SCORING MORE THAN TOUCHDOWNS:

THE IMPACT OF ATHLETIC SUCCESS ON THE

BRAND EQUITY OF A UNIVERSITY

by

Courtney Schmit

Submitted in partial fulfillment of the

requirements for Departmental Honors in

the Department of Marketing

Texas Christian University

Fort Worth, Texas

May 4, 2015

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SCORING MORE THAN TOUCHDOWNS:

THE IMPACT OF ATHLETIC SUCCESS ON THE

BRAND EQUITY OF A UNIVERSITY

Project Approved:

Supervising Professor: Bill Moncrief, Ph.D.

Department of Marketing

Susan Kleiser, Ph.D.

Department of Marketing

John Harvey, Ph.D.

Department of Economics

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ABSTRACT

Previous research and individual cases have indicated a relationship between athletic success, such as winning a conference title or bowl game, and indirect benefits that indicate an increase in the overall status of a university. While much of the research in this area focuses on showing the direction and strength of the advertising effect athletics can have for a university, there has been little research on how a football team’s success can impact the value of a university’s brand overall. This study provides an examination of this phenomenon through a brand equity framework. The analysis begins with an attempt to confirm the impact of athletic performance and conference affiliation on measures of increased awareness, and then extends the analysis through a discussion of four elements of brand equity including brand awareness, associations, perceived quality, and loyalty. In addition to discovering trends, emphasis is placed on evaluating the main drivers behind the relationship and what this means for branding in universities today.

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TABLE OF CONTENTS

INTRODUCTION ...... 1

REVIEW OF LITERATURE ...... 4

Defining Brand Equity ...... 4

Measuring Brand Equity ...... 5

Brand Awareness ...... 6

Brand Associations ...... 7

Perceived Quality ...... 7

Brand Loyalty ...... 8

Branding in the University Context ...... 8

History and Background ...... 8

Success in University Branding ...... 10

Athletics and University Culture ...... 12

RESEARCH QUESTIONS ...... 14

METHODOLOGY AND RESULTS ...... 17

Methodology ...... 17

Data ...... 18

Results ...... 19

DISCUSSION ...... 20

Insight from Opposing Results ...... 20

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Brand Equity and Student Choice ...... 21

Noteworthy Cases ...... 22

Texas Christian University ...... 22

Boise State ...... 24

Explanation through the Brand Equity Framework ...... 24

IMPLICATIONS ...... 28

Limitations ...... 30

Further Research ...... 30

CONCLUSION ...... 31

APPENDIX ...... 33

REFERENCES ...... 48

1

INTRODUCTION

A crowd of purple exploded in celebration and emotions ran wild among players as the game clock hit 0:00 to signify the end of the 2011 Rose Bowl and a perfect 13-0 season for Texas Christian University. Led by quarterback Andy Dalton, now one of the highest paid quarterbacks in NFL history, and linebacker Tank Carder, the Horned Frogs had earned their invitation to the Rose Bowl after ending a perfect regular season with the

Mountain West Conference title. Thought of as a ‘Cinderella story,’ it was a game of many firsts: TCU’s first ever appearance in the Rose Bowl, the first team from the

Mountain West conference to play in a New Year’s Day bowl game and the first time a team from a non-Automatic Qualifying Conference won the Rose Bowl since 1934.

The Rose Bowl trophy not only signified that TCU could compete on the field, but overall interest in TCU as an academic institution increased astronomically as the

University saw a record-breaking number of applications following the team’s victory the previous year. But, TCU is not the only school to witness such a phenomenon. Doug

Chung (2013) notes that the first time significant attention was drawn to such a phenomenon was in 1984 when Doug Flutie, quarterback for Boston College, made his infamous Hail Mary touchdown pass to qualify them for the Cotton Bowl in front of a nationwide television audience. Flutie later won the Heisman Trophy and Boston College saw a thirty percent increase in applications two years later; the increase in the prominence of a university because of athletic success became known as the “Flutie

Effect” (Chung, 2013).

Since then, there have been countless stories following the same trend and a variety of research has been conducted in support of such an intriguing observation. An

2 earlier study by McCormick and Tinsley (1987) cites the Flutie Effect while providing other examples of institutions experiencing such as an increase in applications following an extremely successful year in football history. The University of South Carolina,

Georgetown University, Northwestern University, Boise State University and, more recently, Baylor University are only a few of examples. It seems as though athletic success, such as winning a conference title or bowl game, may have a domino effect and extend beyond athletics to impact the overall status of a university.

Under the premise that the “primary form of mass media advertising by academic institutions in the United States is, arguably, through its athletics program,” (Chung,

2013, p. 3) Chung studied the possible spillover effect of athletic success on the quantity and quality of applications. Similarly, McCormick and Tinsley (1987) used SAT scores to study the link between athletics and academics with an additional focus on comparing the differences between schools that were members of one of the “big-time athletic conferences” and those that were not (p. 1104). Though different approaches, both studies had similar outcomes. Chung (2013) found that, indeed, the Flutie Effect does act essentially as advertising, or earned media, for a university and that “athletic success has a significant long-term goodwill effect on future applications and quality” (p. 28). Along the same lines, McCormick and Tinsley’s (1987) research pointed to a significant symbiotic relationship between a successful athletics program and academics and emphasized the importance of universities having an athletic program. Though

McCormick and Tinsley did not offer any insight as to why the phenomenon occurs,

Chung speculated the main reason to simply be an increase in awareness. For schools that were already well-known, Chung noted that the ‘buzz’ created around a university can

3 increase awareness even further. Additionally, he found that alumni engagement increases with athletic success, which may have a multiplying effect on the school’s level of prestige. Many other academic studies (Stinson & Howard, 2008; Turner, Meserve, &

Bowen, 2001; McDonald (2003); Rhoads & Gerking, 2000; Murphy & Trandel, 1994) have also confirmed that there seems to be a relationship between higher athletic performance and key statistics that indicate increased interest as well as overall quality.

From a slightly different approach, Clark, Apostolopoulou, Branvold, and

Synowka (2009) researched an incidence of specifically using an athletics program to build a university brand. The study looked at Robert Morris University (RMU) as the university underwent a branding campaign centered on their athletics programs, believing it would essentially increase the prestige of the school by increasing brand equity. While many questions remained on how exactly to implement such a strategic plan, the basis for

RMU’s campaign was warranted. A study by Gladden, Milne, and Sutton (1998) found that certain aspects of an athletics program can create brand equity, which led them to create a framework for building brand equity in athletics. Their framework focuses on elements of brand equity including perceived quality, brand awareness, brand associations and brand loyalty. Although the study is aimed at providing a way to build a team brand rather than a university brand, the theory behind the research can be applied at the university level as well.

Much of the research in this area has focused on showing the direction and strength of the relationship between athletic performance and the advertising effect it can have for a university. However, there has been little research on how a football team’s success can impact a university’s brand equity, or the value of the school’s name itself.

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As such, this study first focuses on confirming the relationship between performance and increased awareness measured through key admissions statistics. Additional analysis is performed to determine the impact of a change in conference affiliation on similar measures. From a qualitative standpoint, the phenomenon is discussed through an analysis of four related elements of brand equity: brand awareness, perceived quality, brand loyalty and brand associations. In addition to discovering trends, emphasis is placed on analyzing the main drivers behind the phenomenon and what this means for universities today.

The paper begins with a review of applicable literature, followed by a summary of the research questions, methodology and data used in this study. The next section discusses findings and possible reasons for existing relationships. Finally, the last section concludes with key implications and suggestions for additional research.

REVIEW OF LITERATURE

Defining Brand Equity

The concept of a brand as “a distinguishing name and/or symbol intended to identify the goods or services of one seller and to differentiate [them] from those of competitors” (Aaker, 1991, p. 7) has been central to marketing since ancient history. The idea goes beyond a simple symbol or design on a package to incorporate both “rational and emotional elements” (Chapleo, 2010, p. 170) where meaning is derived directly from customers and their experiences with the brand (Duesterhaus & Duesterhaus, 2014). In the late 1980s, a new focus was brought to defining and modeling the more complex theory of brand equity, the power of which “lies in what customers have learned, felt,

5 seen and heard about the brand as a result of their experiences over time” (Keller, 2003, p. 59). Since that time brand equity has become synonymous with a measure of a brand’s strength and the value of a brand name, one of the most valuable intangible assets of any organization (Gladden et al, 1998).

Measuring Brand Equity

Brand equity, or the strength of a brand, has been measured in various ways depending on the context. But, because it is created in the minds of consumers, it is difficult to measure precisely. It is therefore necessary to understand marketplace perceptions of a brand and how those perceptions influence purchase decisions in order to obtain at least a conceptual idea of its value (Gladden et al, 1998). By understanding its structure and function, managers can obtain an understanding of how brand equity is built and justify investments in an asset that is difficult to measure (Aaker, 1991).

Though there are various approaches to obtaining such understanding, one of the most highly regarded and comprehensive is Aaker’s model where brand equity is derived from four components: brand awareness, perceived quality, brand associations and brand loyalty. This model lends itself to conceptualizing the overall value of brand equity through a theoretical approach as well as through multiple quantitative valuation techniques. Such techniques require measurement through consumer research and include analysis of price premiums or replacement costs, or brand value based on stock price fluctuations or estimated future earnings.

Aaker’s model forms a base framework for this study to explore the complex factors that influence the brand equity of a university and how athletics programs may play a role. This study looks at the value of brand equity through the influence brand

6 name has on customer preferences and attitudes, and how football performance impacts those factors. A summary of Aaker’s components sets up a more detailed discussion of branding in the university context.

Brand Awareness

The first component of brand equity from which all other components stem from is brand awareness, or the “likelihood that a brand name will come to mind and the ease with which it does so” (Keller, 1993, p. 3). Awareness ranges on a continuum from unaware, to brand recognition based on aided recall, to brand recall based on unaided recall, and finally to top of mind awareness where the brand is ahead of all others in a consumer’s mind (Aaker, 1991).

Awareness helps a brand in multiple ways—first by adding the brand to a consumer’s consideration set, crucial in the initial steps of the buying process.

Additionally, increased exposure and experience with the brand makes it feel more familiar, and familiarity can generate liking which may lead to the actual choice of one brand over another. This familiarity is also advantageous in that it signals a product or company’s success to a consumer and, “even in the case of large and involved purchase decisions, brand familiarity and perceptions of substance associated with the brand can make all the difference” (Aaker, 1991, p. 65). In other words, consumers tend to trust a brand more simply because the name sounds familiar. Finally, awareness serves as an anchor from which other connections can be made in consumers’ memories (Keller,

1993).

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Brand Associations

After a consumer becomes familiar with a brand, further connections form as part of an “associative memory network” (Keller, 1993, p. 2). These associations can be functional, experiential—resulting from using a product or interacting with a brand in some way, or symbolic—satisfying a consumer’s need for expression and approval

(Gladden et al., 1998). The more connections formed with the brand name, the easier information and memories can be retrieved in a consumer’s mind. Keller (1993) notes that these associations must be favorable, unique, and strong enough to result in a

“differential response that makes up brand equity, especially in high involvement decision settings,” such as choosing what college to attend (p. 4). Overall, associations provide a reason to buy and create positive feelings toward the overall brand (Aaker,

1991).

Perceived Quality

Perceived quality is a “customer’s perception of the overall quality or superiority of a product or service with respect to its intended purpose, relative to alternatives”

(Aaker, 1991, p. 85). It is important to emphasize that this relates only to perceptions and may not represent actual or objective quality. Thus, objective measures of perceived quality are hard to obtain; measures such as university rankings are not necessarily an accurate representation as perceptions can vary greatly from person to person and from the perspective of groups with different relationships to a university. That being said, this aspect of brand equity is valuable in the university setting in that it provides differentiation and a reason to ‘buy’ or attend. Additionally, perceptions of quality are important in determining the value of a degree from a certain institutions; universities

8 with high perceived quality may see much higher demand for their graduates. Brand extensions are closely related to perceived quality in that perceptions of the brand’s family of products or services can positively or negatively impact equity—namely, perceptions of an athletics program or another aspect of a university besides academics can signal an overall standard of excellence (Gladden et al., 1998).

Brand Loyalty

Brand loyalty, the attachment a customer has to a brand, is central to a high level of brand equity (Aaker, 1991). At one of the highest levels of loyalty are customers who are committed to the brand and are proud to wear the brand’s symbols or encourage others to purchase it over another alternative. Unlike the previously discussed aspects of equity, brand loyalty is unique in that it can’t exist without some type of prior use experience. Collegiate athletics, then, arguably provide an opportunity to have an experience with an institution’s brand before actually enrolling in the university. For industries who offer intangible benefits, such as an education and college experience, it is difficult to determine exactly how loyalty can be generated (Gladden et al, 1998).

Branding in the University Context

History and Background

In the 1980s, Kotler and Fox’s publication of “Strategic Marketing for

Educational Institutions” (Duesterhaus & Duesterhaus, 2014, p. 170) pushed universities to start focusing on their brands and branding in the university context. Since then, a university’s brand has only become more important in their overall strategy and today universities make significant investments in branding not only the institutions themselves, but also their affiliate programs such as athletics and other extracurricular

9 programs (Bunzel, 2007). In fact, as Twitchell (2004) references, this increased focus on branding has led to an evolution in the organizational goals and structure of most institutions:

What used to be the knowledge business has become selling an experience, an

affiliation, a commodity that can be manufactured, packaged, bought and sold.

Don’t misunderstand, the intellectual work of universities is still going strong; in

fact, it has never been stronger… But the experience of higher education—the

accessories, the amenities, the aura—has been commercialized, outsourced,

franchised, branded. (p. 116)

According to the US Department of Education, there are nearly 3,000 four-year degree-granting institutions in the United States (2013). With the number of institutions continuing to rise, “the need to stand out in a crowded and competitive marketplace is a driving force for colleges in the USA to focus on their branding” (Duesterhaus &

Duesterhaus, 2014, p. 169). In alignment with what some may call the commercialization of education, there has been a cultural shift in which students who attend universities are now identified as a part of a massively recruited group rather than one of an exclusive, elite set. With so much competition, a strong brand is now crucial in order “to enhance awareness of [an institution’s] existence and course offering among potential recruits, to differentiate themselves from rivals and to gain market share…and maintain the quality of their students” (Ali-Choudhury, Bennett, & Savani, 2008, 12).

Arguably, building the image and reputation of an institution is more crucial in forming perceptions of prestige and influencing university choice than actual teaching quality (Chapleo, 2010; Riley, 1998). Further, overall university reputation can be broken

10 down into three general dimensions: academic performance or quality of students and faculty, external performance or visibility in the media and community, and emotional engagement or personal connections to the institution (Alessandri, Yan, & Kinsey, 2006).

However, each of these dimensions are intimately related with one another and an improved reputation in one area may be closely linked with the reputation of another or the university as a whole.

Success in University Branding

A university’s reputation is based on its brand—“its personality, psychology, and attitude—as its constituents perceive it. It is the face by which an institution distinguishes itself from others. Its brand…is its most enduring asset” (Plank, 2000). It is the brand that allows an institution to set itself apart from others by sending a message about its ability to satisfy the needs of students and that enables students to choose a school that will provide the type and level of education they seek (Ali-Choudhury et al., 2009). Having a strong brand and presence in the crowded market for higher education can make prospective students trust that an institution is of high quality and that attending will be a positive, value-adding experience; often this is what persuades a student to add an institution to a student’s consideration set (Ali-Choudhury et al.).

Determining what constitutes high brand equity or a ‘successful’ brand, particularly in the university setting, is a “multi-dimensional” concept based on many criteria that are difficult to measure (Chapleo, 2010, p. 171). Chapleo’s (2010) research indicates that, overall, a university brand should be considered successful if it is “clear and consistent in demonstrating a distinct competitive advantage and is congruous with the needs of various customers and stakeholder groups” (p. 172). Thus, each aspect

11 should be looked at through perceptions of various customer segments or stakeholders and their brand experience, which could include prospective students, current students, alumni, donors, potential employers, community members and the general public

(Duesterhaus & Duesterhaus, 2014).

Focusing mainly at the perceptions of prospective students, it’s first necessary to understand the college selection and decision-making process. This process is notably complex and dependent on a multitude of factors. In general, students find out about an institution from their experience with the brand through “printed materials, electronic materials, word of mouth, alumni, faculty and staff, [and] college-sponsored activities including athletic events” (Duesterhaus & Duesterhaus, 2014, p. 170). Clark et al. (2009) found that athletics programs in particular have been found to generate awareness to markets that wouldn’t have otherwise considered the university as an option while at the same time increasing awareness among existing markets. A student then considers a wide variety of criteria in deciding where to actually apply to of all the institutions they are aware of, but it is clear that amenities that enhance the overall experience are consistently extremely important (Shampeny, 2003).

However, the perspective of potential students is not the only one important in understanding university brand equity. Current students and alumni form opinions of the quality of their alma mater based on their overall university experience—“the part of college that will be remembered” (Tan, 2001, p. 64)—which increasingly has less and less to do with the academic work that takes place inside the classroom. Alumni are also influenced by their level of engagement and involvement after graduation. Moreover, perceptions of a school’s brand from the family and friends of prospective or current

12 students and alumni are important, as well as perceptions of potential employers who may see a graduate of one institution as more qualified than another based purely off the institution’s name. In order to understand how those perceptions relate to athletics, it is first necessary to understand the connected role sports have played in the culture of US universities.

Athletics and University Culture

Ali-Choudhury, et al. (2008) found that perceptions of a brand held by prospective students were based first on the ‘ambience’ or the general atmosphere of a university with other important factors including visual images, a distinctive logo and overall reputation. The importance of brand experience and this idea of “ambience” was noted by Duesterhaus and Duesterhaus (2014) as well, while Alessandri (2006) also found a significant link between the visibility of a brand and favorable reputation.

Universities are somewhat unique in that “many have two distinct identities: one that represents the academic side and one that represents the athletic program” that are closely intertwined (Alessandri et al., 2006, p. 260).

Though many critics have placed a divide between athletics programs and higher education, athletics are a “highly visible, effective tool from which to leverage [a university’s] brand platform” and have been a part of university culture for some time

(Clark et al., 2009, p. 60). As higher education culture has evolved from primarily small institutions to much bigger and more expensive large universities, there has continually been an underlying theme that getting the ‘collegiate experience’ is of vital importance and that this experience is related to much more than just academics (Toma, 2003).

Noticing the impact athletics could have on attracting students and unifying the student

13 body, presidents of higher education institutions in the first half of the twentieth century could not resist the attractiveness of athletics for the university as a whole and incorporated athletics into academics through the disguise of physical education (Beyer

& Hannah, 2000). Since then, athletics have been central to the university experience and overall American popular culture (Wolfe, 2000).

In general, sports fulfill a need for “affiliation, health, entertainment, self- expression, and sociability” (Gladden et al., 1998) among other benefits. More specific to the university context, they enhance the student experience by allowing students to

“connect with their school, [providing] a source of pride and affiliation and an opportunity for continued interaction and support after graduation” (Clark et al., 2009, p.

59).

Considering the link between brand experience and brand equity, the divide between athletics and the goals of an overall institution is not so clearly defined. The transfer of sports culture to university culture can happen through many outlets. Perhaps most importantly, this is achieved through media attention and exposure which can not only “legitimize an [athletics] program” but can provide value to the brand name at the university level by creating notoriety (Gladden et al, 1998). This “notoriety helps to fuel increased awareness, interest and applications in future years” (Duesterhaus &

Duesterhaus, 2014, p. 171). Notably, a connection exists between conference affiliation, a tradition of long-standing athletic success or newfound success and the level of media attention achieved (Bruening & Min Yong, 2007).

In addition to national exposure, athletics impacts brand experience for current students. In a study of the impact Tyrone Willingham had as head coach at Notre Dame,

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Bruening and Min Yong (2007) discovered that just one coach can profoundly affect university-wide tradition by “adding a new dimension to [the university’s] brand equity… and projecting awareness into areas not previously reached” (p. 41). From another perspective, even in the absence of a star coach or player, increased merchandise sales as a result of victory or long-standing school tradition not only serve as “walking advertisements” to raise awareness, but also provide a deeper connection with the brand itself as the school or team logo “represents an image important to its purchaser… [and] gives people an identity” (Gladden et al, 1998, p. 8). Additionally, Turner, Meserve, and

Bowen (2001) as well as Stinson and Howard (2008) found that athletic success also impacts other stakeholders, leading to an increase in alumni donations and corporate support, for example.

RESEARCH QUESTIONS

The amount of university resources committed to collegiate athletics programs has led to questions of whether or not athletics can exist in alignment with the academic goals of an institution of higher learning. Among critiques, there have been many justifications of such spending including the impact that athletics may have on aspects that benefit a university overall. Some of these benefits include increased alumni engagement, corporate donations, community support, applications, geographical diversity, academic quality, selectivity, and retention. Each of these aspects signifies part of a university’s brand equity and the impact athletics can have on its overall strength. A multitude of research has shown that an ‘advertising affect’ exists through increased

15 media exposure, and thus increased awareness, resulting in an enhanced brand because of athletic success (Chung, 2013).

With multiple studies already showing a significant relationship between successful football programs and the number and quality of students applying to a university, as well as alumni engagement (Chung, 2013; Clark et al., 2009; Gladden et al., 1998; McCormick & Tinsley, 1987; Murphy & Trandel, 1994; Rhoads & Gerking,

2000; Stinson & Howard, 2008; Turner et al., 2001), this study intends to validate and then go further with those results through an application to the brand equity framework.

With such strong evidence, both theoretically and statistically, for the impact an athletics program can have on the level of awareness of a university, this study begins by looking to confirm such a relationship exists from the perspective of a potential student by examining how football success is related to a few key admissions statistics. However, a slightly different approach is taken compared to earlier studies where only the immediate impact of a football team’s performance on admissions variables in subsequent years are analyzed, regardless of historical performance, in an effort to analyze the impact in marginal changes in on-field performance. Additionally, only schools that are currently in the top five football conferences are included in the study and the analysis includes an examination of how a change in conference affiliation impacts brand equity. Qualitative analysis of the impact of football performance on individual components to equity, such as perceived quality and brand loyalty, is included in the Discussion section. Thus, the following research questions guide the rest of the analysis.

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Research Questions

 RQ1: Does the level of success of a university’s football team have a statistically

significant impact on key admissions variables in the following two years? (Key

admissions variables include number of applications, acceptance rate, yield and

average ACT scores).

 RQ2: Does a change in conference affiliation have an effect on key admissions

variables in the two years immediately following the change?

 RQ3: If a trend does exists, what are the main drivers behind this phenomenon? If

not, why are the results different from the findings of previous studies? How is this

phenomenon related to the brand equity framework?

Hypothesis

Based on the number of individual cases where a football team’s success has led to a huge increase in applications for the university and past academic research on the subject, it is hypothesized that there is indeed a positive correlation between football success and admissions variables related to brand equity. For schools in conferences known more for academics than athletics, the prediction is that there is a lesser effect than for conferences that are known for their athletic success. Regardless, it is hypothesized that conference affiliation does impact the brand in the first few years after a change is made.

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METHODOLOGY AND RESULTS

Methodology

For the first research question, Pearson’s correlation is used to look for a significant relationship between football season performance and enrollment statistics in the following years. Enrollment statistics included in the analysis are: number of applications, acceptance rate, average ACT scores and yield. Acceptance rate is calculated based on the number admitted divided by the number of applicants; yield is calculated as the number enrolled divided by the number admitted. Those statistics that are found to be significant are then analyzed as dependent variables in a regression analysis, with football success measurements acting as independent variables.

For the variables significantly correlated with football performance, Ordinary

Least Squares regression is used to determine whether or not success during a football season indeed has a significant influence on elements of brand equity. Because the analysis is solely intended to show that a significant relationship is present with only one independent variable and the intention is not to predict the admissions variable based on a multitude of controls and other factors, less importance is placed on the overall fit of the model, or the R2 and adjusted R2 values. The regression analysis is executed multiple times with performance measured in three different ways each time to ensure consistent and significant results, regardless of how on-field performance is measured. These performance measures include the following:

 Number of Prior Season Wins

 Prior Season Ranking (Dummy Variable: 0 = Not in Top 25, 1 = In Top 25)

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 Ranking Two Seasons Prior (Dummy Variable: 0 = Not in Top 25, 1 = In Top

25)

The regression model is analyzed looking for statistical significance of the impact of performance on the key admissions variable(s). Though not included in the report, all assumptions of linear regression are tested and can be provided upon request.

In terms of the second research question, Pearson’s correlation analysis is again conducted to look for significant correlation between a change in conference affiliation and enrollment statistics in the following years. Those statistics that are found to be significant are then analyzed as dependent variables in a regression analysis, similar to research question one.

Quantitative results of the first two research questions are then analyzed to determine if this study’s findings are similar to the results of other academic research. If not, specific attention is paid to determining why the results differ and what the main drivers behind the presumed relationship between athletic performance and brand equity are. This part of the analysis is approached through Aaker’s brand equity framework as introduced in the Review of Literature and is included in the Discussion section.

Data

The institutional data used in this study was primarily gathered through the

Integrated Postsecondary Education Data System (IPEDS) where all universities are required to report certain yearly statistics according to standards set by the National

Center for Educational Statistics. Data was collected for the 641 universities that currently make up the five powerhouse conferences—the Atlantic Coast Conference (ACC), the

1 Note: Penn State is not included in the analysis due to inconsistent data.

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Big Ten, the Big Twelve, the Pac Twelve and the Southeastern Conference (SEC)—from

2008 to 2013, leaving a sample size of 320 cases. Specific statistics collected from

IPEDS and used in this study include the following for each year:

 Athletic conference affiliation

 Total number of applicants

 Total number admitted

 Total enrolled

 ACT scores (25th and 75th percentile)

Data on the athletic performance of each football team was collected from various sources including the websites of individual schools and teams as well as from Sports

References, an online database of sports statistics. The number of wins per season was recorded as well as final end-of-season standings in the Associated Press (AP) Poll.

The primary data tables used for analysis are included in Tables 1-5 of the

Appendix, organized by current athletic conference affiliation. Table 6 provides a key for conference codes used in those tables. Again, note that two statistics in these tables were manually calculated using the above data; acceptance rate was calculated as the percent of applicants that were admitted and yield was calculated as the percent of admitted students that actually enrolled in the university.

Results

The results of the Pearson’s correlation test for research question one show no significant correlation between football performance and the number of applications, the acceptance rate or ACT scores of the incoming class in subsequent years. However, there is a significant and positive correlation found between football performance and yield.

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Thus, further regression analysis was performed and showed that football performance, regardless of how it is measured, has a positive and statistically significant impact on yield for the two years following a successful season. An overview of the model is included in Tables 7a-7c of the Appendix and is represented by the simple equation:

YIELD = β0 + β1(PERFORMANCE).

On the other hand, no significant correlation was found between a change in conference affiliation and any enrollment statistics in the immediate years following the change. This is likely due to a lack of power in the sample size where only a limited number of schools included in the sample changed conferences between 2008 and 2013.

As previously mentioned, qualitative analysis of these findings as well as suggestions for further research are included in the following sections.

DISCUSSION

Insight from Opposing Results

Though no statistically significant relationship was found between football success in a given season and the number of applicants, acceptance rate or ACT scores, the findings of this study are intriguing given the amount of existing academic research that supports this phenomenon. In fact, it is this juxtaposition of the significant relationships found between football success and key measures related to brand equity in the past and the lack of significance found in this study that provide key insight into this relationship. Two main differences between this study and previous research reveal the nature of a football team’s relationship to brand equity. First, each of the previous studies in some way account for a longer period of athletic performance, capturing data on which

21 schools have always performed well on the field compared to schools that suddenly experience success. Additionally, their samples include universities that are not already in the five powerhouse conferences.

Given these differences, it is clear that the difference between schools who have historically been known for their athletic success and those that suddenly rise to prominence is key to understanding this phenomenon. More importantly, by including only those schools currently in the five powerhouse conferences—meaning all of the schools in the sample have at some point experienced considerable athletic success—only the impact of marginal improvements in athletic performance on brand equity is evaluated. The statistically insignificant findings in this study show that once a university has already proven themselves on the football field and joined a powerhouse conference, marginal improvements in performance from year to year do not have a phenomenal impact on brand equity. Instead, the brand of a university in a top conference has already realized the beneficial impact of athletic performance on brand equity once the university is in the conference.

Brand Equity and Student Choice

While the results of the regression analysis do not directly support the hypothesis surrounding a few of the measures in research question one, the analysis does show a significant relationship between a successful football season and yield in the next two years. In other words, a good football season increases the percentage of students who go beyond applying and actually accept their offer of admission by enrolling in a university.

In a sense, the yield statistic represents a student’s final ‘purchase’ decision after putting

22 a significant amount of time and consideration into evaluating multiple universities

(Avery, Glickman, Hoxby, & Metrick, 2012).

Though many other factors such as financial aid or academic offerings play a significant role in this final choice, the decision is certainly influenced by a student’s perception of the university’s brand. Research shows that brand equity is extremely important in such “highly visible purchase decisions” (Zeithami, Lemon, & Rust, 2000, p. 86). Thus, though perhaps not consciously, a student likely considers how their personal reputation will be impacted by attending the institution, the type of students they’ll be associated with, what their friends and family will think, and how employers will view the value of their education after graduating from the institution—all of which are linked to reputation and brand equity. These results support the theory that a successful football season does influence a student’s choice of institution, providing some evidence for an enhanced perception of brand equity resulting from marginal improvements in athletic performance. However, it is still evident that athletic performance has a greater impact on brand equity when a team is experiencing new success after a history of mediocre performance.

Noteworthy Cases

Texas Christian University

For those underdog teams who do quickly shift from having a mediocre reputation to seeing quick, nationally-recognized success on the field, the brand equity of the entire university can undoubtedly benefit. Though relatively rare, there have been multiple examples of such an impact at both public and private universities in the past decade.

Texas Christian University (TCU) provides one of the most palpable stories. The TCU

23 football program struggled until the arrival of head coach Gary Patterson in 2000, when

TCU appeared in the end-of-season top twenty-five rankings for the first time in over forty years. By 2010, Coach Patterson had led the Horned Frogs to a pivotal 13-0 season and the team earned an invitation to the Rose Bowl as the first ever from a non-

Automatic Qualifying conference.

Patterson’s ‘Cinderella story’ on the field had noteworthy consequences for TCU as a whole, noticeably impacting each element of the University’s brand equity. In addition to the overall number of applications, the demographics of applicants changed.

Research shows that “the further students travel [to attend a university]…affirms higher brand awareness for the college and indicates a more successful brand” (Duesterhaus &

Duesterhaus, 2014, p. 174). The overall number of applications not only shot up by 36% the year after TCU won the 2011 Rose Bowl, but there was also a 325% increase in the number of students applying from California, a testament to the level of awareness created through the football program. Further, before the era of TCU football, the

University predominately recruited students from Texas and nearby regions. Now, the majority of students attend TCU from out of state with the percentage more than doubling from 24% to 55% from 2003 to 2013. Also during this period, TCU experienced an increase in perceived quality. As one measure, the University moved up thirty-seven spots from their position in 2008 as the 113th Best University according to US News and

World Report to number 76 in 2015. The drivers behind these significant changes are discussed later through the brand equity framework.

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Boise State

This kind of impact has not just been seen at small, private institutions, but public universities as well. Boise State University’s (BSU) recent success on the field also led to an increase in applications following their shocking 2006 Fiesta Bowl win and a 40% increase in applications after their 2010 Fiesta Bowl championship. Beyond awareness,

BSU saw increased brand loyalty during this period. Retention increased noticeably among first year students after the 2009 season and there was an above average increase in graduation rates as well. More impressively, the team’s success generated an enormous amount of alumni and community support—donations increased more than four times between 2006 and 2010. Still not a member of one of the five powerhouse conferences, it will be interesting to see how Boise State’s athletic performance continues to impact the

University and what a conference realignment could do to increase brand equity even further.

Explanation through the Brand Equity Framework

Because of the multitude of perspectives that must be considered and widely differing views of what defines a ‘successful’ university brand, the measurement of brand equity as it relates to universities is even more complex than applying the concept to a typical consumer product. While some research explores brand equity applied to collegiate athletics programs themselves, the conversation is not extended to include the brand at the institutional level. Gladden et al. (1998) apply Aaker’s model as a framework for evaluating the brand equity of athletics programs through a “continual feedback loop”

(p. 7) of antecedents and consequences to brand equity. This continual feedback loop approach provides an essential understanding in the analysis of brand equity, where the

25 same components that help to drive higher brand equity can also be the result of high brand equity. Additionally, it is again emphasized that much of brand equity relates to intangible feelings and perceptions that are difficult to measure. Aaker’s framework, then, provides a conceptual overview of how football performance can drive and relate to equity through a continual feedback loop.

The most evident and quantifiable effect an athletics program has is on awareness, generated by media attention and the ‘hype’ or ‘buzz’ that can surround a team during a successful season. As mentioned previously, one way to measure this awareness effect is by looking at the increase in applications overall as well as the number of students applying or attending from out of state. For underdog schools who come from a mediocre reputation to seeing quick success on the field, this affect is amplified even further as the university has never received that level of media attention before.

Clearly, this type of ‘buzz’ is valuable and provides a type of advertising for a brand. Rather than looking at this ‘advertising’ from a traditional point of view, it is useful to consider paid, owned and earned media in such a crowded media environment with so many channels and new technologies reaching different audiences. Owned media exists in the form of university and athletic program websites, blogs and any other media distribution channels that the university has complete control over. “When a company pays to leverage an existing channel” (Connelly, 2013) and has some control over the content, but is restricted by the format they’ve chosen, it is considered paid media.

Finally, earned media is the most valuable, where customers or other third-party sources produce and share content about a company or institution on their own.

26

According to Forrester Research, earned media “is the most credible of the three—since it is a level of brand awareness voluntarily given and shared by those who don’t ‘own’ the brand” (Connelly, 2013) and is thus more trustworthy information. In fact, a Nielsen study (2014) found that 84% of consumers trust word-of-mouth recommendations from friends and family more than any other source, and the influence of these perceptions on a university’s brand is no different. Thus, the media attention garnered through television contracts and bowl game appearances provides a more meaningful benefit than traditional TV advertising, while also providing an opportunity for paid media in the form of slotted commercials for competing teams.

Nielsen’s report also indicates that sports fans are growing both in number and level of engagement with sports media content. Collectively, “over 33 billion hours of national sports programming was consumed by 255 million people in the U.S.” in 2013 alone (Nielsen, 2014). More relevant to college football, the 2014 BCS National

Championship between Auburn and Florida State (which ended the 2013 regular season) drew in more than 25 million viewers, while the Rose Bowl, Chick-Fil-A Bowl, Fiesta

Bowl, Sugar Bowl and Orange Bowl attracted nearly 70 million combined viewers.

Perhaps more notably, fans aren’t just viewing this content from the couch on their television screens, but more and more are spending time visiting sports sites and watching videos on their computers and mobile devices. Television viewers weren’t the only ones exposed to this content. The same 2014 BCS Championship game “generated substantial Twitter activity (4.4 million Tweets by 1.2 million Unique Authors)… and those Tweets about the game airing were seen by a Twitter TV Unique Audience of 10.4

27 million people” (Nielsen, 2014). Thus, a significant amount of national exposure was created in the form of earned media for the universities participating in these games.

Paid, earned, and owned media provide valuable exposure of a university’s brand.

Other results of athletics include increased merchandise sales and traveling, as well as increased community support. Each of these aspects increase the visibility of the logo and brand name and the importance of this exposure to the brand name should not be minimized. First, fundamental awareness of a brand is necessary before a university can be added to the consideration set of potential places to apply and eventually attend. But, even more, awareness provides the foundation from which the other three components of equity stem.

With so many universities competing in the marketplace, if a prospective student, parent, employer, or other constituent has heard the name of a university and has some familiarity with that brand name, logo, colors, etcetera, there is a much greater likelihood that additional brand associations will form and those associations will connect the university to higher perceived quality. These associations are complex and can take many forms, but conceptually illuminate the connection between athletics and the overall university brand. On a subconscious level, people may associate high-performing teams with a university’s ability to dedicate resources and provide a student experience that many schools cannot, leading to a belief in better quality. Additionally, the fact that a university has a well-recognized name across the country provides legitimacy; the assumption is often made that since more people know about it, the university must have a better reputation.

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Finally, this phenomenon can be viewed from two main perspectives in relationship to brand loyalty. First, as discussed in the literature review, college sports and university culture are closely intertwined with many students looking for a campus experience that has more to do with overall quality of life than just academic quality. A football team can unite a campus through tradition, social activities, and a greater bond with the institution (Smith, 2009). This leads to a better student experience, and thus increases the level of loyalty a student or alumni has with the university brand. This may be seen not only in higher retention and graduation rates, but also lead to favorable associations that last far after graduation and incentivize greater alumni giving in the future. Additionally, even if a team was not successful during the time a student attends, the rise of a football team later on can provide an opportunity for alumni to reconnect with a university long after attending and reinforce loyalty that may have withered over time. This loyalty is central to brand equity and a university’s long-term success.

IMPLICATIONS

The harmony between collegiate athletics and the academic goals of institutions of higher learning has long been debated. Though this analysis does not attempt to put a numerical value on athletic programs or the economic implications of resource allocations, it does indicate the role athletics can play in boosting brand equity through many indirect benefits. By showing that marginal improvements in performance do not have a direct effect on measures related to the brand, it is evident that the success must be new and abnormal in order for a branding effect to be realized. That being said, it is becoming increasingly important for universities to focus on their brand and develop

29 strategic brand initiatives in order to stand out among the crowd, and the relationship discussed in this research does have implications.

Knowing that “high levels of brand awareness and a positive brand image increase the probability of brand choice, as well as produce greater consumer loyalty and decrease vulnerability to competitive marketing actions,” (Keller, 1993, p. 8) universities need to recognize the relationship between athletics and these factors. More than just showing this relationship exists, the key takeaway here is the potential to utilize athletics as an asset to leverage overall brand equity. More importantly, this research shows that unless athletic success is out of the ordinary, universities must be intentional in leveraging this asset in order for it to benefit the overall brand. Though some form of earned media will undoubtedly result from athletic success, a university can choose to magnify the “Flutie Effect” and use this ‘buzz’ to their advantage through owned and paid channels, in addition to encouraging additional earned media from prospective and current students as well as alumni.

In the most recent football season, the University of Oregon provided an exceptional example of capitalizing on their appearance in the first championship playoff series in college football history. Before the season began, the university’s marketing team decided to “kick off a four-year overall marketing campaign using football as a welcome mat” (Peterson, 2015). The strategy had already proved successful in the past when the marketing team used TV and billboard advertisements to push constituents toward the strategically-timed launch of a new academic website right before the football team won the Rose Bowl. An hour after the game, traffic to the site sky-rocketed, with six times more clicks from the homepage to admissions page than the same time frame

30 after the previous year’s bowl game. This year, the focus was on earned media which was incorporated through the use of customized merchandise geared toward engaging non- sports fans with the Oregon brand through multiple social media platforms. By taking such strategic initiatives in conjunction with football success, universities have an opportunity to realize even greater indirect benefits of their athletic programs.

Limitations

The most significant limitation to this research is the intangible nature of brand equity, a concept that can only be fully understood through deep analysis of individual customer perceptions and is hard to boil down to a few statistics. Moreover, the complex nature of the college application and selection process makes brand equity difficult to measure, with students looking for very different attributes in a university experience and other factors affecting decisions to enroll such as financial aid and other scholarships.

Additionally, yield as a statistical measure raises concerns in that it does not significantly change from year to year and thus may not be sensitive enough to changes in other factors. Other critiques include the potential for universities to control or manipulate their yield statistic by adjusting the number of accepted students based on hypothesis of where the student will actually enroll. However, the fact that statistical significance was found between football success and yield indicates that there is a strong relationship.

Further Research

Football success and its relationship to brand equity brings up multiple opportunities for further research. Additional studies could analyze more quantitative measures of each aspect of equity, such as the number of out-of-state applications to indicate awareness levels and retention to indicate brand loyalty. The impact of

31 conference affiliation changes on these measures could further validate this study’s results if a larger sample was included. Further qualitative research into the impact on perceived quality and associations could also prove beneficial for universities to use in branding strategies. However, perhaps the most telling research would be a comprehensive study on perceptions of brands from the general public, including not just direct stakeholders in the university but also people such as employers or parents with children not yet conducting a college search. This could indicate the full impact of athletics on university reputation and would be a more comprehensive measure than any analysis conducted thus far. Using this additional research, an actual valuation could be placed on the brand equity of a university overall and the amount accounted for by athletics programs.

CONCLUSION

Previous research shows that football success does indeed have a positive impact on awareness, applications, quality of students and alumni donations. The results of this study show that the magnitude of this effect depends on the historical performance of the football team and whether or not a university has already experienced an increase in brand equity through such an affiliation. The study does confirm the athletics to equity relationship through a further measure of brand equity by showing that football success leads to an increase in the percentage of students who end up choosing one university over all others in which they were accepted. It was also shown that a change in conference affiliation alone does not have the same effect, though this result was likely due to limitations in the sample size.

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The impact of athletics on university brand equity is driven primarily through the various forms of media that are able to generate unmatched awareness and brand recognition. But, increases in loyalty, perceived quality and brand associations are also directly related. In this competitive era of higher education, the implications of these findings not only play into the debate about high levels of spending on athletics programs and the importance of including brand equity in economic valuations, but also provide insight into the ability of universities to further leverage athletic success through their branding strategies. By recognizing that this relationship does exist, institutions have the opportunity to increase their overall reputation and the value of their brand to maintain a foothold in the competitive market for higher education.

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APPENDIX

Table 1: ACC Data

Institution Year Conference Wins Season Prior Season Prior Ranking Total Applicants Total Admissions Total Enrolled Rate Acceptance Yield Composite ACT %) (75th

Boston College 2013 102 2 24,538 7,905 2,215 32.2% 28.0% 33

Boston College 2012 102 4 34,061 9,813 2,405 28.8% 24.5% 32

Boston College 2011 102 7 32,974 9,227 2,285 28.0% 24.8% 32

Boston College 2010 102 8 29,933 9,310 2,359 31.1% 25.3% 32

Boston College 2009 102 9 29,289 8,804 2,171 30.1% 24.7% 32

Boston College 2008 102 11 10 30,845 8,093 2,167 26.2% 26.8% 32

Clemson University 2013 102 11 11 18,500 10,706 3,463 57.9% 32.3% 31

Clemson University 2012 102 10 22 17,072 10,803 2,935 63.3% 27.2% 30

Clemson University 2011 102 6 16,865 9,724 3,016 57.7% 31.0% 30

Clemson University 2010 102 9 24 14,504 8,355 2,982 57.6% 35.7% 30

Clemson University 2009 102 7 14,504 8,355 2,982 57.6% 35.7% 30

Clemson University 2008 102 9 21 14,255 7,154 2,762 50.2% 38.6% 30

Duke University 2013 102 6 30,374 4,077 1,714 13.4% 42.0% 34

Duke University 2012 102 3 28,145 3,938 1,724 14.0% 43.8% 34

Duke University 2011 102 3 25,462 4,196 1,751 16.5% 41.7% 34

Duke University 2010 102 5 22,280 4,203 1,723 18.9% 41.0% 34

Duke University 2009 102 4 18,774 4,202 1,703 22.4% 40.5% 34

Duke University 2008 102 1 17,748 4,077 1,700 23.0% 41.7% 34

Florida State University 2013 102 12 10 29,579 16,803 6,048 56.8% 36.0% 29

Florida State University 2012 102 9 23 30,040 16,124 5,738 53.7% 35.6% 29

Florida State University 2011 102 10 17 28,313 16,561 6,135 58.5% 37.0% 28

Florida State University 2010 102 7 26,037 15,498 5,952 59.5% 38.4% 28

Florida State University 2009 102 9 21 23,439 14,308 5,967 61.0% 41.7% 28

Florida State University 2008 102 7 25,485 11,901 5,031 46.7% 42.3% 28

Georgia Institute of Technology 2013 102 7 14,645 8,045 3,044 54.9% 37.8% 32

Georgia Institute of Technology 2012 102 8 14,088 7,210 2,695 51.2% 37.4% 32

Georgia Institute of Technology 2011 102 6 13,495 6,976 2,712 51.7% 38.9% 32

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Georgia Institute of Technology 2010 102 11 13 11,432 6,721 2,660 58.8% 39.6% 31

Georgia Institute of Technology 2009 102 9 22 10,258 6,248 2,640 60.9% 42.3% 31

Georgia Institute of Technology 2008 102 7 9,664 6,122 2,628 63.3% 42.9% 31

North Carolina State University 2013 102 7 20,700 10,353 4,398 50.0% 42.5% 30

North Carolina State University 2012 102 8 20,103 10,577 4,697 52.6% 44.4% 28

North Carolina State University 2011 102 9 19,753 10,709 4,705 54.2% 43.9% 28

North Carolina State University 2010 102 5 18,992 10,409 4,772 54.8% 45.8% 28

North Carolina State University 2009 102 6 17,853 10,538 4,804 59.0% 45.6% 27

North Carolina State University 2008 102 5 16,553 9,985 4,907 60.3% 49.1% 27

Syracuse University 2013 102 8 28,269 13,990 3,487 49.5% 24.9% 28

Syracuse University 2012 104 5 25,790 13,240 3,392 51.3% 25.6% 28

Syracuse University 2011 104 8 25,884 12,779 3,410 49.4% 26.7% 28

Syracuse University 2010 104 4 22,935 13,704 3,471 59.8% 25.3% 28

Syracuse University 2009 104 3 20,951 12,596 3,261 60.1% 25.9% 28

Syracuse University 2008 104 2 22,079 11,597 3,186 52.5% 27.5% 28

University of Louisville 2013 372 11 13 9,166 6,519 2,857 71.1% 43.8% 28

University of Louisville 2012 104 7 7,892 5,738 2,569 72.7% 44.8% 28

University of Louisville 2011 104 7 7,749 5,804 2,561 74.9% 44.1% 27

University of Louisville 2010 104 4 7,755 5,625 2,478 72.5% 44.1% 28

University of Louisville 2009 104 5 7,861 5,473 2,609 69.6% 47.7% 27

University of Louisville 2008 104 6 7,280 5,109 2,569 70.2% 50.3% 27

University of Miami 2013 102 7 28,907 11,691 2,140 40.4% 18.3% 32

University of Miami 2012 102 6 27,757 11,020 2,012 39.7% 18.3% 32

University of Miami 2011 102 7 27,747 10,635 2,172 38.3% 20.4% 32

University of Miami 2010 102 9 19 25,899 10,157 2,132 39.2% 21.0% 32

University of Miami 2009 102 7 21,845 9,700 2,006 44.4% 20.7% 31

University of Miami 2008 102 5 21,774 8,411 2,010 38.6% 23.9% 31

University of North Carolina 2013 102 8 28,437 7,847 3,915 27.6% 49.9% 32

University of North Carolina 2012 102 7 22,652 7,469 4,026 33.0% 53.9% 32

University of North Carolina 2011 102 8 22,288 7,552 3,960 33.9% 52.4% 31

University of North Carolina 2010 102 8 23,225 7,345 3,960 31.6% 53.9% 31

University of North Carolina 2009 102 8 21,543 7,315 3,865 34.0% 52.8% 31

University of North Carolina 2008 102 4 20,090 6,999 3,893 34.8% 55.6% 31

University of Notre Dame 2013 113 12 4 17,647 3,936 2,070 22.3% 52.6% 34

35

University of Notre Dame 2012 113 8 16,957 3,947 2,014 23.3% 51.0% 34

University of Notre Dame 2011 113 8 16,548 4,019 2,020 24.3% 50.3% 34

University of Notre Dame 2010 113 6 14,521 4,177 2,067 28.8% 49.5% 34

University of Notre Dame 2009 113 7 14,357 4,113 2,064 28.6% 50.2% 34

University of Notre Dame 2008 113 3 13,945 3,727 2,000 26.7% 53.7% 34

University of Pittsburgh 2013 102 6 24,871 13,959 3,678 56.1% 26.3% 30

University of Pittsburgh 2012 104 6 23,409 13,544 3,768 57.9% 27.8% 30

University of Pittsburgh 2011 104 8 22,616 13,066 3,750 57.8% 28.7% 30

University of Pittsburgh 2010 104 10 15 21,737 12,722 3,642 58.5% 28.6% 30

University of Pittsburgh 2009 104 9 20,685 11,467 3,488 55.4% 30.4% 30

University of Pittsburgh 2008 104 5 19,056 10,591 3,432 55.6% 32.4% 30

University of Virginia 2013 102 4 28,984 8,691 3,520 30.0% 40.5% 33

University of Virginia 2012 102 8 27,178 8,031 3,400 29.5% 42.3% 32

University of Virginia 2011 102 4 23,583 7,847 3,439 33.3% 43.8% 32

University of Virginia 2010 102 3 22,124 7,212 3,246 32.6% 45.0% 32

University of Virginia 2009 102 5 21,109 6,768 3,250 32.1% 48.0% 32

University of Virginia 2008 102 9 18,363 6,735 3,255 36.7% 48.3% 32

Virginia Polytechnic 2013 102 7 19,112 13,432 5,364 70.3% 39.9%

Virginia Polytechnic 2012 102 11 21 20,191 14,210 5,487 70.4% 38.6%

Virginia Polytechnic 2011 102 11 16 20,828 13,860 5,221 66.5% 37.7%

Virginia Polytechnic 2010 102 10 10 19,981 13,389 5,205 67.0% 38.9% 29

Virginia Polytechnic 2009 102 10 15 21,053 14,040 5,050 66.7% 36.0%

Virginia Polytechnic 2008 102 11 9 20,615 13,485 5,460 65.4% 40.5%

Wake Forest University 2013 102 5 11,121 3,915 1,232 35.2% 31.5%

Wake Forest University 2012 102 6 11,407 3,875 1,240 34.0% 32.0%

Wake Forest University 2011 102 3 9,869 3,933 1,240 39.9% 31.5%

Wake Forest University 2010 102 5 10,566 4,256 1,225 40.3% 28.8%

Wake Forest University 2009 102 8 10,553 3,959 1,201 37.5% 30.3%

Wake Forest University 2008 102 9 9,050 3,473 1,202 38.4% 34.6% 31

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Table 2: Big Ten Data

Institution Year Conference Wins Season Prior AP Season Prior Ranking Total Applicants Total Admissions Total Enrolled Rate Acceptance Yield Composite ACT %) (75th

Indiana University 2013 107 4 37,826 27,300 7,604 72.2% 27.9% 30

Indiana University 2012 107 1 35,247 26,228 7,613 74.4% 29.0% 29

Indiana University 2011 107 5 35,218 25,455 7,424 72.3% 29.2% 29

Indiana University 2010 107 4 36,719 25,391 7,020 69.1% 27.6% 29

Indiana University 2009 107 3 33,011 23,975 7,327 72.6% 30.6% 29

Indiana University 2008 107 7 31,177 22,039 7,564 70.7% 34.3% 29

University of Illinois 2013 107 2 33,203 20,716 7,329 62.4% 35.4% 31

University of Illinois 2012 107 7 31,454 19,924 6,921 63.3% 34.7% 31

University of Illinois 2011 107 7 28,751 19,434 7,252 67.6% 37.3% 31

University of Illinois 2010 107 3 27,310 18,324 6,929 67.1% 37.8% 31

University of Illinois 2009 107 5 26,057 17,053 6,984 65.4% 41.0% 31

University of Illinois 2008 107 9 20 23,240 16,043 7,287 69.0% 45.4% 31

University of Iowa 2013 107 4 21,642 17,363 4,460 80.2% 25.7% 28

University of Iowa 2012 107 7 19,430 15,240 4,470 78.4% 29.3% 28

University of Iowa 2011 107 8 18,939 15,105 4,565 79.8% 30.2% 28

University of Iowa 2010 107 11 7 17,220 14,434 4,557 83.8% 31.6% 28

University of Iowa 2009 107 9 20 15,060 12,503 4,063 83.0% 32.5% 28

University of Iowa 2008 107 6 15,582 12,827 4,246 82.3% 33.1% 28

University of Maryland 2013 102 4 26,247 12,333 4,020 47.0% 32.6%

University of Maryland 2012 102 2 25,326 11,889 3,906 46.9% 32.9%

University of Maryland 2011 102 9 23 26,372 11,815 3,994 44.8% 33.8%

University of Maryland 2010 102 2 26,147 11,671 3,933 44.6% 33.7%

University of Maryland 2009 102 8 28,331 11,870 4,202 41.9% 35.4%

University of Maryland 2008 102 6 28,054 10,888 3,912 38.8% 35.9%

University of Michigan 2013 107 8 24 46,813 15,570 6,200 33.3% 39.8% 32

University of Michigan 2012 107 11 12 42,544 15,551 6,148 36.6% 39.5% 32

University of Michigan 2011 107 7 39,584 16,073 6,236 40.6% 38.8% 32

University of Michigan 2010 107 5 31,613 16,006 6,481 50.6% 40.5% 31

University of Michigan 2009 107 3 29,965 14,970 6,079 50.0% 40.6% 31

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University of Michigan 2008 107 9 29,814 12,567 5,783 42.2% 46.0% 31

Michigan State University 2013 107 7 31,479 21,610 8,061 68.6% 37.3% 28

Michigan State University 2012 107 11 11 30,224 21,327 8,354 70.6% 39.2% 28

Michigan State University 2011 107 11 14 28,416 20,728 7,984 72.9% 38.5% 28

Michigan State University 2010 107 6 26,907 18,829 7,375 70.0% 39.2% 28

Michigan State University 2009 107 9 24 25,395 18,392 7,416 72.4% 40.3% 27

Michigan State University 2008 107 7 25,589 17,919 7,555 70.0% 42.2% 27

University of Minnesota 2013 107 6 43,048 19,121 5,544 44.4% 29.0% 30

University of Minnesota 2012 107 3 38,174 18,900 5,514 49.5% 29.2% 30

University of Minnesota 2011 107 3 39,720 18,505 5,368 46.6% 29.0% 30

University of Minnesota 2010 107 6 36,853 17,613 5,323 47.8% 30.2% 30

University of Minnesota 2009 107 7 33,910 16,960 5,400 50.0% 31.8% 29

University of Minnesota 2008 107 1 29,173 15,324 5,107 52.5% 33.3% 29

University of Nebraska 2013 107 10 25 10,929 6,999 4,420 64.0% 63.2% 28

University of Nebraska 2012 107 9 24 10,350 6,662 3,937 64.4% 59.1% 29

University of Nebraska 2011 107 10 20 10,022 5,943 4,093 59.3% 68.9% 28

University of Nebraska 2010 108 10 14 9,768 6,089 4,075 62.3% 66.9% 28

University of Nebraska 2009 108 9 9,455 5,943 3,986 62.9% 67.1% 29

University of Nebraska 2008 108 5 9,709 6,122 4,200 63.1% 68.6% 28

Northwestern University 2013 107 10 17 32,060 4,912 2,037 15.3% 41.5% 34

Northwestern University 2012 107 6 30,926 5,575 2,107 18.0% 37.8% 34

Northwestern University 2011 107 7 27,528 6,367 2,127 23.1% 33.4% 33

Northwestern University 2010 107 8 25,369 6,887 2,128 27.1% 30.9% 33

Northwestern University 2009 107 9 25,013 6,553 2,078 26.2% 31.7% 33

Northwestern University 2008 107 6 21,930 5,872 1,981 26.8% 33.7% 34

Ohio State University 2013 107 12 3 31,359 17,413 7,130 55.5% 40.9% 31

Ohio State University 2012 107 6 25,816 16,521 7,215 64.0% 43.7% 30

Ohio State University 2011 107 12 5 26,100 16,518 7,089 63.3% 42.9% 30

Ohio State University 2010 107 11 5 26,764 16,572 6,672 61.9% 40.3% 30

Ohio State University 2009 107 10 9 21,330 13,840 6,739 64.9% 48.7% 30

Ohio State University 2008 107 11 5 20,932 13,041 6,173 62.3% 47.3% 30

Purdue University 2013 107 6 31,083 18,779 6,422 60.4% 34.2% 30

Purdue University 2012 107 7 31,124 19,127 6,476 61.5% 33.9% 30

Purdue University 2011 107 4 29,721 20,318 6,821 68.4% 33.6% 30

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Purdue University 2010 107 5 30,707 19,993 6,513 65.1% 32.6% 29

Purdue University 2009 107 4 27,213 19,905 6,225 73.1% 31.3% 29

Purdue University 2008 107 8 29,952 21,423 7,025 71.5% 32.8% 29

Rutgers University 2013 104 9 30,631 18,230 6,337 59.5% 34.8%

Rutgers University 2012 104 9 28,635 17,436 6,170 60.9% 35.4%

Rutgers University 2011 104 4 28,602 17,487 6,075 61.1% 34.7%

Rutgers University 2010 104 9 29,532 17,448 6,033 59.1% 34.6%

Rutgers University 2009 104 8 28,624 17,598 5,839 61.5% 33.2%

Rutgers University 2008 104 8 29,547 16,478 5,840 55.8% 35.4%

University of Wisconsin 2013 107 8 23,324 15,841 6,279 67.9% 39.6% 30

University of Wisconsin 2012 107 11 10 21,352 14,651 5,828 68.6% 39.8% 30

University of Wisconsin 2011 107 11 7 21,689 14,417 5,927 66.5% 41.1% 30

University of Wisconsin 2010 107 10 16 21,390 14,225 5,680 66.5% 39.9% 30

University of Wisconsin 2009 107 7 22,613 13,446 5,773 59.5% 42.9% 30

University of Wisconsin 2008 107 9 24 22,289 14,004 5,994 62.8% 42.8% 30

39

Table 3: Pac 12 Data

Institution Year Conference Wins Season Prior AP Season Prior Ranking Total Applicants Total Admissions Total Enrolled Rate Acceptance Yield Composite ACT %) (75th

Arizona State University 2013 127 8 21,770 17,465 7,171 80.2% 41.1% 28

Arizona State University 2012 127 6 29,722 26,425 9,254 88.9% 35.0% 27

Arizona State University 2011 127 6 29,771 25,795 9,544 86.6% 37.0% 27

Arizona State University 2010 127 4 28,304 25,616 9,344 90.5% 36.5% 26

Arizona State University 2009 127 5 27,089 24,473 9,707 90.3% 39.7% 26

Arizona State University 2008 127 10 16 28,644 23,504 9,274 82.1% 39.5% 26

Oregon State University 2013 127 9 20 12,414 9,799 3,516 78.9% 35.9% 27

Oregon State University 2012 127 3 12,197 9,471 3,506 77.7% 37.0% 27

Oregon State University 2011 127 5 11,428 9,269 3,696 81.1% 39.9% 26

Oregon State University 2010 127 8 10,068 8,303 3,506 82.5% 42.2% 26

Oregon State University 2009 127 9 18 8,640 7,270 3,191 84.1% 43.9% 26

Oregon State University 2008 127 9 25 8,149 6,971 3,141 85.5% 45.1% 26

Stanford University 2013 127 12 7 38,828 2,208 1,677 5.7% 76.0% 34

Stanford University 2012 127 11 7 36,632 2,423 1,765 6.6% 72.8% 34

Stanford University 2011 127 12 4 34,348 2,437 1,707 7.1% 70.0% 34

Stanford University 2010 127 8 32,022 2,340 1,672 7.3% 71.5% 34

Stanford University 2009 127 5 30,429 2,426 1,694 8.0% 69.8% 34

Stanford University 2008 127 4 25,299 2,400 1,703 9.5% 71.0% 34

University of Arizona 2013 127 8 26,329 20,251 7,401 76.9% 36.5% 27

University of Arizona 2012 342 4 26,854 19,172 7,300 71.4% 38.1% 27

University of Arizona 2011 127 7 26,629 20,068 7,032 75.4% 35.0% 27

University of Arizona 2010 127 8 26,629 20,068 7,032 75.4% 35.0% 27

University of Arizona 2009 127 8 24,756 19,310 6,966 78.0% 36.1% 27

University of Arizona 2008 127 5 22,544 18,158 6,709 80.5% 36.9% 26

U. of California-Berkeley 2013 127 5 61,717 11,108 4,162 18.0% 37.5% 33

U. of California-Berkeley 2012 127 1 52,982 11,450 4,443 21.6% 38.8% 33

U. of California-Berkeley 2011 127 3 50,374 10,795 4,109 21.4% 38.1% 32

U. of California-Berkeley 2010 127 7 48,682 10,524 4,356 21.6% 41.4% 32

U. of California-Berkeley 2009 127 5 48,473 10,404 4,261 21.5% 41.0% 32

40

U. of California-Berkeley 2008 127 8 44,149 10,251 4,225 23.2% 41.2% 31

University of California-LA 2013 127 9 72,676 15,981 5,620 22.0% 35.2% 31

University of California-LA 2012 127 6 61,556 16,689 5,825 27.1% 34.9% 31

University of California-LA 2011 127 4 61,545 15,719 4,636 25.5% 29.5% 31

University of California-LA 2010 127 7 55,694 12,178 4,472 21.9% 36.7% 31

University of California-LA 2009 127 4 55,423 12,659 4,735 22.8% 37.4% 31

University of California-LA 2008 127 6 50,746 11,960 4,563 23.6% 38.2% 31

U. of Colorado Boulder 2013 127 1 22,473 19,710 5,846 87.7% 29.7% 29

U. of Colorado Boulder 2012 127 3 21,744 18,172 5,472 83.6% 30.1% 28

U. of Colorado Boulder 2011 127 5 20,506 17,828 5,700 86.9% 32.0% 28

U. of Colorado Boulder 2010 108 3 21,150 17,517 5,211 82.8% 29.7% 28

U. of Colorado Boulder 2009 108 5 19,649 16,514 5,555 84.0% 33.6% 29

U. of Colorado Boulder 2008 108 6 23,004 17,933 5,860 78.0% 32.7% 28

University of Oregon 2013 127 12 2 21,263 15,770 4,031 74.2% 25.6% 27

University of Oregon 2012 127 12 4 23,012 16,790 4,167 73.0% 24.8% 27

University of Oregon 2011 127 12 3 18,515 14,588 3,978 78.8% 27.3% 27

University of Oregon 2010 127 10 11 16,780 13,367 3,839 79.7% 28.7% 27

University of Oregon 2009 127 10 10 15,013 12,801 4,260 85.3% 33.3%

University of Oregon 2008 127 9 23 11,287 9,813 3,587 86.9% 36.6%

U. of Southern California 2013 127 7 47,358 9,395 2,922 19.8% 31.1% 33

U. of Southern California 2012 127 10 6 46,104 9,187 3,021 19.9% 32.9% 33

U. of Southern California 2011 127 8 37,210 8,566 2,931 23.0% 34.2% 33

U. of Southern California 2010 127 9 22 35,794 8,715 2,972 24.3% 34.1% 33

U. of Southern California 2009 127 12 3 35,753 8,724 2,869 24.4% 32.9% 32

U. of Southern California 2008 127 11 3 35,900 7,875 2,766 21.9% 35.1% 33

University of Utah 2013 127 5 11,354 9,281 3,124 81.7% 33.7% 27

University of Utah 2012 127 8 11,118 9,187 3,494 82.6% 38.0% 27

University of Utah 2011 127 10 9,545 7,941 3,268 83.2% 41.2% 27

University of Utah 2010 203 10 18 8,364 6,931 3,110 82.9% 44.9% 27

University of Utah 2009 203 13 2 7,890 6,318 3,748 80.1% 59.3% 27

University of Utah 2008 203 9 7,244 5,873 2,642 81.1% 45.0% 27

University of Washington 2013 127 7 30,199 16,679 6,253 55.2% 37.5% 30

University of Washington 2012 127 7 24,540 14,340 5,788 58.4% 40.4% 30

University of Washington 2011 127 7 24,537 14,340 5,774 58.4% 40.3% 30

41

University of Washington 2010 127 5 21,268 12,264 5,338 57.7% 43.5% 30

University of Washington 2009 127 0 20,224 12,327 5,579 61.0% 45.3% 29

University of Washington 2008 127 4 19,906 12,094 5,607 60.8% 46.4% 29

Washington State University 2013 127 3 14,887 12,219 4,163 82.1% 34.1% 25

Washington State University 2012 127 4 14,825 11,269 4,389 76.0% 38.9% 25

Washington State University 2011 127 2 13,094 10,939 4,176 83.5% 38.2% 26

Washington State University 2010 127 1 11,604 8,068 2,980 69.5% 36.9% 26

Washington State University 2009 127 2 11,795 8,995 3,372 76.3% 37.5% 26

Washington State University 2008 127 5 11,326 8,179 3,411 72.2% 41.7% 26

42

Table 4: Big 12 Data

Total

Institution Year Conference Wins Season Prior AP Season Prior Ranking Total Applicants Admissions Total Enrolled Rate Acceptance Yield Composite ACT %) (75th

Baylor University 2013 108 8 29,249 16,809 3,190 57.5% 19.0% 29

Baylor University 2012 108 10 13 27,828 16,879 3,254 60.7% 19.3% 29

Baylor University 2011 108 7 38,960 15,451 3,033 39.7% 19.6% 29

Baylor University 2010 108 4 34,145 16,315 3,259 47.8% 20.0% 29

Baylor University 2009 108 4 31,440 15,699 3,098 49.9% 19.7% 29

Baylor University 2008 108 3 25,501 13,096 3,068 51.4% 23.4% 28

Iowa State University 2013 108 6 16,539 13,648 5,366 82.5% 39.3% 28

Iowa State University 2012 108 6 14,540 12,541 5,048 86.3% 40.3% 28

Iowa State University 2011 108 5 15,066 12,135 4,552 80.5% 37.5% 28

Iowa State University 2010 108 7 12,536 10,662 4,356 85.1% 40.9% 28

Iowa State University 2009 108 2 12,549 10,953 4,546 87.3% 41.5% 28

Iowa State University 2008 108 3 11,058 9,832 4,347 88.9% 44.2% 27

University of Kansas 2013 108 1 12,389 11,433 3,771 92.3% 33.0% 28

University of Kansas 2012 108 2 10,035 9,306 3,580 92.7% 38.5% 28

University of Kansas 2011 108 3 10,157 9,397 3,702 92.5% 39.4% 28

University of Kansas 2010 108 5 10,653 9,740 3,942 91.4% 40.5% 27

University of Kansas 2009 108 8 10,902 10,003 4,483 91.8% 44.8% 27

University of Kansas 2008 108 12 7 10,367 9,554 4,084 92.2% 42.7% 27

Kansas State University 2013 108 11 12 9,839 9,437 3,821 95.9% 40.5%

Kansas State University 2012 108 10 15 9,273 9,180 3,770 99.0% 41.1%

Kansas State University 2011 108 7 8,292 8,204 3,644 98.9% 44.4%

Kansas State University 2010 108 6 8,268 8,147 3,561 98.5% 43.7%

Kansas State University 2009 108 5 8,413 8,283 3,687 98.5% 44.5%

Kansas State University 2008 108 5 9,386 5,249 3,981 55.9% 75.8% 26

University of Oklahoma 2013 108 10 15 10,991 8,841 4,052 80.4% 45.8% 29

University of Oklahoma 2012 108 10 16 11,650 9,220 4,138 79.1% 44.9% 29

University of Oklahoma 2011 108 12 6 11,456 9,377 4,053 81.9% 43.2% 29

University of Oklahoma 2010 108 8 9,996 8,500 3,726 85.0% 43.8% 29

University of Oklahoma 2009 108 12 5 9,252 8,211 3,760 88.7% 45.8% 29

43

University of Oklahoma 2008 108 11 8 10,863 7,958 3,803 73.3% 47.8% 28

Oklahoma State University 2013 108 8 11,064 8,411 3,872 76.0% 46.0% 28

Oklahoma State University 2012 108 12 3 12,056 9,351 4,289 77.6% 45.9% 28

Oklahoma State University 2011 108 11 13 9,914 8,099 3,896 81.7% 48.1% 27

Oklahoma State University 2010 108 9 8,696 7,079 3,554 81.4% 50.2% 28

Oklahoma State University 2009 108 9 16 7,561 6,537 3,148 86.5% 48.2% 27

Oklahoma State University 2008 108 7 6,406 5,702 3,073 89.0% 53.9% 27

Texas Christian University 2013 108 7 18,551 8,791 1,936 47.4% 22.0% 29

Texas Christian University 2012 108 11 14 19,335 7,901 1,854 40.9% 23.5% 30

Texas Christian University 2011 203 13 2 19,170 7,219 1,873 37.7% 25.9% 29

Texas Christian University 2010 203 12 6 14,085 7,428 1,827 52.7% 24.6% 29

Texas Christian University 2009 203 11 7 11,951 7,079 1,821 59.2% 25.7% 28

Texas Christian University 2008 203 8 12,212 6,157 1,630 50.4% 26.5% 28

U. of Texas at Austin 2013 108 9 19 38,161 15,335 7,249 40.2% 47.3% 31

U. of Texas at Austin 2012 108 8 35,431 16,563 8,092 46.7% 48.9% 31

U. of Texas at Austin 2011 108 5 32,589 15,172 7,149 46.6% 47.1% 31

U. of Texas at Austin 2010 108 13 2 31,022 14,583 7,275 47.0% 49.9% 31

U. of Texas at Austin 2009 108 12 4 31,362 14,213 7,243 45.3% 51.0% 30

U. of Texas at Austin 2008 108 10 10 29,501 12,843 6,718 43.5% 52.3% 30

Texas Tech University 2013 108 8 19,170 12,709 4,785 66.3% 37.7% 27

Texas Tech University 2012 108 5 18,027 11,593 4,560 64.3% 39.3% 27

Texas Tech University 2011 108 8 17,569 11,645 4,464 66.3% 38.3% 27

Texas Tech University 2010 108 9 21 16,349 11,720 4,858 71.7% 41.5% 27

Texas Tech University 2009 108 11 12 16,551 11,228 4,585 67.8% 40.8% 26

Texas Tech University 2008 108 9 22 16,143 11,643 4,385 72.1% 37.7% 26

West Virginia University 2013 108 7 16,521 14,060 5,135 85.1% 36.5% 26

West Virginia University 2012 108 10 17 15,815 13,415 5,022 84.8% 37.4% 26

West Virginia University 2011 104 9 14,335 12,475 5,034 87.0% 40.4% 26

West Virginia University 2010 104 9 25 14,229 12,496 4,589 87.8% 36.7% 26

West Virginia University 2009 104 9 23 15,094 13,232 5,135 87.7% 38.8% 26

West Virginia University 2008 104 11 6 13,634 4,731 4,731 34.7% 100.0% 26

44

Table 5: SEC Data

Institution Year Conference Wins Season Prior AP Season Prior Ranking Total Applicants Total Admissions Total Enrolled Rate Acceptance Yield Composite ACT %) (75th

The University of Alabama 2013 130 13 1 30,975 17,515 6,454 56.5% 36.8% 30

The University of Alabama 2012 130 12 1 26,409 14,019 6,397 53.1% 45.6% 30

The University of Alabama 2011 130 10 10 22,136 9,636 5,766 43.5% 59.8% 29

The University of Alabama 2010 130 14 1 20,112 10,776 5,563 53.6% 51.6% 29

The University of Alabama 2009 130 12 6 18,967 11,133 5,148 58.7% 46.2% 28

The University of Alabama 2008 130 7 18,500 11,172 5,116 60.4% 45.8% 27

University of Arkansas 2013 130 4 18,908 11,076 4,339 58.6% 39.2% 28

University of Arkansas 2012 130 11 5 16,749 10,630 4,574 63.5% 43.0% 28

University of Arkansas 2011 130 10 12 16,633 10,129 4,447 60.9% 43.9% 28

University of Arkansas 2010 130 8 14,019 8,468 3,810 60.4% 45.0% 28

University of Arkansas 2009 130 5 12,035 6,751 2,919 56.1% 43.2% 29

University of Arkansas 2008 130 8 12,045 6,945 3,011 57.7% 43.4% 28

Auburn University 2013 130 3 15,745 13,027 3,726 82.7% 28.6% 30

Auburn University 2012 130 8 17,463 13,486 3,852 77.2% 28.6% 30

Auburn University 2011 130 14 1 18,323 12,827 4,202 70.0% 32.8% 30

Auburn University 2010 130 8 15,784 12,417 4,204 78.7% 33.9% 30

Auburn University 2009 130 5 14,862 11,816 3,918 79.5% 33.2% 29

Auburn University 2008 130 9 15 17,068 12,085 3,984 70.8% 33.0% 28

Louisiana State University 2013 130 10 14 16,169 12,326 5,725 76.2% 46.4% 28

Louisiana State University 2012 130 13 2 14,818 11,789 5,290 79.6% 44.9% 28

Louisiana State University 2011 130 11 8 18,214 13,148 5,481 72.2% 41.7% 28

Louisiana State University 2010 130 9 17 15,917 11,012 4,789 69.2% 43.5% 28

Louisiana State University 2009 130 8 15,093 11,092 5,141 73.5% 46.3% 28

Louisiana State University 2008 130 12 1 11,452 8,332 4,596 72.8% 55.2% 28

University of Mississippi 2013 130 7 14,258 8,464 3,582 59.4% 42.3% 27

University of Mississippi 2012 130 2 13,934 8,507 3,373 61.1% 39.6% 27

University of Mississippi 2011 130 4 13,321 10,524 3,569 79.0% 33.9% 27

University of Mississippi 2010 130 9 20 10,909 8,479 3,088 77.7% 36.4% 27

University of Mississippi 2009 130 9 14 8,595 6,839 2,576 79.6% 37.7% 26

45

University of Mississippi 2008 130 3 7,946 6,630 2,473 83.4% 37.3% 26

Mississippi State University 2013 130 8 11,191 7,254 3,156 64.8% 43.5% 28

Mississippi State University 2012 130 7 10,462 6,502 2,894 62.1% 44.5% 27

Mississippi State University 2011 130 9 15 9,862 6,192 2,898 62.8% 46.8% 27

Mississippi State University 2010 130 5 9,300 5,158 2,707 55.5% 52.5% 26

Mississippi State University 2009 130 4 11,281 6,415 2,450 56.9% 38.2% 27

Mississippi State University 2008 130 8 7,479 4,273 3,313 57.1% 77.5% 27

Texas A & M University 2013 130 11 5 31,387 21,803 ##### 69.5% 47.0% 29

Texas A & M University 2012 130 7 27,730 18,546 8,143 66.9% 43.9% 30

Texas A & M University 2011 108 9 19 25,990 16,488 8,254 63.4% 50.1% 30

Texas A & M University 2010 108 6 23,314 16,003 8,176 68.6% 51.1% 30

Texas A & M University 2009 108 4 22,969 15,369 8,104 66.9% 52.7% 30

Texas A & M University 2008 108 7 20,887 14,640 8,091 70.1% 55.3% 29

University of Florida 2013 130 11 9 27,107 12,618 6,373 46.5% 50.5% 31

University of Florida 2012 130 7 27,419 12,092 6,289 44.1% 52.0% 31

University of Florida 2011 130 8 27,295 11,786 6,451 43.2% 54.7% 30

University of Florida 2010 130 13 3 26,512 11,459 6,381 43.2% 55.7% 30

University of Florida 2009 130 13 1 25,798 11,015 6,253 42.7% 56.8% 29

University of Florida 2008 130 9 13 26,326 10,916 6,384 41.5% 58.5% 30

University of Georgia 2013 130 12 5 18,458 10,352 4,936 56.1% 47.7% 30

University of Georgia 2012 130 10 19 17,569 11,062 5,482 63.0% 49.6% 30

University of Georgia 2011 130 6 17,408 10,318 4,679 59.3% 45.3% 29

University of Georgia 2010 130 8 17,776 9,557 4,684 53.8% 49.0% 29

University of Georgia 2009 130 10 13 17,207 9,569 4,851 55.6% 50.7% 29

University of Georgia 2008 130 11 2 16,871 9,242 4,721 54.8% 51.1% 29

University of Kentucky 2013 130 2 19,810 13,592 4,702 68.6% 34.6% 28

University of Kentucky 2012 130 5 15,153 10,362 4,139 68.4% 39.9% 28

University of Kentucky 2011 130 6 13,537 9,275 4,328 68.5% 46.7% 28

University of Kentucky 2010 130 7 12,195 8,966 4,153 73.5% 46.3% 28

University of Kentucky 2009 130 7 11,120 8,757 4,110 78.8% 46.9% 27

University of Kentucky 2008 130 8 10,619 8,172 3,865 77.0% 47.3% 27

University of Missouri 2013 130 5 20,956 16,473 6,194 78.6% 37.6% 28

University of Missouri 2012 130 8 20,564 16,752 6,501 81.5% 38.8% 28

University of Missouri 2011 108 10 18 18,103 14,690 6,138 81.1% 41.8% 28

46

University of Missouri 2010 108 8 20,664 16,730 6,089 81.0% 36.4% 28

University of Missouri 2009 108 10 19 16,455 13,659 5,589 83.0% 40.9% 28

University of Missouri 2008 108 12 4 14,491 12,327 5,783 85.1% 46.9% 28

University of South Carolina 2013 130 11 8 23,429 14,199 4,625 60.6% 32.6% 29

University of South Carolina 2012 130 11 9 21,311 13,451 4,637 63.1% 34.5% 29

University of South Carolina 2011 130 9 22 18,485 12,914 4,468 69.9% 34.6% 29

University of South Carolina 2010 130 7 17,695 11,262 3,917 63.6% 34.8% 29

University of South Carolina 2009 130 7 17,018 9,954 3,967 58.5% 39.9% 28

University of South Carolina 2008 130 6 14,994 8,908 3,813 59.4% 42.8% 28

The University of Tennessee 2013 130 5 14,396 10,435 4,276 72.5% 41.0% 29

The University of Tennessee 2012 130 5 14,398 9,693 4,207 67.3% 43.4% 29

The University of Tennessee 2011 130 6 13,769 9,595 4,188 69.7% 43.6% 29

The University of Tennessee 2010 130 7 12,555 9,352 4,214 74.5% 45.1% 29

The University of Tennessee 2009 130 5 12,234 8,892 3,717 72.7% 41.8% 29

The University of Tennessee 2008 130 10 12 12,824 9,136 4,351 71.2% 47.6% 28

Vanderbilt University 2013 130 9 23 31,099 3,963 1,613 12.7% 40.7% 34

Vanderbilt University 2012 130 6 28,348 4,034 1,608 14.2% 39.9% 34

Vanderbilt University 2011 130 2 24,837 4,078 1,601 16.4% 39.3% 34

Vanderbilt University 2010 130 2 21,811 3,914 1,600 17.9% 40.9% 34

Vanderbilt University 2009 130 7 19,353 3,899 1,599 20.1% 41.0% 34

Vanderbilt University 2008 130 5 16,944 4,292 1,569 25.3% 36.6% 33

47

Table 6: Conference Labels

Conference Labels Value Conference 102 Atlantic Coast Conference 104 Big East Conference 107 Big Ten Conference 108 Big Twelve Conference 111 Conference USA 203 Mountain West Conference 130 Southeastern Conference Western Athletic 137 Conference

Table 7: Research Question 1 Results

Table 7a. Impact of Prior Season Wins on Yield Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) .335 .015 22.374 .000 Prior Season .009 .002 .281 4.562 .000 Wins

Table 7b. Impact of Prior Season Ranking on Yield Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) .381 .007 52.816 .000 Rank_1SeasonPrior .047 .012 .242 3.884 .000

Table 7c. Impact of Rank 2 Seasons Prior to Current Year on Yield Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) .388 .007 52.536 .000 Rank_2SeasonsPrior .027 .012 .137 2.164 .031

48

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