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Maturity Status as a Determinant of the Relationships Between Conditioning Qualities and Preplanned Agility in Young Handball Athletes

Hammami, Raouf1; Sekulic, Damir2; Selmi, Mohamed Amin1; Fadhloun, Mourad3; Spasic, Miodrag2; Uljevic, Ognjen2; Chaouachi, Anis1,4

Author Information
Journal of Strength and Conditioning Research: August 2018 - Volume 32 - Issue 8 - p 2302-2313
doi: 10.1519/JSC.0000000000002390
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Abstract

Introduction

Preplanned agility is a necessary component of a field-sport athlete's physical abilities (8,26,29). This is because the inherent design of field-based team sports (i.e., football, handball, basketball, and lacrosse) places a great emphasis on the ability of the athlete to run quickly and change directions during a game (30). Therefore, it is important for field sport coaches and strength and conditioning practitioners to understand the physical components that can contribute to preplanned agility. Evidence from cross-sectional studies indicate that preplanned agility is significantly associated with muscle strength and power (8,22,24), balance abilities (14,32), and speed (26).

Jumping abilities accounted for 25–45% of the variance of preplanned agility performances in college-level athletes (25,27,32), junior soccer players (9), junior basketball players (1,33), and team sport athletes (29). Furthermore, sprinting abilities and preplanned agility were significantly correlated (25–35% of the common variance) in junior soccer players (9,36), but sprinting and preplanned agility did not correlate in professional soccer players (10). Furthermore, studies have identified the importance of the elastic and reactive strength components in preplanned agility performances for team sport athletes, physical education students, and physically active male adults (22,29,38). However, most of the studies investigated the correlation between predictors and preplanned agility in adult and late-pubescent athletes.

In youth athletes, the importance of agility for long-term athletic development regarding performance enhancement and injury prevention has recently been described, and agility has been highlighted as one of the most underresearched fitness components within the pediatric literature (4,11). In addition, there is a lack of research on the determinants of preplanned agility performances in prepubertal and early-pubertal athletes (17,31). In this context, Negra et al. recently demonstrated a significant relationship between both the Illinois agility test and T-HALF scores and measures of sprint and power performance (r = 0.72–0.85) in a sample of competitive-level young soccer (n = 95) and handball players (n = 92, approximately 12 years old), and the authors concluded that the preplanned agility, speed, and muscle power assess the same physical attributes in the studied athletes (24). However, to the best of our knowledge, no previous study has considered the effect of biological maturation when examining the determinants of preplanned agility.

With the new Youth Physical Development (YPD) model that was demonstrated recently by Granacher and Lloyd, the need for a structured and logical approach to developing different types of agility throughout childhood and adolescence has been highlighted (4,11). Therefore, it would be important to define whether growth and maturation modulate relationships between specific predictors (i.e., power, sprinting, and anthropometric indices) and preplanned agility. In addition, there is insufficient evidence to suggest that training programs should be adapted to coincide with periods of maturation. This is particularly relevant given the recent academic debate on the trainability of youth, the timing of youth agility training, and the potential existence of golden periods of adaptation (18).

To optimize agility training programs, correlation analyses as parts of investigations of different determinants of agility (i.e., sprinting speed, power qualities, and anthropometrics) need to be conducted. In addition, given the differing methodological approaches in the current literature, there is a lack of knowledge about the longitudinal development of key determinants of preplanned agility in boys and about the influence that maturation and the period of peak height velocity (PHV) may exert on these determinants. Therefore, the aim of this study was to examine how the determinants of preplanned agility are affected by the period of PHV, an indicator of maturity status, regarding the anthropometrics, speed, reactive strength, and jumping ability in young male handball players.

Methods

Experimental Approach to the Problem

This cross-sectional study analyzed the relationship between anthropometrics, sprinting speed, and jumping abilities with 2 measures of preplanned agility in young handball players of different maturity status. The players were split into the pre- and post-peak height velocity groups (Pre-PHV and Post-PHV, respectively) according to their biological age of maturity. Pearson's correlation and multiple stepwise regression analyses were used to determine possible interactions between anthropometrics and conditioning qualities with preplanned agility. The independent variable was the sample group (Pre- or Post-PHV). The dependent variables were: the linear 10-m sprint, 30-m sprint, 20-m flying time, vertical countermovement jump (CMJ), vertical squat jump (SJ), reactive strength index (RSI), standing long jump (standing long-J), triple-hop jump, horizontal CMJ (HCMJ), and horizontal squat jump (HSJ).

Subjects

In this study, we included 56 young handball players (male; age range between 12 and 14 years) recruited from a first-division Tunisian handball club (Bni Khiar Club, Tunisia). All players volunteered to participate in this study, and they were available for 2 testing sessions for the study. The players did not have any existing medical conditions, balance disorders, or significant lower-limb injuries that would compromise participation in the study. For all players, pubertal timing was estimated according to the biological age of maturity of each individual, as described by Moore et al. {Maturity offset = −7.999994 + (0.0036124 × age [years]) × height (cm); (R2 = 0.896; standard error of estimate = 0.542)} (23). This assessment has been applied previously (24) and is a noninvasive and practically approved method to predict years from PHV as a measure of maturity offset using anthropometric variables. Therefore, a maturity offset of −1.0 indicates that the player was measured 1 year before this peak velocity, a maturity offset of 0 indicates that the player was measured at the time of this peak velocity, and a maturity offset of +1.0 indicates that the athlete was measured 1 year after this peak velocity (19). Accordingly, players were allocated into 2 groups (Pre-PHV and Post-PHV; 34 and 22 players, respectively). The study was conducted according to the Declaration of Helsinki, and the protocol was fully approved by the ethics committee of the National Center of Medicine and Science of Sports before the commencement of the assessments. All athletes received a clear explanation of the study, including the risks and benefits of participation; written informed consent was obtained from the subjects and their parents or responsible adults before testing.

Procedures

Variables observed in this study included the following: anthropometrics, vertical and horizontal jumping performances, sprinting qualities, and preplanned agility. Anthropometric variables consisted of standing body height (cm), body mass (kg), and body fat (BF), which was estimated on the basis of skinfold measures of triceps and biceps (34). Preplanned agility and sprinting capacities were measured using Brower timing gates (Brower Timing Systems, Salt Lake City, UT, USA), whereas vertical jumps were measured using the Ergojump system (Ergojump apparatus; Globus Italia, Codogne, Italy).

The preplanned agility was evaluated by the T-half test (T-HALF) and the Change of Direction and Acceleration Test (CODAT). The T-HALF assesses movement patterns common in handball defense (i.e., combination of forward-backward sprinting and lateral displacement) (Figure 1). The test was conducted as previously described (28). Time was recorded to the nearest 0.01 seconds. Three trials were attempted with 3 minutes of recovery between trials, and the best time was used for analysis.

Figure 1.
Figure 1.:
T-HALF test. The athlete runs forward from cone A to cone B, then shuffles to the left (cone C), shuffles to the right (cone D), and shuffles back to point B before running backward to the start position (cone A).

The CODAT was used as a second test of the preplanned agility because it assesses movement patterns common in handball offense (Figure 2). As in the T-HALF, athletes completed practice trials of the CODAT during warm-up. Athletes started 30 cm behind the start line; they were required to face forward at all times and to stay outside the markers when running (15). Three trials were completed with 3 minutes of recovery between each trial. The time was recorded to the nearest 0.01 seconds, and the best time was used for the analysis.

Figure 2.
Figure 2.:
Change of direction and acceleration test. Dimensions and complete route; m = meters.

Sprinting performance testes included the following: a 10-m sprint (S10), 30-m sprint (S30), and straight-line 20-m flying time (S20F) measurement. For the S10 and S30, players were asked to take the start position 2 seconds before the assessment, to place the front foot 5 cm ahead of the first timing gate and to await the start signal for the sprint. Photo cells were placed at the start, 10, 20, and 30-m (finish-line) points and were positioned approximately 0.4 m above the ground. Measurement was conducted with an accuracy of 0.01 seconds. The S20F was determined on the basis of results from between the 10 and 30 m when players performed the S30 (5). Tests were performed twice and were separated by a passive recovery period of at least 5 minutes; the best performance was recorded and used for further analysis.

Vertical jumping performance tests included the following: CMJ, SJ, and RSI. All 3 vertical jump procedures were commenced with the hand remaining on the hips (i.e., without arm swing). The CMJ involved athletes lowering themselves from an upright standing position until reaching a knee angle of approximately 90°, which was immediately followed by a maximal vertical jump. During the SJ, participants started from a stationary semi-squatted position (knee angle of 90°) with the gluteal fold resting on the edge of the chair and performed a vertical jump at maximal effort. When the athletes performed a jump, the examiner pulled the chair back to prevent injury. For the evaluation of RSI, the maximal hopping protocol was performed in the same manner as previously reported (12), and it involved athletes performing 5 repeated bilateral maximal vertical hops in place on the contact mat. Athletes were instructed to maximize their jump height and minimize their ground contact time. The first jump in each trial was discounted, whereas the remaining 4 hops were averaged for the analysis of the RSI as previously suggested (3).

For the evaluation of horizontal jumping performances, players were tested on standing long-J, HCMJ, HSJ, and triple-hop test (3HOPT). For the standing long-J, athletes stood behind the starting line and were instructed to push off vigorously and jump forward as far as possible. Throughout this test, countermovement, arm swing, and body swing were allowed. The distance jumped was measured in centimeters using a metal tape measure from the start line at takeoff to the position of the heel on landing. With the 3HOPT, a cloth tape measure was fixed to the ground, perpendicular to the starting line. Participants stood on the designated testing leg, behind the starting line. They performed 3 consecutive maximal hops forward on the same (dominant) limb. Arm swing was allowed. The investigator measured the distance hopped from the starting line to the point where the heel struck the ground on completing the third hop. For HCMJ, athletes performed testing as previously described for the standing long-J, but their hands remained on their hips during the jump, and therefore the jump was performed without arm swing. For HSJ, athletes underwent testing as previously described for the SJ (gluteal fold was resting on the edge of the chair) but they performed a maximal horizontal jump (horizontal displacement). Jumping tests were performed 3 times with 2-minute rest between trials, and the best performance was recorded and used for further analysis.

Before commencement of the study and the initiation of testing, all players were familiarized with the general testing environment, method of testing, technique for each test used, equipment, and experimental procedures. Testing was conducted over 2 days that were separated by 48 hours during the second half of the competitive season (March). The first day consisted of anthropometrics, sprinting and tests of preplanned agility. The remaining tests were conducted on second day in a random order.

At the beginning of the first testing day, athletes performed a standardized warm-up, consisting of 10 minutes of jogging, 10 minutes of dynamic stretching of the lower limbs, and progressive speed runs over the testing distances. The day-2 warm-up consisted of 10 minutes of low-intensity jogging, which was followed by submaximal running and dynamic stretching, low-intensity forward, sideways, and backward running, several accelerating runs, jumping at a progressively increased intensity, and a range of mobility exercises that provided appropriate activation of the lower-limb musculature. Testing was conducted at the beginning of regular handball training. All players were tested in the late afternoon for both testing sessions and in the same order. Players did not eat for 2–3 hours before their testing sessions, and they refrained from intensive lower-body exercise and any form of stimulant (e.g., caffeine) in the 24 hours before testing.

Statistical Analyses

The reliability was established by calculating intraclass correlation coefficients (ICCs) model 3,1 (37). In addition, SEM (square root of mean square error derived from analysis of variance [ANOVA] for repeated measurements) and coefficient of variation {CV; (CV% = [SEM/Mean] × 100)} were calculated (6,37). The Kolmogorov-Smirnov test identified all variables as normally distributed; therefore, descriptive statistics included the mean values and standard deviations. The homoscedasticity of the variables was tested by Levene's test.

Because of low correlations between some pairs of variables and unequal number of players in the studied groups, the differences between observed groups (i.e., Pre-PHV vs Post-PHV) were determined using one-way ANOVA (2). In addition, between-group differences were analyzed using a magnitude-based Cohen's effect size (ES) statistic with modified qualitative descriptors. The ES was assessed using the following criteria: <0.02 = trivial, 0.2–0.6 = small, >0.6–1.2 = moderate, >1.2–2.0 = large, and >2.0 very large differences (6). For those variables where homoscedasticity of the variance was not confirmed (see Results for details), between-group differences were calculated using Kruskal-Wallis ANOVA.

The univariate associations between variables were determined using Pearson's product moment correlation (Pearson's R). Multiple regression analyses (forward stepwise model) were calculated to establish multivariate relationships between predictors and preplanned agility criteria separately for each group. Although Levene's test identified heteroscedasticity for some of the variables (see Results for details), these variables were included in multiple regressions because regressions were separately calculated for each group.

A p-value of 95% was applied and Statistica version 12.0 (StatSoft, Tulsa, OK, USA) was used for all calculations.

Results

The reliability of applied tests was appropriate to high. The reliability ranged from an ICC of 0.87 (for 3HOPT) to 0.95 (for CMJ) and CV of 5.8 to 4.4% (for standing long-J and 3HOPT, respectively) (Table 1).

Table 1.
Table 1.:
Reliability of the applied tests.*

Groups differed significantly in their PHV (F test: 238.79, p < 0.01, very large differences), body mass (F test: 50.45, p < 0.01, large differences), body height (F test: 111.90, p < 0.01, very large differences), SJ (F test: 18.39, p < 0.01, very large differences), CMJ (F test: 18.80, p < 0.01, very large differences), S20F (F test: 23.99, p < 0.01, small differences), and S30 (F test: 16.25, p < 0.01, small differences). Kruskal-Wallis tests indicated significant differences between groups for standing long-J (H value: 15.42, p < 0.01), HSJ (H value: 19.65, p < 0.01), HCMJ (H value: 19.07, p < 0.01), and 3HOPT (H value: 22.19, p < 0.01) (Table 2).

Table 2.
Table 2.:
Descriptive statistics and differences between groups.*

In the Pre-PHV group, T-HALF results were significantly correlated with age, body mass, BF, SJ, CMJ, standing long-J, HSJ, HCMJ, 3HOPT, and all sprinting indices. In the same group, CODAT results were correlated with the body mass, BF, SJ, CMJ, standing long-J, HSJ, HCMJ, and sprinting indices. In general, better agility performance was demonstrated in older boys with better physical abilities, lower body mass, and less BF (Table 3).

Table 3.
Table 3.:
Pearson's moment correlation coefficients between studied variables in pre-PHV group.*

For the Post-PHV group, T-HALF results were significantly associated with age, PHV, BF, SJ, CMJ, RSI, standing long-J, HSJ, HCMJ, 3HOPT, S20F, and S30, whereas CODAT performance significantly correlated with BF and all jumping and sprinting abilities.

Evidently, predictors were more strongly correlated with agility criteria in the Post-PHV than in the Pre-PHV group (average correlations; T-HALF: 0.57 and 0.44 and CODAT: 0.49 and 0.36, for Post-PHV and Pre-PHV, respectively) (Table 4).

Table 4.
Table 4.:
Pearson's moment correlation coefficients between studied variables in post-PHV group.*

When multiple regressions were calculated for the Pre-PHV group, 67% of the variance was attributed to the T-HALF. Significant partial regressors were HCMJ (β: −0.83, p < 0.01), S20F (β: 0.91, p < 0.01), and body mass (β: 0.19, p = 0.02); the best performance was in lighter athletes with greater sprinting and jumping abilities. Predictors accounted for 80% of the variance in T-HALF results in the Post-PHV group, and there was a significant partial influence of S20F (β: 0.52, p < 0.01), RSI (β: −0.24, p = 0.04), and standing long-J (β: −0.57, p = 0.03). The best performance was observed in athletes with superior reactive strength and sprinting and jumping abilities (Table 5).

Table 5.
Table 5.:
Multiple regression calculations (forward stepwise model) for T-HALF test.*

Overall, 45% of the variance in CODAT performance in the Pre-PHV group was explained by predictor variables. The significant partial predictors were BF (β: 0.44, p = 0.04; negative influence) and S20F (β: 0.74, p < 0.01; positive influence). For the Post-PHV group, 79% of the variance in CODAT results was explained for Post-PHV, and significant predictors were as follows: RSI (β: −0.26, p = 0.04; positive influence) and S10 (β: 0.87, p = 0.03; positive influence) (Table 6).

Table 6.
Table 6.:
Multiple regression calculations (forward stepwise model) for CODAT test.*

Discussion

This study is one of the first to examine the association between measures of preplanned agility and proxies of sprinting, muscle power, and anthropometrics in athletes with different maturity statuses. Significant moderate- to large-sized correlations were found between preplanned agility and body mass, BF, and straight sprinting and jumping abilities. In general, the observed predictors accounted for the preplanned agility in the Post-PHV group better than in the Pre-PHV group. Furthermore, horizontal displacement abilities (sprints and horizontal jumps) were the most important predictors of preplanned agility. Finally, irrespective of maturity status, the observed predictors accounted for the T-HALF scores better than for the CODAT scores.

In the Pre-PHV group, body fat percentage (BF%) negatively influenced CODAT performance. This is almost certainly related to the fact that BF acts as ballast, which directly alters agility performance. Therefore, the athletes with a lower BF% were able to adapt their form during locomotion and their running technique according to the necessary change of direction (i.e., shorten their step and lower their center of mass), as previously suggested (33). Our results do not agree with a recent study by Sattler et al. (2016), in which the authors reported no significant correlation between BF% and preplanned agility in male team sport players (29). However, differences between the populations studied almost certainly resulted in such differential findings. Specifically, in this study, we have observed young handball players (12–14 years of age), whereas Sattler et al. investigated young adults involved in team sports (22 years of age on average) (29). Supportively, in our study, BF% was not associated with preplanned agility in the Post-PHV group (i.e., older participants).

This study demonstrated sprinting performances as significant predictors of CODAT scores in both maturity groups (S20F and S10 for Pre- and Post-PHV groups, respectively). In addition, S20F was found as a significant regressor of T-HALF scores in the Pre-PHV group. In this regard, our results support the findings reported by Little and Williams (2009), who studied professional soccer players and found a significant correlation between the 10-m acceleration test results and zig-zag preplanned agility (r = 0.34 and p < 0.01) (10). Similar findings were recently reported by Negra et al. (24), who found that young handball and soccer players had moderate to high correlations between their 10-meter sprint performance and the preplanned agility T-HALF scores (r = 0.66 and 0.82) and Illinois test scores (r = 0.66 and 0.80, for soccer and handball players, respectively). The relatively stronger influence of sprinting performances on CODAT scores (i.e., significant influence in both maturity groups) than on T-HALF scores is a logical consequence of the nonstop character of CODAT (i.e., no moment of zero velocity throughout test execution) (31).

In our study, a stronger association between conditioning qualities and preplanned agility was observed in the more mature group (Post-PHV). These findings suggest that Pre-PHV boys should focus on the development of neural parameters to facilitate improved preplanned agility. Improved neural factors may increase the technical competency and assist in coping with the growth-related anthropometric changes observed at this time (20). Although different combinations of plyometric and strength training and complex training should continue for boys during and after PHV (7,11), the focus of neuromuscular training at these older ages should shift toward improving agility by developing muscle power performance (24), acceleration, and maximal sprinting speed (13) to make optimal use of the maturity-related changes in circulating androgens and increased muscle mass (21). Although previous considerations are mostly based on cross-sectional investigations, future studies should focus on collecting longitudinal data to validate the observations in this study regarding the natural development of agility with advancing maturation and should consider the interaction between maturation and training.

The jumping performance recorded in our study consistently indicated a significant correlation between the horizontal jump test results (HCMJ and HSJ) and agility performance, indicating that greater explosive strength supports preplanned agility. Although previous studies regularly reported strong relationships between jumping performance and preplanned agility (29,33), it is interesting to note that the correlation values established herein were significantly different between the Pre-PHV and Post-PHV conditions, with a stronger correlation in the more mature group (r: 0.26–0.67 and 0.45–0.79 for Pre- and Post-PHV, respectively). Such differences in established relationships could be explained by differences in the techniques used to execute directional changes between the 2 maturity groups. Namely, because CODAT and T-HALF consist of specific handball movement patterns (35), it is probable that older (and more experienced) players were more advanced in their techniques than their younger peers. This advantage resulted in a stronger influence of the studied conditioning capacities on preplanned agility in more experienced (i.e., more mature) players. In addition, it is probable that even the “mental maturity” of the players influenced the execution of the CODAT and T-HALF because knowledge of game tactics almost certainly contributes to performance. In addition, it could be suggested that the high neural demand of rapidly changing direction provided a stimulus that coincided with the natural adaptive response of the Post-PHV boys resulting from growth and mental maturation (13). Developmentally, Post-PHV boys experience morphological changes that facilitate force generation (e.g., increased motor unit size and pennation angles) in addition to continued neural adaptations as a consequence of cognitive maturation (16). Therefore, future studies investigating the effect of mental maturation on preplanned performance development are warranted.

Multiple regression analysis revealed the differential influence of the studied predictors on the observed criteria (T-HALF and CODAT). In general, the predictors accounted for the T-HALF scores (67 and 80% of the common variance for Pre-PHV and Post-PHV group, respectively) better than the CODAT scores (45 and 79% of the common variance for Pre-PHV and Post-PHV groups, respectively). Such differences in common variance are explainable when considering the differences in the structure of the agility tests performed in this study. In short, the CODAT and T-HALF differ in the generic cues incorporated in the movement patterns of each test. Specifically, the T-HALF consists of shuffling movements and backward running, which are both absent in the CODAT. More importantly, the T-HALF involves several moments of “zero velocity” (i.e., stop-and-go movement patterns), whereas the CODAT includes zig-zag movement patterns and no moments of “zero velocity.” Naturally, because of the biomechanical and physiological similarity between jumping tests and the type of change of direction, which appears throughout each “stop-and-go” movement pattern, jumping abilities are more important determinants of T-HALF scores (32). Meanwhile, the CODAT involves relatively longer sprinting distances (up to 10 m), which logically explains the strong influence of sprinting capacities on performance in this test (31,32).

The horizontal jumping test results had stronger correlations with the preplanned agility performance than vertical jumping test results. Interestingly, in practically all studies in which the authors simultaneously observed vertical and horizontal jumping performance and correlated the results with agility, horizontal jumps showed a stronger correlation with agility (32,33). The biomechanical similarity between horizontal jumping and agility-related movement seems to be one of the main causes for such relationships (32). Meanwhile, the positive influence of the RSI on preplanned agility is understandable when considering the similar physiological background of these 2 qualities (i.e., both are highly dependent on the intensive involvement of fast-twitch muscle fibers) (29,32). In addition, the RSI represents an individual's ability to efficiently switch between eccentric and concentric contractions (i.e., to perform plyometric activities), which contributes to agility performances as well.

The main limitation of this study is the cross-sectional design. Therefore, although some causality can be intuitively identified (i.e., anthropometrics are predictors of preplanned agility and not vice-versa), the true cause-effect relationships between conditioning abilities (i.e., sprinting speed and jumping capacities) and preplanned agility should be more precisely studied through longitudinal investigations. In addition, we evaluated a larger sample population of Pre-PHV players than Post-PHV players, which influenced the statistical significance of the established relationships.

Practical Applications

Strength and conditioning programs aimed at improving preplanned agility in less mature players (i.e., before PHV) should be more oriented toward neural training. This type of training should include learning specific movement patterns, improvement of motor competency, perfection of movement skills included in different agile maneuvers, etc. In doing so, special attention should be paid to differences in movement patterns that appear in various maneuvers requiring a high degree of agility (i.e., zig-zag, lateral displacement, and rotational movements). Apart from improving preplanned agility, neural training will assist players in coping with the growth-related anthropometric changes observed at this time.

Our results indicated a stronger association between conditioning abilities (i.e., jumping and sprinting) and preplanned agility in early pubescent handball players of advanced maturity status (i.e., Post-PHV players). This implies the transfer effects from strength/power and sprint training to preplanned agility for this age group. In addition, special attention should be paid to improving reactive strength capabilities because this conditioning quality was significantly related to both types of preplanned agility observed herein (i.e., stop-and-go and non-stop performances). Therefore, although different types of complex training should continue for boys during and after PHV, the focus of neuromuscular training at these older ages should shift toward improving agility by developing muscle power performance, acceleration, and maximal sprinting speed.

We have evidence of the greater importance of conditioning qualities and anthropometric measures in the execution of those maneuvers requiring a high degree of agility where moments of zero velocity repeatedly occur and where plyometric muscular properties are repeatedly challenged (i.e., stop-and-go agility tests such as T-HALF). Therefore, in designing training programs aimed at improving preplanned agility in young athletes, strength and conditioning specialists should pay attention to the specifics of agility because it relates to the sport of interest (e.g., stop-and-go agility in handball and basketball vs. nonstop agility performance in soccer and rugby) and design conditioning programs specifically to address this.

Acknowledgments

Authors are particularly grateful to all athletes who voluntarily participated in the study. The authors have no conflicts of interest to disclose. The results of this study do not constitute the endorsement of the product by the authors or the National Strength and Conditioning Association.

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Keywords:

change of direction speed; predictors; conditioning capacities; sport-specific tests

© 2017 National Strength and Conditioning Association