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Momentum, Rather Than Velocity, Is a More Effective Measure of Improvements in Division IA Football Player Performance
Abstract
Speed, or the time to complete straight runs or agility drills, is commonly used to assess performance in collegiate American football players. However, it is common for players’ speeds to plateau by the second year of eligibility, whereas their body masses continue to increase. The purpose of this study was to track change in speed, body mass, and momentum (body mass · velocity), across Division 1 football players’ 4-year careers (n=512). Complete data were derived for the 40-yd sprint (n=82), the proagility shuttle (n=73), and the L
drill (n=73) from the same NCAA Division 1 team over a 15-year period. Significant changes were seen for velocity between year 1
and the next 3 playing years (p < 0.05), with no differences between years 2 and 4, whereas body mass increased significantly across all playing years (p < 0.05). Further momentum increased across all years for all tests (p < 0.0001). These results indicate the importance of including changes in body mass when evaluating performances during sprints and change of direction drills. Our results also suggest that using sprint or agility drill times to evaluate playing potential across football players’ collegiate careers may be ineffective and can provide players with a false and disheartening picture of their improvements across their careers. Momentum, which incorporates training-induced increases in both speed and body mass, would be a more relevant and supportive measure of players’ improvements. In addition, the simple computation of this variable, using existing speed and body mass data, may be an important addition to the National Football League combine as a measure of playing potential in the professional game.
... A growing body of literature suggests substantial relationships between strength and sprinting performance, with increases in strength coinciding with improvements in sprint performance over shorter distances, in populations such as soccer and rugby (30). However, research reports have also suggested a common trend in which collegiate football players experience gains in strength and body mass over the course of their collegiate career, which are not accompanied by improvements in speed (8,19,24). For instance, Mann et al. (19) recently suggested that over the course of their 4-year collegiate career, American football players experienced significant increases in body mass, which were reflected in significant increases in sprint momentum but not sprint velocity. ...
... However, research reports have also suggested a common trend in which collegiate football players experience gains in strength and body mass over the course of their collegiate career, which are not accompanied by improvements in speed (8,19,24). For instance, Mann et al. (19) recently suggested that over the course of their 4-year collegiate career, American football players experienced significant increases in body mass, which were reflected in significant increases in sprint momentum but not sprint velocity. This makes it difficult to discern to what point improvements in strength aid in the development of speed and to what extent changes in anthropometric measures factor into this discussion. ...
... If permitted by the sample size, future investigations may also investigate respective thresholds across additional subgroups (e.g., position groups). Future studies may use a similar methodological approach, looking at sprint momentum, rather than sprint velocity, as proposed in previous research reports (19). From a methodological standpoint, readers should acknowledge the limitations of measuring jump height using jump mats, which derive jump height from time spent in the air rather than take-off velocity through the impulse-momentum theorem. ...
Philipp, NM, Crawford, DA, Cabarkapa, D, and Fry, AC. Strength and power thresholds to identify high and low linear sprint speed performers in collegiate American football players. J Strength Cond Res XX(X): 000–000, 2023—Lower-body strength and power are commonly measured performance qualities across a number of sports. In recent years, more attention has been given to relationships, primarily between lower-body strength and linear speed performance. While still limited, evidence is in agreement that lower-body strength positively contributes to linear speed performance. However, what is less well understood is if there comes a point in an athlete's development, at which, further working on increasing maximal strength may not fully compliment additional gains in speed performance. Within this study, authors aimed to provide practitioners with lower-body strength and power thresholds that can discriminate between slow and fast performers, within a group of collegiate American football players. The sample was further divided into a high-body and low-body weight group, and authors hypothesized that by using logistic regression, supplemented with receiver operator curve analyses, optimal cut-off points (i.e., relative lower-body strength thresholds) that are able to significantly discriminate between slow and fast linear speed performers may be identified. Findings indicate that optimal cut-off scores differed between the groups of athletes, as well as the lower body strength and power tests. All models were able to significantly distinguish between slower and faster performers, and area under the curve values ranged from 0.695 to 0.903. Although thresholds will likely vary based on factors such as sex, training age, and sport, findings from this investigation may be used to benchmark athletes and to further individualize training aimed at improving linear speed performance.
... The fastest trial (least time) to complete the 0-30 m was used for all further analysis. All splits (0-10 m, 0-20 m, 0-30 m, 10-20 m and 20-30 m) were reported for the variables of time to complete (s), mean velocity (m·s −1 ) and sprint momentum (kg·m −1 ·s −1 ), which is the product of mean velocity and BM (Baker & Newton, 2008;Barr et al., 2014;Mann et al., 2022). ...
... Whilst sprint performance is essential to competitive success in a wide range of sports, in collision-based sports it has been proposed that sprint momentum may be more indicative of performance potential (Baker & Newton, 2008;Barr et al., 2014;Mann et al., 2022;McErlain-Naylor & Beato, 2021). Specifically, as sprint momentum is a product of the athlete's BM and sprint velocity, it provides valuable information to the practitioner regarding an athlete's ability to be physically dominant during collisions with an opposing athlete (Baker & Newton, 2008;Barr et al., 2014;Mann et al., 2022). ...
... Whilst sprint performance is essential to competitive success in a wide range of sports, in collision-based sports it has been proposed that sprint momentum may be more indicative of performance potential (Baker & Newton, 2008;Barr et al., 2014;Mann et al., 2022;McErlain-Naylor & Beato, 2021). Specifically, as sprint momentum is a product of the athlete's BM and sprint velocity, it provides valuable information to the practitioner regarding an athlete's ability to be physically dominant during collisions with an opposing athlete (Baker & Newton, 2008;Barr et al., 2014;Mann et al., 2022). In this study, skating sprint momentum was significantly greater with each progression in the performance pathway across all splits of the 30 m skating sprint, highlight the importance of continuously seeking improvements in this variable throughout an athlete's progression from junior elite to senior elite levels. ...
Skating sprint performance is essential for competitive success in ice hockey; however, it is unknown which component of a skating sprint is most critical for development throughout the performance pathway. Fifty-seven Swiss male ice hockey athletes were subjects (National League [NL], n = 22; Under 20 [U20], n = 20; Under 17 [U17], n = 15). Athletes performed: on-ice 30 m skating sprint, countermovement jump (CMJ), squat jump (SJ), and isometric mid-thigh pull (IMTP) tests in a single day. Linear mixed models, effect sizes and 95% confidence intervals were used to compare sprint performance and CMJ, SJ and IMTP between each performance level, with a correlation matrix used to determine the influence of lower-body strength and power on sprint performance. The NL and U20 athletes were significantly faster and had greater performance in most CMJ, SJ and IMTP variables compared to the U17 athletes, indicating minimum standards of lower-body strength and power are required to optimise technical performance. Significant differences were observed between NL and U20 for 10-20 m skating sprint split time and CMJ concentric relative peak and mean force, and reactive strength index-modified. Therefore, flying acceleration (10-20 m) is likely the most critical variable for pathway progression, with relative concentric force production the greatest influence.
... Sprint velocity was determined by dividing the distance (30 m) by the time (speed = distance (m)/sprint time (s)). This was then multiplied by the subjects' body mass in kg, which gave the momentum (sprint momentum = body mass (kg) × velocity (m/s)) [24]. ...
... It has been mentioned that sprint momentum is a much more effective measurement tool than sprint speed in assessing linear sprint performance [24]. There is no study in the literature that examines the influence of the body types we have identified on sprint momentum. ...
... While no significant difference is found in the literature between the sprint times of professional first and second division rugby players, it has been observed that the momentum scores of first division players with an average body weight of 7% are significantly higher [39]. In another study, it was mentioned that the increase in body mass can have a positive effect on performance in sprinting [24]. In our study, the fact that the highest sprint momentum performance after the balanced mesomorphs belongs to the group of mesomorphic endomorphs also coincides with the rational explanation of this situation. ...
The relationship between an athlete’s somatotype three-numeral rating and his or her athletic performance is well known. However, a direct effect of the different dominant somatotype on jumping and sprinting variables has not yet been reported. The aim of this study was to investigate the effects of dominant somatotype on sport-specific explosive variables. One hundred and twelve physically active young adults (mean ± standard deviation age: 21.82 ± 3.18 years) were somatotype-rated using the Heath–Carter method. Participants were classified as balanced ectomorph, balanced mesomorph, central, mesomorph-endomorph, and mesomorphic ectomorph. Vertical jump and linear sprint tests were performed to measure peak lower body performance and sprint variables (time, speed, and momentum), respectively. The analysis revealed that balanced mesomorph had significantly higher vertical jump (effect size (ES) = 1.10, p = 0.005) and power to body mass (ES = 1.04, p = 0.023) than mesomorph-endomorph. In addition, balanced mesomorph showed significantly superior performance in 30-m sprint time and velocity than central and mesomorph-endomorph (ES range = 0.93–1, p < 0.05). Finally, balanced ectomorph (ES = 1.12, p = 0.009) and mesomorphic ectomorph (ES = 1.10, p = 0.017) were lower in sprint momentum compared to balanced mesomorphs. In conclusion, this study has shown the importance of the interaction between subtypes and athletic performance. The knowledge gained may be important in identifying those who tend to perform well in sports with explosive power and in prescribing training programs.
... In addition, higher approach running velocities make subsequent COD more difficult because more braking is needed to modify the higher linear momentum of the athlete (8). For this reason, linear momentum, which considers both the athlete's body mass and velocity, has been suggested as a relevant parameter to relativize COD measurements to assess the specific physical performance of athletes (13,26). ...
... This calculation was performed to determine players' deceleration capacity for each leg relativized to his linear momentum during the COD. For further information on the above formulas, readers are referred to the following references: (20,26,47). ...
Miralles-Iborra, A, Del Coso, J, De Los Ríos-Calonge, J, Elvira, JLL, Barbado, D, Urban, T, and Moreno-Pérez, V. Deceleration capacity during directional change as a time-efficient (ecological) prescreening of hip adductor force status in amateur soccer players. J Strength Cond Res XX(X): 000-000, 2024-Reduced isometric adductor muscle strength has been identified as a modifiable risk factor contributing to injury in soccer players. However, the measurement of hip adductor muscle strength is habitually laboratory-based, with isolated hip movements that do not reflect soccer-specific movements that induce groin injury during match play. This study aimed to determine the usefulness of deceleration capacity during a change of direction (COD) as a time-efficient (ecological) prescreening of hip adductor force status in soccer players. Nineteen amateur soccer players completed unilateral isometric hip adductor strength assessments and a 180° COD test. Isometric hip strength assessment included the maximum peak torque (PT) and maximum rate of torque development (RTDmax) relative to players' body mass. Players' deceleration capacity during the COD test was determined for each leg through maximum deceleration normalized to the linear momentum. A linear regression analysis was performed to associate isometric hip strength variables with the deceleration capacity during the COD test at each leg. There was not a statistically significant association between deceleration capacity and hip isometric maximum PT of the dominant and nondominant legs (r ≤ 0.14, p > 0.05). Nevertheless, a moderate association was found between deceleration capacity and RTDmax for both legs (r ≥ 0.58, p < 0.05). The optimal linear regression model suggests that measuring deceleration capacity during a directional change test could explain RTDmax by 33 and 43% for the dominant and nondominant legs, respectively. During a 180° COD test, the deceleration capacity captured through GPS-accelerometer device was limited as a prescreening tool to evaluate hip adductor force status in soccer players.
... Coaches and sport scientists should consider using impulse-momentum derived variables when quantifying and evaluating sprint performance. L and P are more informative than sprint time alone (Mann et al., 2022). At the same time, positional and sport related differences likely exist, and the production of normative data will prove useful when evaluating athlete performance ...
... This data can be used to calculate mean velocity or velocity (V) at specific intervals. When combined with publicly available body mass data, linear momentum (L) can be calculated (Baker & Newton, 2008, Mann et al., 2022. The Impulse-momentum equation then creates an opportunity to calculate the force (F) applied during sprint. ...
Sprint performance data of Major League Baseball (MLB) players has become publicly available in recent years via MLB’s Statcast database. This data can be used to calculate mean velocity or velocity (V) at specific intervals. When combined with publicly available body mass data, linear momentum (L) can then be calculated. The impulse-momentum equation then creates an opportunity to calculate the force (F) applied during sprints. Subsequently, power (P) can then be calculated as the product of force and velocity. Positional differences likely exist in each of these variables. PURPOSE: The purpose of this investigation was to determine if there are positional differences in V, L, and P in MLB players. METHODS: This study examined publicly available sprint and body mass data in 249 MLB players from the 2022 season. The sprint performance data includes times at specific intervals from 0 to 27.4 m (90 ft), which was used to calculate V. This was multiplied by each athlete’s body mass resulting in L. Knowing both the L and sprint time, F applied was calculated with the impulse-momentum equation (F*t=m*V). P was calculated as the product of F & V. All data preparation and analyses were completed in R. Data were non-normally distributed, thus, Kruskal-Wallis ANOVAs were used to evaluate the presence of statistical differences (p<0.05) by position for V, L, & P. When necessary, post hoc comparisons were completed via Welch tests with a Holm-Bonferroni correction and effect sizes (2) were included. RESULTS: All 3 variables resulted in statistical and practical differences (V 2 = 0.65, L 2 = 0.25, P 2 = 0.33) between positions. There were many statistically significant positional differences in V, and they are shown in Figure 1. Designated hitters produced statistically greater L than short stops (p = 0.01). Both right fielders (p < 0.000) and center fielders (p < 0.000) showed statistically greater power than catchers. CONCLUSIONS: To the current authors’ knowledge, this is the first investigation to utilize the impulse-momentum equation to evaluate sprinting performance in MLB players. Traditional sprint testing generally only utilizes time for a set distance, but calculating V, L, and P are relatively easy when body mass is known. L and P are likely more robust measures of performance as they quantify more than speed alone. Results demonstrated that positional differences exist, and practitioners may find this useful when evaluating athletes or for setting target training goals. PRACTICAL APPLICATIONS: Coaches and sport scientists should consider using impulse-momentum derived variables when quantifying and evaluating sprint performance. L and P are more informative than sprint time alone. At the same time, positional and sport related differences likely exist, and the production of normative data will prove useful when evaluating athlete performance.
... For example, athlete sprint times may not seem to improve over a collegiate career. However, when body mass is accounted for, it is clear that substantial improvements in momentum could have occurred (42). For collision sports, this is naturally a great advantage. ...
... For example, by calculating mean sprint velocity from the times retrieved during linear sprint testing, then multiplying this value with body mass, initial and peak sprint momentum can be calculated. This information is a valid discriminator between professional and subprofessional athletes and may be useful for monitoring long-term changes in physical capacity (36,42). Alternatively, the consideration of body mass during tests of aerobic capacity, such as the 30-15 IFT, may help account for the influence of body mass and demonstrate to an athlete that there has been an improvement in highintensity running performance despite (93). ...
Understanding the physical qualities of athletes can lead to improved training prescription, monitoring, and ranking. Consequently, testing and profiling athletes is an important aspect of strength and conditioning. However, results can often be difficult to interpret because of the wide range of available tests and outcome variables, the diverse forms of technology used, and the varying levels of standardization implemented. Furthermore, physical qualities can easily be misrepresented without careful consideration if fundamental scientific principles are not followed. This review discusses how to develop impactful testing batteries so that practitioners can maximize their understanding of athletic development while helping to monitor changes in performance to better individualize and support training. It also provides recommendations on the selection of tests and their outcome measures; considerations for the proper interpretation, setup, and standardization of testing protocols; methods to maximize testing information; and techniques to enhance visualization and interpretation.
... In this study, we aimed to elucidate the characteristics of visually perceived momentum of others' motions by focusing on momentum, a key physical characteristic of human motion. Momentum, the product of velocity and mass, holds particular significance in sports, especially those involving contact with a ball or other individuals (Baker & Newton, 2008;Mann et al., 2022;Roane, 2011). To our knowledge, there are no previous studies examining the perception of momentum in human motion. ...
The objective of this study was to elucidate the characteristics of visually perceived momentum of others’ motions. Twenty participants watched and compared two consecutive point-light running motions: one at a fixed velocity of 8.0 km/h and the other at one of seven velocities (5.6, 6.4, 7.2, 8.0, 8.8, 9.6, and 10.4 km/h). They then evaluated which had greater momentum, or if they were the same. The results indicated that as the velocity deviated from the standard velocity of 8.0 km/h, the correct rates increased; in particular, the correct rate at 5.6 km/h, which differed the most from the standard velocity, was the greatest ( p < .001). Additionally, the mean response times at 5.6 and 6.4 km/h, which were relatively smaller than the standard velocity, were significantly lower (both p < .05). This study indicates that humans can accurately perceive the momentum of others’ motions, consistent with previous studies demonstrating an accurate perception of the physical and mechanical properties of human-like motion.
... Jalilvand et al. found that the magnitude of the relationship between vertical jump and sprint variables changed significantly when body weight was considered [40]. A recent study found that momentum is a much more meaningful indicator of performance than sprint speed [41]. It has also been found that velocity approaches peak performance in the 20 s, but momentum can be improved in later periods [42]. ...
Background
Both maximal muscle strength and muscle power are independently important for karatekas. However, the relationship between strength and power in elite male kumite karatekas is under researched. This study aimed to determine the relationship between back-leg-chest (BLC) isometric muscle strength with sprint and jump variables in elite male karatekas.
Methods
Male elite/international level (tier 4) kumite karatekas (n = 14; age, 20.79 ± 1.67 year; height, 1.77 ± 0.06 m; weight, 72.21 ± 5.20 kg) were recruited. BLC strength, sprint and jump values were measured with a dynamometer, a photocell, and an application, respectively. Pearson correlation (trivial r < 0.1; small r < 0.3; moderate r < 0.5; large r < 0.7; very large r < 0.9; nearly perfect/perfect r ≥ 0.9) and linear regression analyses were performed to determine the relationship and shared variance between BLC strength, sprint, and jump performance.
Results
There were large to very large correlations between BLC strength and sprint time (r = − 0.930, p < 0.01), velocity (r = 0.918; p < 0.01), acceleration (r = 0.913; p < 0.01) and running momentum (r = 0.721; p < 0.01). Additionally, BLC strength correlated with jump height (moderate, r = 0.550, p < 0.05), peak anaerobic power (moderate, r = 0.672, p < 0.01) and power to body mass ratio (moderate, r = 0.545, p < 0.05). BLC strength and sprint variables showed an r² = 0.52–0.86 (p < 0.01), while BLC strength and jump variables showed an r² = 0.29–0.45 (p < 0.05).
Conclusions
BLC strength is related to jump and sprint performance in male elite karate athletes. This relationship underscores the importance of including strength training that targets BLC muscle strength in training programs for coaches and athletes.
... There is evidence in studies conducted on CODD time and linear sprint time with several investigations reporting that athletes with faster sprint time showed to have a larger CODD indicating no significant influence of speed on the CODD time [18,19]. Others have also suggested that sprint momentum can be a valuable indicator of CODD and needs further examination [20]. CODD has been classified as an absolute measure of COD since it has proven to provide accuracy when used [16]. ...
The majority of COD execution assessments employ the use of total time as the metric by which COD performance is judged. This study investigated the relationships between CODD time, sprint time, 5-10-5 and jump performance. Performance data of 328 participants of the 2021 NFL Combine (age: 22.35 ± 1.00 years; height: 1.87 ± 0.07m; weight: 108.51 ± 21.61kg) was collected and used for the analysis. CODD correlated to the 5- 10-5 pro-agility (r= 0.69 - 0.71) test but not sprint time (r= 0.15 - 0.27) for both the drafted and undrafted groups. Meanwhile, there was a large to very large association between 5-10-5 proagility time and the sprint variables (r = 0.62 - 0.82) for both drafted and undrafted groups. The correlation between CODD time and momentum was minor (r= 0.26 – 0.28) for both drafted and undrafted groups, but the 5-10-5 pro-agility reported a strong to a very strong association with momentum (r= 0.57 – 0.75). There was an inversely small correlation between CODD time and VJh (r= – 0.27) and BJ (r= -0.25– -0.28) for both drafted and undrafted groups whereas the 5-10-5 pro-agility time reported an inversely large to very large correlation with VJH (r= -0.51 – -0.68) and BJ (r= -0.57 – -0.71) on both groups. The magnitude and impact of the momentum, horizontal jump, and vertical jump of participants on their CODD time indicate that coaches and fitness experts should focus on improving the technical aspects of the COD execution when attempting to improve their CODS.
... Furthermore, in collision sports such as rugby union and rugby league, the ability to physically dominate an opponent has been demonstrated to be important, with traits like sprint momentum (i.e., body mass 3 velocity) being an essential consideration for the long-term progression of an athlete (1,9,18). In American football athletes, sprint momentum has been shown to be a sensitive measure of monitoring performance changes across a collegiate career (12). However, the ability of momentum-based measures to discriminate between higher performing athletes (e.g., starters vs. nonstarters) is still unknown. ...
Mann, JB, Cowley, N, and Weakley, J. The role of speed, change of direction, and momentum by position and starting status in Division 1 collegiate football players. J Strength Cond Res XX(X): 000-000, 2024-This study (a) investigated differences between big, mid, and skill positions in sprint and change of direction times and momentum; (b) compared starting and nonstarting athletes; and (c) investigated whether thresholds can be developed to distinguish between starting and nonstarting Division 1 collegiate football athletes. Data from 496 collegiate football players who completed the 40-yard dash, pro-agility, and L drill were analyzed. Momentum was calculated using body mass and the average velocity during each test. To assess differences between positions and starters and nonstarters, data were analyzed using linear mixed models with effect size 695% confidence intervals. Receiver operating characteristic (ROC) curves were generated to determine whether a cutoff value could be used to distinguish starters from nonstarters. Significant differences for both time and momentum were found between positional groups and starters and non-starters for all tests in all positions. Starting skill position players tended to have greater differences in sprint or change of direction times and starting big players had greater sprint momentum. However, it should be noted that all ROC curves demonstrated relatively poor predictive value. Collectively, these findings demonstrate that bigger, faster players are preferentially selected in collegiate Division 1 football and there may be value in coaches collecting and assessing different outcome measures (e.g., sprint times and sprint momentum) depending on the positional group of the player. Finally, it should be acknowledged that setting binary thresholds to guide selection decisions is ill-advised and that speed, change of direction, and momentum are only one piece of the performance puzzle.
... This is somewhat evident, because in track and field disciplines such as sprinting and jumping events, the ability to sprint faster is directly related to competitive success (Loturco et al., , 2023d. However, it is worth noting that coaches from various team-sports (e.g., rugby union, rugby league, American football, and hockey) (Haro et al., 2020;Kugler and Janshen, 2010;Mann et al., 2022;Zabaloy et al., 2023), have also emphasized that maximal sprint drills play a key role in the preparation of their players. This further supports the notion that incorporating this training method is essential in almost any athletic training program, irrespective of the sport discipline. ...
This is the second article in a three-article collection regarding the plyometric, speed, and resistance training practices of Brazilian Olympic sprint and jump coaches. Here, we list and describe six out of the ten speed training methods most commonly employed by these experts to enhance the sprinting capabilities of their athletes. Maximum speed sprinting, form running, resisted sprinting, overspeed running, uphill and downhill running, and sport-specific movement methods are critically examined with reference to their potential application in different sport contexts. In an era when sprint speed is of critical importance across numerous sports, practitioners can employ the methods outlined here to design efficient training programs for their athletes.
... The adequate development of sprint performance is a key and primary objective in various sports (1,18). Faster athletes in linear sprints, over shorter (e.g., 5-m) or longer (e.g., 40-m) distances, tend to also be faster in directional changes and curve sprints (abilities that are essential for soccer performance) (11,27,28), as well as exhibiting higher levels of sprint momentum in collision sports (e.g., rugby and American football) (7,32). In addition, maximal sprints precede most decisive situations in team sports (e.g., a goal in soccer or a try in rugby) (4,12), which makes sprint speed one of the most important physical capabilities in modern sports. ...
The aim of this study was to analyze the changes in the speed-power performance of elite youth soccer players submitted to two different low-volume resistance training programs during the off-season period. Twenty under-17 players were randomly allocated to “traditional non-ballistic” or “ballistic training” groups. Countermovement jump (CMJ), 20-m sprinting speed, and half-squat (HS) power tests were performed after the final match of the season (pre-testing session) and at the beginning of the subsequent season (post-testing session), after 4 weeks of detraining. Between-group differences were assessed using a two-way ANOVA with repeated measures followed by the Tukey’s post-hoc test. Performance variations were individually analyzed with the use of the “true changes” calculation. At post-tests, CMJ height and HS power remained unchanged (P > 0.05) but similar and significant improvements in sprint speed were observed in both groups (P < 0.05). However, notably, a larger number of players in the ballistic group exhibited “true changes” in HS power (i.e., 55% vs. 33%, compared to the traditional group, respectively). In conclusion, either low-volume ballistic or traditional resistance training schemes were able to increase sprint speed and maintain power output during a short inter-season break in youth soccer players. Despite this apparent similarity, at the individual level, ballistic movements were more efficient at improving lower-body power. Practitioners can use the strategies described here to improve the sprint and power performance of soccer players during short periods of soccer-specific training cessation.
... One less-emphasized variable that has only become a focus in recent jump-training research is that of "jump momentum." Momentum is the product of an object's mass and velocity (35), and the importance of monitoring momentum relative to jumping abilities, general sport success, and the relation between both jump momentum and sprint momentum (20,22,23) and force asymmetry (11) has been previously discussed. Redirecting the focus for jump-training load prescription from velocity loss to maximizing momentum can mitigate some challenges associated with establishing velocity-loss training targets for the exercise because system momentum (i.e., athlete plus load) displays a unimodal profile across loads, which is discussed later in detail alongside data. ...
Velocity-based training is often applied to ballistic exercises, like the barbell jump squat, to improve vertical jump performance. However, determining the ideal training load based on velocity data remains difficult, as load prescriptions tend to be limited to subjective velocity loss thresholds, velocity ranges, or both. Using data from jump squats performed with 0%, 15%, 30%, 45%, and 60% of the 1-repetition maximum squat, we explored subjective and objective methods to determine the ideal training load. Specifically, we explored takeoff velocity and a related metric only recently discussed in the literature, system momentum (i.e., takeoff velocity multiplied by the mass of the athlete-load system). At the group level, an ideal training load could not be revealed objectively using takeoff velocity. With individual participants, the process remained challenging using takeoff velocity. Conversely, an ideal training load could be revealed easily and objectively using system momentum at the group average and individual participant levels. System momentum at takeoff is well-suited to assist practitioners seeking to identify appropriate training loads for jump squat training, and potentially other ballistic exercises. We suggest a pivot from velocity to system momentum when seeking to objectively establish training loads for the jump squat and related exercises.
Jalilvand, F, Banoocy, NK, Rumpf, MC, and Lockie, RG. Relationship between body mass, peak power, and power-to-body mass ratio on sprint velocity and momentum in high-school football players. J Strength Cond Res XX(X): 000-000, 2018-The ability to rapidly shift one's body mass horizontally or vertically is common within American football irrespective of field position, and the capacity to generate power is a favorable physical quality. This requires analysis in high-school football players, especially considering the body mass disparities that exist in this population. Sixteen high-school players (7 backs and 9 linemen) completed the vertical jump (VJ) to determine jump height, peak anaerobic power measured in watts (PAPw), and power-to-body mass ratio (P:BM), and a 36.58-m sprint (0-4.57, 0-9.14, and 0-36.58-m intervals) to determine sprint velocity and momentum. Independent-samples t-tests (p , 0.05) determined differences in these variables between the backs and linemen. Pearson's correlations (r; p , 0.05) computed relationships between body mass, VJ height, PAPw, P:BM, with 36.58-m sprint velocity and momentum on the pooled data. Linemen were heavier, and slower in the 36.58-m sprint, but had greater PAPw and sprint momentum compared with backs. Body mass exhibited negative relationships to velocity across all sprint intervals (r = 20.55 to 0.70), and positive relationships with momentum across all intervals (r = 0.95-0.96). The VJ correlated with sprint velocity across all intervals (r = 0.51-0.83), but not momentum. PAPw was positively correlated with body mass and momentum across all intervals (r = 0.77-0.85), but not velocity. There were significant correlations between P:BM with velocity (r = 0.51-0.85) and momentum (r = 20.53-0.62) across all intervals. Heavier high-school players could focus on improving P:BM to positively influence jumping ability and sprint velocity.
The purpose of this study is to examine players' physical and performance measures taken at the NFL Scouting Combine and compare these to their future performance in the NFL. From 2002-2016, three types of player data (N=5,506) were collected from secondary data sources. Results players earned on various NFL Scouting Combine drills and measurements (e.g., height, weight, 40-yard dash time, vertical jump, bench press repetitions, shuttle run time, and 3-cone drill time), the position players play on the field (e.g., quarterback, running back, wide receiver, tight end, offensive line, defensive line, linebacker, and defensive back), and if players received elite performance awards (e.g., Pro Bowl and All-Pro selections) in the future were collected. After analyzing the data, the results indicate that (1) NFL quarterbacks that received All-Pro and Pro Bowl awards tend to be taller, weigh more, run faster in the 40-yard dash, jump higher, complete more bench presses, and are slower for the shuttle run and 3-cone drill; (2) All-Pro and Pro Bowl NFL running backs tend to weight more, run the 40-yard dash faster, do not jump as high, complete more bench presses, and complete the shuttle run and 3-cone drills slower; (3) NFL wide receivers that were selected for the Pro Bowl or as All-Pros tend to be taller, weigh more, run the 40-yard dash faster, have a higher vertical jump, and run the 3-cone drill faster; (4) NFL tight ends that received All-Pro and Pro Bowl awards tend to be taller, weight more, run the 40-yard dash faster, jump higher, complete more bench presses, run the shuttle run slower, and complete the 3-cone drill faster; (5) All-Pro and Pro Bowl NFL offensive linemen tend to run the 40-yard dash faster, jump higher, are able to complete more bench presses, and run both the shuttle run and 3-cone drill faster; (6) NFL defensive linemen that were selected as All-Pro and Pro Bowl players tend to be taller, weight more, run the 40-yard dash faster, jump higher, complete more bench presses, and run the 3-cone drill faster; (7) NFL linebackers that were named to the All-Pro and Pro Bowl teams tend to be taller, weight more, run the 40-yard dash faster, jump higher, can complete more bench presses, and run both the shuttle run and 3-cone drill faster; (8) NFL wide receivers that were selected for the Pro Bowl or as an All-Pro tend to weigh more, run the 40-yard dash faster, jump higher, can complete fewer bench presses, and run the shuttle run slower. Certified strength and conditioning specialists for college and professional teams will be able to use these results to help train and set performance goals for American football athletes with whom they work and train.
Purpose: This study determined differences in prolonged high-intensity running (PHIR) performance and running momentum (pIFT) between competition levels and positional groups in rugby league.
Methods: Elite Australian National Rugby League (NRL), sub-elite [state-based competition (SRL); National Youth Competition (NYC); local league (LL)] and junior-elite (U18; U16) rugby league players completed the 30–15 Intermittent Fitness Test (30–15IFT) to quantify PHIR performance. Final running momentum (pIFT; kg·m∙s⁻¹) was calculated as the product of body mass and final running velocity (VIFT; m∙s⁻¹). Effect sizes (ESs) were used to examine between-group differences.
Results: 30–15IFT performance was possibly to likely higher in NRL players (19.5 ± 1.0 km·h⁻¹; mean ± SD) when compared with SRL (ES = 0.6 ± 0.5; ES ± CI), NYC (ES = 0.6 ± 0.5) and U18 (ES = 0.8 ± 0.5) players. NRL players (537 ± 41 kg·m·s⁻¹) possessed possibly to very likely greater pIFT than SRL (ES = 0.7 ± 0.5), NYC (ES = 1.2 ± 0.5), U18 (ES = 2.3 ± 0.6), U16 (ES = 3.0 ± 0.7) and LL players (ES = 2.0 ± 0.7). Middle forwards attained a likely superior pIFT (ES = 0.5 − 1.8) to all other positional groups.
Conclusions: This study demonstrated that elite rugby league players possess superior PHIR capacities, whilst highlighting that pIFT can account for the disparities in body mass between groups.
Whilst there are various avenues for performance improvement within collegiate American football (AF), there is no comprehensive evaluation of the collective array of resources around performance, physical conditioning and injury and training/game characteristics to guide future research and inform practitioners. Accordingly, the aim of the present review was to provide a current examination of these areas within collegiate AF. Recent studies show that there is a wide range of body compositions and strength characteristics between players, which appear to be influenced by playing position, level of play, training history/programming and time of season. Collectively, game demands may require a combination of upper and lower body strength and power production, rapid acceleration (positive and negative), change of direction, high-running speed, high intensity and repetitive collisions and muscular strength endurance. These may be affected by the timing of, and between, plays and/or coaching style. AF players appear to possess limited nutrition and hydration practices, which may be disadvantageous to performance. AF injuries appear due to a multitude of factors: strength, movement quality, and previous injury whilst there is also potential for extrinsic factors such as playing surface type, travel, time of season, playing position and training load. Future proof of concept studies are required to determine the quantification of game demands with regards to game style, type of opposition and key performance indicators. Moreover, more research is required to understand the efficacy of recovery and nutrition interventions. Finally, the assessment of the relationship between external/internal load constructs and injury risk is warranted.
The purpose of the present study was to evaluate the anthropometric, sprint and high-intensity running profiles of English academy rugby union players by playing positions, and to investigate the relationships between anthropometric, sprint and high intensity running characteristics. Data was collected from 67 academy players following the off-season period and consisted of anthropometric (height, body mass, sum of 8 skinfolds [∑SF]), 40 m linear sprint (5, 10, 20 30 & 40 m splits), the Yo-Yo intermittent recovery test level 1(Yo-Yo IRTL-1) and the 30-15 intermittent fitness test (30-15IFT). Forwards displayed greater stature, body mass and ∑SF; sprint times and sprint momentum, with lower high-intensity running ability and sprint velocities than backs. Comparisons between age categories demonstrated body mass and sprint momentum to have the largest differences at consecutive age categories for forwards and backs; whilst 20-40 m sprint velocity was discriminate for forwards between Under 16s, 18s and 21s. Relationships between anthropometric, sprint velocity, momentum and high-intensity running ability demonstrated body mass to negatively impact upon sprint velocity (10 m; r = -0.34 to -0.46); positively affect sprint momentum (e.g., 5 m; r = 0.85 to 0.93), with large to very large negative relationships with the Yo-Yo IRTL-1 (r= -0.65 to -0.74) and 30-15IFT (r= -0.59 to -0.79). These findings suggest that there are distinct anthropometric, sprint and high-intensity running ability differences between and within positions in junior rugby union players. The development of sprint and high-intensity running ability may be impacted by continued increases in body mass as there appears to be a trade-off between momentum, velocity and the ability to complete high-intensity running.
The 40-yd sprint is the premier event for evaluating sprint speed among football player's at all competitive levels. Some question remains concerning the validity of hand timing compared to electronic timing, as well as the lack of assessment reliability of each method. The purpose of this study was to evaluate the validity of hand timing by experienced and novice timers compared to electronic timing and to establish the reliability and smallest worthwhile difference (SWD) of each method for the 40-yd sprint. NCAA Division I college football players (n = 81) ran two 40-yd sprint trials, with each being timed electronically (touch pad start and infrared beam stop) and with hand-held stop watches by two experienced and four novice timers. There was no significant difference between trials timed electronically or by experienced and novice timers. Hand timing (experienced = 4.90 ± 0.34 s; novice = 4.86 ± 0.33 s) produced a significantly faster 40-yard sprint time than electronic timing (5.12 ± 0.35 s) by 0.22 ± 0.07 and 0.26 ± 0.08 s, respectively. Relative reliability was extremely high for all comparisons with ICC > 0.987. The SWD was 0.12 s with electronic timing and 0.14 s with hand timing. In conclusion, hand timing produces faster sprint times than electronic timing in college football players, independent of timer experience. Repeated 40-yd sprint trials have high relative reliability regardless of timing method. A meaningful change in 40-yd sprint performance is dependent on timing method employed.
Speed and sprint momentum are considered to be important physical qualities for rugby. The purpose of the study was to understand the development of these qualities in senior and junior international rugby players. In Part 1 of the study, a group of senior (n=38) and junior (n=31) players were tested for speed over 40 m. Initial Sprint Velocity (ISV), Maximal Sprint Velocity (MSV), Initial Sprint Momentum (ISM) and Maximal Sprint Momentum (MSM) were calculated using 10 m splits. In Part 2 of the study, a group of junior (n=12) and senior (n=15) players were tracked over a two year period for body mass, ISV, MSV, ISM and MSM. In Part 1, senior backs and forwards were not found to have significantly greater ISV and MSV than junior players but were found to have greater ISM and MSM. Forwards were found to have significantly greater ISM and MSM than backs but significantly lower ISV and MSV than backs. In Part 2, no significant differences were found over the two years between senior and junior players but greater effect sizes for juniors were generally found when compared to seniors for improvements in ISV (d=0.73 vs 0.79), MSV (d=1.09 vs 0.68), ISM (d=0.96 vs 0.54) and MSM (d=1.15 vs 0.50). Sprint momentum is a key discriminator between senior and junior players and large changes can be made by junior players as they transition into senior rugby. Speed appears to peak for players in their early twenties but sprint momentum appears to be more trainable.
Success in rugby league football seems heavily reliant on players possessing an adequate degree of various physical fitness qualities, such as strength, power, speed, agility, and endurance, as well as the individual skills and team tactical abilities. The purpose of this study was to describe and compare the lower body strength, power, acceleration, maximal speed, agility, and sprint momentum of elite first-division national rugby league (NRL) players (n = 20) to second-division state league (SRL) players (n = 20) players from the same club. Strength and maximal power were the best discriminators of which players were in the NRL or SRL squads. None of the sprinting tests, such as acceleration (10-m sprint), maximal speed (40-m sprint), or a unique 40-m agility test, could distinguish between the NRL or SRL squads. However, sprint momentum, which was a product of 10-m velocity and body mass, was better for discriminating between NRL and SRL players as heavier, faster players would possess better drive forward and conversely be better able to repel their opponents'drive forward. Strength and conditioning specialists should therefore pay particular attention to increasing lower body strength and power and total body mass through appropriate resistance training while maintaining or improving 10-m sprint speed to provide their players with the underlying performance characteristics of play at the elite level in rugby leagues.
Performance data for 261 NCAA Division 1A collegiate football players were analyzed to determine if player position, body weight, body fat, and training time were correlated with changes in performance in the following events: power clean (PC), bench press (BP), squat (SQ), vertical jump (VJ), 40-yd dash (40yd), and 20-yd shuttle (20yd). Individual positions were combined into the following groups: (A) wide receivers, defensive backs, and running backs, (B) linebackers, kickers, tight ends, quarterbacks, and specialists, and (C) linemen. Increases in body weight were positively correlated with increases in BP and PC performance for all groups. Increases in body fat were negatively correlated with performance in the PC and VJ for all groups. For group C, increases in body fat were also negatively correlated with performance in the 40yd and 20yd. Group and training time exhibited no linear relationship with performance in any of the tested events. No linear relationships were observed between the independent variables and performance in the SQ. When individual training data were analyzed longitudinally, a nonlinear increase in performance in the PC, BP, and SQ was observed as training time increased, with the greatest rate of change occurring between the first and second semesters of training.
Vincent, LM, Blissmer, BJ, and Hatfield, DL. National Scouting Combine scores as performance predictors in the National Football League. J Strength Cond Res XX(X): 000-000, 2018-The National Football League (NFL) hosts an annual scouting combine to evaluate the approximately 300 elite college football players who are most likely to be selected in the upcoming NFL draft. Given the public interest, player obligations, coaching staff commitments, and business aspects of the combine, several questions have arose in recent years concerning the applicability of combine scores to eventual draft NFL performance. The primary purpose of this study is to investigate the relationship between specified National Scouting Combine (NSC) scores and measures of performance by player position. A secondary aim was to determine whether correlated variables could predict player performance at the quarterback (QB), running back (RB), wide receiver (WR), defensive end (DE), defensive tackle (DT), and linebacker (LB) positions. Subjects in this study were combine participants between the years 2005-2010 who subsequently played in the NFL. The positional groups investigated were QBs (N = 44), RBs (N = 82), WRs (N = 116), LBs (N = 139), DEs (N = 59), and DTs (N = 72). Combine raw scores for 40-yd dash time, countermovement vertical jump (CMVJ) height, standing long jump (SLJ) distance, and pro-agility time were recorded. Measures of horizontal and vertical power were calculated for the 40-yd dash and CMVJ. Combine scores and on-field positional statistics for the first 4 years for QBs and 3 years of all other players' careers were analyzed to investigate relationships. Significant correlations were shown between at least one combine measure and on-field success at every position. Hierarchal regression showed combine measures could predict between 4% and 62% of the variance for individual on-field variables. Quarterback rushing yards were significantly correlated with 40T, CMVJ, vertical jump power (VJP), vertical jump relative power (VJRP), and horizontal power (HP), and those factors accounted for 62.2% of the total variance. Horizontal power and VJP were predictive of QB rushing attempts (r = 0.370). At RB, 40T and SLJ combined were predictive of total rushing yards (r = 0.200), rushing attempts (r = 0.195), and yards per game (r = 0.197). Power variables were predictive of total tackles for DEs' 40HP (r = 0.096) and VJP (r = 0.018), accounting for a total of 21% of the variance. The current study suggests that combine tests are modest predictors of future performance. Should the NFL change the current NSC testing battery, the addition of horizontal and vertical power measurements, as well as position-specific skill tests are recommended.
This investigation analyzed the sprint velocity profiles for athletes who completed the 40-yard (36.6m) dash at the 2016 NFL Combine. The purpose was to evaluate the relationship between maximum velocity and sprint performance, and to compare acceleration patterns for fast and slow athletes. Using freely available online sources, data were collected for body mass and sprint performance (36.6m time with split intervals at 9.1 and 18.3m). For each athlete, split times were utilized to generate modeled curves of distance vs. time, velocity vs. time, and velocity vs. distance using a mono-exponential equation. Model parameters were used to quantify acceleration patterns as the ratio of maximum velocity to maximum acceleration (vmax / amax, or τ). Linear regression was used to evaluate the relationship between maximum velocity and sprint performance for the entire sample. Additionally, athletes were categorized into fast and slow groups based on maximum velocity, with independent t-tests and effect size statistics used to evaluate between-group differences in sprint performance and acceleration patterns. Results indicated that maximum velocity was strongly correlated with sprint performance across 9.1m, 18.3m, and 36.6m (r of 0.72, 0.83, and 0.94, respectively). However, both fast and slow groups accelerated in a similar pattern relative to maximum velocity (τ = 0.768 ± 0.068s for the fast group and τ = 0.773 ± 0.070s for the slow group). We conclude that maximum velocity is of critical importance to 36.6m time, and inclusion of more maximum velocity training may be warranted for athletes preparing for the NFL Combine.
The study aimed to evaluate the mediating effect of biological maturation on anthropometrical measurements, performance indicators and subsequent selection in a group of academy rugby union players. Fifty-one male players 14-17 years of age were assessed for height, weight and BMI, and percentage of predicted mature status attained at the time of observation was used as an indicator of maturity status. Following this, initial sprint velocity (ISV), Wattbike peak power output (PPO) and initial sprint momentum (ISM) were assessed. A bias towards on-time (n = 44) and early (n = 7) maturers was evident in the total sample and magnified with age cohort. Relative to UK reference values, weight and height were above the 90th and 75th centiles, respectively. Significant (p ≤ .01) correlations were observed between maturity status and BMI (r = .48), weight (r = .63) and height (r = .48). Regression analysis (controlling for age) revealed that maturity status and height explained 68% of ISM variance; however, including BMI in the model attenuated the influence of maturity status below statistical significance (p = .72). Height and BMI explained 51% of PPO variance, while no initial significant predictors were identified for ISV. The sample consisted of players who were on-time and early in maturation with no late maturers represented. This was attributable, in part, to the mediating effect of maturation on body size, which, in turn, predicted performance variables.
The aim of this study was to determine the changes in anthropomorphism and performance over a four year eligibility career of American football players. A total of 92 offensive and defensive linemen and 64 skill (wide receivers and defensive backs) player observations were included in the analysis. Data from pre-season testing over a seven year period were compiled, sorted and analyzed by players' year in school. Assessments of strength included 1RM bench press, squat, power clean and a 225 lb. maximum repetition muscle endurance test. Power and speed measures included the vertical jump (VJ) and 40 yd (36.6m) sprint. All strength measures improved significantly (p<0.05) over the years of training. Skill players demonstrated a significant increase in power (W) between years 1 and 2, but at no other time. Linemen did not demonstrate significant changes in VJ. Speed did not change significantly for either group over the four years of training. These data provide a theoretically predictable four-year rate of change in anthropometric, strength and power variables for Division I football players. By having a longitudinal assessment of expected physical improvement it may be possible for strength training personnel to determine those who may need additional attention in an area in order to more closely improve as expected. Additionally, it is suggested that elite athletes may possess genetically superior attributes and therefore, when selecting athletes particular attention should be paid to the selection of those who have previously demonstrated superior speed and power.
The purpose of this study was to compare anthropometric and athletic performance variables during the playing career of NCAA Division III college football players. Two hundred and eighty-nine college football players were assessed for height, body mass, body composition, 1-repetition-maximum (1RM) bench press, 1RM squat, vertical jump height (VJ), vertical jump peak, and vertical jump mean (VJMP) power, 40-yd sprint speed (40S), agility, and line drill (LD) over an 8-year period. All testing occurred at the beginning of summer training camp in each of the seasons studied. Data from all years of testing were combined. Players in their fourth and fifth (red-shirt year) seasons of competition were significantly (p < 0.05) heavier than first-year players. Significant increases in strength were seen during the course of the athletes' collegiate career (31.0% improvement in the 1RM bench press and 36.0% increase in squat strength). The VJ was significantly greater during the fourth year of competition compared to in the previous 3 years of play. Vertical jump peak and VJMP were significantly elevated from years 1 and 2 and were significantly higher during year 4 than during any previous season of competition. No significant changes in 40S or LD time were seen during the athletes playing career. Fatigue rate for the LD (fastest time/slowest time of 3 LD) significantly improved from the first (83.4 ± 6.4%) to second season (85.1 ± 6.5%) of competition. Fatigue rates in the fourth (88.3 ± 4.8%) and fifth (91.2 ± 5.2%) seasons were significantly greater than in any previous season. Strength and power performance improvements appear to occur throughout the football playing career of NCAA Division III athletes. However, the ability to significantly improve speed and agility may be limited.
Side-to-side differences in lower-extremity biomechanics may be predictive of increased risk of lower-extremity injuries in athletes. The purpose of this report is to provide field testing methodology for tests designed to isolate lower-extremity asymmetry and to demonstrate the potential for these tests to provide reliable measures. Six athletes (3 females, 3 males) were tested on 2 consecutive days for activities incorporated into a replicated National Football League (NFL) combine setting. Vertical hop power (VHP) and jump height were measured on a portable force platform as athletes performed maximum effort hops for 10 seconds. The modified agility T-test (MAT) incorporates two 90-degree single-leg cuts during the trial and was measured as total time for completion. Intraclass correlations (within ICC [3,k], between ICC [3,1]) were calculated. The VHP test had good to excellent within-session reliability for peak power of both the right (ICC = 0.942) and left (ICC = 0.895) sides. Jump height showed excellent within-session reliability for both the right (ICC = 0.963) and left (ICC = 0.940) sides. The between-session reliability for peak power between jumps was good for the right (ICC = 0.748) and left (ICC = 0.834) sides. Jump height showed good to excellent between-session reliability on the right (ICC = 0.794) and left (ICC = 0.909) sides. The MAT also showed good reliability between days (ICC = 0.825).The results indicate that the VHP test provides reliable assessment of both within- and between-session jump height and power production. The MAT also provides good reliability between testing days. Both the VHP and the MAT may be useful for clinicians to identify the presence of lower-limb asymmetry and potential injury risk factors in athletic populations.
The authors investigate the correlation between National Football League (NFL) combine test results and NFL success for players drafted at three different offensive positions (quarterback, running back, and wide receiver) during a recent 6-year period, 1999-2004. The combine consists of series of drills, exercises, interviews, aptitude tests, and physical exams designed to assess the skills of promising college football players and to predict their performance in the NFL. Combine measures examined in this study include 10-, 20-, and 40-yard dashes, bench press, vertical jump, broad jump, 20- and 60-yard shuttles, three-cone drill, and the Wonderlic Personnel Test. Performance criteria include 10 variables: draft order; 3 years each of salary received and games played; and position-specific data. Using correlation analysis, we find no consistent statistical relationship between combine tests and professional football performance, with the notable exception of sprint tests for running backs. We put forth possible explanations for the general lack of statistical relations detected, and, consequently, we question the overall usefulness of the combine. We also offer suggestions for improving the prediction of success in the NFL, primarily the use of more rigorous psychological tests and the examination of collegiate performance as a job sample test. Finally, from a practical standpoint, the results of the study should encourage NFL team personnel to reevaluate the usefulness of the combine's physical tests and exercises as predictors of player performance. This study should encourage team personnel to consider the weighting and importance of various combine measures and the potential benefits of overhauling the combine process, with the goal of creating a more valid system for predicting player success.
The vertical jump-and-reach score is used as a component in the estimation of peak mechanical power in two equations put forth by Lewis and Harman et al.
The purpose of the present study was to: 1) cross-validate the two equations using the vertical jump-and-reach test, 2) develop a more accurate equation from a large heterogeneous population, 3) analyze gender differences and jump protocols, and 4) assess Predicted Residual Sum of Squares (PRESS) as a cross-validation procedure.
One hundred eight college-age male and female athletes and nonathletes were tested on a force platform. They performed three maximal effort vertical jumps each of the squat jump (SJ) and countermovement jump (CMJ) while simultaneously performing the vertical jump-and-reach test. Regression analysis was used to predict peak power from body mass and vertical jump height.
SJ data yielded a better power prediction equation than did CMJ data because of the greater variability in CMJ technique. The following equation was derived from SJ data: Peak Power (W) = 60.7x (jump height cm]) +45.3x(body mass [kg])-2055. This equation revealed greater accuracy than either the Lewis or previous Harman et al. equations and underestimated peak power by less than 1%, with a SEE of 355.0 W using SJ protocol. The use of one equation for both males and females resulted in only a slight (5% of power output) difference between genders. Using CMJ data in the SJ-derived equation resulted in only a 2.7% overestimation of peak power. Cross-validation of regression equations using PRESS reveals accurate and reliable R2 and SEE values.
The SJ equation is a slightly more accurate equation than that derived from CMJ data. This equation should be used in the determination of peak power in place of the formulas developed by both Harman et al. and Lewis. Separate equations for males and females are unnecessary.
The purpose of this study was to examine performance differences between drafted and nondrafted athletes (N = 321) during the 2004 and 2005 National Football League (NFL) Combines. We categorized players into one of 3 groups: Skill, Big skill, and Linemen. Skill players (SP) consisted of wide receivers, cornerbacks, free safeties, strong safeties, and running backs. Big skill players (BSP) included fullbacks, linebackers, tight ends, and defensive ends. Linemen (LM) consisted of centers, offensive guards, offensive tackles, and defensive tackles. We analyzed player height and mass, as well as performance on the following combine drills: 40-yard dash, 225-lb bench press test, vertical jump, broad jump, pro-agility shuttle, and the 3-cone drill. Student t-tests compared performance on each of these measures between drafted and nondrafted players. Statistical significance was found between drafted and nondrafted SP for the 40-yard dash (P < 0.001), vertical jump (P = 0.003), pro-agility shuttle (P < 0.001), and 3-cone drill (P < 0.001). Drafted and nondrafted BSP performed differently on the 40-yard dash (P = 0.002) and 3-cone drill (P = 0.005). Finally, drafted LM performed significantly better than nondrafted LM on the 40-yard dash (P = 0.016), 225-lb bench press (P = 0.003), and 3-cone drill (P = 0.005). Certified strength and conditioning specialists will be able to utilize the significant findings to help better prepare athletes as they ready themselves for the NFL Combine.
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