1. Redshirting and Academic Performance: Evidence from NCAA
Student-Athletes
Ethan Wilkes
Montana State University
Supervising Faculty: Randal R. Rucker and D. Mark Anderson
November 3, 2014
Abstract
Redshirting is common in NCAA athletics. Many student-athletes forgo playing time as a
true freshman and extend their eligibility in order to develop physically before they suit up for
their first game the following year. A redshirt year also provides student-athletes an additional
year to finish degree requirements. Although it is clear that redshirting can have important
impacts on an athlete’s athletic performance, the academic effects of redshirting are essentially
unknown. Student-Athletes have a federal graduation rate of 65 percent compared to the general
student body’s rate of 63 percent. The Football Bowl Subdivision reached a federal graduation
rate of 59 percent and men’s basketball scored a 47 percent (Trends 2012). These comparisons
show that although there have been improvements in student-athlete academic achievement, there
is still much work to be done, especially in high-revenue producing sports. This project will
examine the effects of redshirting on academic performance. In doing so, the results will be of
interest to those who are considering redshirting as a tool to aid student-athlete academic
achievement.
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2. Introduction
To prepare a student-athlete for collegiate competition, college programs often give players
a year to practice with their team, learn the playbook, and develop physically without seeing
game action. This is known as redshirting and is a practice that is commonly employed by NCAA
athletic programs. The redshirting player does not see game action during her redshirt season and
receives an additional year of eligibility. Although redshirting is widely used for athletic reasons, it
may also have academic benefits. This possibility has received little attention among researchers.
The NCAA publishes yearly reports that highlight the scholastic achievement of their
student-athletes. Using data from the 2005 cohort, the overall Division I federal graduation rate
was 65 percent, 2 percent points higher than the general student body. The Football Bowl
Subdivision scored a 51 percent while men’s basketball reached a federal graduation rate of 47
percent (Christianson 2012). The federal graduation rate treats all transfers as dropouts so it
does not reflect the true number of athletes that are graduating in six years; however, it is the
only measurement that can be used to compare student-athletes to the general student body. The
NCAA’s academics are improving every year but their main revenue earning sports’ athletes are
lagging behind.
In this study, redshirting’s effect on many different academic success variables will be
estimated using data from football players from SuperPrep Magazine and data from
student-athletes from Montana State University.1 These findings will have the potential for policy
relevance as colleges are continuously seeking methods to promote academic performance among
their student-athletes.
Literature Review
Although there is little to no research on the academic impacts of redshirting, there have
been many economic studies that can guide us in assessing the expected impact of redshirting on
academic achievement.2 The determinants of retention have been examined using a variety of
1
Professor George Haynes, the Faculty Athletic Representative at Montana State University is on the faculty in the
Department of Agricultural Economics and Economics. He is providing us with access to the data that is maintained
by the athletic department.
2
A portion of an unpublished study conducted by NCAA research personnel using data from the 1994 cohort
of freshman student-athletes found that redshirting has no significant effect on first-year GPA, first-year credits,
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3. data and statistical techniques. The literature that I highlight below is recent and uses relatively
modern statistical analyses to determine what factors influence a student’s choice to return to
school. The additional theory that each analysis provides in respect to redshirting is then
discussed.
Singell (2003) used student data from the University of Oregon (UO) and survey data
from the UO dropouts to determine what factors contribute to the dropout decision. He found
that important determinants of dropout rates include grade point average (GPA), inadequate
financial aid, problems with advising, problems with health, and needing to work. Redshirting
can theoretically impact a few of the factors that Singell discussed. GPA may be influenced by
redshirting if players allocate a portion of the time that the rest of the team is traveling to
studying and schoolwork. Redshirting may also improve health by allowing players to practice
with the team and use the team’s trainers and health and fitness facilities without the risk of
being injured during games. To determine if redshirting impacts first year GPA, our study will
estimate the effect using the MSU data.
Social connectedness, college GPA, attending orientation class, having a campus job and
receiving additional financial aid have been found to increase the probability of retention. Allen
et al. (2008) used student-level Student Readiness Inventory data to determine factors that affect
retention and found that social connectedness improves the odds of staying, rather than dropping
out. This applies to redshirting in much the same way as GPA does; the student-athlete will have
more time to allocate to making friends and recreating as opposed to traveling with the team.
Using Winona State University data, Yu et al. (2012) provided further evidence that college GPA
and receiving additional financial aid increase the probability of retention. Attending orientation
classes and having a campus job also increased the probability of retention.
Stinebrickner and Stinebrickner (2012) used Berea Panel Study data to assess the
importance of future expectations in the dropout decision. They found that students enter college
too optimistic about their academic performance. They also found that the dropout decision is
based on current GPA and expected future GPA. Redshirting could provide the student-athlete
with additional time during their freshman year to get acclimated to college courses. This could
first-year quality points or final GPA. We are currently in the process of obtaining additional information on this
study.
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4. provide realistic expectations about the remainder of their college careers without the burden of
traveling during their true freshman year.
McArdle and Hamagami (1994) regressed graduation rate on redshirting. The coefficient
on redshirting was positive and significant but the regression that was used did not control for
any other variables. There were no further regressions run including redshirting as an
independent variable. The significant coefficient suggests that redshirting may have a positive
effect on graduation; however, without addressing potential omitted variable bias and including
controls the results are not likely to reflect the true effect of redshirting on graduation rate. Our
analysis will employ propensity score matching to account for potential bias and will include
athletic, academic and institutional controls.
Methodology
The effects of redshirting on a host of academic outcomes will be estimated in this study.
The dependent variables of interest include college GPA, graduation, summer class attendance,
receiving a minor, receiving a second major and being named to academic all-conference or
academic all-American teams. Individual demographic and academic variables such as high school
GPA and SAT/ACT scores will be controlled for to determine the effects of redshirting on
academic outcomes. Athletic variables will be used to evaluate the propensity to redshirt in the
propensity score matching portion of our analysis, which is explained below. Institutional
demographic, athletic and academic characteristics will also be included as controls.
Data
Two separate data sets will be used in the analysis. The first will be built using data from
SuperPrep Magazine (Wallace 2000-2008) and will contain information on high school football
recruits. The second will be comprised of individual student-athlete data at Montana State
University. This will allow us to examine how redshirting affects top quality athletes and athletes
in many different sports at a middle-tier athletic university.3
The first that is being constructed features the top high school football recruits in the
3
Montana State University has 15 different varsity sports.
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5. nation. The dataset will include approximately 10,000 of the nation’s top recruits that were
featured in issues of SuperPrep Magazine from the years 2000-2008 (Wallace 2000-2008).
SuperPrep Magazine was a recruiting magazine created by Allen Wallace that ranked and
assessed approximately one thousand recruits annually. Data to be collected from the magazines
include: name, position(s), height, weight, 40-yard-dash time, high school, high school state,
player’s SuperPrep ranking, high school GPA and test score.4 SuperPrep Magazine is used
because other recruiting publications and sources do not include players’ academic characteristics.
Players’ athletic and academic characteristics must be controlled for to determine the true effect
of redshirting.
Once high school information is collected for each player, other data will be collected from
college profile pages. The data to be collected from each player’s collegiate career are: height and
weight in last year of college football, whether they played in community or junior college (JC),
Division I team played for, major, whether they were elected to all-conference or all-American
teams, whether they were chosen for academic all-conference or all-American teams and if they
redshirted.5 Graduation is determined by calling registrars offices, from LinkedIn accounts or
from National Student Clearinghouse data. In addition to the variables listed above, being signed
with an NFL team, playing in a game, starting a game, and the draft year and draft position for
players that were drafted will also be recorded. NFL information is included because it could be a
source of bias in the estimates of redshirting on academic performance. Some athletes may have
NFL potential that makes them less likely to redshirt and less likely to succeed academically. For
example, the athlete could allocate less time to studying because of the increased opportunity
cost or could leave early to play professionally.
Institutional characteristics will also be included in the analysis. Institutional data will be
collected from each institution’s individual website. Average GPA and test scores of incoming
freshman, percent of students that graduate, Football Bowl Subdivision or Football
Championship Subdivision conference, games won by school’s football team in previous year, and
enrollment will be included. I will also use Google Earth to measure the distance between the
athletes high school and the university they attend. There will be a variable that indicates if the
4
Either ACT or SAT scores are reported. ACT scores will be converted into SAT scores using equivalency tables.
In the case that both are recorded, the ACT score will be converted and the higher one will be used.
5
If the student-athlete transferred it will be noted and both schools will be recorded.
5

6. student attends school in the same state. These variables are likely to be collinear; however, in
regions with smaller states, the distance is still important even though it is out of state. Recruits
often admit that their mothers play a large role in their decisions, which in general leads them to
prefer schools closer to home (Jackson 2013).
There are limitations to the SuperPrep dataset. First, our sample of players are not
representative of the typical Division I football players. Most of these players attended top notch
football programs in the Football Bowl Subdivision. These athletes could be less likely to redshirt
and graduate. If the assumption is made that coaches play their best possible players every game,
these players are certainly expected to redshirt less often than less elite players. An elite athlete
may opt to forgo graduation in favor of utilizing their athletic talent to play professionally earlier.
The opportunity cost of staying in school is very high when playing in the NFL is an option, so
the decision to leave in favor of beginning an NFL career is rational.6 Elite players may also be
less likely to graduate because they allocate less time to improving their cognitive human capital
and more time building their physical human capital.
The second dataset that we use to determine the effects of redshirting includes all
student-athletes at Montana State University from 2000-2013. The data that are available are
much more detailed than the first dataset and include college GPA data, semester by semester
credit hours, race, gender, sport, if the player redshirted and other socioeconomic variables.
Athletic performance variables to be used in the propensity score matching portion of our analysis
are readily available as well for these student-athletes. These data will provide a different
perspective on how redshirting affects Division I athletes in a variety of sports and will provide an
opportunity for redshirting’s effect on players of different races and both genders to be examined.
Montana State University currently has a Division I athletics program and the football team is
part of the Football Championship Subdivision. MSU is part of the Big Sky Conference and has
an enrollment of 14,660 students.
The MSU student-athlete dataset has limitations as well. MSU is a single institution that
may not be representative of the typical Division I school; therefore, care will be taken in
generalizing the results to other programs across the country. There are 351 universities classified
as Division I in the NCAA. To compare MSU to the typical Division I university a database will
6
The minimum rookie salary in the NFL is $405,000 in 2013, and will increase to $420,000 in 2014 (Florio 2011).
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7. be created with institutional summary statistics, such as enrollment and demographic
characteristics; the mean and median values will be compared to MSU’s.
Model
Because redshirting is determined based on athletic ability, time spent in the weight room,
time spent on the playing field and other athletic characteristics, players that redshirt are likely
different from non-redshirts. Any determinants of redshirting that are correlated with
determinants of graduation will present selection bias to our estimates. We will control for
selection bias using propensity score matching (PSM) techniques. We begin with a linear model:
Ai = f(Ri, Xi, Zi, εi), (1)
where Ai is the academic dependent variable of interest (academic dependent variables are listed
above), Ri is an indicator variable that is equal to one for redshirts and zero for nonredshirts, Xi
is a vector of student-athlete characteristics, Zi is a vector containing institutional characteristics
of the student-athlete’s school, and εi is an error term. Variables that will be included as
student-athlete characteristics and institutional characteristics are listed above.8
To estimate the effects of redshirting without selection bias, each player’s propensity to
redshirt will be calculated. Propensity score matching relies on the assumption that, conditional
on observable characteristics, players that redshirt and players that do not redshirt do not
systematically differ along unobservable dimensions. We will compare students that redshirt with
students that do not redshirt who have similar estimated propensities to redshirt. Propensity
scores will be calculated using a probit model, specified below:
Ri = φ(Xi, Zi, vi), (2)
where Xi and Zi are specified as above and vi denotes unobserved determinants of redshirting.
After each player’s propensity to redshirt is calculated, we use three commonly-used
8
There will be interaction terms included for height and position, weight and position, height and sport, weight
and sport, returning players and sport, returning players and position, and college team wins in previous year and
sport.
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8. matching methods: nearest neighbor, kernel density and caliper. In the nearest neighbor
approach, each redshirted athlete is matched with a number of non-redshirts that have the closest
propensity score. The kernel density approach matches each redshirt with a weighted average of
all non-redshirts’ propensities. Each non-redshirt’s value is weighted by the inverse difference
between the redshirt’s and his score. The caliper method matches each redshirt with
non-redshirts whose propensity scores fall within a certain range.
The assumption that unobserved determinants of academic success are unrelated to
redshirting, conditional on the propensity score, must be satisfied for our PSM estimates of the
effect of redshirting on academic success to be unbiased. If this assumption is satisfied the
propensity score captures all of the differences between redshirts and non-redshirts that affect
academic achievement and the effect of redshirting can be estimated by examining mean
differences in academic outcomes between redshirts and propensity score matched nonredshirts.
Significance and Potential Applications
The NCAA has done well in raising the academic achievement of the players that
participate in Division I athletics; however, many student-athletes in high-revenue sports are still
struggling in the classroom. Previous studies on other determinants of academic success suggest
that redshirting could potentially have positive benefits for student-athletes. A careful empirical
evaluation of the effect of redshirting on academic achievement is the first step in determining the
potential for redshirting to be used as a tool to prepare student-athletes, not only for the playing
field, but also for academic success.
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9. References
Allen, J., Robbins, S. B., Casillas, A., & Oh, I.-S. (2008). Third-year College Retention and
Transfer: Effects of Academic Performance, Motivation, and Social Connectedness.
Research in Higher Education, 49(7), 647664. doi:10.1007/s11162-008-9098-3
Athletics Scholarships. (2011). Behind the Blue Desk. Retrieved March 08, 2013, from
http://www.ncaa.org/wps/wcm/connect/public/NCAA/Resources/Behind+the+Blue
+Disk/How+Do+Athletic+Scholarships+Work
Christianson, E. (2012). Mens basketball,FBS football grad rates highest ever. NCAA.org.
Retrieved August 02, 2013, from
http://www.ncaa.org/wps/wcm/connect/public/NCAA/Resources/Latest+News/2012
/October/2012+GSR+Release
Florio, M. (2011). Minimum Salaries Shoot Up Under New Deal. NBC Sports.
Hossler, D. (n.d.). Transfer & Mobility : A National View of Pre-Degree Student Movement
in Postsecondary Institutions.
Jackson, D. (2013). Moms often take center stage in recruiting process. rivals.com. Retrieved
April 24, 2013, from http://footballrecruiting.rivals.com/content.asp?CID=1498573
McArdle, J. J., & Hamagami, F. (1994). Logit and Multi Level Logit Modeling of College
Graduation for 1984-1985 Freshman Student-Athletes. Journal of the American
Statistical Association, 89(427), 11071123.
Singell, L. D. (2004). Come and Stay A While: Does Financial Aid Effect Retention
Conditioned on Enrollment at a Large Public University? Economics of Education
Review, 23(5), 459471. doi:10.1016/j.econedurev.2003.10.006
Stinebrickner, T., & Stinebrickner, R. (2012). Learning about Academic Ability and the
College Dropout Decision. Journal of Labor Economics, 30(4), 707748.
Trends in Graduation-Success Rates and Federal Graduation Rates at NCAA Division I
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10. Institutions. (2012). NCAA Research. Retrieved August 02, 2013, from
http://www.ncaa.org/wps/wcm/connect/public/ncaa/pdfs/2012/2012+gsr+and
+fed+trends
Wallace, A. (2000-2008). SuperPrep: Americas Recruiting Magazine (All-American Issue).
Yu, W., Lin, T.-C., Chen, Y.-C., & Kaufman, D. (2012). Determinants and Probability
Prediction of College Student Retention: New Evidence from the Probit Model.
International Journal of Education Economics and Development, 3(3), 217236.
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