The Relationship Between College Football Success and College

The Relationship Between College Football Success and College Admissions

Yiming Benjamin Wang

June 7, 2010

Mathematical Methods in the Social Sciences

2

Acknowledgments

I would like to thank Professor Steffen Habermalz for his role as my

faculty advisor. His insight and guidance were instrumental to completing this

project. Another thank you goes out to my friends and family for supporting me

throughout my undergraduate education and making this work possible.

3

Abstract

In this paper, I expound upon the results constructed by Tucker in his

paper Big-Time Pigskin Success: Is There an Advertising Effect? (Tucker 2005). In the

original paper, Tucker found a positive relationship between college football

success and SAT scores of incoming freshmen in the years after the establishment

of the current Bowl Championship Series system. Thus, he concluded this was

significant evidence for the existence of an advertising effect. After constructing

and analyzing an updated dataset, I find that, first of all, the positive correlation

is not observed for schools with high SAT scores. This was found using several

quantile regressions. Also, short-term on-the-field football success does not

translate into increased SAT scores when a fixed effects model of the panel data

is used. Because the observed effect from the ordinary least squares model is no

longer apparent in the fixed effects model, any positive academic externalities

derived from college football can only be attributed to school-specific factors.

4

Table of Contents

Introduction __________________________________________________________ 5

Literature Review _____________________________________________________ 7

Descriptions of Data __________________________________________________ 10

Ordinary Least Squares (OLS) _________________________________________ 14

Quantile Regression __________________________________________________ 17

Fixed Effects Model ___________________________________________________ 19

Conclusions __________________________________________________________ 21

Limitations __________________________________________________________ 23

Appendix 1 ___________________________________________________________ 24

Appendix 2 ___________________________________________________________ 28

Bibliography _________________________________________________________ 34

5

Introduction

The Division I Football Bowl Subdivision is unique in the sense that it is

the only NCAA sport to not have a champion determined by an NCAA

sanctioned championship event. Instead, schools compete in the Bowl

Championship Series (BCS). This system was established in the 1990’s.

Originally, it was called the Bowl Coalition from 1992-1994, then the Bowl

Alliance from 1995-1997, and the modern BCS system began in 1998.

Since the establishment of the Bowl Championship Series, college football

has been a prominent fixture in major sports programming in the United States.

The BCS system relies on computer rankings and sports polls to determine which

teams may participate in the championship game. Of course, this system is not

perfect, and the system is seemingly faced with criticism at the end of every

season. This often involves many sports analysts trying to predict which bowls

different schools will get, and in this process, many schools receive a large

amount of media coverage. Year after year, television networks devote large

chunks of time towards broadcasting college football games and events. In

recent years, cable channels, such as ESPNU and the Big Ten network, dedicated

solely to college sports have sprouted.

With its prominent place in the media, college football also comes with its

costs. The University of Alabama’s athletic program in the 2007-2008 school year

6

had operating expenses of $123.4 million1. The University of California-

Berkeley’s athletic department ran a $5.8 million1 deficit in 2008-2009 that the

university had to cover. The most telling statistic, however, is that in 2009, only

14 schools1 out of the over 115 Division I FBS schools had athletic departments

that generated more revenue than expenses. In this calculation, revenue

included such items as ticket sales, donations, and media rights. A USA Today

analysis of college sports financial data from 2005-2009 found that only seven

schools had self-supporting athletic programs in those five years. Thus, very few

schools are able to cover their athletic expenses with athletic revenues alone yet

schools continue to invest heavily in athletic programs. These costs are of

interest especially considering the vast majority of Division I FBS schools are

public schools that are funded in part by tax payers in the state. The large

allocation of resources towards football programs by institutions of higher

education certainly begs the question of whether or not these investments have

positive academic externalities. That is, do investments in college athletics

further a school’s academic mission?

1 Statistic from USA Today article

7

Literature Review

Previous literature has varied in methods and approaches used to

evaluate the relationship between college athletics and academic spillovers. One

of the first papers to address the issue of football success affecting academic

quality of incoming students was by McCormick and Tinsley in 1987. They

showed that being in a major sports conference was associated with a small

increase in average freshmen SAT scores. The data was limited to 44 top

performing football programs. The variable used in that study was winning

percentage of a program over a 15 year period. A Robert H. Frank report in 2004

concluded that athletic success only offers small indirect spillover effects.

However, in a 2005 report by Irvin B. Tucker, Tucker concluded that a positive

externality exists for schools since the formation of the Bowl Alliance in 1995.

The Tucker paper in 2005 found the relationship between success in the

BCS system and SAT scores to be statistically significant at the 5% level. The

estimates for coefficients in the Tucker model suggest that an increase of 10% in

winning percentage over a 5-year period will increase average SAT scores by 14

points. Additionally, Tucker’s paper suggested that an extra bowl appearance in

a 5-year period is related to an increase of more than 12 points in the average

SAT score.

8

The methodology in the Tucker paper used an ordinary least squares

regression to estimate the impact various factors had on SAT scores. In this

paper, a fixed effects model with panel data and a quantile regression will be

used to explore the topic further. Also, Tucker’s paper used only 78 schools

while this paper includes 105 schools.

Although a spillover effect may be observed, Robert H. Frank in a 2004

article argues that the net effect is still very costly for colleges. The investment in

sports is akin to a game theoretic model of an expenditures arms race where

costs escalate with seemingly no limit. That is, relative investment is what is

most important since schools compete for the same players and coaches. If one

school raises its investment level, another school has an incentive to increase

sports expenditures to keep relative expenditures at comparable levels. The net

effect is both schools spend more money, but the level of the sports programs

remains static.

The length of time a spillover effect lasts may be able to offset some of the

high costs of sports programs. Schools need to keep investing in sports

programs if peers are doing the same, but ultimately, once costs become

prohibitively high, resources may be better spent funding other programs such

as financial aid. This analysis will hopefully deepen the current understanding

9

of any lasting positive advertising effects schools experience from investments in

college football.

10

Descriptions of Data

The dataset contains 105 Division I Football Bowl Subdivision schools.

The following variables were used in this paper:

SAT25 is the 25th percentile combined math and verbal score on the SAT

for the entering freshmen class. The score on the SAT ranges from 400 to

1600 and is used widely across the United States for college admissions

purposes. A new SAT test was introduced in 2006 that includes a writing

section, but in order to remain consistent with the data before 2006, only

the math and verbal scores will be considered for the new SAT. Also,

schools that report only ACT scores had those scores converted to an SAT

equivalent score using a conversion table. This is the dependent variable

used to roughly measure if academic quality of freshmen is related to

college football success metrics.

SAT75 is the 75th percentile combined math and verbal score on the SAT

for the entering freshmen class.

SATDIST is the absolute value of the difference between SAT75 and

SAT25. This number gives us some indication of the distribution of SAT

11

scores for the incoming freshmen class. If students who are more likely

influenced by football success have different SAT scores, this variable

would be likely to change with football success.

SFR is the student-faculty ratio. It is student enrollment divided by the

number of faculty at each institution. Lower ratios indicate greater

opportunities for students to receive personalized attention, and thus,

students with high test scores may choose to attend schools with lower

student-faculty ratios for the individual attention. In general, student-

faculty ratios were collected from college self-reporting using the

Common Data Set standard.

AP is a binary variable that is equal to one if the school is ranked in the

top 25 at the end of the season in the ranking published by the Associated

Press. The AP poll is based on votes from sports writers who consider the

school’s record while keeping in mind the strength of opposition.

Although there is no perfect measurement or definition for a “good”

college football season, a ranking in the AP poll generally signifies a high

caliber football program. As such, this measure is sufficient in

approximating football success.

12

Rank1 counts the number of weeks a school is ranked in 1st place in the AP

poll in a given season. This number is meaningful in the context of trying

to determine evidence for an advertising effect. The advertising effect

hypothesis is dependent upon schools receiving positive press coverage

from having success on the field. Certainly, being ranked in first place is a

great way to receive positive press coverage especially since the media

tends to place a focus on covering the schools ranked first.

A private school dummy variable will take on a value of 0 for public

schools and 1 for private schools. This may have a positive coefficient

with SAT score if private schools are perceived to be better at educating

students and can somehow attract the students with higher scores.

A dummy variable will be used for each year to account for any sort of

effects that are related to events in a given year. For example, the strength

of the economy in one year could result in more people applying to public

schools since they would rather pay lower tuition. These kinds of effects

can be controlled for by including a year dummy variable.

13

SAL is a variable that represents the average full time instructional faculty

salary for that school year. The salary numbers are reported from the

Integrated Postsecondary Education Data System (IPEDS). The nominal

wages are then adjusted for inflation using the Consumer Price Index

published by the Bureau of Labor Statistics.

DPI is the disposable personal income of the state the school is in. This

variable serves as a proxy for the local economy for the particular school.

Students may want to go to a school with higher DPI since that represents

more lucrative career and job opportunities near the school. The DPI

numbers in the data set were adjusted for inflation using the Consumer

Price Index published by the Bureau of Labor Statistics.

14

Ordinary Least Squares (OLS)

An OLS regression was used on this dataset with the AP rank lagged one

year, and the results are consistent with those found in Tucker’s paper.

An OLS regression on the 25th percentile SAT scores:

SAT 25th Percentile Coefficient Std. Err. 95% Confidence Interval

AP Rankt-1 32.6700 5.3066 22.2489 43.0912

SFRt -2.3043 0.9811 -4.2308 -0.3777

Enrollmentt -0.0001 0.0004 -0.0008 0.0006

DPIt -0.0009 0.0007 -0.0023 0.0005

Private Schoolt 80.2116 8.7812 62.9671 97.4561

SALt 0.0053 0.0003 0.0048 0.0058

2002 0.6121 10.1068 -19.2355 20.4597

2003 14.9797 9.9555 -4.5709 34.5302

2004 16.1229 9.9249 -3.3675 35.6134

2005 26.5727 9.8025 7.3226 45.8227

2006 22.3093 9.8498 2.9663 41.6523

2007 19.7430 9.8297 0.4395 39.0464

2008 27.3324 9.8221 8.0439 46.6209

Constant 640.7776 32.4018 577.1472 704.4079

OLS Regression on the 75th Percentile SAT scores:


AP Rankt-1 24.5018 4.8083 15.0593 33.9443

SFRt -1.0644 0.8889 -2.8101 0.6813

Enrollmentt -0.0003 0.0003 -0.0009 0.0003

DPIt -0.0017 0.0006 -0.0030 -0.0005

Private Schoolt 64.3874 7.9566 48.7623 80.0126

SALt 0.0054 0.0002 0.0050 0.0059

2002 -2.7489 9.1577 -20.7327 15.2348

2003 8.0171 9.0206 -9.6975 25.7317

2004 10.7543 8.9929 -6.9058 28.4145

2005 22.9006 8.8820 5.4583 40.3430

2006 17.6512 8.9249 0.1246 35.1778

2007 17.4793 8.9066 -0.0115 34.9700

2008 22.3718 8.8997 4.8946 39.8490

Constant 854.4918 29.3591 796.8367 912.1469

15

This OLS model was considered to offer comparability to the results

found in the existing literature. This dataset is comprised of data from 105

schools, and regressions were run on data primarily collected after 2001. The

results here are largely consistent with Tucker’s findings in 2005 that post-1995

SAT scores are positively correlated with college football performance. The AP

rank variable has a statistically significant positive relationship with SAT scores

at the 95% confidence level.

An interesting observation that can be made about the data is that the 25th

percentile SAT score seems to be affected more by football success than the 75th

percentile score is affected. The coefficient on AP rank is 32.67 compared to 24.50

for the 25th and 75th percentile scores, respectively. This discrepancy warranted a

deeper investigation of the data to see if football success had any effect on the

distribution of SAT scores for incoming classes. To assess this, a variable called

SAT distance was created to measure the difference between the 75th and 25th

percentile SAT scores for a school.

16

OLS Regression on SAT distance:

SAT Distance Coefficient Std. Err. 95% Confidence Interval

AP Rankt-1 -8.1683 2.4979 -13.0737 -3.2629

SFRt 1.2398 0.4618 0.3330 2.1467

Enrollmentt -0.0002 0.0002 -0.0006 0.0001

DPIt -0.0008 0.0003 -0.0015 -0.0002

Private Schoolt -15.8242 4.1335 -23.9415 -7.7069

SALt 0.0001 0.0001 -0.0001 0.0004

2002 -3.3610 4.7574 -12.7037 5.9816

2003 -6.9626 4.6862 -16.1654 2.2402

2004 -5.3686 4.6718 -14.5431 3.8060

2005 -3.6721 4.6142 -12.7334 5.3893

2006 -4.6581 4.6365 -13.7632 4.4471

2007 -2.2637 4.6270 -11.3502 6.8228

2008 -4.9606 4.6234 -14.0400 4.1189

Constant 213.7142 15.2522 183.7622 243.6663

From this analysis, the coefficient on AP rank is statistically significantly

different from 0 and has a value of approximately -8. Therefore, being ranked in

the AP top 25 has some effect on the distribution of SAT scores within a school.

That is, the effect is not uniform across students of varying SAT scores and may

have a bigger impact on students with low SAT scores than students with high

SAT scores. To address this issue, a quantile regression is used.

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Quantile Regression

A quantile regression approximates the effects on the median of the

response variable, SAT scores in this case. The more common OLS method

measures the effects on the mean. While the mean and median both measure

central tendencies, the quantile regression does have some other nice features.

The most notable is the fact that any quantile can be estimated (Appendix 1).

In this model, it was found that quantile regressions on the 50th percentile

and 15th percentile schools ranked by SAT scores are all statistically significant at

the 99% level when looking at

lagged AP. In both cases (left), SAT

scores were statistically different

from zero and had coefficients

ranging from 27 to 36. Thus, a

successful football program at a school with median SAT scores or lower could

expect to approximately have a 30 point increase in SAT scores of incoming

freshmen compared to schools without successful football programs. However,

this changes when the same regression is run on the 85th percentile schools. For

these schools with high SAT scores, football success on the field did not translate

to increased SAT scores of incoming freshmen for the 75th percentile SAT scores.

SAT75 Quantiles

0.1500 0.5000 0.8500

AP 34.62994** 27.05828** 7.8291

Rank1 2.4550 -0.1270 0.3881

SAT25 Quantiles

0.1500 0.5000 0.8500

AP 33.24985** 35.36776** 25.0187**

Rank1 0.7735 2.2141 0.6343

18

The quantile regressions show that while an effect can be observed, this

effect is not as strong for schools that already have high SAT scores. This

analysis implies that the result is not uniform across students, and the effect

varies depending on SAT scores. Students with high SAT scores do not respond

as well as other students to positive performance on the football field.

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Fixed Effects Model

The OLS and quantile regression models are useful tools for assessing the

relationship between college football performance and SAT scores of incoming

freshmen, but both of those models do not take into consideration any school

specific effects. In other words, those models do not control for factors

inherently part of certain schools. To remove school specific effects from the

model, a fixed effects model is used on the panel data.

Similar to the quantile regression model, the fixed effects regression was

run with lagged AP and Rank1 variables. This was done with the same

reasoning as above. Interestingly, the results change considerably from the OLS

regression. With the exception of Rank1 lagged two years, no other coefficient

for football success is found to be statistically significant (Appendix 2). Also, the

coefficients all tend to be very small with many negative numbers as well. The

coefficient on the only variable that is statistically significant is 1.1779. This

shows that being ranked in first place for an additional week in one season is the

equivalent of a less than 2 point boost to a school’s SAT 75th Percentile score for

incoming freshmen. A 2 point increase to a test that is scaled from 400 to 1600 is

virtually irrelevant. Thus, this model shows very little evidence for the

advertising effect. This is especially surprising considering that Rank1 was

expected to be statistically significant. That is, being ranked in first place was not

20

enough for a school to see meaningful and statistically significant improvements

in its incoming freshmen class SAT scores.

This result may imply that any realized advertising effects do not come

from having a few great seasons. The spillover effect does not come from short

term performance on the field, but instead, it may only be attributed to factors

intrinsic to schools with successful football programs. That is, the effect is not

immediately realized after having a successful season, but because of significance

in the OLS model, school-specific long term factors such as a school’s sports

culture or tradition of sports may play the more significant role in driving the

effect.

21

Conclusions

The models used in this paper show no evidence for the advertising effect

in a fixed effects regression. Also, there are virtually no statistically significant

results when looking at the coefficient on the Rank1 variable. Thus, the

advertising effect may not be as driven by on field performance as originally

presumed.

Instead, the effect may have more to do with a school’s football culture

and traditions. These effects are certainly affected by performance on the

football field, but these are also long term effects developed over time.

Therefore, while a surprise season in which a school is successful in football may

have a negligible effect on the SAT scores of incoming freshmen, the

development of a school culture where football plays a big role is likely to

influence test scores of a school’s incoming class.

Regardless of what the driving force is behind the advertising effect, many

of these athletic departments in public schools are running a deficit and are using

taxpayer money to cover losses. The positive spillovers seem to be negligible

unless large investments are made to develop a strong and long-standing

tradition of football. Schools may need to reevaluate their athletic budgets since

the money spent on athletic programs might help a school’s academic mission

22

more if it were spent on research facilities and financial aid programs rather than

football stadiums and a large coaching staff.

23

Limitations

This analysis considers only one specific positive externality from college

football success. While academic quality of incoming students is an important

aspect of a university’s academic mission, it is only one measure and using SAT

scores as a proxy is a crude approximation for actual student quality. There are

other externalities to consider when looking at spillover effects from collegiate

football success. For example, this model did not include alumni giving rate

which may be affected by successes on the field. Also, there are factors which are

hard to measure such as a school’s perceived reputation and the value in having

increased school pride and student cohesion. These ulterior factors could also be

influenced by football performances.

24

Appendix 1

Quantile Regression (0.5 Quantile):

SAT 25th

Percentile Coefficient Std. Err.

95% Confidence

Interval

AP Rankt-1 35.3678 5.1523 25.2497 45.4859

SFRt -2.0827 0.9329 -3.9148 -0.2506

Enrollmentt 0.0002 0.0003 -0.0004 0.0009

DPIt -0.0001 0.0007 -0.0015 0.0012

Private Schoolt 94.9125 8.3805 78.4550 111.3701

SALt 0.0048 0.0002 0.0043 0.0053

2002 -5.0103 9.7878 -24.2316 14.2110

2003 8.6907 9.6356 -10.2316 27.6130

2004 15.1327 9.5891 -3.6982 33.9637

2005 25.5156 9.4386 6.9801 44.0510

2006 14.5233 9.5107 -4.1539 33.2004

2007 13.1854 9.5168 -5.5035 31.8744

2008 16.5983 9.5006 -2.0588 35.2555

Constant 646.7721 30.6989 586.4859 707.0583

SAT 75th


95% Confidence

Interval

AP Rankt-1 27.0583 6.9958 13.3200 40.7966

SFRt -0.3366 1.2967 -2.8830 2.2098

Enrollmentt -0.0014 0.0005 -0.0023 -0.0004

DPIt -0.0022 0.0009 -0.0040 -0.0004

Private Schoolt 46.8039 11.5768 24.0694 69.5384

SALt 0.0060 0.0003 0.0054 0.0067

2002 -12.3510 13.2142 -38.3009 13.5990

2003 -3.5005 13.0169 -29.0630 22.0620

2004 -1.3629 12.9540 -26.8019 24.0762

2005 20.0330 12.8316 -5.1655 45.2315

2006 14.7593 12.8455 -10.4667 39.9852

2007 18.3162 12.8172 -6.8540 43.4865

2008 19.5508 12.8381 -5.6606 44.7622

Constant 836.0794 42.8614 751.9086 920.2501

25

SAT Distance Coefficient Std. Err.

95% Confidence

Interval

AP Rankt-1 -11.3373 3.6146 -18.4357 -4.2389

SFRt 1.3440 0.6629 0.0421 2.6458

Enrollmentt -0.0002 0.0002 -0.0007 0.0003

DPIt -0.0007 0.0005 -0.0016 0.0002

Private Schoolt -10.6856 5.7234 -21.9251 0.5539

SALt -0.0001 0.0002 -0.0004 0.0003

2002 -1.5346 6.7822 -14.8535 11.7842

2003 -9.1769 6.6836 -22.3022 3.9483

2004 -7.7840 6.6267 -20.7975 5.2295

2005 -6.9972 6.5781 -19.9151 5.9208

2006 -7.0126 6.6117 -19.9966 5.9714

2007 -3.7840 6.5803 -16.7063 9.1382

2008 -6.7512 6.5910 -19.6946 6.1921

Constant 227.7186 22.0244 184.4673 270.9700


SAT 25th


95% Confidence

Interval

AP Rankt-1 33.2499 6.7455 20.0031 46.4966

SFRt -4.6749 1.1562 -6.9454 -2.4044

Enrollmentt -0.0001 0.0004 -0.0010 0.0007

DPIt 0.0002 0.0008 -0.0014 0.0017

Private Schoolt 86.1853 10.5093 65.5473 106.8234

SALt 0.0047 0.0003 0.0041 0.0053

2002 0.7315 12.7444 -24.2959 25.7588

2003 8.8981 12.3616 -15.3775 33.1736

2004 8.1042 12.2402 -15.9330 32.1415

2005 21.6958 12.3959 -2.6471 46.0388

2006 17.3687 12.4652 -7.1105 41.8478

2007 21.1988 11.9973 -2.3614 44.7591

2008 24.0035 12.1376 0.1678 47.8391

Constant 634.2103 38.8667 557.8841 710.5364

26

SAT 75th


95% Confidence

Interval

AP Rankt-1 34.6299 5.8663 23.1098 46.1501

SFRt -2.6764 1.0520 -4.7423 -0.6104

Enrollmentt 0.0005 0.0004 -0.0003 0.0012

DPIt -0.0001 0.0007 -0.0015 0.0013

Private Schoolt 86.1379 9.2219 68.0280 104.2479

SAL 0.0045 0.0003 0.0040 0.0050

2002 -5.7951 11.7369 -28.8439 17.2536

2003 4.3291 11.5278 -18.3092 26.9674

2004 -1.7148 11.7335 -24.7570 21.3274

2005 5.2185 11.4282 -17.2241 27.6610

2006 3.5129 11.3102 -18.6979 25.7237

2007 2.1524 11.3164 -20.0707 24.3755

2008 3.1347 11.4220 -19.2957 25.5651

Constant 840.6840 34.9755 771.9994 909.3685


SAT 25th


95% Confidence

Interval

AP Rankt-1 25.0181 8.2846 8.7489 41.2873

SFRt -1.2350 1.6418 -4.4592 1.9891

Enrollmentt 0.0006 0.0005 -0.0004 0.0017

DPIt -0.0032 0.0010 -0.0051 -0.0013

Private Schoolt 63.2610 15.4175 32.9843 93.5378

SALt 0.0064 0.0004 0.0056 0.0072

2002 4.2554 15.1107 -25.4189 33.9297

2003 20.0591 14.9915 -9.3811 49.4992

2004 25.0831 15.0614 -4.4943 54.6604

2005 29.1784 15.0213 -0.3202 58.6770

2006 30.0748 15.1525 0.3185 59.8311

2007 30.7329 14.8396 1.5910 59.8747

2008 37.6145 15.2277 7.7105 67.5184

Constant 642.9581 49.6202 545.5145 740.4018

27

SAT 75th


95% Confidence

Interval

AP Rankt-1 7.8291 5.7269 -3.4175 19.0756

SFRt 0.7436 1.1382 -1.4915 2.9787

Enrollmentt -0.0005 0.0004 -0.0012 0.0003

DPIt -0.0022 0.0007 -0.0036 -0.0009

Private Schoolt 61.3577 8.6854 44.3014 78.4141

SALt 0.0058 0.0003 0.0053 0.0063

2002 0.5111 10.7930 -20.6841 21.7062

2003 14.9989 10.7154 -6.0438 36.0416

2004 21.1002 10.7418 0.0056 42.1948

2005 25.8482 10.6464 4.9409 46.7554

2006 24.5730 10.3854 4.1783 44.9677

2007 23.9186 10.6213 3.0605 44.7767

2008 24.4218 10.3902 4.0175 44.8261

Constant 859.5604 36.3556 788.1656 930.9552

28

Appendix 2

Fixed effects:


AP Rankt-1 -0.2555 2.2997 -4.7730 4.2621

SFRt -0.1806 0.7596 -1.6728 1.3116

Enrollmentt -0.0010 0.0008 -0.0024 0.0005

DPIt -0.0020 0.0012 -0.0044 0.0004

SALt 0.0000 0.0003 -0.0006 0.0005

2001 -35.4208 4.9457 -45.1364 -25.7051

2002 -28.6230 4.2787 -37.0282 -20.2178

2003 -16.9056 3.8311 -24.4315 -9.3797

2004 -10.3954 3.2059 -16.6933 -4.0975

2005 -3.9289 3.0703 -9.9603 2.1024

2006 -2.2597 2.7585 -7.6786 3.1592

2008 7.0811 2.7636 1.6520 12.5101

Constant 1156.8710 47.0550 1064.4340 1249.3070


AP Rankt-1 -2.6086 2.2036 -6.9376 1.7203

SFRt -0.5826 0.7279 -2.0125 0.8473

Enrollmentt -0.0006 0.0007 -0.0020 0.0008

DPIt -0.0002 0.0012 -0.0025 0.0021

SALt 0.0003 0.0003 -0.0002 0.0008

2001 -25.8290 4.7393 -35.1390 -16.5190

2002 -21.9542 4.1000 -30.0085 -13.8999

2003 -14.9305 3.6711 -22.1422 -7.7188

2004 -10.0052 3.0721 -16.0401 -3.9703

2005 -2.0844 2.9421 -7.8640 3.6952

2006 -3.9051 2.6433 -9.0978 1.2876

2008 4.5748 2.6483 -0.6276 9.7772

Constant 1274.3370 45.0906 1185.7590 1362.9150

29

SAT Distance* Coefficient Std. Err. 95% Confidence Interval

AP Rankt-1 -2.3532 2.0590 -6.3980 1.6916

SFRt -0.4020 0.6801 -1.7380 0.9341

Enrollmentt 0.0003 0.0007 -0.0010 0.0017

DPIt 0.0018 0.0011 -0.0004 0.0039

SALt 0.0003 0.0002 -0.0001 0.0008

2001 9.5917 4.4282 0.8928 18.2906

2002 6.6688 3.8309 -0.8568 14.1945

2003 1.9750 3.4302 -4.7633 8.7134

2004 0.3902 2.8705 -5.2486 6.0290

2005 1.8445 2.7490 -3.5557 7.2448

2006 -1.6455 2.4699 -6.4973 3.2064

2008 -2.5063 2.4745 -7.3672 2.3546

Constant 117.4666 42.1310 34.7027 200.2306 *SAT Distance = SAT 75th Percentile Score – SAT 25th Percentile Score


AP Rankt-2 0.1718 2.2518 -4.2517 4.5954

SFRt -0.1763 0.7589 -1.6671 1.3144

Enrollmentt -0.0010 0.0008 -0.0024 0.0005

DPIt -0.0020 0.0012 -0.0044 0.0004

SALt 0.0000 0.0003 -0.0006 0.0005

2001 -35.4658 4.9452 -45.1803 -25.7513

2002 -28.6473 4.2832 -37.0615 -20.2332

2003 -16.9327 3.8306 -24.4577 -9.4076

2004 -10.4196 3.2020 -16.7097 -4.1294

2005 -3.9446 3.0706 -9.9766 2.0875

2006 -2.2684 2.7577 -7.6858 3.1490

2008 7.0818 2.7638 1.6525 12.5110

Constant 1157.2220 47.0829 1064.7300 1249.7140

30


AP Rankt-2 -1.7861 2.1592 -6.0278 2.4556

SFRt -0.5490 0.7277 -1.9785 0.8804

Enrollmentt -0.0007 0.0007 -0.0021 0.0007

DPIt -0.0002 0.0012 -0.0025 0.0021

SALt 0.0003 0.0003 -0.0002 0.0008

2001 -25.9227 4.7419 -35.2379 -16.6076

2002 -21.8714 4.1071 -29.9396 -13.8031

2003 -14.9913 3.6731 -22.2070 -7.7757

2004 -10.1477 3.0704 -16.1794 -4.1161

2005 -2.1019 2.9444 -7.8860 3.6822

2006 -3.9708 2.6444 -9.1655 1.2239

2008 4.5402 2.6502 -0.6659 9.7463

Constant 1274.3530 45.1474 1185.6630 1363.0420


AP Rankt-2 -1.9579 2.0168 -5.9199 2.0041

SFRt -0.3727 0.6797 -1.7079 0.9625

Enrollmentt 0.0003 0.0007 -0.0010 0.0016

DPIt 0.0018 0.0011 -0.0004 0.0039

SALt 0.0003 0.0002 -0.0001 0.0008

2001 9.5431 4.4292 0.8423 18.2440

2002 6.7759 3.8363 -0.7603 14.3121

2003 1.9413 3.4309 -4.7985 8.6812

2004 0.2718 2.8679 -5.3620 5.9057

2005 1.8426 2.7502 -3.5600 7.2453

2006 -1.7024 2.4700 -6.5545 3.1497

2008 -2.5416 2.4754 -7.4043 2.3212

Constant 117.1306 42.1701 34.2898 199.9713

31


Rank1t-1 0.1067 0.6284 -1.1277 1.3411

SFRt -0.1714 0.7595 -1.6634 1.3205

Enrollmentt -0.0010 0.0008 -0.0025 0.0005

DPIt -0.0020 0.0012 -0.0044 0.0004

SALt 0.0000 0.0003 -0.0006 0.0005

2001 -35.4152 4.9433 -45.1261 -25.7043

2002 -28.6057 4.2806 -37.0147 -20.1967

2003 -16.9046 3.8295 -24.4274 -9.3819

2004 -10.4018 3.2021 -16.6922 -4.1114

2005 -3.9045 3.0754 -9.9460 2.1370

2006 -2.2462 2.7604 -7.6689 3.1765

2008 7.0915 2.7644 1.6609 12.5221

Constant 1156.5150 47.1318 1063.9280 1249.1030

SAT 75th

Percentile Coefficient Std. Err. 95% Confidence Interval

Rank1t-1 0.4117 0.6027 -0.7722 1.5957

SFRt -0.5232 0.7284 -1.9542 0.9077

Enrollmentt -0.0007 0.0007 -0.0021 0.0007

DPIt -0.0002 0.0012 -0.0025 0.0021

SALt 0.0003 0.0003 -0.0002 0.0008

2001 -25.9810 4.7412 -35.2948 -16.6672

2002 -21.9396 4.1056 -30.0048 -13.8745

2003 -15.0326 3.6729 -22.2477 -7.8174

2004 -10.1512 3.0712 -16.1844 -4.1180

2005 -2.0456 2.9497 -7.8401 3.7489

2006 -3.9012 2.6475 -9.1022 1.2997

2008 4.6065 2.6514 -0.6020 9.8151

Constant 1274.0970 45.2045 1185.2960 1362.8990

32


Rank1t-1 0.3050 0.5632 -0.8013 1.4114

SFRt -0.3518 0.6807 -1.6889 0.9854

Enrollmentt 0.0003 0.0007 -0.0011 0.0016

DPIt 0.0018 0.0011 -0.0004 0.0039

SALt 0.0003 0.0002 -0.0001 0.0008

2001 9.4342 4.4303 0.7311 18.1374

2002 6.6661 3.8364 -0.8703 14.2024

2003 1.8721 3.4321 -4.8700 8.6142

2004 0.2506 2.8698 -5.3870 5.8883

2005 1.8589 2.7563 -3.5556 7.2735

2006 -1.6550 2.4739 -6.5149 3.2049

2008 -2.4849 2.4776 -7.3519 2.3821

Constant 117.5820 42.2406 34.6029 200.5612

SAT 25th

Percentile Coefficient Std. Err. 95% Confidence Interval

Rank1t-2 1.1779 0.5341 0.1288 2.2270

SFRt -0.6229 0.7257 -2.0485 0.8027

Enrollmentt -0.0007 0.0007 -0.0021 0.0007

DPIt 0.0000 0.0012 -0.0023 0.0023

SALt 0.0003 0.0002 -0.0002 0.0008

2001 -25.7206 4.7214 -34.9954 -16.4458

2002 -21.6942 4.0891 -29.7270 -13.6613

2003 -14.8575 3.6581 -22.0436 -7.6714

2004 -10.1985 3.0577 -16.2052 -4.1917

2005 -2.1327 2.9317 -7.8918 3.6264

2006 -3.9188 2.6341 -9.0934 1.2559

2008 4.4371 2.6403 -0.7496 9.6238

Constant 1271.5060 44.9680 1183.1690 1359.8430

33


Rank1t-2 0.8960 0.4997 -0.0857 1.8776

SFRt -0.4272 0.6790 -1.7611 0.9068

Enrollmentt 0.0003 0.0007 -0.0011 0.0016

DPIt 0.0019 0.0011 -0.0002 0.0041

SALt 0.0003 0.0002 -0.0001 0.0008

2001 9.6348 4.4179 0.9561 18.3135

2002 6.8547 3.8263 -0.6618 14.3713

2003 2.0066 3.4230 -4.7176 8.7308

2004 0.2156 2.8612 -5.4050 5.8363

2005 1.7952 2.7432 -3.5938 7.1841

2006 -1.6668 2.4648 -6.5088 3.1753

2008 -2.6129 2.4706 -7.4662 2.2404

Constant 115.5705 42.0776 32.9116 198.2294

34

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Documents

The Relationship Between College Football Success and College