The Wonderlic-NFL Performance Relationship Revisited: Positional

Wonderlic and NFL 1

Running head: WONDERLIC AND NFL PERFORMANCE

The Wonderlic-NFL Performance Relationship Revisited:

Positional Analyses and Generalizability

Brian D. Lyons

University at Albany, State University of New York

Brian J. Hoffman

University of Tennessee, Knoxville

John W. Michel

University at Albany, State University of New York

Paper to be presented at the 21st Annual Meeting of the Society for Industrial and Organizational

Psychology, April 2005, Dallas, TX. Please direct all correspondence concerning this

manuscript to Brian D. Lyons, University at Albany, SUNY, 1400 Washington Ave., BA 339,

Albany, NY 12222; Phone: (518) 456-1209; e-mail: [email protected].

Wonderlic and NFL 2

Abstract

The purpose of this research was to re-examine relationship between the Wonderlic and NFL

performance by accumulating data from another draft class. A total of 521 professional football

players from the 2002 and 2003 draft classes were included in our sample. Results indicated that

scores on the WPT are not predictive of future NFL performance in either draft class or by

position and overall selection in the NFL Draft. Additionally, the use of this measure produced

significant racial discrepancies. The implications of these findings and avenues for future

research are discussed.

Wonderlic and NFL 3

The Wonderlic-NFL Performance Relationship Revisited:

Positional Analyses and Generalizability

Within a selection system, a predictor can be evaluated in terms of its efficiency (i.e.,

validity) and equity (i.e., gender/ethnic differences; Murphy, 2002). Voluminous empirical

research supports the validity of general mental ability (GMA; i.e., g) as a predictor of job

performance (e.g., Schmidt & Hunter, 1998; Viswesvaran & Ones, 2002). To that end, GMA

consistently demonstrates the strongest criterion-related validity evidence of existing predictors

(Murphy, 2002; Ree, Earles, & Teachout, 1994; Schmidt, 2002; Schmidt & Hunter, 1998).

Meta-analytic estimates of this relationship typically result in an uncorrected validity coefficient

of approximately .30 (Bobko, Roth, & Potosky, 1999; Schmitt, Rogers, Chan, Sheppard, &

Jennings, 1997) and a corrected validity coefficient of .51 (Schmidt & Hunter, 1998). However,

ethnic group differences associated with measures of GMA have also garnered substantial

attention (e.g., Herrnstein & Murray, 1994). Given the legal landscape and a demographic shift

to a more heterogeneous workforce (Offermann & Gowing, 1993; Outtz, 2002), the potential for

adverse consequences to minorities is a probable determinant against the use of GMA in

selection contexts (e.g., Outtz, 2002; Schmitt et al., 1997). Still, GMA remains a frequently

assessed construct in personnel selection.

For example, GMA is used as an evaluation tool by the National Football League (NFL)

at its annual NFL Combine, held approximately two months before the NFL Draft. Professional

football can be conceptualized as an indirect extension of an employment setting, with

professional athletes being evaluated, compensated, promoted, and terminated based on their

performance. An additional similarity between professional athletics and employment settings is

that professional athletes are selected via the NFL Draft. A preliminary study examined the

Wonderlic and NFL 4

relationship between GMA and subsequent performance in the NFL (Citation withheld, 2005).

However, only one draft class of players was included in the sample. As a result, the inability to

generalize the results to other draft classes was a methodological issue as well as the failure to

provide specific position predictability of the GMA-performance relationship because of low

sample sizes. Thus, the purpose of this study is to expand the initial sample to include another

draft class which, in turn, will strengthen the analysis of the efficiency and equity of GMA in this

employment context.

Study Hypotheses

An on-line publication produced by the Wonderlic organization (2004), HR

Measurements, stated that the WPT is an essential assessment during the Combine because

“smarter people make better teammates and deliver more wins to the team.” Such a

determination should be based on evidence demonstrating a relationship between the WPT and

subsequent performance. Given the import of "on-the fly" processing of information, the

complex schemes associated with present day professional football, and the research suggesting

that GMA is related to performance across job settings (Schmidt & Hunter, 1998; Schmidt,

Hunter, & Pearlman, 1981), it is expected that GMA will possess a positive, nonzero relationship

with NFL performance. Thus, the following hypothesis is proposed:

Hypothesis 1: The WPT will be positively related to performance in the NFL.

A recent Wonderlic, Inc. press release (2005) suggested that a positive relationship exists

between the WPT and the number of games an athlete starts during a given NFL season.

However, we were unable to locate any empirical research examining this assertion. It is

reasonable to assume that in the competitive environment of the NFL, playing time will be a

direct function of a player's performance. In other words, those draftees who play in more games

Wonderlic and NFL 5

have a greater probability of attaining performance-related statistics. In turn, those draftees who

elicit immediate playing time may be those who digested the playbook quicker and more

efficiently. After all, GMA has been shown to be causally related to the acquisition of job

knowledge (Schmidt, 2002). Thus, it is hypothesized that:

Hypothesis 2: The WPT will be positively related to number of games started.

The efficiency of GMA might not compensate for the potential threat of adverse impact

and subsequent litigation (Ryan, Ployhart, & Friedel, 1998; Schmitt et al., 1997). The issue of

equity is of concern because GMA tests are frequently associated with large mean differences

between African-Americans and Caucasians (Sackett & Wilk, 1994). More specifically, on

average, African-Americans score about one standard deviation lower than Caucasians (Hunter &

Hunter, 1984; Roth, BeVier, Bobko, Switzer, & Tyler, 2001). In addition to the general trend of

Caucasians outscoring their African-American counterparts on measures of GMA, the WPT is a

speeded test of GMA (Murphy, 1984), which may exacerbate this discrepancy. Consistent with

previous research investigating racial differences in cognitive ability testing (e.g., Hunter &

Hunter, 1984; Roth et al., 2001), the following hypothesis is proposed:

Hypothesis 3: Caucasian prospects will score higher on the WPT than African-American

prospects.

Similarly, disparity in WPT scores may be further explained and examined through

positional differences in GMA. For instance, some positions may require more cognitive

functioning than other positions. Specifically, the quarterback position may encompass more

decision making (e.g., deciding which receiver is the best option) and problem solving (e.g.,

reading defensive schemes) than other positions. Because GMA has been positively related to

Wonderlic and NFL 6

problem solving (Stevens & Campion, 1999) and decision making (Gully, Payne, Koles, &

Whiteman, 2002) activities, it is hypothesized that:

Hypothesis 4: Quarterbacks will score higher, on average, on the WPT than other

positions.

In addition to these specific hypotheses, two research questions will be offered to further

determine the WPT’s efficiency and equity in this employment realm. It has been reported that

some NFL teams question the validity of the WPT (Mulligan, 2004) while other teams consider

the test results a vital part of their selection processes (Merron, 2002). Given the apparent

disagreement among league decision makers over the utility of the WPT, this research sought to

examine the relationship between a prospect’s WPT score and his overall draft selection number

in order to elucidate the potential influence WPT scores have during the draft selection process.

Research Question 1: Is there a relationship between GMA and draft selection

number?

A meta-analysis by Schmidt and Hunter (1998) demonstrated that although GMA is

related to performance across levels of job complexity, the relationship between GMA and

performance decreases as job complexity decreases. Thus, to the degree that some positions are

less complex than others, the relationship between GMA and performance would be expected to

fluctuate by position. To that extent, certain positions require more problem solving and

decision making ability (e.g., quarterbacks) than other positions that primarily rely on skill and

instinct (e.g., running back and wide receivers). Consequently, the strength of the relationship

between the WPT and performance might vary by position.

Research Question 2: Does position type influence the relationship between GMA and

NFL performance?

Wonderlic and NFL 7

Method

Participants

A total of 521 professional football players, 261 taken in the 2002 NFL Draft and 260 in

the 2003 NFL Draft, were included in the sample. Draftees over these two years consisted of

365 African-Americans (70.1%), 142 Caucasians (27.3%), and 14 Other (2.7%). Table 1 depicts

the racial distribution by position and draft year (i.e., 2002 and 2003).

--------------------------------

Insert Table 1 about here

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Measures

Prior to the annual NFL Draft, the NFL Combine provides owners and coaches an

opportunity to evaluate prospects’ physical and mental ability. During the NFL Combine, GMA

is measured with the WPT. First published in 1938, the WPT was originally adopted from the

Otis Self-Administering Test of Mental Ability. Designed as a speeded test, the WPT is a 12

minute timed test consisting of 50-items comprised of multiple-choice and short answer items

that purport to measure verbal, numerical, general knowledge, analytical, and spatial relations.

Test scores range from 1 to 50. Internal consistency estimates for the WPT range from .88 to

.94, test-retest values range from .82 to .94, and alternate form estimates range from .73 to .95

(Wonderlic, Inc., 2000). WPT data for all draftees were collected from a secondary source,

CBS.sportsline.com.

To serve as the criterion, data from the first two years of athletic performance were

collected for both draft classes. Specifically, data was taken from the 2002 and 2003 NFL

seasons for the 2002 draft class and 2003 and 2004 NFL seasons for the 2003 draft class.

Wonderlic and NFL 8

Depending on the draft class, each year of statistical data represented Year 1 and Year 2 of

performance. This information was accumulated from nationally recognized sports websites

available on the world-wide-web such as ESPN.com and NFL.com. A priori decision rules were

imposed for performance criteria inclusion. Our goals were to include those statistics that were

(1) available on the world-wide-web and discernable by position, (2) not redundant within

position (e.g., we included total tackles instead of including solo and assisted tackles), and (3)

accurately portrayed performance. Performance criteria by position are summarized in Table 2.

--------------------------------


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Procedure

The 2002 and 2003 NFL Combine data were collected from a nationally recognized

sports website, CBS.sportsline.com. This information included each participant’s name, weight,

height, football position, WPT score, and overall selection number in either the 2002 or 2003

NFL Draft. As noted above, the first two years of athletic performance were collected per

draftee to serve as the criteria. Performance data was collected from two NFL seasons to

mitigate the potential effects of a draftee’s decreased playing time, which would attenuate their

statistics, because of rookie status and/or injury. As a result, two years of performance data may

provide a more reliable estimate of the criterion.

Data Analysis

To ascertain and compare the relationship of the WPT and performance across positions,

all performance criteria within each position were standardized. We negatively coded raw scores

for adverse performance criteria such as fumbles, sacks allowed, holding, and false starts (e.g., a

Wonderlic and NFL 9

value of 5 for fumbles was changed to –5). Subsequently, the raw scores for each draft class

were transformed into z-scores within each position per year. We then summated all of the

representative z-scores and divided this value by the number of performance criteria that position

encompasses to create an overall averaged estimation of their performance per year (i.e., Year 1

and Year 2). In essence, each z-score represents a player’s performance relative to other players

at the same position (e.g., a score of zero indicates average performance, while a z-score of one

indicates performance at one standard deviation above average performance for a respective

position). Games played and games started were not included in this performance metric

because of the decision to control for these two variables in one of the analyses.

To create an averaged performance value for the performance in both Year 1 and Year 2,

we summated the z-score totals for each year and divided this value by the total number of

performance criteria. In sum, three z-score performance values were created for each draftee:

Year 1, Year 2, and overall averaged performance. In addition, z-score values for games played

and games started were created for Year 1 and Year 2, and an overall averaged value for both

years. One undesirable result of using z-scores is that half of the scores in the distribution will

be negative (Murphy & Davidshofer, 2001). Since we did not want to use or interpret negative

performance values, the three z-score performance composites and the games played and games

started values were transformed into T-scores to produce the final performance estimates.

Finally, to test the hypotheses and research questions, bivariate correlations, partial correlations,

an independent-samples t-test, and a one-way analysis of variance were produced.

Results

Descriptive statistics and bivariate correlations among study variables are presented in

Tables 3 and 4. The first hypothesis stated that the WPT would be positively related to

Wonderlic and NFL 10

performance in the NFL. Results indicated that the WPT is not related to any of the performance

criteria. Specifically, the bivariate correlations between the WPT and Year 1 performance (r =

.01, p = ns), Year 2 performance (r = .00, p = ns), and overall performance across both Years 1

and 2 (r = .01, p = ns) were not significant. In order to rule out possible attenuation to the WPT-

performance relationship due to playing time, partial correlations were utilized to control for the

number of games started and played. In particular, three partial correlations were calculated

between the WPT and NFL performance (i.e., one for Year 1, one for Year 2, and one for

overall), holding games played and games started constant for each analysis. These analyses

indicated that each of the partial correlations for Year 1 performance (pr = .06, p = ns), Year 2

performance (pr = .01, p = ns), and overall performance (pr = .05, p = ns) were not significant.

Thus, our results suggest that the number of games played and games started have little impact

on the relationship between the WPT and NFL performance. Overall, the results from both

correlation analyses suggest that scores on the WPT are not predictive of subsequent NFL

performance.

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Insert Tables 3 and 4 about here

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The second hypothesis predicted that the WPT would be positively related to the number

of games started. Similar to the previous results, this hypothesis was not supported.

Specifically, these data indicated there is no relationship between the WPT and Year 1 games

started (r = -.08, p = ns), Year 2 games started (r = -.01, p = ns), or overall games started

(r = -.05, p = ns).


The third hypothesis stated that Caucasian prospects would score higher on the WPT than

African-American prospects. This hypothesis was supported as Caucasians (M = 27.66, SD =

5.92), on average, scored significantly higher than African-Americans (M = 19.54, SD = 5.97) on

the WPT, t(443) = -12.83, p < .001.

The fourth hypothesis stated that quarterbacks would score higher on average on the

WPT than other positions. A one-way analysis of variance (ANOVA) computed to determine if

mean differences on the WPT existed between positions. This analysis indicated that statistically

significant mean group differences were present, F(7, 444) = 9.46, p < .001. Consequently,

Scheffé post-hoc comparisons were produced to examine position level differences. As depicted

in Table 5, quarterbacks (M = 27.52) were found to significantly score higher on the WPT than

running backs (M = 19.27, p < .01), wide receivers (M = 20.52, p < .01), defensive linemen (M =

20.72, p < .01), line backers (M = 20.89, p < .05), and defensive backs (M = 19.7, p < .01).

However, quarterback WPT scores on average did not significantly differ from those of tight

ends (M = 24.74) or offensive linemen (M = 25.19). As a result, this hypothesis was mostly

supported in that statistically significant differences were found between most positions.

--------------------------------


--------------------------------

The first research question speculated whether a relationship existed between the WPT

and draft selection number. As depicted in Table 4, the correlation between the WPT and overall

selection was not significant (r = .05, p = ns). Therefore, these data suggest that there is no

relationship between how well a prospect scores on the WPT and how high he is selected in the

NFL draft.


The second research question sought to determine if position type influences the

relationship between the WPT and NFL performance. As illustrated in Table 6, no statistically

significant relationships between the WPT and any year of performance were detected. In some

positions, the strength of the relationship between the WPT and performance increased or

decreased by year. Nevertheless, the results from this analysis suggest that regardless of

position, the WPT was not significantly correlated with performance in the NFL.

--------------------------------


--------------------------------

Discussion

The primary purpose of this research was to examine whether the GMA-job performance

relationship can generalize to a non-traditional employment setting, professional football. The

results provided implications and future research directions germane to the extant GMA-job

performance literature.

As indicated by the results, the WPT has a nonsignificant relationship with future NFL

performance in either Year 1, Year 2, or overall (i.e., averaged performance from both years). In

addition, the number of games a prospect starts in the NFL displayed a nonsignificant

relationship with the WPT. In other words, the WPT predicted neither NFL performance nor the

number of games started. These results call into question the utility of using the instrument at

the NFL Combine and are uncharacteristic of those typically found in employment settings (e.g.,

Schmidt & Hunter, 1998; Schmidt et al., 1981).

Consistent with previous GMA literature, Caucasian prospects scored higher on the WPT

than African-American prospects. Because the majority of athletes in the NFL are African-


American (Turner, 2003), this discrepancy will most likely not be eliminated in the near future.

Although racial differences exist, the results from first research question indicated that the WPT

is not related to where a prospect is selected in the draft. In that the WPT is not used in the

selection of these athletes, adverse impact is not a concern in this setting. Furthermore, since the

relationship between the WPT and performance was approximately near zero, differential

prediction by race is not a concern.

The GMA discrepancy was also reflected by the type of position. The fourth hypothesis

was mostly supported in that quarterbacks did significantly score higher than all positions except

tight ends and offensive linemen. The second research question examined whether the predictive

strength of the relationship between the WPT and performance would vary by position. As

depicted in the results section, the value of the WPT-performance relationship fluctuated by

position but its statistical strength in predicting performance was nonsignificant. However, some

of correlation coefficients approached significance, but in those positions that did, the results

were unexpected. For example, the WPT-performance relationship was negative for the

quarterback position where cognitive processing and decision making is said to be critical

(Wonderlic, Inc., 2005). On the other hand, the running back position, where skill and instinct

are thought to be more dominant, demonstrated a positive relationship in the second year of

performance.

The historical notion that GMA predicts job performance, regardless of context, has most

likely been the foundation for the NFL's continued use of the WPT (e.g., Goldstein, Zedeck, &

Goldstein, 2002). Other types of assessment strategies, related to the industrial psychology field,

could be utilized during the NFL Combine. One such strategy would be to assess the propensity

and susceptibility to deviant or counterproductive behavior. This could be measured by using a


biographical data inventory, situational judgment test, or a personality instrument. Another

strategy would be to assess the fit between the prospect’s ability and personality with certain

team climates (e.g., Bowen, Ledford, Nathan, 1991). For example, some prospects could be

more suited for the West-Coast offense (i.e., emphasizes short, quick passes and ball control)

than the Run-and-Gun (i.e., emphasizes a strong running game and passing the ball downfield).

In general, future research should explicate whether assessment instruments in the traditional

employment realm can be applied to this employment context.

Limitations

Several limitations of this study should be noted. First, the WPT and performance data

were gathered from nationally recognized secondary data sources, CBS.sportsline.com,

NFL.com, and ESPN.com. Although these websites are secondary data sources, we believe that

these are reputable sources for sporting information. Another possible limitation is criterion

deficiency. More specifically, the ultimate criterion that represents job performance in the NFL

may entail multiple years of performance that encompasses objective (e.g., statistical) and

contextual performance behaviors (e.g., Borman & Motowidlo, 1993). However, in this

employment context, statistical output and ability potential are most likely the precursors for

compensation beyond the initial contract; thus, statistics may capture or possess a greater amount

of the variance in the ultimate criterion. In addition, more than one year of performance was

gathered to provide a longitudinal estimate of performance. A final limitation is statistical power

and sample size within position. For example, the correlational results obtained for the

quarterback and tight end positions should be interpreted with caution.

Conclusion


Empirical research has supported the validity of GMA as a predictor of job performance

in traditional employment settings. However, in this employment context, the results from this

research suggest that the WPT is not predictive of future performance overall or by position, has

adverse consequences to minorities, and is not related to where a prospect is selected during the

NFL Draft. Therefore, its use is neither efficient nor equitable in this context. Future research

examining the efficiency and equity of other predictors in this context is clearly warranted.


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Table 1

Race Distribution by Position and Draft Year

Position

African-American Caucasian Other Cumulative Total

2002 2003 2002 2003 2002 2003 2002 2003 Total

QB 2

(13%)

2

(15%)

13

(87%)

10

(77%)

0

1

(8%)

15

13 28

RB 25

(96%)

18

(78%)

1

(4%)

4

(17%)

0

1

(4%)

26 23 49

WR 34

(97%)

31

(89%)

1

(3%)

4

(11%)

0

0

35 35 70

TE 11

(46%)

5

(36%)

13

(54%)

8

(57%)

0

1

(7%)

24 14 38

OL 16

(44%)

12

(27%)

17

(47%)

29

(66%)

3

(8%)

3

(7%)

36 44 80

DL 33

(75%)

41

(82%)

10

(23%)

8

(16%)

1

(2%)

1

(2%)

44 50 94

LB 17

(71%)

22

(81%)

6

(25%)

4

(15%)

1

(4%)

1

(4%)

24 27 51

DB 49

(94%)

47

(92%)

3

(6%)

3

(6%)

0

1

(2%)

52 51 103

K 0 0 3

(100%)

1

(100%)

0 0 3 1 4

P 0 0 2

(100%)

2

(100%)

0 0 2 2 4

Total 187

(72%)

178

(68%)

69

(26%)

73

(28%)

5

(2%)

9

(3%)

261 260 521

Note. QB = quarterback; RB = running back; WR = wide receiver; TE = tight end; OL = offensive line; DL = defensive line; LB = line backer;

DB = defensive back; K = kicker; P = punter.


Table 2

Performance Criteria by Position

Position

Rating*

Fumbles

Rush

Yards

Carries Receptions

Reception

Yards

Total Touch

Downs

Sacks

Allowed

Holding

QB

X

X

RB

X

X

X

X

X

X

WR

X

X

X

X

TE

X

X

X

X

OL

X

X

DL

LB

DB

Note. Cells with an “X” designation represent those criteria for which each position is measured. * Rating produces a value that estimates a quarterback’s

efficiency. The equation, widely used in the NFL, includes percentage of completions per attempt, average yards gained per attempt, percentage of touchdown

passes per attempt, and percentage of interceptions per attempt.


Table 2 (Cont.)

Performance Criteria by Position

Position

False

Starts

Total

Tackles

Forced

Fumbles

Sacks

Interceptions

Passes

Defended

Games

Started

Games

Played

QB

X

X

RB

X

X

WR

X

X

TE

X

X

OL

X

X

X

DL

X

X

X

X

X

X

X

LB

X

X

X

X

X

X

X

DB

X

X

X

X

X

X

X


Table 3

Means, Standard Deviations, and Minimum-Maximum values by Draft Year

Note. *Minimum and maximum values were provided since T-scores were used in the analyses.

Variable

N M SD Min-Max*

2002 2003 2002 2003 2002 2003 2002 2003

WPT 240 217 21.82 21.83 6.83 7.23 6 – 42 5 – 48

Overall Selection 261 262 131 131.5 75.49 75.78 1 – 261 1 – 262

Year 1 Avg. Performance 183 189 50 50 6.97 7.39 27.13 – 76.88 28.09 – 83.45

Year 2 Avg. Performance 184 199 50 50 7.27 7.03 28.49 – 71.96 30.29 – 74.58

Overall Avg. Performance 203 222 49.59 49.74 6.37 6.52 27.81 – 68.93 30.36 – 76.25

Year 1 Games Played 183 189 49.76 50 9.84 9.81 20.97 – 65.95 24.09 – 63.81

Year 1 Games Started 183 189 49.90 50 9.81 9.81 39.78 – 76.00 37.57 – 80.36

Year 2 Games Played 185 199 50 50 9.81 9.82 20.68 – 64.96 19.66 – 64.16

Year 2 Games Started 185 199 50 50 9.81 9.82 35.36 – 75.88 36.24 – 71.88

Overall Avg. Games Played 205 222 49.03 49.17 8.97 8.99 20.97 – 63.75 23.02 – 62.52

Overall Avg. Games Started 205 222 49.46 49.36 8.57 8.71 37.57 – 72.87 37.57 – 76.12


Table 4

Intercorrelations between the WPT and NFL Criteria

Note. *p < .01.

Variable 1 2 3 4 5 6 7 8 9 10 11 12

1. WPT -

2. Overall Selection 0.05 -

3. Year 1 Avg. Performance 0.01 -0.35* -

4. Year 2 Avg. Performance 0.00 -0.31* 0.60* -

5. Overall Avg. Performance 0.01 -0.37* 0.90* 0.91* -

6. Year 1 Games Played -0.06 -0.37* 0.32* 0.27* 0.35* -

7. Year 1 Games Started -0.08 -0.44* 0.47* 0.32* 0.45* 0.50* -

8. Year 2 Games Played -0.07 -0.23* 0.17* 0.29* 0.26* 0.32* 0.19* -

9. Year 2 Games Started -0.01 -0.47* 0.39* 0.48* 0.47* 0.39* 0.52* 0.50* -

10. Overall Games Played -0.07 -0.38* 0.32* 0.32* 0.35* 0.86* 0.45* 0.86* 0.55* -

11. Overall Games Started -0.05 -0.52* 0.50* 0.45* 0.52* 0.53* 0.88* 0.44* 0.89* 0.58* -

12. Race 0.45* 0.14* -0.01 0.00 0.01 -0.04 -0.08 -0.02 -0.05 -0.06 -0.09 -


Table 5

Means and Standard Deviations of the WPT by Position

Position N M SD

QB 25 27.52 6.33

RB 45 19.27** 6.58

WR 58 20.52** 7.04

TE 34 24.74 8.31

OL 70 25.19 5.83

DL 85 20.72** 7.49

LB 46 20.89* 4.99

DB 89 19.70** 5.66

Note. Scheffé post-hoc comparisons were used in the one-way ANOVA analysis. An

asterisk beside a mean score indicates that a significant mean difference between the

quarterback (QB) position and that specific position was detected. **p < .01. *p < .05.


Table 6

Intercorrelations between the WPT and Performance by Position

Position

Year 1

Performance

Year 2

Performance

Overall

Performance

QB

(n)

-0.27

(11)

-0.41

(17)

-0.30

(19)

RB

(n)

-0.02

(32)

0.28

(33)

0.13

(36)

WR

(n)

0.07

(43)

-0.11

(40)

0.01

(47)

TE

(n)

-0.33

(27)

-0.11

(25)

-0.22

(28)

OL

(n)

0.17

(43)

0.14

(50)

0.15

(54)

DL

(n)

0.12

(64)

0.20

(63)

0.15

(71)

LB

(n)

0.14

(42)

0.11

(42)

0.12

(45)

DB

(n)

-0.12

(73)

-0.20

(75)

-0.17

(80)

Documents

The Wonderlic-NFL Performance Relationship Revisited: Positional