is used
as a proxy for uncertainty of outcome for the
given game. Rascher (1999) suggests that fans will
be more interested in a sporting contest that pits
two equally matched teams against each other.8
While in their analysis, a similar variable
(difference in win percentage) was insignificant,
Price and Sen (2003) predicted the variable to have
a negative influence on game day attendance.
Thus, the predicted sign for (DIFFSAGSQ) is
negative; it is predicted fans will prefer the
uncertainty associated with a game that pits
two teams of comparable quality. It is also
predicted, per Rascher (1999), that fans will be
more influenced by the home team’s win-loss
record than by the difference in the quality of
the two teams playing. It is unclear as to
directionality of the variables (SATURDAY)
and (NIGHT). Fans may prefer the traditional
Saturday afternoon game or may prefer the
novelty of a weeknight game. It is also unclear as
to
the
directionality
of
the
variable
(NEWCONFOPP). This variable is intended to
assess if there is a novelty effect when playing an
unfamiliar opponent as a conference rival for the
first time. Fans may be intrigued at the prospect of
playing an unfamiliar conference opponent, or
fans may require a rivalry to develop before it
sparks their interest.
There is an econometric concern with including
both independent variables (SAGARIN) and
(WINPCT). These variables have the potential to
be highly correlated, thereby raising concerns of
possible multicollinearity in the model.9 One may
assume that teams with high winning percentages
would have high Sagarin Computer Ratings. It is
important to note, however, that Sagarin Ratings
Copyright r 2010 John Wiley & Sons, Ltd.
523
and winning percentage are not perfectly
correlated. This is because often teams have good
records but play in weak conferences. Unlike
professional football, the disparity between the
qualities of opponents at the college level across
conferences can be quite large. For example, a
team from a weak conference may have a higher
winning percentage than a team from a stronger
conference but in reality may be an inferior team.
To compare teams more accurately, computer
ratings are used in the model. USA Today’s
Sagarin Computer Ratings, a well-respected
objective rating system, are included in this
analysis for that purpose. With this said, fans of
teams in weak conferences may appreciate high
winning percentages even if their team has a low
Sagarin Rating. This is the reason for inclusion of
the (WINPCT) variable.
To account for idiosyncrasy, unobservable
characteristics that are specific to each university,
the fixed effects method is used which allows
each team to have its own constant term.
UNIVERSITY is a set of dummy variables that
are unique to each team but constant across all
seasons. The descriptive statistics as well as the
source of the data for each variable used in this
analysis are reported in Table 3.
The model is first run using an ordinary least
squares regression model (OLS). The results of the
OLS estimation are reported under Model 1 in
Table 4. An issue in previous sport demand models,
and an issue in this model is that for certain games
demand outpaces supply. In this analysis, 100 of the
612 games included (16.3%) were sold out. There
are a relatively fixed number of seats in athletic
stadiums and once the stadium is at capacity no
other spectators can enter. Welki and Zlatoper
(1994) and Price and Sen (2003) (among others)
deal with this problem by running a Tobit Analysis
in addition to an OLS regression. As a robustness
check, the model is run a second time using a Tobit
estimation. The results of the Tobit Analysis are
reported under Model 1 in Table 5.
The Tobit Analysis confirms the robustness of
the OLS estimations in that both statistical
techniques yield similar goodness of fit statistics
and parameter estimates. Both techniques yield a
similar parameter estimate for the variable of
interest, CHANGE. The model estimates after a
program changes conference affiliation they
experience a statistically significant increase in
attendance relative to stadium capacity. The model
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524
Table 3.
M. D. GROZA
Summary Statistics of Variables
Variable
Source of data
N
Mean
Std Dev
Min
Max
PCTCAP
CHANGE
TREND
YEARSFOOT
HTBOWL
SAGARIN
WINPCT
DIFFSAGSQ
SATURDAY
NIGHT
NEWCONFOPP
OPPYRFOOT
OPPBOWL
OPPWINPCT
OPPSAGARIN
(1)
(3)
(1)
(2)
(2)
(4)
(3)
(4)
(3)
(3)
(3)
(2)
(2)
(3)
(4)
612
612
612
612
612
612
612
612
612
612
612
612
612
612
612
0.677
0.542
0.719
85.500
1.977
67.104
0.497
235.736
0.840
0.346
0.183
89.105
9.361
0.456
66.678
0.27
0.50
0.02
33.18
2.07
13.11
0.25
394.44
0.37
0.48
0.39
29.22
9.15
0.23
11.05
0.10
0
0.69
3
0
43.67
0
0.0001
0
0
0
3
0
0
42.31
1.13
1
0.74
119
5
95.05
1
5478
1
1
1
137
45
1
95.05
Notes: (1) Refers to the ‘Single Game Team Report Div 1 FBS: Attendance’ provided by the NCAA. www.NCAA.org/stats.
Stadium sizes were gathered from the NCAA Division I Football Records Books. (2) Refers to the ‘Official 2002, 2003, 2004, 2005,
2006, and 2007 NCAA Division I Football Records Books,’ found on the NCAA’s official website. (3) Refers to the NCAA’s
official statistical website found at: http://web1.ncaa.org/stats/StatsSrv/rankings?doWhat 5 archive&sportCode 5 MFB. (4) Refers
to Sagarin computer ratings taken from the USA Today website: http://www.usatoday.com/sports/sagarin-archive.htm.
also yields a number of statistically significant
parameter estimates for the control variables. As
predicted, it is estimated that fans prefer teams with
high winning percentages and high Sagarin
Computer Ratings. It is also estimated fans prefer
night contests. Contrary to initial expectations, the
model yielded a positive parameter estimate for the
variable DIFFSAGSQ. While positive and
significant, the parameter estimate for this variable
is extremely small. Interestingly, the parameter
estimate for the variable NEWCONFOPP is
negative and significant. It appears games that pit
unfamiliar new conference opponents against each
other do not appeal to fans. Again, the variable of
interest, CHANGE, estimates that teams experience
an increase in attendance in the games they play
after a change in conference affiliation. While fans
may not be enthusiastic about playing new
unfamiliar conference opponents, it appears they
are enthusiastic about their team playing as a
member of the new conference. A closer
examination of what changed for these universities
after the realignment may help to explain this
increase in attendance.
DISSECTING CHANGE
To find the possible sources of this predicted
increase in attendance, it is important to closely
examine exactly what changes when a team
Copyright r 2010 John Wiley & Sons, Ltd.
changes conference affiliation. Variables specific
to the university are not expected to change simply
because its athletic program now competes in a
different conference. The enrollment, size of the
alumni, size of the university’s host city, and other
university specific variables can be expected to
remain constant through a conference change. The
tradition of a football program takes years to
develop and thus is expected to remain constant
over the relatively short time period being
examined. There are only two major changes
that take place: the new schedule the team plays
and the athletic program’s association with a
different conference.
It has been argued there was a systematic nature
to the movement that took place. All of the teams
that changed conference affiliation moved into
better football conferences. The systematic nature
of the movement makes it important to specifically
look at how the strength of schedules changed for
those teams involved in the realignment. The
on-field strength of the opposition, as well as
the tradition of the opposition greatly improved
after teams changed conference affiliation.
On average, the teams that changed conferences
improved their schedules by three Sagarin
Rating points (roughly 5%) and on average
played teams that appeared in three more
bowl games than compared to the teams they
played before the change. A complete list of
the Sagarin strength of schedule ratings as well
Manage. Decis. Econ. 31: 517–529 (2010)
DOI: 10.1002/mde
525
NCAA CONFERENCE REALIGNMENT
Table 4.
OLS Estimations
Dependent variable: PCTCAP
Independent variable
Intercept
CHANGE
TREND
YEARSFOOT
HTBOWL
SAGARIN
WINPCT
DIFFSAGSQ
SATURDAY
NIGHT
NEWCONFOPP
OPPYRFOOT
OPPBOWL
OPPWINPCT
OPPSAGARIN
Predicted sign
1
1
1
1
1
1
1/
1/
1/
1
1
1
1
Model 1
Model 2
Parameter estimate
T. Stat
Parameter estimate
T. Stat
0.415
0.043
0.088
0.0007
0.017
0.005
0.295
0.00004
0.029
0.061
0.063
1.45
2.70
0.23
1.12
1.32
4.75
6.40
2.52
1.50
3.66
3.43
0.254
0.033
0.10
0.0009
0.017
0.005
0.280
0.00004
0.056
0.058
0.065
0.0002
0.004
0.045
0.001
0.92
2.15
0.27
1.54
1.38
4.84
6.36
2.77
2.97
3.68
3.72
0.70
4.99
1.20
1.21
612
0.6874
0.6712
42.28
N
R2
Adj. R2
F statistic
612
0.7171
0.7004
43.01
Notes: The individual coefficient is statistically significant at the 95% level or 99% level.
Table 5.
Tobit Analysis Estimations
Dependent variable: PCTCAP
Independent variable
Intercept
CHANGE
TREND
YEARSFOOT
HTBOWL
SAGARIN
WINPCT
DIFFSAGSQ
SATURDAY
NIGHT
NEWCONFOPP
OPPYRFOOT
OPPBOWL
OPPWINPCT
OPPSAGARIN
N
Censored observations
Noncensored Values
Log likelihood
Predicted sign
1
1
1
1
1
1
1/
1/
1/
1
1
1
1
Model 1
Model 2
Parameter estimate
Chi-square
Parameter estimate
Chi-square
0.363
0.066
0.366
0.001
0.027
0.006
0.333
0.00001
0.032
0.068
0.074
1.31
13.44
0.76
1.47
3.54
24.49
40.97
7.06
2.04
13.47
12.54
0.152
0.050
0.424
0.001
0.028
0.006
0.315
0.00001
0.069
0.067
0.071
0.000
0.006
0.054
0.001
0.25
8.48
1.13
2.73
4.11
26.45
40.81
6.40
10.38
14.67
12.84
0.04
38.51
1.63
1.52
612
100
512
116.88
612
100
512
155.52
Notes: The individual coefficient is statistically significant at the 90% level or 95% level or 99% level.
as the average bowl appearances of opponents
before and after the change can be found in
Table 6.
Copyright r 2010 John Wiley & Sons, Ltd.
To estimate the impact this increase in
the quality and tradition of competition has
on game day attendance, the following model
Manage. Decis. Econ. 31: 517–529 (2010)
DOI: 10.1002/mde
526
Table 6.
2007)
M. D. GROZA
Quality of Opposition: Before the Change (2002 and 2003) Versus After the Change (2006 and
Team
Sagarin strength of schedule ratings
Florida International
Florida Atlantic
Connecticut
Louisville
Cincinnati
South Florida
Utah State
Idaho
Army
Texas Christian
Central Florida
Rice
Marshall
Virginia Tech
Tulsa
Boston College
Texas El Paso
Troy State
Miami (Fla.)
New Mexico State
Temple
Southern Methodist
Average
Bowl appearances of opponents
02’ 03’
Ave
06’ 07’
Ave
Change
02’ 03’
Ave
06’ 07’
Ave
Change
44.27
50.92
62.22
64.74
63.68
66.91
66.17
63.28
63.87
65.24
64.58
62.97
65.31
71.02
64.04
72.00
61.75
64.63
74.38
64.27
71.39
71.46
64.50
66.77
64.81
72.36
72.93
71.34
73.01
69.18
65.91
66.50
67.76
66.69
65.01
67.01
72.43
64.62
72.41
61.96
63.97
70.59
60.45
66.52
62.77
67.50
22.50
13.89
10.14
8.19
7.66
6.10
3.01
2.63
2.63
2.52
2.11
2.05
1.70
1.41
0.58
0.41
0.21
0.66
3.79
3.82
4.86
8.69
3.00
0.0
0.0
6.8
8.9
8.5
9.9
8.4
6.7
7.8
7.2
7.4
10.2
5.3
13.4
9.5
13.8
7.4
9.8
18.1
7.7
13.2
10.3
8.7
11.0
6.7
10.3
11.3
11.3
12.5
10.2
8.4
10.7
13.3
11.2
15.0
11.4
18.3
10.5
17.9
10.0
10.4
18.6
7.2
8.5
10.9
11.7
11.0
6.7
3.5
2.4
2.8
2.6
1.8
1.7
2.9
6.1
3.8
4.8
6.1
4.9
1.0
4.1
2.6
0.7
0.5
0.5
4.7
0.6
3.0
is proposed:
PCTCAPi;t ¼ b0 1b1 CHANGEi;t 1b2 TRENDt
1b3 HTEAMi;t 1b4 GAMEDAYi;t
1b5 UNIVERSITYi
1b6 OPPTEAMi;t 1ei;t
ð2Þ
Model 2 is identical to Model 1 except that a
new set of variables OPPTEAM is added. This
new set of variables includes characteristics of the
visiting team; characteristics that can be expected
to change drastically after a given team changes
conference affiliation. OPPTEAM includes
variables that represent the tradition and current
success of the visiting team. Included in this set of
variables are the number of seasons the visiting
team has played football (OPPYRFOOT), the
number of bowl games the visiting team has
participated in (OPPBOWL), the winning
percentage of the visiting team the previous 11
games (OPPWINPCT), and the final Sagarin
computer rating of the visiting team for the
current season (OPPSAGARIN). It is predicted
that the OPPTEAM variables will all positively
influence game day attendance. Fans are expected
to prefer to watch their team play an opponent
that has traditionally had success and is having
Copyright r 2010 John Wiley & Sons, Ltd.
success currently. In addition to identifying
OPPTEAM’s influence on the dependent variable
PCTCAP, it will also be important to identify the
interaction between this set of variables and the
independent variable of interest CHANGE. In
other words, these variables will help to show if the
increase in attendance was simply a result of
playing better opponents with more tradition.
The OLS as well as the Tobit estimations of
Model 2 can be found under Model 2 in Tables 4
and 5, respectively. It appears Model 2 fits the data
better; the adjusted R-squared for Model 2 is 0.70
as compared to 0.67 for Model 1. In addition, an
F-test to measure joint significance was performed.
The test yields a significant (po0.01) F-value of
15.14. Model 2 also estimates that the tradition of
the opponent does in fact matter for game day
attendance. The number of bowl games the visiting
team has participated in positively influences game
day attendance. However, it appears attendance is
not greatly influenced by visiting teams’ current
on-field success.
For the purposes of this analysis, the impact the
inclusion of the OPPTEAM set of variables has on
the CHANGE variable is extremely important.
The addition of the OPPTEAM variables only
slightly lowered the parameter estimate on the
Manage. Decis. Econ. 31: 517–529 (2010)
DOI: 10.1002/mde
NCAA CONFERENCE REALIGNMENT
CHANGE variable. The OLS estimate of the
CHANGE variable in Model 2 is positive and
significant at the 95% confidence level and the
Tobit estimate of the same variable is positive and
significant at the 99% confidence level. Even after
accounting for the difference in quality and
tradition of the individual opponents, the games
played after the change in conference affiliation
drew significantly more fans than the games before
the change. This empirical finding seems to
indicate that the reputation of the conference
may influence game day attendance.
DISCUSSION
Between the 2004 and 2007 football seasons, 21 of
the 119 programs comprising the NCAA’s FBS
changed conference affiliation. This provides the
opportunity for a natural experiment aimed at
identifying how a dramatic change in schedule
affects game day attendance. Analysis of the nature
of college football prior to the conference
realignment indicates there was a systematic
nature to the movements. Those teams that
changed conference affiliation moved into better
conferences as judged by bowl appearances,
average attendance, and computer ratings.
Individual athletic departments most certainly
knew this before they agreed to make the
voluntary switch. With this being said, it is likely
one of the major reasons athletic departments
made this voluntary switch was to increase football
game day attendance and associated revenues.
To analyse the impact a conference swap has on
game day attendance, Model 1 was created with a
number of control variables predicted to affect game
day attendance. These variables include an
attendance trend in college football, characteristics
of the home football team, characteristics of the
game itself, and a series of dummy variables intended
to account for characteristics of the home university.
Model 1 predicts teams that change conference
affiliation experience a statistically significant
increase in attendance as a percent of capacity.
To estimate possible sources of this increase,
Model 2 was created. Model 2 includes the
variables of Model 1 plus a set of variables
representing the quality and tradition of the
opposition team. Model 2 indicates that while
bowl appearances of the visiting team is a driver of
Copyright r 2010 John Wiley & Sons, Ltd.
527
game day attendance, the increase in quality and
tradition of the visiting team are not responsible
for all of the increase in attendance after a
conference change. This implies that even after
accounting for playing better competition, teams
that change conference affiliation enjoy an
increase in attendance. One possible explanation
for this increase in attendance is that fans are
excited about the opportunity to play new
unfamiliar conference rivals. The significant
negative parameter estimate for the dummy
variable NEWCONFOPP yielded by both
Model 1 and Model 2, however, suggests that
fans are not necessarily enthusiastic about
attending a game that pits their team against an
unfamiliar rival. In other words, the results of this
analysis suggest that it is not simply the new
opposition the drives attendance after a change in
conference affiliation.
A possible explanation for the increase in
attendance is that individual conferences
themselves have a certain drawing power. Sport
consumers may be attracted at the prospect of
their team playing in a well-known traditional
conference. Better conferences also accentuate the
importance of each game. Stronger conferences
have larger national television contracts and have
more automatic bids with larger post-season bowl
games. For example, the champion from a BCS
conference has the opportunity to play in a
prestigious BCS bowl game. In whole, this
analysis adds support to Price and Sen’s (2003)
conclusion that ‘membership of specific
conferences influence fan support (p. 45).’ The
findings in this study provide additional
motivation for managers of college athletic
programs to work towards placing their
programs into better athletic conferences.
LIMITATIONS AND FUTURE RESEARCH
As is true with all studies, there are certain
limitations to this study. Due to the lack of time
passed since the conference realignment, this
analysis is only able to show the impact on game
day attendance in the short run. The increase in
attendance credited to the conference swap may in
fact be nothing more than a honeymoon effect. The
novelty of playing in a new stronger conference
may wear off after more seasons have been played.
Manage. Decis. Econ. 31: 517–529 (2010)
DOI: 10.1002/mde
528
M. D. GROZA
Another important limitation to note is the
tradition and quality of the opposing team are
limited to four variables: number of years the team
has played football, the number of bowls the team
has played in, the winning percentage of the team,
and the Sagarin Rating of the team. These four
variables may not completely account for the
tradition and quality of the opposing team. In
other words, there may be other characteristics of
the change in schedule that drive game day
attendance and are not included in this analysis.
Future research can help to distinguish the
honeymoon effect from the true drawing power of
conferences by including a greater number of
seasons following the change. This obviously can
only be done after more time has passed. Future
studies can also look at the many other financial
and non-financial impacts of changing conference
affiliation. While it has been argued in this paper
that football game day attendance is an excellent
proxy for athletic revenues, there may be other
factors motivating a conference change. Also,
future research can look at the impact conference
realignment has on those teams that do not change
but whose conference admitted and lost members.
Research has examined the effects of scholarship
limits (Sutter and Winkler, 2003), telecast
deregulation (Bennett and Fizel, 1995), and other
NCAA regulations (Eckard, 1998) on competitive
balance in college football. Future studies can
examine the impact conference realignment has on
competitive balance within the conferences that are
part of the realignment. A study of the impact of
accepting new conference members on the current
members could have significant policy implications.
Finally, future research should continue to look at
the importance of conference affiliation on the
institutions that comprise the conference.
2.
3.
4.
5.
6.
7.
Acknowledgements
The author wishes to thank Francesco Renna, University of
Akron, Department of Economics, Neil Longley, University of
Massachusetts, Isenberg School of Management and two
anonymous referees for their comments on previous versions
of this article. The author is solely responsible for any errors in
this paper.
8.
9.
NOTES
1. Athletic departments are especially susceptible to
university wide budget cuts as their multi-million
dollar budgets are often controversial within the
Copyright r 2010 John Wiley & Sons, Ltd.
university and throughout the general public
(Watson, 2009). In addition to reduced funding,
increased expenses have also placed a heavy
financial
burden
on
athletic
departments.
Technological advancements have rapidly increased
the expenses associated with training and
rehabilitating student-athletes. Finally, employees in
athletic departments, including both coaches and
other staff, are garnering higher salaries placing
additional pressure on budgets. For example, in
2009 24 of the 25 highest-paid coaches in college
football all had base salaries of $2 million or more
(Smith, 2009).
Sagarin Ratings are an objective computer generated
rating published by The USA Today.
Many factors may have been considered when a
conference was deciding which programs to admit as
new members. For example these factors may
have included: the geographical distance between
the new member and the existing members, the
quality of the new member’s basketball program,
and the size of the new member’s home television
market.
Army is not included in the data analysis. While
Army was a part of the realignment, the program did
not enter into a new conference; they remained
independent. Thus, including this program in an
analysis designed to assess the impact of changing
conference affiliation would be inappropriate.
Ticket price is omitted from this model. While
demand models traditionally include price, previous
research has found price to be insignificant in
college football game day demand models after
controlling for unobservable individual school
characteristics (DeScriver and Jensen, 2002). Since
this model will be estimated with a fixed effect
method, the omission of a price variable should not
bias the results.
Three of the 21 football programs included in this
analysis changed the capacity of their stadiums
during the time frame analyzed: Connecticut,
Central Florida and Troy State. The stadium size at
the time the game was played is used as the
denominator.
This variable is designed to capture the impact
current success (or lack of success) has on game day
attendance. Price and Sen (2003) use eleven games
because the college football season has traditionally
been eleven games long.
In the context of Major League Baseball, Rascher
(1999) examines other measures of uncertainty of
outcome including a probability of the home team
winning variable based on pre-game betting odds
data. Such betting odds data is not available in
college football where betting lines are based on a
point spread and not odds.
SAGARIN and WINPCT yield a correlation
coefficient of 0.77. In the full OLS estimated
model the variance inflation factors for
SAGARIN and WINPCT are 4.58 and 3.5,
respectively; both well under the acceptable level of
10.0 (Belsey, 1980).
Manage. Decis. Econ. 31: 517–529 (2010)
DOI: 10.1002/mde
NCAA CONFERENCE REALIGNMENT
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