At MindMedners Clinic, we can help athletes and employees improve his
or her Wonderlic Personnel Test (WPT) to ensure career success. Dr. St.
Laurent will address the root causes of any dysfunction, properly fuel
the brain, then Pat Stafford will provide elite cognitive training to improve the score.
The research below is a small look into the benefits of proper brain
function and success in the National football League (NFL).
Testing the Relationship Between a
Cognitive Ability Test and Player Success:
The National Football League Case
Arthur J. Adams & Frank E. Kuzmits
University of Louisville, USA
The purpose of this study was to examine the
relationship between the Wonderlic Personnel Test (WPT) and subsequent
National Football League (NFL) success. The WPT is a well known and
established measure of cognitive ability that is used by the NFL in
their annual combine, a pre-draft assessment of player mental and
physical skills. The authors investigated the relationship between WPT
scores and NFL success for all players drafted at 3 different offensive
positions over a recent 6-year period. Using multiple measures of NFL
player success, we found no evidence of any relationship. Accordingly we
question the usefulness of the WPT as a combine measure. However given
that other psychological constructs (for example; aggression,
leadership, coachability, and self-confidence) have shown a relationship
with athletic success, it seems wise for the NFL to consider expanding
their mental assessments at the combine to include higher level
cognitive measurements and also to examine the current research on peak
performance that may provide for potential improvements in the NFL
player selection process.
Identifying the psychological variables that help
athletes achieve athletic success continues to be an important goal of
researchers in the field of sports and athletics (Beilock &
McConnell, 2004; Hays & Kenkel, 2006). The field of sports
psychology has grown markedly in the past two decades, and the discovery
of psychological constructs that help athletes achieve peak
performance, defined as the “superior use of human potential” (Privette,
1981) and “outstanding accomplishments” (Jackson & Roberts, 1992)
has led to advancements in the understanding of success in a wide
variety of athletic endeavors. One research study involving peak
performance and successful athletes showed that the psychological
profile of peak performing athletes included high self-confidence,
energy, feelings of control, concentration, positive attitudes,
determination, and commitment (Krane & Williams, 2006).
Predicting Athletic Success
The development of psychological profiles of
athletic success also enhances the development of selection models that
assist in choosing successful athletes. In a research review of
personnel selection practices in athletics, Humara (2002) found a number
of psychological constructs related to various athletic endeavors,
including aggression, leadership, coachability, and self-confidence.
Further, affiliation and conformity may predict athletic performance;
e.g.; an athlete low in conformity and affiliation may not perform well
in team sports or under an autocratic coach. Based on his review, Humara
(2002) concluded that in the selection of athletes, in addition to an
assessment of an athlete’s past performance and bio (physiological)
data, administrators should make greater use of psychological
assessments, including the Athletic Motivation Inventory (AMI), the Test
of Attentional and Interpersonal Style (TAIS), and the Profile of Mood
Cognitive Ability as a Psychological Construct
While the examination of psychological constructs and
their relationships to athletic performance is not new, the role of
cognitive ability and its part in athletic success, particularly in the
realm of predicting athletic success, has received scant attention in
the literature. Cognitive ability, also called general mental ability
(GMA), remains one of the most widely used predictors of job performance
(Brody, 1992; Jensen, 1986; Schmidt & Hunter, 1998). Several
definitions have been put forth for cognitive ability. One approach
views cognitive ability as the ability to reason, solve problems,
understand complex ideas, and learn from experience (Gottfredson, 1997).
Another conceptual framework views cognitive ability as a single
general ability, labeled g, which includes multiple abilities such as
memory, learning, cognitive speed, retrieval ability, and visual and
auditory perception (Carroll, 1993). Finally, a simple definition of
the concept puts cognitive ability as “the ability to learn” (Schmidt,
General Cognitive Ability, Job Performance, and Training Success
As a predictor of human behavior, cognitive ability
has long been of interest to scientific investigators, with research
dating back to WWI when the U.S. Army used a paper and pencil version of
the Binet intelligence test to screen and classify conscripts. (Ree
& Earles, 1992). In the workplace, cognitive ability has been
demonstrated to be one of the strongest and most consistent predictors
of job performance in traditional employment settings (Campbell, 1990;
Hunter & Hunter, 1984; Schmidt & Hunter, 1998; Thorndike, 1986).
Further, the predictive power of cognitive ability extends across
multiple job families, including managers, salespersons, law
enforcement, service workers, trades and crafts, industrial workers and
vehicle operators (Hunter & Hunter, 1984). In addition cognitive
ability predicts job performance for military as well as civilian
occupational classes (Hunter, 1986; Ree, Earles & Teachout, 1994).
However the predictive power of cognitive ability becomes stronger in
more complex employment situations, such as managerial jobs and jobs
facing continual and unexpected change (Hunter & Hunter, 1984;
LePine, Colquitt, & Erez, 2000).
Cognitive Ability, Wonderlic Personnel Test (WPT) and the NFL
One widely used measure of cognitive ability is the
Wonderlic Personnel Test (WPT). Material contained on the Wonderlic
company website claims that “Since 1937, more than 120 million people at
thousands of organizations worldwide have taken the WPT”…and that the
test “Can be used to match job candidates or employees to jobs in which
they will be effective and satisfied. WPT information helps shorten
training time” (Wonderlic Inc., 2006).
The WPT measures the two principal dimensions of
general intelligence: fluid intelligence (intellectual development
resulting from biological factors, for example, heredity) and
crystallized intelligence (intellectual development resulting from
education, experience, and acculturation). Test items include word and
number comparisons, disarranged sentences, and geometric figures and
problems that require mathematical and logical problem solving
(Furnham, Rawles, & Iqbal, 2006). Sample questions are available at
Wonderlic advises employers to gauge the utility of
test scores by comparing test takers’ scores to norms for the relevant
occupational level. For example test score norms (The maximum score is
50) for attorneys, bookkeepers, and food service workers are 29.67,
23.36, and 16.31, respectively. Accordingly Wonderlic suggests that
employers seek a minimum test score of 24 for bookkeepers. Wonderlic
also recommends certain test scores for broad occupational groups, e.g.,
28 and above for upper level management, 20 to 26 for general clerical
workers and first line supervisors, and 10 to 17 for workers who operate
simple process equipment and perform routine jobs. Wonderlic also
cautions employers that employment tests be used in conjunction with
other job related performance predictors (Wonderlic, 2002).
As part of the NFL draft each spring, the NFL and team personnel conduct a series of tests, termed the combine,
for the most promising professional prospects among college football
players. The weeklong series of tests are primarily physical tests
(e.g. speed, agility, and strength) and a single mental exercise, the
WPT. The NFL began giving combine invitees the WPT in the early 1970s,
when coach Tom Landry of the Dallas Cowboys stated that smarter players
were better players and that the test results would provide important
information in the NFL draft selection process (Mirabile, 2004). Since
then the WPT has become a required measure during the combine, with
great media attention given to test scores, which range from 0 to 50.
For example a Google search of ‘2006 NFL combine and the Wonderlic
personnel test’ resulted in 10,500 hits, with several of the top 20 hits
focusing on star University of Texas quarterback Vince Young’s
(reportedly) very low score of six (Dougherty, 2006).
As a predictor of success in the NFL, the WPT is not
without controversy. NFL teams themselves question the validity of the
WPT and also worry about the possibility of cheating (Mulligan, 2004).
Some teams minimize the significance of the test except for extreme
outliers (Merron, 2002). Still others see the WPT as a sinister NFL tool
to influence a player’s marketability. As one sports writer puts it,
“It’s (the WPT) a manipulative tool in the NFL’s strategy of
misinformation in the run-up of head fakes and spin moves to a draft
selection” (Roberts, 2006).
Predictably the Wonderlic organization supports the
use of the test as part of the combine exercises. Quotes from a
Wonderlic press release include:
The coaches understand that players have to be smart and think quickly to succeed on the
field, and the closer they are to the ball the smarter they need to be… Whether you are
hiring a mailroom clerk or a CEO, a defensive lineman or a quarterback, intelligence is
an accurate determiner of success (Wonderlic Inc., 2005).
NFL and WPT Research
In spite of the NFL’s continued use of the WPT as a
measure of cognitive ability, no scientific support of the WPT as a
predictor of NFL success has been provided by either the NFL or the
Wonderlic organization (Lyons, Michel & Hoffman, 2005). Further, a
review of the literature uncovered only two studies that have examined
the ability of the WPT to predict professional football success. In the
first study the WPT results of 261 NFL players selected in the 2002
draft were correlated with NFL performance for 2002 and 2003.
Performance data were position specific; for example performance
criteria for the quarterback position included the NFL quarterback
rating (discussed in a later section); criteria for the running back
position included yards gained, pass receptions and yards, and
touchdowns. The results failed to show a statistical relationship
between the WPT and NFL performance for either year or both years
combined. The WPT also failed to predict overall draft order for 2002
and 2003 (Lyons, Michel & Hoffman, 2005). In the second study the
relationship between WPT scores and NFL performance was examined for 82
quarterbacks drafted in the years 1989-2004. WPT scores predicted
neither first-year NFL performance (passing efficiency) nor first-year
salary (Mirabile, 2004).
The purpose of this research is to further
investigate whether the WPT has the ability to predict NFL performance.
Specifically we examine the relationship between the WPT and multiple
measures of success at three NFL offensive positions (quarterback,
running back, and wide receiver) over a multi-year period, 1999 through
2004. Results of this research should be of interest to NFL and team
personnel in determining whether there is statistical justification for
requiring combine participants to complete the WPT. In a broader sense
this research may also encourage greater interest in the use of
psychological construct testing, whether it be the WPT or other types of
cognitive ability tests, in not only professional football but also
other athletic endeavors (e.g.; baseball, basketball) and at other
levels (e.g.; Division I and II college and university athletics).
Selecting the quarterback (QB) position for inclusion
in this research was based upon three factors. First, performance
data for the QB position were believed to be more objective and readily
obtainable as compared to other positions such as guards, tackles, and
centers. Second, one may surmise that the QB position requires greater
cognitive ability than other positions, as the quarterback must handle
the ball on the vast majority of offensive plays and must make
play-calling strategic decisions within seconds; for example, by reading
the defense and changing plays at the line of scrimmage. Third, the
relationship between the WPT and quarterback performance had previously
been investigated; however this research will define success in broader
terms. We also examined running backs (RB) and wide receivers (WR) – the
other primary so-called “skill” positions that involve handling the
ball on offense. Further, RBs and WRs met the Wonderlic criterion that
“…the closer they are to the ball the smarter they need to be
(Wonderlic, Inc., 2005).”
A total of 68 NFL QBs were included in the study.
This number represented all combine invitees who were drafted by the NFL
from 1999 through 2004. The study also included data for 86 drafted
RBs and 152 drafted WRs for the same 6-year period. (Typically the
number of players drafted per year was slightly more than half of those
participating in the combine). Table 1 shows the number of draftees by
position for the years under study.
First published in 1937, the WPT is a 50-item, timed
12-minute test. The test yields a single score that reflects the total
number of questions answered correctly. Originally adopted from the
Otis Self-Administering Test of Mental Ability (Beck, 1986), the test
includes multiple choice and short answer questions designed to measure
verbal, numerical, general knowledge, and spatial relationship
abilities. Few people complete the test because of time constraints.
The great majority of research concerning the
validity and reliability of the WPT has been performed in traditional
employment settings. In six employment categories (supervision, blue
collar, general office, data processing, engineering, and professionals)
the Wonderlic organization reports validities ranging from .22 to .67.
Validity studies criteria included a variety of measures, primarily job
performance rankings, productivity and production records, and
supervisory ratings. The highest validities were in the professional
group (e.g.; insurance agents, medical personnel, police officers), with
validities ranging from .39 to .67. The lowest validities were in the
general office group (e.g.; bank tellers and typists) with validities
ranging from .27 to .35. The WPT also correlates highly with the other
tests of cognitive ability widely used in employment settings, including
the Weschler Adult Intelligence Test (WAIS) (Weschler, 1955), the
WAIS-Revised (WAIS-R) (Bell, Matthews, Lassiter, & Leverett, 2002;
Dodrill, 1981; Dodrill & Warner, 1988; Edinger, Shipley, Watkins,
& Hammett, 1985; Hawkins, Faraone, Seidman, & Tsaung, 1990;
Weschler, 1981), and the General Aptitude Test Battery (GATB)
(Wonderlic, Inc., 2002). Various forms of reliability estimates for the
WPT (test-retest, longitudinal, alternate form, and internal
consistency) range from .82 to .95 (Dodrill & Warner, 1988; Furnham
& Petrides, 2003; McKelvie, 1989; Wonderlic Inc., 2002).
WPT data for individual combine participants are not
made public by either the NFL or Wonderlic, Inc. Wonderlic, Inc. does
make available a single mean WPT score for all combine prospects and
also mean scores for each position. Nonetheless WPT scores achieved by
combine participants are widely available on the Internet. WPT scores
for each combine participant included in this study were retrieved from
nfldraftscout.com (“Rankings,” n.d.), a website devoted to current and
historical information on the NFL, NFL teams, and NFL players. Author
discussions with nfldraftscout.com representatives indicated that
Wonderlic scores were obtained from NFL team personnel, player scouts,
and agents. For each position included in this study, a random sample of
players’ reported Wonderlic scores were checked and verified against at
least two Internet sources; no disparities were found.
Player performance information was obtained from
nfl.com (“Players,” n.d.), and includes regular season games only
(post-season play is excluded). This website contains career statistics
for all players included in this sample. NFL salary data were collected
from usatoday.com. According to the usatoday.com website (“Sports,”
n.d.), salary data are obtained from player agents and NFL Players
Association research documents. Nfl.com and usatoday.com are owned,
respectively, by the National Football League and the Gannett Co. As
with the WPT scores, performance data collected for each position were
checked and verified against other Internet sources.
Correlation analysis was used to determine if the WPT
is linearly related to any one of several possible measures of
performance of drafted players. Treating the professional player
prospect WPT score as the independent variable, the following possible
dependent variables were examined:
Draft order. The players are picked in the NFL
draft through several rounds. If the WPT is related to a player’s
talent and/or potential, there should be a negative correlation between
WPT and draft order, as the “best” players should have a high WPT and a
low (selected early) draft order number while the less promising
prospects should have a low WPT and a high draft order number.
Salary. Hypothetically players are paid
according to their promise and performance on the field, so we
anticipate a positive relationship between WPT and salary if indeed the
WPT is a performance predictor. We consider the WPT and salaries for
each of the first three years in the NFL. (Note however that for players
drafted in 2004, there is only one year of salary and other data
described below. Similarly for players drafted in 2003, as we have data
for their first and second but not their third year.)
On-field performance measures. Quarterback
performance was defined by applying the NFL’s quarterback rating system.
According to nfl.com, this official NFL statistic (in use since 1973)
is based on a formula that takes into account four aspects of passing
performance: pass completion rate, average yards gained per attempt,
percentage of interceptions, and percentage of passes that result in
touchdowns. Again if the WPT predicts job performance, there should be a
positive correlation for WPT with this on-field measure. As with salary
we consider the first three years of data for QB rating. For RBs the
success measure was average yards per carry; for WRs the success measure
was average yards per reception.
Games played. Though a less complex measure
than the others, it is possible to argue that WPT should be positively
related to the number of games a player appears in per season, as a
higher performing player should make more game appearances than a lower
performing one and vice versa. As is the case for the other variables,
we will analyze a player’s first three years in the NFL. (One potential
drawback of this measure is that it does not consider actual playing
time per game. Conceivably a “game played” could involve simply one
play, several plays, or the entire game. However the NFL does not make
playing time per game data available, and neither do other websites or
professional football statistics sources.)
We will consider the correlations for the 6-year
sample period separately for each position (QB, RB, and WR). This first
analysis involves only combine participants who were drafted by an NFL
team. We will also conduct two-sample t-tests to see if WPT scores
differ depending on whether a combine participant is drafted or not
drafted. Each position will be treated separately for the comparisons of
means (i.e., there will be three t-tests).
Sample Summary – Correlations by Position
Table 2 presents correlations for each WPT-success
measure pair and for each player position over the 6-year time period.
Sample sizes are 68 QBs, 86 RBs, and 152 WRs. For instance the first
correlation shown is -.138, representing the WPT-draft order
correlations for all 68 QBs drafted over the 6-year study period; the
correlations for RBs are -.028 and for WRs the result is .086. None of
these is significant at the .05 level; in fact only 2 of the 30
correlations in Table 2 generate a p-value < .05, an outcome that is
consistent with a random chance model. Further one of these two
correlations (-.259 for WR-Year 2 Salary) is in the “wrong” direction,
assuming Year 2 Salary should have a positive association with WPT
(It is possible to argue that the three draft order
correlations in Table 2 should be determined via a nonparametric
procedure since draft order could be interpreted as an ordinal number.
This was done; yielding values for Spearman’s rho little changed from
the parametric correlations reported in Table 2: -.123, -.020, and .084,
Sample Summary – Drafted versus Non-drafted Players
A second way to approach the question of how the WPT
relates to NFL success is to compare the WPT scores of drafted players
against those who were not drafted on a position-by-position basis. See
Table 3 for WPT results for QBs, RBs, and WRs over the 6-year period of
For QBs the average WPT for drafted players was 2.15
points higher than that of non-drafted players; however a two-sample
t-test shows this difference is not statistically significant (at α
= .10). WPT scores for drafted versus non-drafted RBs and WRs were
nearly identical. The difference between the two groups for RBs was .18
point (p = .85) and the difference for WRs was .05 point (p
= .94). Therefore for these three “skill” positions we fail to find
that drafted players have significantly higher WPT scores than players
who were not drafted. If assertions made by Wonderlic, Inc. were in fact
true and believed to be true by the NFL teams selecting players, then
we would expect drafted players to have higher WPT scores than those not
drafted, but who play the same position.
Viewing the results as a whole we conclude that for
the years, positions, and performance criteria we examined in this
study, the WPT is unrelated to a player’s draft order, salary, games
played in, and on-field position-specific success measures. Further
overall tests of drafted players versus those not chosen in the draft
failed to reveal any meaningful differences with respect to scores
obtained on the WPT.
Although it may have utility in more general employment
situations, the WPT appears to lack validity in this athletic oriented
setting; this despite its advocates and some perhaps self-serving
statements from Wonderlic Inc. Although (see limitations below) there
may be multiple variables interacting and even masking the effects of
other variables, we would still expect to find more correlations above
and beyond random chance, or a significant difference in drafted versus
non-drafted means if the WPT had any merit.
Given the research that has been published on the
relationship between various psychological constructs and athletic
performance discussed earlier and the obvious monetary risks of false
positives and false negatives when selecting professional football
players, it is surprising that the NFL has not adopted a more
sophisticated approach to the measurement of cognitive ability and other
psychological measures for combine participants. While the validity
and reliability of the WPT in traditional employment settings has been
established, the instrument does not appear to have utility in the
professional football arena.
The results of this study raise serious questions
about the degree of sophistication employed by the NFL in its player
selection process. While the actual selection process used by NFL teams
remains confidential and cloaked in a degree of secrecy, it could be
assumed that NFL teams could benefit from using contemporary human
resource selection models to the choosing of player personnel. While the
selection process is obviously constrained by NFL draft rules and
requirements, the drafting of a player essentially remains an employee
hiring process, one in which an organization attempts to predict how an
employee will perform in the future. Contemporary human resource
management models for hiring employees stress the importance of job
analysis, “fit” between the person and the organization, and the
development and validation of selection instruments (Heneman &
Judge, 2005). Efforts to predict success are not new to collegiate
athletes and other sports contests; for example, Olympic events (Humara,
2000; Spieler, Czech, Joyner & Munksay, 2007), but there is no
evidence, at least in the literature, that the NFL undertakes the level
of personnel selection research found in non-professional sports.
Assuming such research is absent among the professional football ranks,
NFL teams could well profit from utilizing employee selection models
that are commonplace in traditional business settings and by examining
the potential utility of the human performance research that has taken
place in non-professional athletic environments.
This study has several limitations. First both WPT
predictor and performance data were collected from secondary sources:
nfldraftscout.com, usatoday.com, and nfl.com. While the data were
collected from such sources, they are nonetheless considered valid
sources for professional football statistics. Second, although we
examined six draft classes, the question remains as to whether the
results are generalizable to other draft classes. A historical analysis
of the WPT over a greater time period might add insight into the ability
to generalize the predictability of the WPT. Third, we examined
performance variables over a three-year period. Conceivably, a player
might not ‘blossom’ until after several years of experience; on the
other hand, the average NFL career lasts less than four years (Zaslow,
In addition the performance criterion ‘games played’
might be moderated by the quality of a player’s competition for playing
time. That is, an excellent recently drafted QB, RB, or WR may see
limited playing time because an experienced, superior player is
currently performing for the team; conversely, a lesser quality recently
drafted player may see considerable playing time for another team
because he is the best available from a weaker roster of current
The relationship between the WPT scores and several
aspects of skilled player performance was examined for NFL combine
participants who were drafted in the period 1999-2004. In general the
findings suggest that WPT scores are not related to draft order, salary,
games played, or QB/RB/WR rating. Further the WPT scores do not appear
to be related to whether or not a player is drafted. Thus “smarter” as
measured by the WPT does not seem to translate into “better.”
Future research should continue to focus on the WPT
as a predictor of player performance by examining additional draft
classes. Further the predictive merits of the WPT should also be
examined for other NFL positions, both offensive and defensive. Beyond
the WPT, the NFL may improve the overall validity of the combine process
by including a test battery comprised of additional psychological
constructs, for example, those that have been shown to correlate with
peak athletic performance. In addition the other combine predictors in
current use (running, jumping, and strength tests) should be analyzed to
determine their relationships with success measures.
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