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Original Research

Combine Performance Descriptors and Predictors of Recruit Ranking for the Top High School Football Recruits from 2001 to 2009: Differences between Position Groups

Ghigiarelli, Jamie J

Author Information
Journal of Strength and Conditioning Research: May 2011 - Volume 25 - Issue 5 - p 1193-1203
doi: 10.1519/JSC.0b013e318215f546
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Abstract

Introduction

Physical performance characteristics required to play football at the collegiate and professional level have been extensively reported (2-4,8,10,12,13,25,27,29) and follow-up investigations explored the relationship between these descriptives and football playing ability (FPA) (1,2,9,16,24). At the professional and collegiate levels, explosive power and maximum running speed are the best predictors of FPA, with greater explosive power and faster maximal running speed predicting improved FPA. Similarly, these variables also have the strongest association with draft pick status and coach's subjective rankings (16,19,24). Over the past 3 decades, football players have become bigger, faster, stronger, and more powerful (22,27,31), and the physical characteristics needed for these abilities are undoubtedly key factors for performance in Division I-A collegiate and professional players.

Despite the criticisms of using performance measures to evaluate on-field performance (16), a great deal of time, effort, and resources are directed at evaluating players and assessing measures that are assumed to underlie FPA and functional ability (1,3,4,7,9,10,12,18,20,25,28,32). Using these off-field performance measures, coaches can evaluate the efficacy of their respective strength program and target position-specific physiological benchmarks such as 40-yd sprint time, vertical jump height, and broad jump distance. This can be applicable to goal setting and ensuring that players have an optimal response to training regimens (20). In addition, this also lends an insight into which factors promote the development of a football player to produce maximum FPA (2).

Previous literature has reported the physical descriptive information in national football league (NFL) and college players (2-4,8,10,12,13,25,27,29), but few have examined these data at the high school level. Dupler et al. (11) were the first to analyze cross-sectional data describing the physical and anthropometric characteristics of high school players in grades 9-12. In addition, recent findings (5,33) have examined the relationship between maximum squat strength, vertical jump, short agility, and 40-yd sprint time between high school players across position. However, no study has evaluated these data in a cross-sectional analysis examining the relationship between performance descriptors and FPA. This may identify specific attributes that contribute to success in obtaining more scholarship offers.

Therefore, the purposes of this study were to (a) perform a cross-sectional analysis examining the differences across position between highly recruited and recruited players, (b) create position-specific regression models using the performance variables to predict recruit star value, and (c) tabulate the physical attributes of all the high school players recruited from 2001 to 2009. We hypothesize that highly recruited players will be able to run faster, jump higher, and be physically larger in size compared to recruited players. Additionally, we hypothesize that the 40-yd sprint and the vertical jump will be the best predictors of star value, with faster 40-yd sprint times and higher vertical jump scores predicting higher star values.

Methods

Experimental Approach to the Problem

This retrospective study was designed to report the basic descriptors and physiological benchmarks of 5 performance tests and 2 anthropometric measures across 10 positional groups for the top high school football recruits per year between 2001 and 2009, inclusive. A 1-way analysis of variance (ANOVA), 2-way ANOVA, and backwards linear regression statistical models were used to analyze the data set. A 1-way ANOVA was calculated to detect the differences between each star value for height, weight, 40-yd sprint time, 20-yd shuttle time, and vertical jump height. Star value was denoted as the number of stars (from 1 to 5) recruiters from Scout Inc. assigned to each prospective recruit. To investigate the differences between positions, data were stratified by star value and comparisons were performed between highly recruited (5 and 4 stars) and recruited (3 and 2 stars) players. Ten (10 positions) separate 2-way (group by test) ANOVAs were calculated to examine the differences between performance and group. Finally, 10 backward regression models were created to identify which performance or anthropometric measures significantly contributed to star value. Height, weight, 40-yd sprint, vertical jump, and 20-yd shuttle run were the independent variables, and star value was the dependent variable for the regression models. All procedures were exempted by the Institutional Review Board at Hofstra University.

Subjects

Subjects for this study included highly recruited high school football players across 10 positions for the years 2001-2009. The positions consist of cornerbacks (CBs), defensive ends (DEs), defensive tackles (DTs), linebackers (LBs), offensive lineman (OL), quarterbacks (QBs), running backs (RBs), safeties (S), tight ends (TEs), and wide receivers (WRs).

Data Collection

This study used archived data that were extracted from a publicly available website www.scout.com. Data were aggregated to yield a sample size of 2,560 players. Scout Inc. is a commercial web site that reports a wide variety of historical and current data on the most highly recruited high school football players in the country. Each recruit is ranked on a star value system (1-5 stars), which is assigned from Scout Inc. Star values range from 1-5 stars with 5 stars indicating the highest recruited players. Scouts base this score on the player's physical attributes and on-field performance. Scout Inc. is a media company consisting of a team of scouts around the country, including local scouts that work in smaller areas and submit their rankings to a regional manager. Typically, most scouts are reporters although some are also a combination of coaches and former players (26). It is assumed that many of the scouts have adequate knowledge of the game of football and can make accurate evaluations of the players being scouted.

Combine Results Reported in this Study

Performance and anthropometric variables were height, weight, 40-yd sprint, 20-yd shuttle run, vertical jump, angle drive drill, and broad jump. In an attempt to verify the integrity of the data, the primary author contacted the director of Scout Inc. It was confirmed that all testing was administered in a supervised and controlled environment. This consisted of trained Scout Inc. personnel reporting and conducting the testing, which was similar to the NFL combine battery. The description for each of the drill is documented in Table 1.

Table 1
Table 1:
Description for each anthropometric and combine test administered by Scout Inc.

Statistical Analyses

Five separate 1-way ANOVAs were performed to examine the data by star value. This was used to examine the overall differences between 5-, 4-, 3-, and 2-star recruits. Ten separate 2-way 2 × 5 (group × tests) factorial ANOVAs were performed for each position with Tukey post hoc testing when appropriate to test the difference between the tests (height, weight, 40-yd sprint, 20-yd shuttle, vertical jump) across the 2 groups (highly recruited, recruited). Angle drive drill and broad jump were not entered into the ANOVA because of a lack of data for these values. Highly recruited and recruited players were grouped together on the basis of star value, which entailed the 5-and 4- star recruits and the 3-and 2-star recruits.

Ten multivariate stepwise backward regression models were created using 3 performance variables and 2 anthropometric variables to predict star value. Angle drive drill and broad jump were not entered into the model because of a lack of data for these values. Nonsignificant variables were excluded from the model. Lastly, mean ± SD for height, weight, and combine tests were computed and tabulated for all 5-, 4-, 3-, and 2-star recruits. Statistical significance for all the analyses was defined by p ≤ 0.05. All statistical analyses were performed through the use of the statistical software package (SPSS Version 18.0 SPSS Inc., Chicago, IL, USA).

Results

One-way ANOVAs detected significant differences in 4 of the 5 tests across star value (height, weight, 40-yd sprint, vertical). Five- and 4-star recruits were significantly taller (Figure 1) compared to 3-star (p < 0.05, p < 0.05) and 2-star (p < 0.05, p < 0.05) recruits. No other height differences were noted. There was a significant difference in body mass between recruit star values (Figure 2), with 5-star recruits being significantly heavier than 3-star (p < 0.05) and 2-star (p < 0.05) recruits. No other body mass differences were noted. Five- and 4-star recruits were significantly faster (Figure 3) than 3-star (p < 0.01, p < 0.05) and 2-star (p < 0.001, p < 0.001) recruits, and 3-star recruits were significantly faster than 2-star recruits (p < 0.001). Five-star and 4-star recruits jumped significantly higher (Figure 4) than 3-star (p < 0.05, p < 0.01) and 2-star (p < 0.05, p < 0.05) recruits. There were no significant differences in the 20-yd shuttle run across star value.

Figure 1
Figure 1:
Height comparisons between star values. All data are reported as mean ± SD. a, Significantly different from 2-star recruits; b, significantly different from 3-star recruits.
Figure 2
Figure 2:
Weight comparisons between star values. All data are reported as mean ± SD. a, Significantly different than 2-star recruits; b, significantly different from 3-star recruits.
Figure 3
Figure 3:
Vertical jump height comparisons between star values. All data are reported as mean ± SD. a, Significantly different from 2-star recruits; b, significantly different from 3-star recruits.
Figure 4
Figure 4:
Forty-yard sprint and 20-yd shuttle run comparisons between star values. All data are reported as mean ± SD. a, Significantly different from 2-star recruits; b, significantly different from 3-star recruits.

The 2 by 5 (group × test) 2-way ANOVA revealed significant differences in each position, except for the S (Tables 2 and 3). Cohen d effect sizes were identified and tabulated in Table 4. Height was significantly different in 7 of 10 positions (CB, WR, LB, OL, QB, RB, TE), 40-yd sprint was significantly different in 4 positions (WR, LB, OL, DT), and weight was significant in 3 positions (CB, QB, DE). Twenty-yard shuttle was significant in 1 position (QB), and vertical jump was significant in 1 position (TE).

Table 2
Table 2:
Performance comparisons between recruit classification and CBs, WRs, RBs, QBs, and S.*†‡
Table 3
Table 3:
Performance comparisons between recruit classification and LBs, TEs, and lineman.*†‡
Table 4
Table 4:
Effect sizes for the variables denoting significant differences between highly recruited and recruited players.*

According to the regression analysis, 40-yd sprint, height, and weight were the most consistent significant predictors across all positions (Table 5). Forty-yard sprint significantly predicted the star value for all 10 positions. Height and weight were significant predictors for 7 of the 10 positions.

Table 5
Table 5:
Multivariate backward stepwise regression models to predict star value within position for height, weight, 40-yd sprint, vertical jump, and 20-yd shuttle run.*

The means and SDs for all anthropometric and performance data for 5-star, 4-star, 3-star, and 2-star high-school football recruits are given in Tables 6-9. Across all players, the mean star value was 3.3, with a median of 3.5, and a mode of 3, producing a normal to slightly negatively skewed bell curve distribution. This is also illustrated by the unequal samples sizes across star value. One hundred forty-two players were 5-star recruits, 625 were 4-star recruits, 1,057 were 3-star recruits, and 191 were 2-star recruits.

Table 6
Table 6:
Descriptive statistics (mean ± SD) for anthropometric measures and combine performance tests for all 5-star recruits across each position.*
Table 7
Table 7:
Descriptive statistics (mean ± SD) for anthropometric measures and combine performance tests for all 4-star recruits across each position.*
TABLE 8
TABLE 8:
Descriptive statistics (mean ± SD) for anthropometric measures and combine performance tests for all 3-star recruits across each position.*
Table 9
Table 9:
Descriptive statistics (mean ± SD) for anthropometric measures and combine performance tests for all 2-star recruits across each position.*

Discussion

In this study, we hypothesized that higher recruited players will be able to run faster, jump higher, and be physically larger in size compared to recruited players. On examining the differences between these 2 groups, this hypothesis was partially supported. Higher ranked players were able to run faster and jump higher, which supports the findings of previous studies that 40-yd sprint time and explosive power are associated with FPA and coaches' evaluation of athletic potential (2,12,16). Despite the significant predictive ability of the 40-yd sprint, the r2 values ranged from 0.025 to 0.27. These low r2 values are likely a result of the unequal sample sizes for each star value for each position. Despite this, we still found that the best predictive variable for star value (40-yd sprint time) explained up to 27% of the variance. This is an important finding that should not be overlooked by persons attempting to quantify recruitment potential based on off-field performance measures. Unfortunately, the regression analysis did not support our hypothesis regarding vertical jump. Vertical jump was included in the predictive model for only one position. The forty-yard sprint was a significant predictor for all the positions, whereas height and weight were predictors for 7 positions. This finding does support the trend similar to the ANOVAs emphasizing physical size to have the greatest variability among the recruits.

Grouping the players into highly recruited and recruited players provided a closer examination of the data. In doing so, we attempted to place the groups into homogenous clusters, thus increasing the sensitivity of the design to identify smaller effects between the performance variables. Notwithstanding the significant differences found for 9 of 10 positions, there was little clinical significance to those differences, with Cohen effect sizes ranging from 0.12 to 0.39. The largest effect sizes were observed in height for the CBs (0.39) and 40-yd sprint for the DEs (0.32).

Our findings suggest that a relationship exists between greater physical size (height and weight) and greater recruitment potential. The emphasis on body size contrasts earlier studies, which found different attributes, specifically explosive movements such as vertical jump, 40-yd sprint, and short shuttle runs to be significantly correlating to coach's evaluation of athletic ability (2,12,24,29). Sierer et al. (29) found no significant differences in body mass and height when comparing drafted and undrafted players in the 2004-2005 NFL combine. Sawyer et al. (24) found low correlations between height and weight and FPA for offensive and skill position players for Division 1-A players. Other authors (10) have reported a long-standing contention that lighter athletes, who can run and cut faster than their weaker and heavier counterparts, will be able to perform better on functional tests such as vertical jump and 20-yd shuttle run (2,12). Scoring high on both of these performance measures have been associated with high FPA (2,4,12).

Even at the NFL level, except for body mass of offensive and defensive lineman, basic body structure such as height, weight, and body fat has remained the same over the past decades (15). The difference between the previous investigations and this study may be attributed to the fact that at the Division 1-A and professional levels, players have been carefully recruited or drafted from a larger pool of players. This substantially reduces the variation from player to player at each position. Once a player reaches an elite level of competition such a major Division 1-A college football program or on a NFL team, the morphological features of each player at each position would typically be the same because all of these players have reached the peak of their maturation process. This is a stark contrast from the recruitment pool at the high school level. Recent studies have shown that a continual maturation process exists at the high school level as athletes move up to higher levels of play (11). College scouts are going to be more inclined to rank the physically larger and taller players higher indicating these players have obtained or will likely reach a physical maturity benchmark similar to Division 1-A college players.

This study also identifies the descriptives that characterize physiological benchmarks associated with elite high school football players. Although many of these previous reports evaluated collegiate and professional players (10,12,19,27,29), the present results expand the knowledge base to include high school players. This information builds on prior literature describing the physical and performance characteristics of American football players. Furthermore this study used a relatively large sample size compared to other studies that reported similar descriptors in American football players (11,19,23,29) and use these descriptors to predict future athletic success (16,23,24).

Comparing these data to the published normative values for successful high school and Division III football players, the 5-star, 4-star, and 3-star recruits in the current sample were taller, heavier, and could jump higher (25,32). Players were more comparable to Division 1-A players (8,13,14,24), which was expected considering most of the recruits evaluated in this study were being recruited for Division 1-A. In fact, the highly recruited players were more similar to drafted and undrafted NFL players, particularly in the 40-yd sprint, weight, height, and vertical jump (29). Prior literature that has reported the normative values for the descriptive statistics of American football players has concluded players are heavier, stronger and more agile across multiple levels of competition (22,27,31). Wang et al. (31) reported a significant increase in body mass index after 1971-1989 among All-American high school football players, which raise interesting research questions. Of particular interest is an understanding of whether these gains are from improved nutrition and training or from other means, including performance-enhancing drugs. Brophy et al. (6) have found an upward trend in average number of injuries per player in the NFL combine from 1987 to 2000. It has been suggested that a change to the bigger, faster, stronger athlete may result in stronger players sustaining more injuries; however, part of this trend may be because of detection bias from advances in both the awareness and sophistication of physical examination techniques (6). Some authors consider the widespread emphasis of strength training and nutritional programs for college and high school players has led to increased player size, strength, and power (27,30). This supports the belief that players' physical norms are continually changing, and a periodic review of data is warranted.

The results from this study validate the use of performance measures in developing recruiting criteria, player position decisions, and off-season program design. However, it is not without limitations. Although off-field performance measures explain a portion of the variance of on-field success, many other factors contribute to actual performance. The off-field performance tasks cannot differentiate between skill and ability and also do not consider many other factors that may affect on-field performance. According to Noe et al. (21), skill narrowly focuses on a particular task, whereas ability broadly identifies a set of tasks. For example, in this data set, the short shuttle run was a significant predictor in the QB position for star value. This supports previous data documenting a similar drill, the 3-cone drill, to be the most significant predictor for draft status at the QB position (19). Logically, these agility tests would apply to the QB position that requires agile foot work in the pocket and the ability to move quickly in a small space. Despite this association, many other factors need to be considered when evaluating FPA in the QB position such as coordination, vision, football sense, accuracy, and stress tolerance (17). Other confounding factors may also have affected the star value reported by Scout Inc., which would have limited the generalizability of the results. Factors such as school size, geographical area, high school winning percentage, and coaches' ability to advertise the player are several factors that were not accounted for in this study. Although these are not quantifiably weighted by Scout Inc., it is conceivable to think these factors, which were not entered into our regression model, may contribute to the star value. Another limitation to this study is the lack of test-retest reliability correlation measures. It has been assumed that the test data collected at these combine events were done so appropriately. However, because of secondary data collection, the primary author did not develop the data set. Therefore, this method of data mining does not allow the author to comment on data collection rigor and is unable to report inter and intraclass correlations. A final limitation is the qualifications of the scouts at Scout Inc. As stated earlier, each scout employed by the company does not need to meet specific criteria to become a scout and begin evaluating players.

Future research should focus on establishing predictors of FPA in high school players using primary methods of data collection, opposed to secondary analyses. This would provide a more accurate depiction of what performance variables truly measure on- field performance and allow the researcher to control external biases that are inherent in the currently available databases. Further investigations should also incorporate assessments of which type of players are being recruited at specific institutions. This may validate certain recruiting criteria for specific institutions on the basis of offensive and defensive schemes employed by the school.

Practical Applications

The multiple descriptive tables listing the performance benchmarks add to the scant body of knowledge of physical performance descriptors in high school football players. The practical significance of these findings can be used for goal setting, player evaluation, and understanding player profiles for specific positions. Primarily, players who have ambitions of competing at the Division 1-A level should become familiar with these data and understand the physical traits required to play their respective position. These physical characteristics can be used as obtainable training goals and if these goals are reached, it's plausible a player may increase the likelihood of obtaining more Division 1-A scholarship offers. Coaches can also use the benchmarks for evaluation by comparing a player's performance on a particular test of size and speed to the mean performance for his position. If the player is below the standard, coaches can tailor and devise training programs to improve the physical characteristics required to play that position.

Another application for this data is for specific-position profiling. By assessing the player's height, weight, and performance measurements a coach could predict the position where the player may increase their recruiting (star) value. This reasonably would potentially maximize the chances of success at a certain position, particularly during an athlete's recruiting period. For example, high school coaches may want to consider placing taller players on the offensive side of the ball vs. defensive. In this study, greater variability was observed for height for offensive down lineman positions (OL, TE) compared to defensive down linemen positions (DE, DT). It can be suggested that height may be a more critical attribute for increasing recruit value for offensive down linemen vs. defensive down lineman.

Of course, the author is aware that there is no scientific formula for predicting positions, and it is provincial to think by purely analyzing one's physical traits a coach can accurately place a player in the correct position. This is merely a suggestion. In fact, the theory of using combine performance measures to predict athletic success is speculative (16,23), which has generated some controversy for the NFL combine testing battery and its reflection on FPA. In conclusion, this study does provide an interesting perceptive on how to scientifically evaluate high school players and these results can be used as a baseline evaluation of a player's strengths and weaknesses which could be the key to facilitating them to be a more attractive college recruit.

Acknowledgments

The author would like to thank Phil Giackette, Sarah Pletz, and James DaVolio for their assistance with this manuscript. The authors disclose professional relationships with companies or manufacturers who will benefit from the results of this study. Results of this study do not constitute endorsement of the product by the authors or the National Strength and Conditioning Association.

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Keywords:

star value; testing; assessment; cross-sectional analysis

© 2011 National Strength and Conditioning Association