Evidence from NCAA Sports

Competition Among Athletic Conferences for New Members: Evidence from NCAA Sports

Jane E. Ruseski∗ Patrick A. Reilly† West Virginia University Skidmore College Brad R. Humphreys‡ West Virginia University Abstract

Elite college athletics conferences’ television broadcast rights provide additional revenue to their members. These “big-time” athletics programs derive most of their value from football. Unlike a typical market, football programs cannot compete on price because of weekly games and the limited length of the college football season. Instead, conferences must rely on other forms of competition. In this paper, we investigate nonprice competition in the form of changes in conference aﬃliates. We seek to estimate how conferences value program ranking and program popularity when adding conference members. Our data consist of 73 conference changes for 120 FBS college football programs between 2002 and 2015. Using probit models, we estimate the likelihood of a conference adding a team. Our ﬁndings suggest that the most prestigious conferences value both our measures of success and team popularity when seeking new conference members. Less prestigious conferences put a greater value on success and less on team popularity.

Introduction

This paper analyzes a novel form of competition in sports: competition among US college sports conferences for new members. A 1984 Supreme Court of the United States ruling, the Board of Regents (BOR) decision, prohibited the National Collegiate Athletic Association (NCAA) from collectively bargaining for television rights fees with broadcast networks for games played by big-time college football teams and instead granted this power to conferences and individual schools. The BOR decision clearly impacted conferences, spurring realignment and other outcomes in big-time college sports (Carroll and Humphreys, 2016). This ruling generated enormous increases in broadcast revenues earned by US universities and caused

∗Department of Economics, Chambers College of Business and Economics, PO Box 6025, Morgantown WV 26506-6025; email: [email protected]; phone: 304-293-7835. †Department of Economics, Skidmore College, 815 N. Broadway, Saratoga Springs, NY 12866; email: [email protected]; phone: 518-580-8432 ‡Department of Economics, Chambers College of Business and Economics, PO Box 6025, Morgantown WV 26506-6025; email: [email protected]; phone: 304-293-7871. Fax: 304-293-7061.

1 significant changes in conference composition and size. Televised NCAA athletics began with a $1.1 million ($10.5 million in 2018 dollars) broadcast rights deal in 1952 between NBC and the entire NCAA. Today, broadcast rights can earn upwards of half a billion dollars annually for an individual conference. These increased stakes and limited number of agents (for the most part five or six conferences controlled the market) make this a highly lucrative oligopolistic setting. However, conferences face inelastic supply, i.e. the number of games cannot expand very much due to time constraints and concerns for players’ health. This limits conferences’ ability to compete with price per game. Instead, to increase profits, conferences attract demand for their particular brand. To do this, conferences must generate a high quality product and, perhaps more importantly, attract viewers away from their competitors through advertising. This is how we see conference expansion and realignment, conferences are acting to increase their consumer base by selectively choosing schools with large fan bases that will draw larger television audiences. In this paper, we investigate whether or not teams with larger fan bases are more attractive to conferences contemplating realignment. This research will help shed light on the important role played by commercial incentives in “amateur” US intercollegiate athletic competitions. This question has not been explored empirically in this context. Roberts and Samuelson (1988) developed a model of nonprice competition and empirically studied the cigarette industry. Cigarette companies cannot compete with price due to federal and state price floors. They found that advertising as a form of nonprice competition in the cigarette industry increased the market demand rather than demand for the advertised brand. In its current form, our paper does not explore the payoff of conferences’ advertising efforts via conference expansion/realignment, rather we focus on the determinants of selection of programs involved in expansion/realignment. The outcome of this analysis has potential implications outside of oligopolistic nonprice competition. Many researchers have used athletic success to measure advertising effect of big-time athletic programs on academic outcomes at universities, e.g. incoming student SAT scores (Mixon et al., 2004, Tucker and Amato, 2006, Pope and Pope, 2009, Segura and Willner, 2016 and others). However, athletic success is likely endogenous to other university outcomes. For example, donors are not solely giving to athletics departments, thus, athletic success might accompany improvements in other parts of the university. Conference changes provide a potentially exogenous change in advertising effect. It creates advertising since a school moving to a better conference likely receives more national attention. If we can identify why schools are selected to join big-time athletics conferences, we can clarify whether or not conference changes are exogenous with respect to academic outcomes at universities. With evidence of exogeneity, conference changes could prove a useful tool to study the impact of big-time athletics on academic outcomes at universities. The paper will analyze the characteristics of teams that switched conference affiliation in this period. We estimate probit models that explain which teams switch conferences. We collected data from 121 NCAA Division 1 Football Bowl Subdivision (FBS) colleges and universities over the period 2002-2015 from a number of sources. We combine data on university characteristics from the Integrated Postsecondary Education Data System (IPEDS), state and local macroeconomic indicators from Bureau of Economic Analysis (BEA) Regional Economic Information System (REIS), and data on Division 1 athletic conference affiliation

2 from publicly available news outlets. Seventy-three conference changes occurred over the sample, so there is ample variation in conference aﬃliation to analyze. We ﬁnd evidence that, when looking for new conference members the most prestigious conferences value our proxies for success and team popularity. Less prestigious conferences put a greater value on success and less on team popularity.1.

Background

The Board of Regents Decision and Aftermath A 1984 Supreme Court of the United States ruling, the Board of Regents (BOR) decision, prohibited the National Collegiate Athletic Association (NCAA) from collectively bargaining for television rights fees with broadcast networks for games played by big-time college football teams and instead granted this power to conferences and individual schools. The BOR decision clearly impacted conferences, spurring realignment and other outcomes in big-time college sports (Carroll and Humphreys, 2016). This ruling generated enormous increases in broadcast revenues earned by US universities and caused significant changes in conference composition and size. Following the BOR decision, conferences expanded, adding new members. Most new conference members came from existing conferences, as the high cost of big-time college sports programs deters colleges with smaller programs from expanding to meet the requirements of big-time college sports. We analyze the competition between conferences for new members from the existing ranks of big-time college athletic programs and the role of factors like enrollment and the local population in determining which schools switched conferences and which remained in their current conference. Athletic conference changes reflect the growing importance of television broadcast rights revenues in big-time college sports. Until the early 1980s, the NCAA negotiated with television networks for the rights to broadcast college football and men’s basketball games on behalf of schools and tightly controlled the number of games televised and the number of television appearances any school could make in a season. Relatively few networks expressed interest in televising NCAA sporting events. NBC began broadcasting college football games in 1952. ESPN began operation on 7 September 1979, and broadcast a few college football games on tape delay starting in the 1979 regular season, but did not begin broadcasting live college football games until the 1984 season. The NCAA sold the first football television broadcast rights deal to NBC in 1952 for $1.1 million ($10.5 million in 2018 dollars).2 The contract allowed NBC to broadcast one regular season game per week to a national audience on Saturday afternoon. In 1953 the NCAA allowed multiple regional broadcasts of different games on selected weeks. This contract was terminated after the 1953 season. In 1955 the contract was again sold, this time for the rights to broadcast one nationally-televised regular season Saturday afternoon game per week for eight Saturdays and a small number of regionally-televised games in a single time

1We proxy team success with rankings and popularity with state population and size of school. 2The ﬁrst game broadcast under this contract was TCU versus Kansas on 20 September 1952.

3 slot on Saturday afternoon for ﬁve weeks during the regular season.3 This system remained in place until 1981, when the Board of Regents of the University of Georgia and University of Oklahoma sued the NCAA on the grounds that the NCAA college football television broadcast regulation violated anti-trust law. In June 1984 the U.S. Supreme Court ruled that the NCAA was in violation of anti- trust law and could no longer negotiate and control college football broadcasts. The right to negotiate football broadcast rights, and distribute the revenues, devolved to conferences, and in some cases, individual universities. Unshackled from NCAA control, conferences underwent substantial changes. Traditional conferences like the Big 10 added schools from Pennsylvania, Maryland, and New Jersey, well outside their original midwestern territory. The PAC-10 Conference expanded eastward, adding teams in Utah and Colorado. Long-lived conferences like the SWC disintegrated and new power conferences like the Big 12 emerged. Conference membership in big-time college sports was stable in the pre Board of Regents era.4 The Big 10 Conference included the same members from its founding in the 1890s until 1990, with the exception of the (ex post wise) choice by the University of Chicago to eliminate all intercollegiate sports in 1940 and the addition of Ohio State (1912) and Michigan State (1950). Ten of the founding members of the Southeastern Conference (SEC) have been continuous members since 1932. Only a few membership changes occurred in SEC in the pre Board of Regents era, notably the (ex post wise) choice by the University of the South (Sewanee) to eliminate all intercollegiate sports in 1940. Schools occasionally left major athletic conferences for independent status in the pre Board of Regents era. Georgia Tech and Tulane left the Southeastern Conference, and South Carolina left the Atlantic Coast Conference in the mid 1960s. In this era, television broadcast rights were controlled by the NCAA so leaving a conference resulted in no change in broadcast rights fees. The Board of Regents decision clearly impacted conference incentives, spurring realignment and other outcomes in big-time college sports (Carroll and Humphreys, 2016). The Board of Regents decision put conferences in charge of negotiating television broadcast rights contracts. At the same time, the number of broadcasters televising college football exploded, driving up the value of these rights fees and revenues (Sanderson and Siegfried, 2018).

Changes in Conference Membership Sanderson and Siegfried (2015) and Sanderson and Siegfried (2018) emphasize that conference realignment was/is driven by conferences’ desires to increase television broadcast revenues, and not by university-led attempts to improve academic outcomes or by attempts to improve the quality of teams playing in the conference. We deﬁne conference changes or

3Bowl games were exempted from NCAA broadcast regulation and have been regularly televised since the 1950s. 4A notable exception was University of Arizona and Arizona State University joining the Pacific 8 Con- ference to form the Pacific 10 Conference in 1978. Although this move was not based on broadcast rights, it was based on revenues. The University of Southern California (USC) lost money when traveling to play in the Pacific Northwest in front of small crowds. Because of this, USC threatened to leave the Pac 8 and join a new conference with Notre Dame, Penn State and others unless a membership change was made to the PAC 8 Hansen (2016).

4 Figure 1: Conference Changes by Year, First Time Switchers

switches as the case where institutions switch from one athletic conference to another. A relatively large number of NCAA Division 1-A/FBS schools switched conferences since the 1984 Board of Regents (BOR) decision. These changes in conference affiliation reflect the fact that the BOR decision reduced the power of the NCAA to regulate television schedules and appearances in college football and increased the power of athletic conferences. Figure 1 illustrates the number of universities switching conferences for the first time since the formation of the Big 12 Conference (1996) by year. Some teams change conferences more than once over the period, so the conference changes shown on Figure 1 represent a majority but not the full extent of realignment over this time period. Notice that conference changes are not uniformly distributed over time. Rather, they tend to cluster during specific periods, e.g. during the collapse of the Big East Conference in 2013. Not all conference changes are equivalent. This holds true even for conference changes regarding the same school. Consider the experience of Texas Christian University (TCU). TCU left the Southwest Conference (SWC) in 1996 when the Big 12 formed from a merger of some members of the SWC and the Big 8 Conference. Lacking an invitation to join the newly formed Big 12, TCU moved to the Western Athletic Conference, which expanded into a mega-conference by adding six teams (from the Big West Conference and the SWC) to the ten incumbents. TCU, unwillingly, traded membership in a conference with powerhouse football schools like Texas and Texas A&M for membership in a conference with Texas-El Paso and Tulsa. This realignment was a step down for TCU in terms of prestige of their conference foes.

5 TCU’s most recent conference change saw the Horned Frogs join some of their old SWC rivals (Baylor, Texas, and Texas Tech) and most of the former Big 8 conference in the Big 12 conference.5 In this case, TCU found itself joining more prestigious conference aﬃliates, which increased the proﬁle of TCU’s athletic programs. This was undoubtedly a positive move for TCU. To account for this heterogeneity, we create a number of conference change indicator variables. Conference change indicates any move from one FBS conference to another. Power Five conference change accounts for joining a Power Five conference. “Conference improvement”, where a team moves to a conference with a better recored. We also use the complementary sets of the aforementioned categories of conference changes, i.e. non-Power Five conference change and lateral change.

Conference Expansions as Advertising In economic terms, we interpret conference expansions as analogous to advertising in the context of the dynamic oligopolistic non-price competition model developed by Roberts and Samuelson (1988). In that model firms in an oligopolistic industry compete on a non-price basis by advertising their products. This advertising builds up long-lived goodwill among customers and increased demand for each firm’s product as well as drawing existing customers away from competitors. This model captures the essential features of conference expansion. Football conferences maximize broadcast rights fee revenues from conference games, their product. A relatively small number of differentiated conferences exist in FBS, resembling an oligopolistic market.

Empirical Analysis

Data Our data is structured as an unbalanced panel of 109 universities from various years between 2002 to 2015. We combine data from multiple sources. Sportsreference.com and conferences themselves provide data on conference affiliation and team records. Integrated Postsecondary Education Data System (IPEDS) provides university specific enrollment data. State population data comes from Bureau of Economic Analysis (BEA) Regional Economic Information System (REIS). See Table 1 for summary measures of the data. To determine which attributes conferences deem important when inviting new conference members, we estimate a probit model to identify which attributes best predict conference changes. The basic outcome variable is Changeit, which takes the value of 1 if team i is a member of a different conference in year t than in year t − 1. As mentioned previously, conference changes are not homogenous, for example TCU joining the Big 12 was not the same as Temple University “leaving” the Big East, so we separate conference changes into more homogeneous categories. The first category identifies schools joining a Power Five conference, P 5changeit = 1. The second category, which we

5Former SWC member Texas A&M moved from the Big 12 to the Southeastern Conference in 2012 and former Big 8 member Colorado moved to the PAC 10 conference in 2011.

6 Table 1: Summary Statistics

mean sd min max Conference change 0.036 0.186 0 1 Conference improvement 0.025 0.156 0 1 Conference Change, Lateral 0.013 0.111 0 1 Power Five conference change 0.012 0.108 0 1 Non-Power Five conference change 0.026 0.159 0 1 Winning percentagea 56.8 14.1 23.3 92.3 Bowl TV ratinga 3.65 2.60 0.20 17.80 Bowl TV ratings residuala -0.01 0.59 -3.44 5.28 Yearly conference changes 6.05 6.65 0 22 Power Five Conference 0.565 0.496 0 1 FTE share of state population (Mil) 2.64 1.85 0.33 10.29 FTE enrollment (1000s) 24.3 11.1 3.8 64.6 Observations 1276

Note: D1/FBS Universities, 2002 to 2015. a denotes past 5 year averages. Limited to team-seasons with at least one bowl game appearance in the previous 5 seasons. Conference improvements occur when a team changes conferences from x to y in year t where conference y has a better record than conference x in year t−1. Power Five Conference Change occurs when a school changes conference aﬃliation to a Power Five conference (ACC, B10, B12, PAC10/12, and SEC). Yearly conference changes are the total number of conference changes in a given year. Win % - conference win % for team u in conference v is the winning percentage of team u divided by the combined winning percentage of conference v. Bowl TV Ratings Residual is the residual of bowl tv ratings regressed on indicators for bowl game opponent year, and network. FTE stands for full-time equivalent. FTE share of state population is the state’s population normalized by the enrollment at the university divided by the total enrollment at all FBS universities in that state.

7 call a conference improvement, is a little more arbitrary. CImprovementit = 1 if team i changed from conference x in year t − 1 to conference y in year t and the overall record for teams in conference y was better than the overall record for teams in conference x in year t − 1. We also use complementary sets of these two categories of conference changes, namely NP 5change, a change to a non-Power Five conference, and CLateral, which represents a lateral move to and from a similar or worse conference. We posit that television rights contracts drive most conference decisions. Therefore, if a team moves to a better conference, we assume it moved because that team will increase the number of potential television viewers for conference games. With this increased popularity, a conference can improve the terms during their next round of TV broadcast rights negoti- ations. Our variables of interest should capture team popularity in terms of the number of people who would watch that team’s games on television. Our first popularity measure is enrollment at the school. F T E enrollmentit is the number of full time equivalent (FTE) students enrolled at university i in year t. Although this measure does not reflect the popularity of the football team directly, it does represent one possible measure the size of the school’s television audience. All else equal, we expect larger schools to have larger fan bases, and more alumni than smaller schools, although many small to medium size schools have very large fan bases nationally, for example Notre Dame, Stanford, and Miami (FL). Additionally, there may be very large universities that draw smaller television audiences because they are not national powerhouses and they do not have a very large regional population. For instance, West Virginia, Kansas, New Mexico have smaller populations in their re- spective states and so there may be fewer viewers than, say, Ohio State or Florida. Therefore, we want to use some measure of surrounding population to approximate the number of potential viewers. We choose to use state population, however, many states contain multiple FBS teams and it is unlikely that an individual will support more than one team in their state. For instance, we do not expect an Auburn fan to also support Alabama. Thus, we derive a variable called F T E share of state populationit, which is the population of the state normalized by the enrollment of university i divided by the total FTE enrollment of all FBS/D1 universities in university i’s state. For example, the state of Kansas has 2 FBS teams, University of Kansas (KU) and Kansas State University (KSU). Say the state of Kansas has a population of 10 million, KU has a FTE enrollment of 30,000 and KSU has a FTE enrollment of 20,000. Then KU has a F T E share of state population = (30000/50000) ∗ 10 million = 6 million. KSU has a F T E share of state population = (20000/50000) ∗ 10 million = 4 million. Our final measure of popularity, which actually provides data on the size of TV viewership, is derived from bowl game TV ratings. Bowl games are postseason games reserved for above average regular season performers.6 We have data on the Nielsen Ratings for each of the bowl games in our sample period. If a team receives high ratings at bowl games than expected it likely has a large fan base and vice versa. So, we model bowl game TV ratings with indicator variables for bowl game played, the opponent, the year, and the network broadcasting the bowl game and save the residuals. Because teams do not receive invitations to bowl games each year, we average the residuals over the previous 5 year period to produce

6Typically bowl game eligibility was dependent on a winning season, but due to recent expansions in the number of bowl games, a number of .500 and sub-.500 teams have received invitations.

8 Bowl T V ratingsit. Other than pure size of fan base, conferences may compete for national prominence based on results on the gridiron. So conference might care about team success rather than popularity. Also, popularity and success are difficult to differentiate. So, to reduce any bias in our proposed measures of popularity, we want to control for team success. Therefore, we use winning percentage, W inning percentageit as a measure of team success. We also use Average Bowl TV Rating in the previous 5 years (Average bowl T V ratingit since the rating depends mostly on the prestige of the bowl, which depends on the team’s success that season. Since winning percentage does not account for strength of opponents and bowl television ratings are dependent on many other factors than success, we use Kenneth Massey’s composite year-end ranking (downloaded from Kaggle.com) as an additional measure of success. Including all pertinent variables improves the precision of parameter estimates. Teams from Power Five Conferences do not often change conferences. Therefore, we include a variable in the empirical model that reflects whether or not a team is affiliated with a Power Five conference, P ower 5 conferenceit, in our models predicting conference changes. Additionally, conference changes cluster temporally. Conferences might fill multiple spots at once or an entire conference might collapse. This happened to the Big East Conference. Additionally, conference changes in more prestigious conferences typically generate shuffling in lower tiered conferences. This generates temporal clustering, which control for by creating a variable Y early Conference Changest, which reflects the total number of conference changes in year t. This variable reflects decisions made by competing conferences and proxies for strategic interaction. We included only lagged Y early Conference Changes(t−1) in the empirical models. We estimate the following probit model to identify specific characteristics of teams that switch conferences

P (Yit = 1|Xit) = Φ(α + XitB + it) (1)

Probit estimates whether or not the bivariate dependent variable, Yit, equals 1 using a maximum likelihood approach. The type of conference change estimated determines Yit. Φ( ) is the cumulative distribution function of the unit-normal distribution. α is the in- tercept to be estimated. X is a n × m matrix where n is the number of observations and m is the number of regressors. In our main specification Xit is composed of average values over the prior 5 years for the following variables: W inning percentageit, Bowl T V ratingsit, and Bowl T V ratings residualsit and lagged values of the remaining variables: P ower 5 conferencei(t−1), T otal conference changest−1, Normalized state- populationi(t−1) and F T E enrollmenti(t−1). B is a m × 1 vector of unknown parameters estimated. epsilonit is an independent and normally distributed around 0 random variable reflecting other unobservable factors affecting teams that change conferences.

Preliminary Results

Table 2 presents average marginal eﬀects estimated using equation (1). These results are consistent with many of our priors and provide some insights into the mechanisms that guide

9 Table 2: Predicting Conference Changes: Average Marginal Eﬀects

Type of Conference Change (1) (2) (3) (4) (5) Any Power Five Improvement Non-Power Five Lateral Winning percentage (a) 0.00087∗ 0.00047 0.00074 0.00048 0.00028 (0.00042) (0.00029) (0.00043) (0.00027) (0.00047) Bowl TV ratings residuals a -0.010 -0.0040 -0.0054 -0.0086 -0.011 (0.0092) (0.0052) (0.0078) (0.0068) (0.011) Bowl TV Ratings a -0.00077 0.00069 -0.00081 -0.0035 0.00068 (0.0029) (0.0014) (0.0025) (0.0027) (0.0037) Power 5 conference b -0.075∗∗∗ -0.017∗ -0.041∗∗∗ -0.065∗∗∗ (0.015) (0.0081) (0.011) (0.017) Total conference changes b 0.0012 0.00014 0.00073 0.00099∗ 0.0011 (0.00061) (0.00043) (0.00055) (0.00043) (0.00059) FTE normalized state pop (mil) b 0.0035 0.0035∗ 0.0041 -0.00093 -0.0038 (0.0033) (0.0016) (0.0026) (0.0024) (0.0035) FTE enrollment (1000s) b 0.00036 0.000018 0.000033 0.00043 0.00097 (0.00045) (0.00025) (0.00038) (0.00042) (0.00074) Observations 1331 1331 1331 1331 594

Note: Standard errors in parentheses. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. a indicates past 5 year averages. b indicates a one year lag. Limited to team-seasons with at least one bowl game appearance in the previous 5 seasons. Conference improvements occur when a team changes conferences from x to y in year t where conference y has a better record than conference x in year t − 1. Power Five Conference Change occurs when a school changes conference aﬃliation to a Power Five conference (ACC, B10, B12, PAC10/12, and SEC). Yearly conference changes are the total number of conference changes in a given year. Win % - conference win % for team u in conference v is the winning percentage of team u divided by the average winning percentage of conference v. Bowl TV Ratings Residual is the residual of bowl tv ratings regressed on indicators for bowl game opponent year, and network. FTE stands for full-time equivalent. FTE share of state population is the state’s population normalized by the enrollment at the university divided by the total enrollment at all FBS universities in that state.

10 conference changes. The strongest and most predictable effect is that schools cherish Power Five conference affiliations. We estimate that Power Five teams are 7.5 percentage points less likely to change to any conference, 4.1 percentage points less likely to change to a conference with a higher winning percentage, and 1.7 percentage points less likely to move to a Power Five conference. This result is unsurprising since over half of the team-years are from Power Five conferences and only 12.5% of all conference changes were movements made by Power Five conference schools. Conference changes cluster temporally as teams replace first movers. Estimates suggest that increasing the number of conference changes in the previous year by 5 increases the likelihood that a university would move to a non-Power Five conference by about half a percentage point. Although imprecise, the parameter estimate of conference changes in the previous year on a lateral movement in conference is approximately that same, whereas the estimated effect on movement to Power Five conferences is much closer to zero. This supports our observation that Power Five conference expansions lead to vacancies in second tiered conferences who reload with schools from other secondary or tertiary conferences, who in turn leave vacancies. Although these confirmations to the more basic mechanisms ruling conference changes are important, the crux of our analysis is the relative importance of success on the field versus popularity/fan base. Our somewhat crude proxy measurements still garner attention. We find imprecise estimates of bowl ratings and ratings residuals for each type of conference change outlined above. This suggests one of three possibilities. First, television ratings are not considered when asking teams to join conferences. This suggests that conferences do not consider television right contracts when making conference change decisions. We find this notion farfetched. For the majority if not all of the decision makers, the goals of conference realignment is to increase revenues and/or increase the visibility of their schools (advertise to potential applicants, donors, etc). Boosting television viewership seems like the only mechanism through which conference realignment accomplishes either of these goals. Second, bowl television ratings are a poor proxy for television ratings throughout the year. For instance, schools without prestigious football programs might actually draw larger television audiences to bowl games because they have exceeded expectations, whereas prestigious teams only met expectations or provided disappointment. If a team exceeds expectations, team-specific fans, non-fans associated with the school, and football fans in general will watch them play their bowl game. On the other hand, schools that have larger fan bases might be invited to bowl games even when they have a mediocre year, therefore, not all of their fans will be as excited or willing to watch the bowl game.7 Finally, empirical challenges in this model likely impede precision and/or bias estimates of the relationship between bowl television ratings and conference changes. Collinearity between bowl television ratings and winning percentage and between bowl television ratings and its residual inflate standard errors. Additionally, our original method for dealing with heterogeneity or conference changes (specifying dependent variable as change to power five, conference improvement, etc.) is imperfect. For instance, the Big 12 likely used different criteria to replacing teams than the SEC used when it expanded by adding Missouri and Texas

7The data supports this. The average residual for a non-Power Five school in a bowl game with a TV rating less than 4 is -0.076, whereas the average residual for a Power Five school is -0.130.

11 Figure 2: Marginal analysis of how normalized state population aﬀects conference changes

(a) Conference change (b) Power Five

(e) Lateral movement

A & M. This empirical challenge is addressed further in the group analysis that follows. Although the TV ratings variables proved to be insignificant, we find that alternative measurements of fan base, which we proxy with state population and school size, have statistically significant impacts on the likelihood of changing conferences. Teams with larger

12 Figure 3: Marginal analysis of how winning aﬀects conference changes

(a) Non-Power Five (b) Conference improvement

potential fan bases are more likely to join Power 5 conferences. An increase of one million to our normalized state population measure was associated with a 3.5 percentage point increase in the likelihood of joining a Power Five conference. Using Colorado State University (CSU) to illustrate, our measure of CSU’s normalized state population grew by 0.4 million between 2006 and 2015, which our model associates with a 1.4 percentage point increase in CSU’s likelihood of joining a Power Five conference. Estimates for other types of conference changes provide suggestive, albeit imprecise, evidence that universities with larger normalized state population have a greater probability of joining a “better conference” and no change in or less probability of moving to another conference “laterally.” Figure 2 presents point estimates and 95% confidence bands of the average marginal effects of FTE normalized state population at specific levels of FTE normalized state population. We can see an upward trend in the point estimates for the effect on the likelihood of Power Five conference changes as well as conference improvements, although there is no significant differences based on the confidence bands. Graph (e) suggests that at very high levels of FTE normalized state population there is a significant negative average marginal effect of FTE normalized state population on the likelihood of lateral movement, suggesting

13 that larger schools or schools in larger states are less likely to move to a worse conference as their enrollments or state populations grow. One may interpret these results to suggest that football programs with larger fan bases are more insulated or have more options when forced to move conference and are more desirable to the premier conferences. The last result of consequence is winning percentage. Table 2 suggests that a greater winning percentage will improve the likelihood of conference changes, although it is unclear that this will be a change to a better conference or a worse conference. We estimate that a 10 percentage point increase in the previous 5 year winning percentage (6 or 7 more wins in 5 years) leads to a 0.87 percentage point increase in the likelihood of a conference change. The average marginal effect of winning percentage on the likelihood of moving to a better conference or Power Five conference is imprecise. Focusing on particular winning percentages does not improve clarity all that much. Figure 3 reiterates the fact that teams from Power Five conferences stay in that conference. Panels (a) and (b) suggest that non-Power Five conference members that are mediocre are significantly more likely to move to another non- Power Five conference or even improve conferences than their counterparts who are below average. However, the effect of winning percentage on teams that are mediocre to good is not precisely estimated.

Results by Group

The methods used previously fall short in two empirical respects. First, even when splitting the dependent variable by Power Five/Non-Power Five or Improvements/Lateral moves there still may be a lack of homogeneity across conference changes. Within the aforementioned bins, not all conference changes occur due to similar circumstances. For instance, within the Power Five bin the SEC expanded their conference with Texas A & M University and Uni- versity of Missouri, whereas the Big 12 replaced those teams (and others) with West Virginia University and Texas Christian University. As different circumstances governed each type of conference change, lumping these conference changes together muddles the interpretation of the results. Combining heterogenous conference changes, would split the difference, i.e. if winning is very important to one type of conference change and fan base is important to the other, then combining all conference changes as the dependent variable might cause the estimated effect of these factors to be of middling importance or insignificantly different from zero. A second set of empirical challenges relates to the suitability of control schools, i.e. selection is an issue. In particular, we control every conference change with the full sample of all FBS schools, but a large subset of those schools would never be considered because of fit or match. Therefore, we would be modeling which of our variables of interest affects the likelihood of conference changes where the conference in question would never consider inviting most of these school in the first place. A more meaningful question might be: given a set of plausible matches, why is one team selected to join a conference over another? There are a number of reasons that a team-conference pair would not make a logical match. Perhaps the conference does not benefit the team. Teams will not move to a worse conference voluntarily. Teams that are stalwarts in a top tier conference, say Texas, Ohio State, or Alabama, have a lot of power, standing, and brand recognition within the conference

14 Table 3: Ranking Conferences by Conference to Conference Movement

Group Conference Leaves for Joins from 1 Southeast Conference (SEC) none B12 1 Big 10 (B10) none B12, ACC 1 PAC 12 (P12) none B12, MW 2 Atlantic Cost Conference (ACC) B10 BE, AAC 2 Big 12 (B12) B10, P12, SEC MW, BE 3 Big East (BE) ACC, B12, AACa CUSA 3 Mountain West (MW) B12 WAC, CUSA 3 American Athletic Conference ACC, B10 CUSA, BEa 4 Conference-USA (CUSA) MW, AAC MAC, WAC, SUN 5 Western Athletic Conference (WAC) MW, CUSA, SUNb SUN 6 Mid American Conference (MAC) CUSA, BE Independent, FCS 6 Sunbelt Conference (SUN) CUSA, WAC WACb, Independent, FCS

Note: a indicates the collapse of the Big East (BE) and creation of American Athletic Conference (AAC). This should not be viewed as an improvement in conference or conference change for the teams moving from BE to AAC. b indicates the collapse of the Western Athletic Conference, where some of the remaining teams moved toward the Sunbelt (SUN) conference. This does not indicate the the SUN is better than the WAC. and would likely not enjoy, or profit in, relinquishing it. Second, the team does not benefit the conference. There could be a number of reasons for this including geographic considerations and quality mismatch. It is important to note that team quality can be too high or too low for teams to match conferences. Thus, “quality,” or any measures we use, would have a nonlinear effect on conference change without restricting the control group. The final consideration is timing. If there is no vacancy, teams cannot change conferences to fill it. In what follows we strive to find more appropriate sets of schools to mitigate these selection issue. Inspection of group movements, see Table 3, provides insight on both sets of empirical challenges mentioned above. Specifically, we have identified “groups” in such a way that produces the following general rule: within a group, switching teams move to the next highest group, they do not move within the group or to a lower group.8 Notice that this also means that conferences within a group typically draw from the group directly below. In other words, in the absence of conference collapse, a school switching to a conference in group g originates from conferences in group g + 1. Thus, teams from group g + 1 would make a better control group than all FBS schools. Moreover, the reasoning behind conference invitations to group g should be more homogeneous than all conference changes since those

8Exceptions to this general rules occur when conferences fail. The collapse of the Big East Conference (BE) created the American Athletic Conference (ACC). This should not be viewed as an improvement in conference or conference change for the teams moving from BE to AAC. The collapse of the Western Athletic Conference (WAC) saw some teams join the Sunbelt conference (SUN). This does not indicate the the SUN belongs in a group above the WAC. Finally, Temple left the Big East in 2004 and became independent of conference aﬃliation (ironically, Temple would later join the conference formerly known as the Big East, the American Athletic Conference).

15 conferences add schools from the same group. Because of these important factors, we propose (1) limiting the control group to those groups or conferences from which schools originated, (2) limiting the dependent variable for a particular regression to a conference change into a particular group or controlling for lagged group, and (3) in consideration of temporal nature of conference changes, limiting analysis by group-years abandoned by conference switchers and controlling for year. The following results account for the insights described above. Tables 4 and 6 predict the likelihood of a “conference step-up”–a movement to a higher group as defined in Table 3. To improve the control group, Table 4 restricts the sample to team-seasons of schools affiliated with a group (specifications 1 and 2) or a conference (specifications 3 and 4) that lost a member to a conference step-up in the previous year. Although we do not restrict conference step-ups to a particular group, we do include indicators for lagged group (specifications 1 and 2) or lagged conference (specifications 3 and 4) to control for unobservable factors common to groups and conferences respectively. We also include indicators for group year. These indicator variables should control for the temporal nature of conference expansions that targets specific groups/conferences. Even specifications do not count conference changes from one Power Five conference to another as conference step-ups. Results are fairly uniform in both the group sample and the conference sample, so this analysis will focus on the group sample (specifications 1 and 2). Surprisingly, we fail to find evidence that team ranking significant affects the likelihood of stepping up to a better group. This fails to reject our hypothesis that conferences are not looking for the most competitive teams when expanding/filling in openings in their conference. This does not prove that conferences have no preference based on success when adding conference affiliates. On the contrary, it could be that some conferences desire to add better teams to make their conference more competitive, while other conferences have incumbent members that prefer to inflate their win totals by inviting a team that is more easily defeated. We take a closer look at this explanation in Figure 4 and Table 6. Although it may be plausible that conference expansion preferences are heterogeneous when it comes to team success, all conferences should prefer adding a more popular school, all else the same, in order to increase bargaining power over television rights contracts. Of these three popularity proxies, normalized state population is the most imprecisely estimated. It may be a weak proxy for popularity because less populated states lack professional sports and instead have hugely popular university football teams. Estimates of the effect of bowl ratings on group step-up are marginally significant (p- value of 0.116 for specification 4) for specifications that omit step-ups from one Power Five conference to another. This inclusion/omission of Power Five conference changes influences the estimate because of teams like Oklahoma, Texas, and Clemson who are very popular teams, highly unlikely to move from their conference affiliations because of the power they hold within conference, and are counted as part of the control group. Estimates provide marginal evidence that conferences value teams that have higher television ratings.9

919 percent of observations in the group sample and 21 percent of observations in the conference sample do not appear in bowl games in their previous 5 years. We count these as Bowl ranking = 0. However, because, had these teams had a bowl game, they would not have received zero television rating. Therefore, we include an indicator variable for teams who have had zero appearances in the previous 5 years. Thus, the interpretation of the estimate of x for Bowl Game TV Ratings is: given that a team has played a bowl

16 Table 4: Predicting Conference Step-Up, Average Marginal Eﬀects, Restricted to Abandoned Groups/Conferences

P (Stepped up = 1) Group Sample Conference Sample (1) (2) (3) (4) mean = 0.159 mean = 0.186 mean = 0.251 mean = 0.270 Composite Rankingb -0.0012 -0.0011 -0.0012 -0.00092 (0.00082) (0.00100) (0.0012) (0.0014) Normalized State Populationb -0.00096 -0.0011 -0.0019 -0.0016 (0.0021) (0.0022) (0.0029) (0.0029) FTE Enrollmentb 0.0061∗ 0.0064∗ 0.0084∗ 0.0088∗ (0.0024) (0.0030) (0.0033) (0.0039) Bowl Ratingsa 0.0079 0.037 0.010 0.051 (0.019) (0.025) (0.024) (0.032) Lagged group indicators Yes Yes No No Lagged conference indicators No No Yes Yes Year indicators Yes Yes Yes Yes Bowl appearance indicator Yes Yes Yes Yes Omit moves away from P5 No Yes No Yes Observations share lagged group- Yes Yes No No year with conf changers Observations share lagged conf- No No Yes Yes year with conf changers Observations 290 220 209 173

Note: Standard errors clustered by school in parentheses. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. A conference step-up occurs when a team moves to a higher ranked group according to table 3. a indicates sum of past 5 years or all previously available data if 5 years are not available. b indicates a one year lag. Specifications 1 and 2 limit sample to team-seasons for teams affiliated with a group that lost a member to a conference step-up in the previous year. Specifications 3 and 4 limit sample to team-seasons for teams affiliated with a conference that lost a member to a conference step-up in the previous year. Specifications 2 and 4 omit moves away from a power 5 conferences from the analysis. FTE stands for full-time equivalent. FTE share of state population is the state’s population normalized by the enrollment at the university divided by the total enrollment at all FBS universities in that state.

17 Figure 4: Group Speciﬁc Average Treatment Eﬀects

(a) Bowl TV Ratingsb (b) Composite Ratinga

a indicates lagged value, b indicates value for previous 5 years.

The most distinctive result from this model suggests that larger schools are more likely to step-up to a more prestigious group. Estimates from the group sample suggests a school with 1,000 additional students will increase its likelihood of stepping-up to better group by 0.6 percentage point. This may seem small, but in the sample used in specification (1) only 0.6 16% of observations step up so this is a 0.16 = 3.75 percent increase in the likelihood of stepping up to a new group. Larger schools are likely a bigger draw with larger stadiums, bigger fan bases, and deeper pockets. As stated previously, conference prestige may influence the attributes that conferences rely on to select new conference members. Table 4 does not provide insight into this question. Using the same probit model as Table 4, Figure 4 presents 95% confidence intervals of the average marginal effects at each group. These results lack precision. For each variable of interest, group confidence intervals overlap one another. This may indicate that conferences

game in the past 5 seasons, an increase of 1 percentage point of all viewership in the past 5 years increases probability of a conference change by x.

18 Table 5: Group subsample breakdown of conference changes

FTE Enrollmenta Origin Group Observations Changed Conferences Mean S.D. 2 70 0.0714 24.67 1.15 3 104 0.1154 22.30 0.84 4 47 0.2766 17.55 1.44 5 29 0.4138 15.55 1.06 6 94 0.1277 18.95 0.63

Note: a indicates lagged values in thousands. Sample limited to team-seasons for teams affiliated with a group that lost a member to a conference step-up in the previous year. of any prestige have similar preferences for adding new teams, or it may indicate the need for more data in order to improve estimates. If we focus solely on point estimates, it seems as though FTE enrollment has a greater effect on groups 4 and 5 (and so is more important to groups 3 and 4 when considering inviting new members) relative to other groups. We can attribute some of this difference to the fact that teams from groups 4 and 5 are simply more likely to switch conferences. This is certainly the case for group 5 where 41.38% of all university years end up with the team moving to a higher group. Much of this was due to the WAC collapsing. Group 4, which is made up solely of Conference-USA (CUSA), did experience more conference step- ups than origin groups 2 and 3, but not as much conference step-ups as group 5 (27.66%). A supplementary explanations is that the teams in group 4 have a much greater variance in FTE enrollment. Table 5 reveals that although group 4 universities have the second smallest schools on average, the standard error of enrollment is greater than any other group. It follows that group 3 avoids the smallest schools from group 4 when inviting new teams because these teams are much smaller than average. Although this is perhaps an extreme case, it demonstrates that university size is considered when conference choose to invite new teams. The above findings act as a small step toward uncovering what conferences of different prestige gravitate towards when inviting new conference members. A limit to this analysis is that, although figure 4 presents estimates at group specific values, these estimates assume the average values of all other variables used. As we can see from Table 5, the average FTE enrollment varies considerably from conference to conference. For example, the effects from Figure 4 reflect estimates for FTE enrollment of 20,481 (average for the sample). A school of this size is quite small for origin group 2 (about 4 SD below origin group 2 mean) and very large for origin group 5 (about 4 SD above origin group 5 mean). To overcome this challenge, Table 6 presents average marginal effects of the same probit model limited to observations sharing lagged group-year with a school ascending to a specific group (1, 2, 3, 4) or a combination of two groups (2 and 3 or 4 and 5). As expected, we find heterogeneity by group. Movement to the most prestigious groups (1, 2, and 3) is more likely for higher quality teams, although it seems that this is most important for those ascending to non-power 5 conferences (group 3). Normalized state population and FTE enrollment,

19 Table 6: Predicting Conference Change: Average Marginal Eﬀects, Levels

(1) (2) (3) (4) (5) (6) Probability stepped up to group(s) ... 1 2 3 4 2 and 3 4 and 5 Composite Rankingb -0.0016∗ -0.0022∗∗ -0.0036∗∗∗ 0.00087 -0.0029∗∗∗ 0.00045 (0.00085) (0.0011) (0.0014) (0.0019) (0.0011) (0.0021) Normalized State Populationb 0.0023∗∗∗ 0.00098 -0.0093∗∗ 0.0076 -0.0055∗∗∗ 0.012∗∗∗ (0.00085) (0.0020) (0.0042) (0.0056) (0.0020) (0.0037) FTE Enrollmentb 0.0050∗∗∗ -0.0043 0.0079∗ 0.0033 -0.000022 0.0015 (0.0018) (0.0035) (0.0042) (0.0076) (0.0027) (0.0077) Bowl Ratingsc -0.036∗∗ 0.053 0.0098 -0.017 0.019 -0.015 (0.015) (0.034) (0.048) (0.037) (0.019) (0.038) Observations 103 75 84 77 171 77 Year indicator Yes Yes Yes Yes Yes Yes Bowl appearance indicator Yes Yes Yes Yes Yes Yes Observations share lagged 1 2 3 4 2 & 3 4 & 5 group-year with conference changers to group(s)

Note: Standard errors clustered by school in parentheses. ∗p < 0.1, ∗∗p < 0.05, ∗∗∗p < 0.01. b indicates a one year lag. c indicates sum of past 5 years or all previously available data if 5 years are not available. Limited to team-seasons for teams affiliated with a conference that lost a member to a conference step-up in the previous year. A conference step-up occurs when a team moves to a higher ranked group according to table 3. FTE stands for full-time equivalent. FTE share of state population is the state’s population normalized by the enrollment at the university divided by the total enrollment at all FBS universities in that state. measures of team popularity, are significant factors for movements to the top half of the Power 5 (group 1) but insignificant to movements to the lower half of the power 5 (group 2). We find group 1 prefers to add teams with lower bowl ratings, but, as mentioned before, this can be explained by teams like Clemson, Oklahoma, and Texas who would not move conferences SEC or Big10 or PAC12 and would have received very high bowl rankings. Group 3 conferences (Big East, AAC, and Mountain West) seem to invite better teams, larger schools, and schools from smaller states/states with many other FBS football teams. Group 4, Conference USA, does not take any of these factors into account on average. One potential reason for the null results for group 4 was the necessary reactionary conference changes in 2004 and again in 2013. As the last domino to fall, perhaps C-USA had limited choice during these transitional periods. Including a pair of groups as the set of observations does not change the parameter estimates much more than averaging the estimates from the two groups.10

10We ﬁnd some discrepancies in observations in Table 6. We attribute these to variables perfectly predicting failure, which leads to dropped observations. For instance group 2, 75 observations, plus group 3, 75 observations, are 150 observations, not 162 (group 2 plus 3) due to bowl ratings of zero perfectly predicting failure and thus dropping 12 observations (162-12=150

20 Conclusion

We find evidence suggesting that Power Five conferences, those who drive conference realignment, have preferences for football programs with larger potential fan bases, but do not necessarily prefer winning football teams. This is consistent with Roberts and Samuelson (1988) theory on non-price competition of oligopoly where conference expansion teams allow firms to advertise in order to compete for viewership. Future studies should look for more precise measurements of fanbase size. With college football’s popularity at an all-time high and new rounds of television broadcasts rights contracts in the not so distant future, it is almost certain that conferences will continue to reshape to find the group of programs that makes them most competitive.

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21 Appendix

22 Table A1: Predicting Conference Change: Average Marginal Eﬀects, Levels

(1) (2) (3) (4) (5) (6) Probability stepped up to group(s) ... 1 2 3 4 2 and 3 4 and 5 Composite Rankingb -0.0012∗ -0.0024∗∗∗ -0.0039∗∗ 0.0023 -0.0035∗∗∗ 0.0013 (0.00068) (0.00070) (0.0016) (0.0016) (0.0010) (0.0019) Normalized State Populationb 0.0085∗∗ 0.00082 -0.011∗∗ 0.0033 -0.0047∗∗ 0.0079∗∗ (0.0033) (0.0021) (0.0045) (0.0050) (0.0019) (0.0032) FTE Enrollmentb 0.0044∗∗ -0.0090∗∗∗ 0.0099∗∗ 0.0099 0.0022 0.0076 (0.0019) (0.0032) (0.0045) (0.0064) (0.0026) (0.0065) Bowl Ratingsc -0.034∗∗ 0.047 -0.0060 -0.0041 0.022 0.0034 (0.014) (0.040) (0.044) (0.034) (0.019) (0.036) Observations 95 75 75 77 162 77 Lagged conference indicator Yes Yes Yes Yes Yes Yes Year indicator Yes Yes Yes Yes Yes Yes Bowl appearance indicator Yes Yes Yes Yes Yes Yes Only observations that share Yes Yes Yes Yes Yes Yes lagged group-year with conference changers

23 Table A2: Predicting Conference Change: Average Marginal Eﬀects, Levels

(1) (2) (3) (4) (5) (6) Probability stepped up to group(s) ... 1 2 3 4 2 and 3 4 and 5 Composite Rankingb -0.00063 -0.0016∗∗ -0.0013 -0.00025 -0.0013∗ -0.00031 (0.00045) (0.00060) (0.00076) (0.00077) (0.00055) (0.00083) Normalized State Populationb 0.00045∗ -0.00010 -0.0050∗ 0.0027 -0.0027∗ 0.0039∗∗∗ (0.00021) (0.00093) (0.0025) (0.0017) (0.0011) (0.00090) FTE Enrollmentb 0.0024∗ -0.00100 0.0028 0.0011 0.00084 0.00029 (0.0010) (0.0014) (0.0015) (0.0031) (0.0012) (0.0033) Bowl Ratingsc -0.012 0.0060 -0.0088 -0.0052 0.0023 -0.0072 (0.0072) (0.0035) (0.019) (0.014) (0.0061) (0.015) Observations 247 151 208 191 357 191 Lagged conference ﬁxed eﬀects Yes Yes Yes Yes Yes Yes Bowl appearance indicator Yes Yes Yes Yes Yes Yes Observations share abandoned Yes Yes Yes Yes Yes Yes group and are within 1 year of switch of conference switcher

24 Table A3: Predicting Conference Change: Average Marginal Eﬀects, Levels

(1) (2) (3) (4) (5) (6) Probability stepped up to group(s) ... 1 2 3 4 2 and 3 4 and 5 Composite Rankingb -0.0011∗∗ -0.0019∗∗ -0.0015 0.000062 -0.0015∗ -0.00037 (0.00040) (0.00068) (0.00094) (0.00077) (0.00061) (0.00089) Normalized State Populationb 0.0052∗ -0.00054 -0.0067∗ 0.0017 -0.0029∗ 0.0033∗ (0.0021) (0.00099) (0.0028) (0.0023) (0.0011) (0.0014) FTE Enrollmentb 0.0024∗ -0.0036∗ 0.0053∗ 0.0043 0.0018 0.0033 (0.0010) (0.0014) (0.0022) (0.0033) (0.0012) (0.0035) Bowl Ratingsc -0.022∗ 0.0023 -0.020 -0.0047 0.0062 -0.00071 (0.0088) (0.0045) (0.021) (0.017) (0.0066) (0.018) Observations 202 131 160 176 320 176 Lagged conference indicator Yes Yes Yes Yes Yes Yes Year indicator Yes Yes Yes Yes Yes Yes Bowl appearance indicator Yes Yes Yes Yes Yes Yes Only observations that share Yes Yes Yes Yes Yes Yes lagged group-year with conference changers