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Original Research

Do Force–Time and Power–Time Measures in a Loaded Jump Squat Differentiate between Speed Performance and Playing Level in Elite and Elite Junior Rugby Union Players?

Hansen, Keir T; Cronin, John B; Pickering, Stuart L; Douglas, Lee

Author Information
Journal of Strength and Conditioning Research: September 2011 - Volume 25 - Issue 9 - p 2382-2391
doi: 10.1519/JSC.0b013e318201bf48
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Abstract

Introduction

The preparation of athletes in collision sports, such as rugby union, rugby league, and American football, traditionally involves a large strength and power training component. Effective prescription of resistance training programs for athletic performance in these sports therefore relies heavily upon accurate assessment of strength and power qualities. This assessment process has recently been termed strength diagnosis (32). The assessment of strength and power, or strength diagnosis, quantifies the importance of a given strength quality to an athletic activity, identifies deficiencies in muscular function, monitors training interventions, and aids in the identification of individual talent in a given athletic endeavor (1).

Currently, the most common method of assessment of closed chain, multijoint lower limb strength, and power uses isoinertial dynamometry (22,30,31), although the use of both isometric (37) and isokinetic (38) dynamometry is also documented. Despite the current popularity of isoinertial dynamometry, the best measures for assessing force, velocity and power qualities of performance during isoinertial lower body movements remain unclear. Measures commonly used include peak and mean force (8,13,39), peak velocity (PV [22,26]), and peak and mean power (6,9,10,12,22,26,35). Yet the validity of some of these measures has been a point of debate in the literature (15,27). One shortcoming is that they do not consider the temporal aspects of force measurement such as rate of force development (RFD).

Temporal measures are thought to be important to muscular performance for a number of explosive activities. A number of temporal measures of force have been discussed in the literature, yet their ability to differentiate performance levels and track training-induced changes has not been well documented. For example, Tidow (36) suggested that starting strength (force or impulse produced at 30 milliseconds), explosive strength (steepest point on the force-time curve or maximum RFD), and force or impulse at 100 milliseconds were crucial to performance in explosive tasks. However, the rationale for the selection of these qualities is not clear. The selection of starting strength as a crucial strength quality seems to be arbitrary (36). Likewise, many of the RFD measures discussed by Zatsiorsky and Kraemer (40) (index of explosive strength, reactivity coefficient (RC), starting gradient, and acceleration gradient) have received limited attention in the literature when measured using isoinertial dynamometry, and their application to strength and conditioning practice has not been discussed in the literature in any great depth. Finally, rate of power development (RPD) measures have received some limited research attention of late (11,24), but their reliability and validity and thus their application for the strength and conditioning professional requires further research.

Previous research has attempted to establish the discriminative ability of a number of tests of muscular function by differentiating between performance levels in a nominated functional task (5,14,16,19,23). For example, numerous studies have investigated the ability of force and power values during jumping movements to differentiate sprinting performance over a variety of distances (5,14,20,23). Yet very few studies have addressed the relationship between temporal aspects of force and power and sprinting performance or addressed the ability of these temporal measures to differentiate between performance levels. Young et al. (39) investigated relationships between a number of force and force-time variables during jumps with and without a countermovement, and speed over 2.5 and 10 m in male and female track and field athletes. They found that peak force (PF), average power, and force at 100 milliseconds all expressed relative to body weight (where the absolute force or power value is divided by the body weight of the athlete) were significantly correlated (r = −0.73 to −0.86) with 2.5-m speed (from a block start). Force at 100 milliseconds and average power output (both relative to body weight) were also significantly correlated (r = −0.80 and −0.79, respectively) with 10-m performance. Wilson et al. (37) also investigated relationships between sprint ability in athletes from a variety of team and individual sports, and temporal aspects of force production, in both concentric only and countermovement jumps, and isometric contractions. In this study, the only variable to correlate significantly with sprint performance (30 m) was force at 30 milliseconds in a concentric only jump squat (r = −0.616). Unfortunately, both these studies were conducted with relatively small subject populations (15-20 subjects), and the reliability of many of the measures discussed was either below what would be deemed acceptable or not stated. Additionally, neither study addressed RPD measures, which also warrant investigation.

The ability of tests of strength and power to discriminate between performance levels in specific sports has also interested strength and conditioning researchers (2,3,7,34). For example, Baker (4) found that peak power (PP) in a jump squat with an external load of 20 kg was significantly greater in professional rugby league players than other playing levels. Sheppard et al. (34) reported that PP and relative PP were significantly different between senior elite and elite junior volleyball players. However, there remains little information about the efficacy of isoinertial force-time and power-time values in differentiating performance levels of athletes.

The best mode of muscular assessment in collision sports, such as rugby union, which require a combination of both speed and strength, is not well documented. The purpose of this study was to investigate the discriminative ability of force-time and power-time measures, specifically investigating their ability to differentiate speed performance and competition level in elite and elite junior rugby union players. This will help identify the force and power measures, which are determinants of speed (as a key aspect of performance in many collision sports) and playing level, in this population. These measures are likely to be the most appropriate for assessment of force and power capabilities in collision sports and key foci in programming for performance enhancement.

Methods

Experimental Approach to the Problem

Forty full time rugby union players from a professional club performed 3 jump squats with an external load of 40 kg on a portable force plate and 3 maximal sprints over 30 m. Force-time and power-time curves from the jump squats were analyzed for a number of temporal variables and sprint times were recorded from a standing start over 5, 10, and 30 m. Subsequently, the group's force-time and power-time variables were analyzed in 2 ways to ascertain the ability of these variables to differentiate performance level in the group. Firstly, subjects were ranked from 1 to 40 in speed performance for each of the 3 sprint distances investigated. An independent sample t-test was then used to investigate if there were significant differences between the fastest 20 and slowest 20 players over each distance in jump squat force-time and power-time variables. Secondly, the group was divided based on their playing levels using methods similar to those reported by Baker and Newton (7). This involved the players being classed as elite or elite junior based on their playing level. Those who played in the first team (premiership squad) were categorized as elite, and those in the academy squad yet to play first team rugby were categorized as elite junior. An independent sample t-test was used to investigate if there were significant differences between the 2 playing levels in jump squat force-time and power-time performance and speed performance.

Subjects

Forty male elite and elite junior rugby union players, between 18 and 34 years of age, volunteered to participate in this study. Mean age, height, and body mass for the elite group and the elite junior group together with pooled data for all subjects can be observed from Table 1. All elite subjects had a strength training background of >5 years and thus are described as highly trained using the definitions of Rhea (33). All elite junior subjects had a strength training history of between 2 and 5 years and thus can be described as recreationally trained using the aforementioned definition system. Testing was conducted as part of the subjects' preseason strength and conditioning program. All subjects were informed of the risks and benefits of participation in the research and signed informed consent forms. Procedures were approved by the institutional Human Research Ethics Committee.

T1-5
Table 1:
Mean (±SD) age, height, and weight for elite, elite junior, and all subjects.

Procedures

Subjects attended 2 testing sessions 48 hours apart. Both sessions were performed at the same time of the day and were the first exercise bout of the day. No high exertion training was performed between sessions, but some low-intensity rugby skills training was undertaken by all subjects.

Sprint Testing

On day 1 of testing, subjects performed a standardized warm-up consisting of sprint technique drills, dynamic stretching, and submaximal sprints, which lasted approximately 20 minutes. They then performed 3 maximal sprints over 30 m. Sprint times over 5, 10, and 30 m were measured using electronic timing gates (Smart Speed, Fusion Sport, Queensland, Australia). These sprint distances were chosen because they are common in rugby union (18). The Smart Speed timing light system is a double beam modulated visible red-light system with polarizing filters and consists of 4 sets of gates. Athletes started in a 2-point crouched position with the left toe 30 cm back from the starting line and the right toe approximately in line with the heel of the left foot. All sprints were performed on an indoor rubber based artificial training surface, and all subjects wore rubber-soled track shoes. Approximately 4 minutes of rest was allowed between sprints. The 2 best times for each distance were averaged and used for analysis.

Jump Squat Testing

In session 2, after a standardized warm-up, each subject performed 3 jump squats with an external load of 40 kg using a methodology similar to that described by Hori et al. (22). This involved the subjects standing at a self selected foot width with an Olympic bar placed on their upper trapezius immediately below C7. The subject then performed a countermovement to a self-selected depth and immediately performed a maximal jump. Subjects were instructed to keep the depth of countermovement consistent between jumps and “jump for maximum height” on each repetition. All subjects were familiar with the jump squat movement because they previously performed it as part of both training and testing programs. All jumps were performed on a portable force plate (Accupower, AMTI, Watertown, MA, USA). Ground reaction force (GRF) data were sampled at 500 Hz via an analog to digital converter (16-Bit, 250 kS·s−1 National Instruments, Austin, TX, USA.) and collected by a laptop computer using custom-built data acquisition and analysis software (Labview 8.2, National Instruments).

Force-Time Analysis

From the resultant vertical GRF data, PF and time to peak force (TTPF) were determined. Subsequently, a number of force-time variables were calculated with analysis commencing at the lowest point on the force-time curve encompassing the latter portion of the eccentric phase and the concentric phase of the movement (8). The PF and TTPF were used to calculate the RC using the formulae of Zatsiorsky and Kraemer (40) (RC = PF/[TTPF × Body Mass]). A moving average (MA) was also used to find the greatest RFD within a 50-milliseconds interval. This moving average RFD (RFD-MA) was conducted over a window length of 50 milliseconds from the start point of analysis until attainment of PF.

Impulse was calculated over the 30-, 100-, and 200-millisecond time intervals (I30, I100, I200) and absolute force at 30, 100, and 200 milliseconds (FA30, FA100, FA200) from the lowest point on the force curve (eccentric-oncentric [EC]). Additionally, impulse and absolute force variables for the concentric phase were also calculated (concentric only [CO]). The concentric phase was defined as starting at the lowest point on the displacement-time curve (8). Both impulse and absolute force were calculated over 30 and 100 milliseconds from the start of the concentric phase. All force variables were expressed as absolute values and relative to body weight because both approaches have been used previously in the literature (37,39). All force-time variables had either an intraclass correlation coefficient (ICC) of .0.85 or a coefficient of variation (CV) of less than 10% or achieved both of these reliability standards.

Power-Time Analysis

Power-time data were calculated from GRF data using the impulse-momentum (forwards dynamics) approach to calculate the system power as outlined previously in the literature (10,17). Because the initial velocity of the system was zero, at each time point throughout the jump, vertical GRF was divided by the mass of the system to calculate acceleration of the system. Acceleration due to gravity was then subtracted so that only the acceleration generated by the subject was multiplied by time data to calculate instantaneous velocity of the systems center of mass. The resultant velocity data were then multiplied by the original GRF data to calculate power.

From the integrated power and velocity data PP, PV, time to PP (TTPP), and time to PV (TTPV) were determined. Additionally, 2 rates of RPD measures were calculated. The calculations were initiated at minimum power encompassing the latter portion of the eccentric phase and the concentric phase of the jump. The first variable calculated was RPD using a moving average (RPD-MA), which was calculated over a window length of 50 milliseconds from the start point of analysis until PP. The second variable was the RC described by Zatsiorsky and Kraemer (40) for the force-time curve, applied to the power-time curve (P − RC = PP/[TTPP × body mass]). As with the force-time variables, all power variables were expressed as absolute values and relative to body weight because both approaches have been used previously in the literature (23). All power-time variables had either an intraclass correlation coefficient (ICC) of 0.85 or a coefficient of variation (CV) of 10% or achieved both of these reliability standards.

Statistical Analyses

All statistical analyses for force and power variables were performed on the mean of trials 2 and 3 with the first trial excluded from analysis (21). Statistical analyses of speed times were performed on the mean of the 2 fastest trials. Means and SDs were used as measures of centrality and spread of data. The data obtained were analyzed using SPSS statistical software (SPSS 15, Chicago, IL, USA). In the first instance, all subjects were ranked from 1 to 40 based on the average of their 2 best sprint times for each distance. An independent sample t-test was then used to ascertain significant differences between groups for force and power variables of interest at each distance. Additionally, independent sample t-tests were conducted between the elite group (n = 25) and the elite junior group (n = 15), also to ascertain whether these groups differed significantly in the force and power variables of interest. An alpha level of 0.05 was used for all statistical comparisons.

Results

Mean sprint times over the 3 distances (5, 10, and 30 m) for the fast and slow groups can be observed from Table 2. The difference between the 2 groups was significant at all distances (8.2, 8.2, and 8.0% for 5, 10, and 30 m, respectively). Mean values for force variables for the fast and slow groups over each distance can be observed from Table 3. The only force-time variable to show a significant difference between the fast and slow groups was EC I200 where the fast group at 10 m was significantly lower (9.1%) than the slow group at 10 m. Mean values for power variables for the fast and slow groups can be observed from Table 4. Relative PP was significantly greater in the 10-m fast group and the 30-m fast group (10.8 and 13.9%, respectively). Additionally, PV and relative RPD-MA were significantly greater (7.4 and 24.4%, respectively) in the 30-m fast group.

T2-5
Table 2:
Mean (±SD) sprint times for fastest and slowest subjects over 5, 10, and 30 m.
T3-5
Table 3:
Mean (±SD) force variables and p values for fastest 20 and slowest 20 players over 5, 10, and 30 m.*
T4-5
Table 4:
Mean (±SD) power and velocity variables and p values for top 20 and bottom 20 players over 5, 10, and 30 m.*

Mean sprint times over the 3 distances (5, 10, and 30 m) for the elite and elite junior groups can be observed from Table 5. There were no significant differences between the 2 groups at any of the 3 distances. Mean values for force variables for the elite and elite junior groups can be observed from Table 6. In terms of absolute values, PF, RFD-MA, EC-FA100, and EC FA200 were all significantly greater (% difference = 10.3-37.4%) in the elite group compared to the in elite junior group. In terms of relative values, RFD-MA, EC FA30, and EC FA200 were all significantly different between the 2 groups. Relative RFD-MA and FA200 were significantly greater in the elite group (34.5 and 19.0%, respectively) compared to in the elite junior group. Conversely, relative EC FA30 was significantly greater (25.0%) in the elite junior group. Mean values for the power variables for the elite and elite junior groups can be observed from Table 7. The PP and RPD-MA were significantly greater (12.6 and 21.2%, respectively) in the elite group when compared to in the elite junior group.

T5-5
Table 5:
Mean (±SD) sprint times for elite and elite junior subjects over 5, 10, and 30 m.
T6-5
Table 6:
Mean (±SD) force variables and p values for elite vs. elite junior players.
T7-5
Table 7:
Mean (±SD) power and velocity variables and p values for elite vs. elite junior subjects.

Discussion

This study aimed to establish the discriminative ability of force and power values calculated from the force-time and power-time curve of a loaded rebound jump squat. Specifically, we investigated 2 qualities; the ability of these values to differentiate between the fastest and slowest sprinters in the population of elite rugby union players, and, second, the differences in force-time and power-time parameters between elite and elite junior players. Both absolute and relative force values and absolute power values differentiated playing levels, whereas only power values expressed relative to body weight were able to differentiate speed performance. These are novel findings that have not been published previously with these measures in this population.

Our results do not suggest that any force variables expressed as a relative or absolute value are able to differentiate speed performance over any of the distances investigated. These findings are similar to other studies that have shown that force variables in a rebound jump squat are not strongly related to speed performance over 30 m in team sport athletes (20,37). The only force variable to be significantly different between the fastest and slowest group in this study was I200, which was significantly greater in the slow group. Although not statistically significant, a number of force variables were greater in the slow group. These results are likely to be a reflection of the weight of the players in the 2 groups with heavier players typically being slower, but because of their greater mass being able to generate greater absolute force values. A clear strong correlation (r = 0.64) has previously been reported between 30- and 40-m sprint times and body weight in a population of professional rugby union and rugby league players (20) with faster players typically weighing less. This finding may be a reflection of the body composition of larger players who may carry greater fat mass, although this was not quantified in the study of Harris et al. (20) or in this study.

The fact that RFD values, even when expressed relative to body weight, were not significantly greater in fast athletes when compared to slow athletes over all sprint distances contradict the suggestions of Tidow (36) who postulated that these physical qualities are crucial to athletic performance. This may be related to the biomechanical differences between the jump squat and sprinting, particularly in the acceleration phase of the sprint. The literature suggests that a good sprinter is capable of directing GRFs as horizontally as possible (29) in the acceleration phase of the sprint, whereas a rebound jump squat requires that the athlete direct GRFs vertically. Thus, where sprinting is dependent on horizontal impulse, jumping patterns are dependent on vertical impulses.

The PP and RPD-MA when expressed relative to body weight and PV were all significantly greater in faster athletes when compared to in slower athletes over 30 m. Additionally, PP relative to body weight was significantly greater over 10 m in the fast group. These findings are consistent with previous studies which have reported significant relationships between PP relative to body weight in loaded jump squats and speed performance over similar distances in team sport athletes (5,14,23). The finding that the difference in these variables was greatest at 30 m may again be because of the movements being functionally more similar over the longer distance (10-30 m). That is, as the sprint progresses, the vertical braking forces during the stance phase increase (29), and thus, the contribution of the stretch shorten cycle (SSC) to sprint performance increases (25). Therefore, a common factor between sprinting (after the initial steps) and a rebound jump squat is the ability of the athlete to use the SSC. The most notable difference between the 2 movements (sprinting and jumping) being that sprinting requires that the resultant force and power must be directed horizontally and jumping requires that they must be directed vertically. These findings have implications for the strength and conditioning professional in that relative power, RPD and velocity may be better used to identify talent and monitor training in explosive sports. This also suggests that in sports where running speed is of importance, resistance training should be focused on generating PV, and PP and RPD relative to body mass in training rather than high absolute forces, which has been the traditional approach in resistance training for explosive sports.

The RPD-MA was the only temporal variable able to differentiate fast athletes from slow over any distance. This variable is calculated by conducting a MA over the power-time curve and thus represents the peak RPD over this time period (50 milliseconds). The fact that faster sprinters generated greater values in RPD-MA suggests that unlike force development the ability to generate power rapidly or “explosively” during jumping is functionally similar to the ability to generate power and velocity explosively when sprinting. However, it is noteworthy that although RPD-MA was able to differentiate speed performance over 30 m, PP and PV also differentiated speed performance at this distance. Therefore, for the practitioner using the jump squat to assess lower body muscular function, the use of PP and PV which is simpler to calculate and has greater reliability may be sufficient, and the calculation of RPD-MA may not be necessary. Nonetheless, the application of RPD-MA to strength and conditioning practice warrants further investigation.

Our results showed no significant difference between elite and elite junior rugby union players in terms of speed performance over 5, 10, and 30 m. Previous research by Baker and Newton (7) reported similar findings in a population of professional rugby league players. Because they are collision sports, it could be argued that momentum is crucial to performance in both rugby union and rugby league and thus the ability to generate momentum rather than speed will differentiate performance level. Baker and Newton in the aforementioned research reported sprint momentum, calculated by multiplying body mass by the average sprint velocity over 10 m. In this quality, there was a significant difference between national level athletes and state level athletes. In this study, the elite group was heavier (99.7 ± 12.4 kg) than the elite junior group (93.8 ± 10.7 kg), and thus, it is likely that their ability to generate momentum would be greater.

There were however significant differences between elite and elite junior players in force and power capabilities. Absolute PF plus a number of temporal force variables were found to be significantly greater in elite players. Additionally, absolute PP and RPD-MA were significantly greater in the elite group with no significant difference found in relative values. With regards to PP, these findings are consistent with a number of previous studies, which have reported that lower body PP is significantly greater in elite compared to in elite junior athletes (3,7,34). Although a number of force-time values and RPD-MA were significantly different between groups, given that PF and PP were also able to differentiate groups, it may be that as with speed performance, the use of these traditional variables is sufficient in strength and power assessment for rugby union and other similar sports. Temporal analysis of the force-time and power-time curves may not be necessary.

Although with speed performance only relative values differentiated faster times, absolute values differentiated between elite and elite junior rugby players. This is likely to be principally because of the greater mass of the elite group when compared to the elite junior group. This study did not directly quantify lean mass and fat mass in the various groups compared. Nonetheless, it may be surmised that the greater body weight of the elite group compared to the elite junior group was because of lean mass, leading to the greater absolute values in the aforementioned measures through an increased ability to generate force. Future research would benefit from quantifying lean mass and fat mass and comparing between groups. From a practical perspective, it can be concluded that, although resistance training for an athlete training for speed should be focused on developing power relative to body mass, a developing rugby union player may be best served to focus on increasing absolute force and power capabilities through increasing lean mass and maximum force production (without compromising speed performance).

It should be noted that caution is to be exercised when interpreting these results. In comparing strength and power characteristics between Olympic lifters, power lifters, and sprinters, McBride et al. (28) reported that strength and power profiles reflected the training approaches of each group. This being the case, the fact that absolute force and power values were greater in elite rugby players may simply reflect the high training age of these players and the strong influence of high resistance training used in rugby union in recent years to increase lean mass and strength in players. Hypothetically, should focus shift to a greater emphasis on velocity and relative power in the future, the physical attributes differentiating elite from elite junior players may also change.

Practical Applications

One purpose of strength and conditioning assessment is to determine those predictor variables that are fundamental to performance in sport specific tasks, such as the sprint ability of rugby union players. For the purposes of guiding resistance training prescription and assessing athletic development, it is important for coaches to identify the force and power variables crucial to performance. In the cohort of rugby union players investigated in this study, PV, and PP and RPD relative to body weight, differentiated fast from slow players. These variables therefore can be used by the coach to guide programming and track training adaptation during resistance training for speed development. Resistance training programs for speed development should be designed to focus on the velocity of movement in training. The mass of the player is also a critical consideration given that the predictor variables were expressed relative to body weight. Decreasing fat mass will increase the power to weight ratio. Accordingly, the coach needs to consider the ideal anthropometry of the players related to their positional requirements.

Another focus of strength and conditioning assessment is to determine the variables that distinguish elite from subelite athletes. This is particularly important in talent identification and serves to focus training prescription around variables that are thought critical to “elite” performance. For the rugby union players used in this study, a number of force and power variables differed significantly between playing levels. These included PF, PP, force at 100 milliseconds from minimum force and force and impulse at 200 milliseconds from minimum force. The additional 6-kg body mass of the elite players no doubt affected the magnitude of many of these variables and the significant differences between groups. When testing and training rugby union players, it would seem most appropriate therefore for the coach to target absolute force and power measures. For the purposes of player development and training strategies for rugby union players to transition to elite status, adding lean mass is likely to be most beneficial. However, given the metabolic demands of rugby union, it is likely that this strategy of increasing lean mass is only appropriate to a certain point, which is likely to be position specific.

References

1. Abernethy, P, Wilson, G, and Logan, P. Strength and power assessment: Issues, controversies and challenges. Sports Med 19: 401-417, 1995.
2. Baker, D. A comparison of running speed and quickness between elite professional and young rugby league players. Str Cond Coach 7: 3-7, 1999.
3. Baker, D. The effects of an in-season of concurrent training on the maintenance of maximal strength and power in professional and college-aged rugby league football players. J Strength Cond Res 15: 172-177, 2001.
4. Baker, D. Differences in strength and power among junior-high, senior-high, college-aged, and elite professional rugby league players. J Strength Cond Res 16: 581-585, 2002.
5. Baker, D and Nance, S. The relation between running speed and measures of strength and power in professional rugby league players. J Strength Cond Res 13: 230-235, 1999.
6. Baker, D, Nance, S, and Moore, M. The load that maximizes the average mechanical power output jump squats in power trained athletes. J Strength Cond Res 15: 92-97, 2001.
7. Baker, DG and Newton, RU. Comparison of lower body strength, power, acceleration, speed and agility and sprint momentum to describe and compare playing rank among professional rugby league players. J Strength Cond Res 22: 1, 2008.
8. Chiu, LZF, Schilling, BK, Fry, AC, and Weiss, LW. Measurement of resistance exercise force expression. J Appl Biomech 20: 204-212, 2004.
9. Cormie, P, Deane, R, and Mcbride, JM. Methodological concerns for determining power output in the jump squat. J Strength Cond Res 21: 424-430, 2007.
10. Cormie, P, Mcbride, JM, and McCaulley, GO. Validation of power measurement techniques in dynamic lower body resistance raining. J Appl Biomech 23: 103-118, 2007.
11. Cormie, P, Mcbride, JM, and Mccaulley, GO. Power-time, force-time, and velocity-time curve analysis of the countermovement jump: Impact of training. J Strength Cond Res 23: 177-186, 2009.
12. Cormie, P, McCaulley, GO, Travis-Triplett, N, and McBride, JM. Optimal loading for maximal power output during lower-body resistance exercise. Med Sci Sports Exerc 39: 340-349, 2007.
13. Cronin, J, Hing, R, and McNair, PJ. Reliability and validity of a linear position transducer for measuring jump performance. J Strength Cond Res 18: 590-593, 2004.
14. Cronin, JB and Hansen, KT. Strength and power predictors of sports speed. J Strength Cond Res 19: 349-357, 2005.
15. Cronin, JB and Sleivert, G. Challenges in understanding the influence of maximal power training on improving athletic performance. Sports Med 35: 213-234, 2005.
16. Dowson, MN, Nevill, ME, Lakomy, HK, Nevill, AM, and Hazeldine, RJ. Modelling the relationship between isokinetic muscle strength and sprint running performance. J Sports Sci 16: 257-265, 1998.
17. Dugan, EL, Doyle, T, Humphries, B, Hasson, CJ, and Newton, RJ. Determining the optimal load for jump squats: A review of methods and calculations. J Strength Cond Res 18: 495-504, 2004.
18. Duthie, G, Pyne, D, and Hooper, S. Time motion analysis of 2001 and 2002 super 12 rugby. J Sports Sci 23: 523-530, 2005.
19. Harris, N, Cronin, J, and Keogh, N. Contraction force specificity and its relationship to functional performance. J Sports Sci 25: 201-212, 2007.
20. Harris, NK, Cronin, JB, Hopkins, W, and Hansen, KT. Relationship between sprint times and the strength/power outputs of machine squat jump. J Strength Cond Res 22: 691-698, 2008.
21. Hopkins, W, Schabort, E, and Hawley, J. Reliability of power in physical performance tests. Sport Med 31: 211-234, 2001.
22. Hori, N, Newton, RU, Andrews, WA, Kawamori, N, Mcguingan, MR, and Nosaka, K. Comparison of four different methods to measure power output during the hang power clean and the weighted jump squat. J Strength Cond Res 21: 314-320, 2007.
23. Hori, N, Newton, RU, Andrews, WA, Kawamori, N, Mcguigan, MR, and Nosaka, K. Does performance of hang power clean differentiate performance of jumping, sprinting, and changing of direction? J Strength Cond Res 22: 412-418, 2008.
24. Hori, N, Newton, RU, Kawamori, N, Mcguigan, MR, Kraemer, WJ, and Nosaka, K. Reliability of performance measurements derived from ground reaction force data during countermovement jump and the influence of sampling frequency. J Strength Cond Res 23: 874-882, 2009.
25. Jacobs, R, Bobbert, MF, and Van Ingen Schenau, GJ. Function of mono-and biarticular muscles in running. Med Sci Sports Exerc 25: 1163-1173, 1993.
26. Jidovtseff, B and Croisier, JL. The concept of iso-inertial assessment: Reproducibility analysis and descriptive data. Isokinet Exerc Sci 14: 53-62, 2006.
27. Knudson, DV. Correcting the use of the term “power” in the strength and conditioning literature. J Strength Cond Res 23: 1902-1908, 2009.
28. McBride, JM, Triplett-Mcbride, T, Davie, A, and Newton, RU. A comparison of strength and power characteristics between power lifters, Olympic lifters and sprinters. J Strength Cond Res 13: 58-66, 1999.
29. Mero, A. Force-time characteristics and running velocity of male sprinters during the acceleration phase of sprinting. Res Quart Exerc Sport 59: 94-98, 1988.
30. Murphy, AJ and Wilson, GJ. The ability of tests of muscular function to reflect training-induced changes in performance. J Sports Sci 15: 191-200, 1997.
31. Murphy, AJ, Wilson, GJ, and Pryor, JF. The use of the isoinertial force mass relationship in the prediction of dynamic human performance. Eur J Appl Physiol 69: 250-257, 1994.
32. Newton, RU and Dugan, E. Application of Strength Diagnosis. Strength Cond J 24: 50-59, 2002.
33. Rhea, MR. Determining the magnitude of treatment effects in strength training research through the use of the effect size. J Strength Cond Res 18: 918-920, 2004.
34. Sheppard, JM, Cormack, SJ, Taylor, K, Mcguigan, MR, and Newton, RU. Assessing the force-velocity characteristics of the leg extensors in well-trained athletes: The incremental load power profile. J Strength Cond Res 22: 1320-1326, 2008.
35. Sleivert, GG and Taingahue, M. Maximal jump-squat power and sprint acceleration in athletes. Paper Format. University of New Brunswick, Faculty of Kinesiology, Fredericton, 2003.
36. Tidow, G. Aspects of strength training in athletics. New Stud Athl 1: 93-110, 1990.
37. Wilson, GJ, Lyttle, AD, Ostrowski, KJ, and Murphy, AJ. Assessing dynamic performance: A comparison of rate of force development tests. J Strength Cond Res 9: 176-181, 1995.
38. Wilson, GJ, Walshe, AD, and Fisher, MR. The development of an isokinetic squat device: reliability and relationship to functional performance. J Appl Physiol 75: 455-461, 1997.
39. Young, W, Mclean, B, and Ardagna, J. Relationship between strength qualities and sprinting performance. J Sports Med Phys Fitness 35: 13-19, 1995.
40. Zatsiorsky, VM and Kraemer, WJ. Science and Practice of Strength Training (2nd ed.). Champaign, IL: Human Kinetics, 2006.
Keywords:

force; power; rugby union

Copyright © 2011 by the National Strength & Conditioning Association.