Exploring the relationship between reactive strength index,leg stiffness,and physical performance in adolescent male soccer players: The mediating role of maturation

Abstract

The purpose of this study was twofold: i) to examine the association among maturation, reactive strength index (RSI), leg stiffness, and physical performance tests, and ii) to investigate how maturation influences the relationship among RSI and leg stiffness and physical performance tests. Forty-six male soccer players (age: 11.8 ± 1.5 years; height: 152.9 ± 10.2 cm; weight: 45.9 ± 10.5 kg) participated in this cross-sectional observational study. Players’ biological maturity was assessed using peak height velocity (PHV). Physical tests were conducted in two separate sessions on different days: one included anthropometry, standing long jump, sprint, dribbling and agility; the other included RSI, leg stiffness, and kick velocity. Correlations and bootstrap-based mediation analyses were used to examine the mediating role of PHV between RSI, leg stiffness, and performance. Mean years to PHV was −1.61 ± 0.99 years, and all players were in the pre-PHV period. RSI was strongly correlated with sprint (r = −0.73; CI_95%= −0.840 to −0.533), agility (r = −0.67; CI_95%= −0.803 to −0.471), and SLJ (r = 0.68; CI_95%= 0.487 to 0.811), while leg stiffness was moderately correlated with sprint (r = −0.38; CI_95%= −0.602 to −0.102) and kick velocity (r = 0.43; CI_95%= 0.166 to 0.642). PHV was moderately to highly correlated with all performance indicators (r = 0.37–0.65). Mediation models showed that PHV partially explained the relationship between RSI and performance tests (17–70%),whereas the relationship between leg stiffness and performance tests was fully mediated by PHV (68–96%)(p < 0.05). In conclusion, the findings indicate that considering PHV's mediating effect in assessing neuromuscular performance in young soccer players is essential and should not be overlooked in research and practice.

Keywords

Agility anthropometry association football dribbling kicking peak height velocity sprinting

Introduction

Monitoring athletes’ physical performance is a critical process for evaluating performance changes¹ and adjusting periodization by observing their adaptation to training.² However, physical performance is influenced by biological factors such as age and does not follow a linear model; it initially increases with age, then slows down³ and ultimately declines.⁴ Exclusively, performance is significantly affected by growth spurts that occur during adolescence, along with growth and maturation.⁵ Individuals exhibit considerable variability in both the onset and tempo of biological maturation.⁶ Significant differences between individuals make it difficult to monitor performance and increase complexity. In this context, the influence of maturation on athletic performance has been acknowledged as a complex yet essential factor, positioning it at the forefront of contemporary sport science debates.

Previous studies investigated the relationship between maturity and physical skills such as speed, strength, and power,^7,8 which are important for many sports, and technical skills related to on and off-the-ball skills in sports such as soccer and basketball.^9,10 Although maturity status was monitored using a variety of methods in these studies, peak height velocity (PHV) was used as the easiest and most accessible method.¹¹ Changes in maturity status in childhood were found to be accompanied by continuous improvement in aerobic endurance, jumping, sprinting, and agility performance.^7,8 However, how maturing affects technical performance is unclear, as it hasn't been studied as much as physical performance,^9,10 despite its critical role in talent identification and match performance. Clarifying the mechanisms and establishing a consensus on how maturation affects technical performance is critical for guiding talent selection and development practices in youth academies.

Building on this interest, exploring the fundamental nature of maturation has prompted researchers to conduct more comprehensive investigations aimed at clarifying the specific mechanisms through which this process influences performance. For this reason, recent studies have examined the effects of morphological factors that enable these activities to better understand the mechanism of maturation.^12,13 Preliminary studies have shown that maturation increases fascicle length, muscle thickness, and pennation angle.¹² Follow-up studies have reported that jumping and sprinting performance improve with maturation, as these physical capacities are directly influenced by accompanying increases in morphological variables.¹³ Performance improvements have been observed with maturation in activities involving the stretch-shortening cycle (SSC), such as jumping and sprinting,¹⁴ and these improvements have been attributed to leg stiffness development due to morphological changes.¹⁵ Achieving high performance in movements involving SSC requires a short ground contact time, which can be achieved with high muscle-tendon stiffness. Muscle-tendon stiffness plays a fundamental role in stretch-shortening cycle (SSC) movements, with established relationships to sprint, agility, and jump performance.^16,17 While stiffness naturally improves with age during adolescence,^14,18 recent evidence suggests its predictive value depends on measurement type: relative vertical stiffness (normalized to body mass and leg length) correlates more strongly with sprint performance than absolute vertical stiffness and responds preferentially to targeted training rather than maturation alone.¹⁹ However, leg stiffness changes with maturation.¹⁹ This distinction is critical, as the normalization parameters for relative leg stiffness (body mass, leg length) are themselves maturation-dependent. Thus, understanding leg stiffness-performance relationships requires explicit consideration of biological maturity—a gap our study addresses. The Reactive Strength Index (RSI) is used to represent SSC capacity, which indicates how efficiently a movement involving an SSC pattern can transition from eccentric muscle contraction to concentric muscle contraction.²⁰ Although the relationship between maturation and leg stiffness and RSI has been investigated,^14,21 the mediating effect of maturation on the relationship between leg stiffness and RSI and performance tests is not known. A recent systematic review on RSI suggested evaluating RSI performance by considering anthropometric differences.²⁰ Regarding the potential effects of maturation on anthropometric changes, assessing the relationship between RSI (and stiffness, due to similar neurological mechanisms) and physical tests, along with the mediating effect of PHV (maturation), will help explain an important gap in understanding the effect of maturation on the relationship between them.

Considering its influence on both field tests and morphological structures, maturation provides a strong rationale for examining its potential mediating role in these relationships. Only a limited number of studies have included maturation in mediation analyses.^22,23 Basically, mediation analysis tests whether the relationship between two variables (X and Y) is explained by a third variable. This third variable is referred to as the mediator variable (M).²⁴ Mediation analysis offers a powerful tool to disentangle the complex relationships between biological maturation and athletic performance. Unlike simple correlations, this approach reveals whether maturation acts as a hidden driver (mediator) in the relationship between neuromuscular capacities (e.g., RSI, leg stiffness) and on-field performance. Such insights are crucial for distinguishing between training-induced adaptations and natural developmental processes, ultimately enabling more precise athlete evaluation and individualized long-term development strategies.

Previous research has predominantly examined the mediating influence of anthropometric characteristics and body composition in the relationship between biological maturation and sports performance.^22,25 In contrast, the potential contribution of neuromuscular adaptations occurring during maturation, such as muscle–tendon stiffness and reactive force production to performance development has received considerably less attention. Notably, Abbott et al.²³ demonstrated that biological maturation may act as a direct mediator in the association between strength and performance. Building on this evidence, the present study makes a novel contribution by investigating the mediating role of maturation in the relationship between neuromuscular markers, specifically RSI and stiffness, and physical performance tests. By moving beyond prior approaches that emphasized anthropometric factors alone, our study aims to provide a more comprehensive understanding of how maturation shapes performance development through neuromuscular pathways. Therefore, the aim of this study was twofold: i) to examine the relationship among physical performance tests, maturation, RSI, and leg stiffness, and ii) to examine the mediating effect of maturation on the relationship among RSI, leg stiffness, and physical performance tests. We hypothesize that there may be a relationship among physical performance tests and RSI and leg stiffness, and that this possible relationship is mediated by maturation.

Method

Participants

This study was designed as a cross-sectional investigation. Forty-six male soccer players (mean ± SD; age 11.78 ± 1.46 years, height 152.89 ± 10.21 cm, weight 45.85 ± 10.49 kg, BMI 19.31 ± 2.65 kg/m²) competing in academy groups volunteered to participate in the study. When the effect from X to M (a) is 0.50 and the effect from M to Y (b) is 0.50, the mediation effect (a × b) equals 0.25, which can be considered a medium-sized effect. In this case, when the effect from X to M (a) is 0.50 and the effect from M to Y (b) is 0.50, the mediation effect (a × b) is equal to 0.25, which can be considered a medium-sized effect. In this case, the sample size required to detect this effect with 80% power using mediation analysis is estimated to be approximately 40. The sample size was calculated using the pwrMediation package in the R program (version 4.2.2). All subjects were within the timeframe before the PHV range of from −3.5 to −0.1 years. All of the subjects were members of the same team, and they had trained and competed regularly in soccer for at least two years. Players have regular sixty-minute soccer training sessions three days a week. The study excluded players who were currently injured and those who had experienced a serious injury in the preceding season that resulted in an absence from training exceeding four weeks. Before the study, each participant's coaches and parents were informed about measurement procedures. Written consent to participate in the study was obtained from them in accordance with the Declaration of Helsinki. The Cukurova University Ethics Committee approved the study (16.05.2025, 155).

Procedures

The data collection consisted of two testing sessions. In the first session, anthropometric and demographic data (height, weight, age, sitting height, and leg height), standing long jump, linear sprint, and Illinois agility test were measured and recorded in the order specified by the research team. The second session, RSI, leg stiffness, and soccer kick tests were performed in the order specified. Participants performed before the physical tests a self-selected warm-up consisting of dynamic stretching and practice jumps (10 minutes) on both session days. The typically observed warm-up consisted of squatting movements, toe touches, hopping, and practice jumps, and lasted 3–5 minutes in both sessions. Following the warm-up, players performed in a 5-minute active recovery consisting of walking. On both testing days, a rest interval of 3–5 minutes was provided between each tests.

Maturity offset

The maturational status of the players was assessed noninvasively by calculation of years from peak height velocity (PHV) using the sex appropriate equation of Mirwald et al.,¹¹ derived from anthropometric variables, including standing height, sitting height, leg length, age, and weight. Maturity offset (year) was calculated by subtracting the chronological age at the time of measurement from the age in PHV. This method is a reliable (standard error of estimate ±0.592 years), non-invasive, inexpensive, and straightforward way to assess biological maturity, with the potential to predict adult height in healthy adolescent children.^11,26

Standing long jump test

A standard 5 m tape measure was used. The subjects started the test standing with their feet placed behind the line and their arms relaxed. They were instructed to jump the maximum possible horizontal distance with bilateral legs, executing a controlled landing and maintaining balance on the performing both legs (3 s) until the examiner recorded the fall position. The length was measured from the jump line to the rearmost heel in the subject's landing. Two jump attempts were performed, each with 60 s of recovery between jumps; the best jump was recorded for later analysis.²⁷ The standing long jump (SLJ) test is a reliable (ICC = 0.94) and valid field-based assessment of lower-body muscular power in children aged 6–12 years.28

Linear sprint test

The 30-meter distance was accurately measured and marked before positioning the electronic timing gates (TC Photogate; Brower Timing Systems LLC, Draper, UT, USA). Gates were set up at the start of the 30-m track, and each player started the test 30 cm behind the first timing gate. Two plastic markers were placed 2 m beyond the last pair of timing gates, and each player was encouraged not to decelerate until they were past these markers. Each player completed three maximum effort sprints (3 minutes rest between trials), with the fastest time for the 30-meter distance recorded.29

Illinois agility test

The agility course is arranged with four cones to create the test setup. Upon instruction, the athlete sprints 9.20 meters, executes a turn, and then returns to the initial starting line. Following this return, the athlete maneuvers through four markers, weaving in and out, and completes two 9.20-meter sprints to conclude the agility course. Performances were recorded using an electronic timing system (TC Photogate; Brower Timing Systems LLC, Draper, UT, USA). The infrared timing gates were positioned at the start and the finish lines at a height of approximately 1.00 m. The best performance of the 3 trials was recorded for statistical analyses.30

Dribbling test

For the dribbling test, players departed running 3 m behind the initial set of gates. Players performed 3 m of straight running, entered a 3-m slalom section marked by three aligned sticks (1.6 m of height) placed 1.5 m apart, and then cleared a 0.5-m hurdle placed 2 m beyond the third stick. Finally, players ran 7 m to break the second set of photocell gates, which stopped the timer.³¹ In this modified version, players performed the entire test while dribbling a ball, adding a technical component to the original protocol. Prior to testing, a familiarization session was conducted.

Leg stiffness

Absolute leg stiffness was measured during the submaximal 2-legged hopping test, which was performed at a hopping frequency of 2.5 Hz. This frequency was selected to ensure movement patterns were reflective of typical spring mass model behavior.³² Participants were asked to hop 2-legged on top of the contact mat for 20 consecutive hops (Optojump, Microgate, Italia). Hopping frequency was maintained using a quartz metronome (SQ-44, Seiko, Berkshire, United Kingdom). Participants performed the jumps in synchrony with a metronome, keeping their hands on their hips, legs straight, landing at the same place, and focusing on a fixed point for balance. All jumps were monitored by the researcher. Leg stiffness (kN·m⁻¹) was calculated using measures of body mass, contact times, and flight times³³ (Equation 1). Prior to testing, a familiarization session was conducted to ensure players could perform movements in synchrony with a metronome.

L e g S t i f f n e s s = M π (T f + T c) / T \begin{matrix} 2 \\ c \end{matrix} [(T f + \frac{T c}{π}) - (\frac{T c}{4})]

(1)

Within the equation, M is the total body mass, Tc ground contact time, and Tf represents the flight time.

Reactive strength index

The measure was determined during the maximal hopping test, which involved participants performing 5 repeated bilateral maximal vertical hops on the contact mat (Optojump, Microgate, Italia). Participants were instructed to maximize jump height and minimize ground contact time.³⁴ Participants were instructed to keep their hands on their hips during all jumps, land at the same place on each jump, keep their legs straight when jumping, and look at a fixed point to maintain their balance. All jumps were carefully monitored by the researcher conducting the study. The first jump in each trial was discounted, whereas the remaining 4 hops were averaged for analysis of RSI.³² The RSI was calculated from the equation of Flanagan and Comyns³⁵ (Equation 2). Prior to testing, a familiarization session was conducted.

\begin{aligned} R e a c t i v e S t r e n g t h I n d e x & = J u m p H e i g h t (c m) \\ / c o n t a n c t t i m e (m i l l l i s e c o n d s) \end{aligned}

(2)

Maximum kick velocity

The players’ kick velocity was measured with a highly reliable handheld radar gun (Bushnell 101911, Overland Park, KS, USA), which has previously shown a strong correlation (r = 0.88).³⁶ FIFA-approved balls, which were specifically designed for players with dimensions of size 4 (65 cm diameter, 375 g), as used in the study conducted by Cerrah et al..³⁷ Soccer players executed three maximum-intensity kicks from a distance of 11 meters, using a standardized 2-meter approach. Accuracy was not considered in the assessment, which focused solely on the maximum ball speed.

Statistical analysis

Reactive strength index, leg stiffness, PHV, and physical performance tests (SLJ, sprint, agility, dribbling, and kick velocity) were summarized as mean and standard deviation. To examine the relationships among RSI, leg stiffness, PHV, and physical tests, either the Pearson correlation coefficient or the Spearman rank correlation coefficient was applied, depending on whether the assumptions of parametric statistics were met. The correlation coefficient was classed as weak r = 0.10–0.29, moderate r = 0.30–0.49, strong r ≥ 0.50.³⁸ Analyses were performed using IBM SPSS Statistics Version 20.0 statistical software package. The RSI and leg stiffness were treated as independent variables, performance tests as dependent variables, and PHV as a potential mediator (Figure 1). Mediation analysis was conducted using the medmod module (version 2.3.5) in Jamovi, which employs the bootstrap-based method (1000 bootstrap) developed by Preacher and Hayes to assess partial or full mediation effects.24

Figure 1.

Mediation concept modified according to the variables in our study.

Results

Descriptive statistics for demographic, maturational, and performance variables of the participants are presented in Table 1.

Table 1.

Descriptive statistics for demographic, maturational, and performance variables.

Variable	Mean ± SD	Median (min-max)
Age	11.78 ± 1.46	12.35 (9.00 to 13.90)
Maturity offset (years from Peak Height Velocity)	−1.61 ± 0.99	−1.40 (−3.50 to −0.10)
Standing Long Jump (m)	1.69 ± 0.23	1.70 (1.19 to 2.35)
Sprint (s)	5.00 ± 0.41	5.00 (4.02 to 5.97)
Agility (s)	17.85 ± 1.16	17.57 (16.07 to 21.11)
Dribbling (s)	5.21 ± 0.71	5.08 (4.20 to 7.03)
Kick Velocity (km/h)	71.21 ± 8.50	71.00 (57.00 to 89.00)
Reactive Strength Index (cm/ms)	113.24 ± 28.88	114.64 (53.97 to 184.58)
Leg Stiffness (kN·m⁻¹)	20.22 ± 5.80	18.74 (11.67 to 35.28)

Figure 2 shows the correlation coefficients and significance levels of PHV, SLJ, sprint, agility, dribbling, kick velocity, RSI, and leg tiffness. RSI shows significant positive correlations with SLJ (p < 0.001; CI_95%= 0.487 to 0.811), and kick velocity (p = 0.002; CI_95%= 0.163 to 0.642). These relationships indicate that individuals with higher RSI tend to have SLJ performance and higher kick velocity. Significant negative correlations with sprint (p < 0.001; CI_95%= −0.840 to −0.533) and agility (p < 0.001; CI_95%= −0.803 to −0.471) suggest that higher RSI is correlated with lower sprint times and improved agility. A lower negative correlation with dribbling (p = 0.015; CI_95%= −0.583 to −0.071) indicates a lower inverse relationship, suggesting that higher RSI may slightly reduce dribbling ability. A significant positive correlation is observed between leg stiffness and kick velocity (p = 0.002, CI_95%= 0.166 to 0.642). Conversely, leg stiffness is negatively correlated with sprint performance (p = 0.004; CI_95%= −0.602 to −0.102).

Figure 2.

Correlations among the measured variables.

PHV shows significant positive correlations with SLJ (p < 0.001; CI_95%= 0.368 to 0.754), kick velocity (p < 0.001; CI_95% (0.424 to 0.782)), RSI (p < 0.001; CI_95%= 0.320 to 0.729), and leg stiffness (p < 0.001; CI_95%= 0.381 to 0.760). These relationships indicate that individuals with higher PHV tend to have standing long jump performance, higher kick velocity, higher RSI, and higher leg stiffness. Significant negative correlations with sprint (p < 0.001; CI_95%= −0.789 to −0.441) and agility (p < 0.001; CI_95%= −0.701 to −0.264) suggest that higher PHV is correlated with lower sprint times and improved agility. A lower negative correlation with dribbling (p = 0.011; CI_95%= −0.594 to −0.088) indicates a lower inverse relationship, suggesting that higher PHV may slightly reduce dribbling ability.

The mediation models are presented in Tables 2 and 3. The relationships between RSI and SLJ, and RSI and sprint, showed a significant relationship partially mediated by PHV (β = 0.001, p = 0.021, β = −0.003, p = 0.007, respectively). There was a significant direct effect between the independent and dependent variables in the presence of the mediator (β = 0.004, β = −0.008, p < 0.001). The proportion of total effect mediated by PHV was 25.3% for SLJ, 26.6% for sprint. RSI and Kick Velocity showed a significant relationship that was fully mediated by PHV (β = 0.089, p = 0.002). Since the direct effect between independent and dependent variables was not significant in the presence of the mediator (p > 0.05, β = 0.038). The proportion of total effect mediated by PHV was 70.0% for Kick Velocity. PHV did not mediate the relationship between RSI and agility (β = −0.005, p = 0.105). Although PHV did not significantly mediate the relationship between RSI and dribbling (β = −0.003, p = 0.135), the proportion of the total effect mediated by PHV was 38.3%.

Table 2.

Mediation models of peak height velocity for the correlation between reactive strength index and standing long jump, sprint, agility, dribbling and kick velocity.

Independent Variable	Mediator Variable	Dependent Variable	Direct EffectStandardized Coefficients (β)(CI95%)	p	Indirect EffectStandardized Coefficients (β)(CI95%)	p	Total EffectStandardized Coefficients (β)(CI95%)	p	Proportion Mediated(%)
Reactive Strength Index	Peak Height Velocity	Standing Long Jump	β = 0.004 (0.002 to 0.006)	<0.001	β = 0.001 (0.000 to 0.003)	0.021	β = 0.005 (0.004 to 0.007)	<0.001	25.3
		Sprint	β = -0.008 (-0.011 to -0.01)	<0.001	β = -0.003 (-0.005 to -0.001)	0.007	β = -0.0104 (-0.013 to -0.008)	<0.001	26.6
		Agility	β = -0.022 (-0.032 to -0.012)	<0.001	β = -0.005 (-0.010 to 0.001)	0.105	β = -0.027 (-0.036 to -0.018)	<0.001	17.4
		Dribbling	β = −0.005 (-0.013 to 0.002)	0.168	β = −0.003 (-0.008 to 0.001)	0.135	β = −0.009 (-0.015 to -0.002)	0.010	38.3
		Kick Velocity	β = 0.038 (-0.038 to 0.114)	0.326	β = 0.089 (0.032 to 0.147)	0.002	β = 0.127 (0.052 to 0.203)	<0.001	70.0

Table 3.

Mediation models of peak height velocity for the correlation between leg stiffness and standing long jump, sprint, agility, dribbling and kick velocity.

Independent Variable	Mediator Variable	Dependent Variable	Direct EffectStandardized Coefficients (β)(CI95%)	p	Indirect EffectStandardized Coefficients (β)(CI95%)	p	Total EffectStandardized Coefficients (β)(CI95%)	p	Proportion Mediated(%)
Leg Stiffness	Peak Height Velocity	Standing Long Jump	β = −0.004 (−0.015 to 0.008)	0.520	β = 0.015 (0.006 to 0.023)	0.001	β = 0.011 (−0.001 to 0.022)	0.053	79.9
		Sprint	β = −0.001 (−0.020 to 0.018)	0.900	β = −0.026 (−0.041 to −0.011)	<0.001	β = −0.027 (−0.046 to −0.008)	0.005	95.5
		Agility	β = 0.032 (−0.027 to 0.091)	0.290	β = −0.069 (−0.113 to −0.025)	0.002	β = −0.037 (−0.094 to 0.019)	0.197	68.4
		Dribbling	β = 0.008 (−0.048 to 0.031)	0.680	β = −0.023 (−0.047 to 0.002)	0.045	β = −0.031 (−0.065 to 0.003)	0.070	73.4
		Kick Velocity	β = 0.158 (−0.234 to 0.549)	0.430	β = 0.479 (0.181 to 0.776)	0.002	β = 0.636 (0.259 to 1.013)	<0.001	75.2

Leg stiffness and SLJ, sprint, agility, dribbling, and kick velocity showed a significant relationship that was fully mediated by PHV (β = 0.015, p = 0.001, β = −0.026 p < 0.001, β = −0.069 p = 0.002, β = −0.023 p = 0.045, β = 0.479 p = 0.002, respectively). In the presence of the mediator, the direct effect between the independent and dependent variables was not significant (β = −0.004, p = 0.520, β = −0.001 p = 0.900, β = 0.032 p = 0.290, β = 0.008 p = 0.680, β = 0.158 p = 0.430). The proportion of total effect mediated by PHV was 79.9% for SLJ, 95.5% for sprint, 68.4% for agility, 73.4% for dribbling, and 75.2% for kick velocity.

Discussion

The first aim of this study was to examine the relationships among physical performance tests, maturation, RSI, and leg stiffness. The results revealed moderate to strong correlations among the variables, with the highest coefficient of determination observed between sprint and agility performance. RSI was moderately and significantly negatively correlated with both sprint and agility outcomes. The second aim was to examine the mediating effect of maturation on the relationship among RSI, leg stiffness, and physical performance tests. We hypothesized that PHV would mediate the potential relationship among RSI, leg stiffness, and physical performance. We hypothesized that PHV would mediate the relationship among RSI, leg stiffness, and physical performance indicators. The main finding of this study supports this hypothesis, as PHV significantly mediated the associations among leg stiffness, RSI, and various measures of physical performance.

Specifically, both PHV and RSI demonstrated significant associations with all assessed performance indicators, including sprint time, agility, ball dribbling, kick velocity, and standing long jump. In addition, muscle stiffness showed strong relationships with sprint performance, RSI, and kick velocity. These findings highlight the critical role of maturation status in evaluating physical performance and in designing training programs for young players, as biological maturation directly shapes the interplay between muscle characteristics and performance outcomes. Mediation analyses further indicated that PHV exerted distinct influences on the associations between muscle stiffness, RSI, and specific performance measures. Collectively, these results underscore the necessity of accounting for biological maturation when interpreting neuromuscular contributions to the physical performance of pre-PHV athletes.

In our study, direct associations were also observed between RSI and physical performance tests. RSI was strongly correlated with sprint performance, agility, and standing long jump, moderately associated with kick velocity, and weakly related to dribbling. Although several studies have examined the relationships between RSI and sprint, lower-body power, and agility performance,^39–42 few have focused specifically on youth athletes with comparable age and maturational status to those in our sample.⁴¹ Previous research has demonstrated a significant correlation between the RSI and sprint performance in pre-PHV children,^41,43 aligning with our current findings. This relationship likely stems from shared neuromuscular demands, particularly rapid force generation and short ground contact time, during both reactive jumps and sprint acceleration. Notably, while RSI-sprint relationships are established, no studies to date have investigated potential links between RSI and sport-specific skills like ball-kicking velocity or dribbling performance, representing a key gap in the literature.

Although various methods exist for the assessment of stiffness,^32,44 in our study, we adopted a 20-repetition bilateral hopping stiffness evaluation protocol, which was deemed more appropriate for our participants, given their age.³² Higher musculotendinous stiffness enables more effective elastic energy utilization, rapid force development, and efficient force transmission.²⁰ These characteristics contribute to enhanced sprinting and jumping performance by reducing ground contact time and minimizing energy loss.¹⁶ However, participants in the present study exhibited uniformly low and narrowly distributed stiffness values. This may explain the weak relationship observed between stiffness and sprint performance, and the absence of significant associations with jumping and agility. Our findings revealed a moderate but significant correlation between PHV and leg stiffness, prompting further investigation into maturation's mediating role in stiffness-performance relationships. The strong associations between RSI and performance variables (e.g., sprint and jump) likely reflect their shared dependence on SSC mechanics. However, these relationships may be confounded by training history, anthropometry, and playing position,²⁰ as well as training-induced adaptations in plantar flexor morphology.⁴⁵ Mediation analysis (testing whether PHV explains X→Y relationships) showed that while neither RSI nor stiffness had complete direct effects on all performance outcomes, both were significantly associated with PHV. This supports our primary aim of examining PHV as a mediator between these neuromuscular factors (RSI/stiffness) and athletic performance, thereby accounting for the systemic influence of maturation on developing athletes.

Our findings revealed that maturation fully mediated the relationship between stiffness and all physical performance tests. Previous research has also indicated that maturation mediates the relationship between strength and swimming performance.²³ Although the direct effects between stiffness and performance variables were not statistically significant, the significant mediating effect of PHV, accounting for 68% to 96% of the total effect, underscores the importance of maturation. This mediation role may be attributed to several physiological adaptations that occur with maturation, including increases in testosterone and growth hormone levels during puberty,^46,47 as well as corresponding increases in muscle mass,⁴⁷ muscle cross-sectional area,⁴⁸ and pennation angle⁴⁹; reductions in muscle-tendon compliance; increases in fascicle length¹⁵ and neural firing rates⁵⁰; and decreases in stretch reflex sensitivity.¹⁸ These maturational changes are believed to contribute to the increase in muscle stiffness and likely explain the substantial mediating role of PHV in the stiffness-performance relationships. RSI is also a maturation-related metric, typically measured in tasks involving the SSC.⁵¹ However, unlike stiffness, which directly affects SSC-based mechanics, the mediating effect of PHV on the relationship between RSI and physical performance variables was relatively lower. PHV partially mediated the associations between RSI and both standing long jump and sprint performance. The direct effects observed may be attributable to the natural biomechanical similarities between RSI, sprinting, and SLJ.⁵² Maturation influences stiffness, while stiffness impacts RSI.⁵³ Stiffness represents a morphological adaptation that increases with maturation, while RSI is a measurement influenced by multiple factors besides biological growth. These include body composition, training history, type of training (e.g., strength or plyometric), and playing position,²⁰ all of which may also affect the relationships between RSI and performance outcomes. Moreover, the temporary reduction in RSI performance often observed during the “adolescent awkwardness” phase of early maturation may have contributed to the relatively weaker mediating role of PHV.34

PHV fully mediates between stiffness and physical tests, while RSI partially mediates between stiffness and physical tests (excluding kick velocity). This difference may stem from the differing mechanisms between stiffness and RSI. Increased stiffness of the muscle-tendon unit is one of the mechanisms that increases RSI. RSI is also directly influenced by motor unit activation, reflex control, preactivation, and muscle-tendon dimensions.⁵⁴ In other words, stiffness is already a prerequisite for RSI and is also dependent on other conditions. In addition, a potential reason why PHV does not mediate the relationship between RSI and dribbling may be that it is a skill-based movement, and all these factors may have a synergistic effect. Furthermore, apart from these physiological mechanisms, the learning effect is a separate component. Sport-specific skill learning is linked to a motor learning process. The learning process is shaped by exposure to factors such as attention, feedback, practice design, and perceptual training.⁵⁵ Considering the inter-individual differences in the maturation of cognitive function,⁵⁶ all these processes may have contributed to the lack of a relationship with dribbling skill.

Overall, the findings indicate that the simple correlation methods commonly employed to investigate relationships among performance tests in young athletes may be insufficient. These relationships should be critically reconsidered, particularly through the inclusion of maturation processes. Furthermore, the applicability of these findings extends to other sports, irrespective of whether they share similar performance demands.

Limitations and future directions

A key limitation of our study is the restricted sample size, confined to pre-PHV players from a single academy team, which reduces external validity. The cross-sectional design and limited data points for mediation analysis also prevent causal inference. In contrast, previous longitudinal studies²³ (albeit not in soccer players) have identified maturation-related relationships, indicating that such designs may better capture developmental changes over time. Another limitation is the reliance on predictive equations for estimating maturity (PHV-based formulas), which may introduce error; radiographic or multi-method assessments could enhance validity in future research. In addition, only dribbling and kick velocity were evaluated as technical skills, narrowing the scope of conclusions.

Future studies should include larger and more diverse youth populations, adopt longitudinal designs, and examine a wider range of soccer-specific technical skills and other factors potentially influencing performance.

Practical implications

Current findings suggest that PHV may act as a mediating variable between physical tests and indicators of neuromuscular performance, such as RSI and leg stiffness. These insights could be valuable for coaches and practitioners. In particular, they may consider that the relationship between the measured variables can be fully and/or partially explained by the mediation of maturation, especially in players who are in the pre-maturation and maturation stages. Therefore, when examining such relationships, coaches and practitioners might take into account whether their players’ maturation is early, on time, or late. This awareness could help them make more informed decisions when evaluating players, as proximity to PHV may influence the mediation of maturation. Furthermore, this factor might be considered in tests administered for talent selection purposes. However, since the extent to which maturation mediates for players post-PHV remains unclear, caution may be advisable when applying these insights to players at that stage.

Conclusion

In conclusion, RSI and leg stiffness are meaningful indicators of performance in youth soccer players, but their effects should not be interpreted without considering biological maturation. These findings underscore the importance of accounting for PHV when evaluating performance and guiding talent development. Mediation analysis provides a developmentally sensitive framework that is more appropriate than simple correlation approaches, especially for players in the pre- and circa-PHV stages.

Footnotes

ORCID iDs

Abdullah Kilci

Selcen G. Korkmaz Eryilmaz

Ömer Cumhur Boyraz

Muhammed Emin Koç

Hülya Binokay

Okan Kamiş

Gibson Moreira Praça

Ethical considerations

The Cukurova University Ethics Committee approved the study (16.05.2025, 155).

Consent to participate

Written parental informed consent was obtained for all participants in accordance with the Declaration of Helsinki.

Author contributions

Conceived and designed research, A.K. and S. G. K. E.; Data collection, A.K. and Ö. C. B; Analyzed data, H.B.; Interpreted results of statistics, H.B. and A.K.; Prepared figures, H.B., M.E.K.; drafted manuscript, A.K., S. G. K. E., Ö. C. B., M.E.K. O.K. and G.M.P.; Edited and revised manuscript, A.K., S. G. K.E., Ö. C. B., M.E.K. O.K. and G.M.P… All authors have approved the final version of the manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

Data are available for research purposes upon reasonable request to the corresponding author.

References

Weakley

Black

McLaren

, et al. Testing and profiling athletes: recommendations for test selection, implementation, and maximizing information. Strength Cond J 2024; 46: 159–179.

McGuigan

. Monitoring training and performance in athletes. Champaign, IL, USA: Human Kinetics, 2017.

Haugen

Solberg

Foster

, et al. Peak age and performance progression in world-class track-and-field athletes. Int J Sports Physiol Perform 2018; 13: 1122–1129.

Leversen

Haga

Sigmundsson

. From children to adults: motor performance across the life-span. PLoS One 2012; 7: e38830.

Lloyd

Oliver

Faigenbaum

, et al. Chronological age vs. biological maturation: implications for exercise programming in youth. J Strength Cond Res 2014; 28: 1454–1464.

Eisenmann

Till

Baker

. Growth, maturation and youth sports: issues and practical solutions. Ann Hum Biol 2020; 47: 324–327.

Towlson

Cobley

Parkin

, et al.

When does the influence of maturation on anthropometric and physical fitness characteristics increase and subside?

Scand J Med Sci Sports 2018; 28: 1946–1955.

Fernandez-Fernandez

Canós-Portalés

Martinez-Gallego

, et al. Effects of maturation on lower-body neuromuscular performance in youth tennis players. J Strength Cond Res 2023; 37: 167–173.

da Costa

Borges

Ramos-Silva

, et al. Body size, maturation and motor performance in young soccer players: relationship to technical actions in small-sided games. Biol Sport 2023; 40: 51–61.

10.

Guimarães

Ramos

Janeira

, et al.

How does biological maturation and training experience impact the physical and technical performance of 11–14-year-old male basketball players?

Sports 2019; 7: 243.

11.

Mirwald

Baxter-Jones

Bailey

, et al. An assessment of maturity from anthropometric measurements. Med Sci Sports Exerc 2002; 34: 689–694.

12.

Radnor

Oliver

Waugh

, et al. The influence of maturity status on muscle architecture in school-aged boys. Pediatr Exerc Sci 2020; 32: 89–96.

13.

Radnor

Oliver

Waugh

, et al. Muscle architecture and maturation influence sprint and jump ability in young boys: a multistudy approach. J Strength Cond Res 2022; 36: 2741–2751.

14.

Rumpf

Cronin

Oliver

, et al. Vertical and leg stiffness and stretch-shortening cycle changes across maturation during maximal sprint running. Hum Mov Sci 2013; 32: 668–676.

15.

Kubo

Kanehisa

Kawakami

, et al. Growth changes in the elastic properties of human tendon structures. Int J Sports Med 2001; 22: 138–143.

16.

Kalkhoven

Watsford

. The relationship between mechanical stiffness and athletic performance markers in sub-elite footballers. J Sports Sci 2018; 36: 1022–1029.

17.

García-Santamaría

Abelairas-Gómez

Carrera

, et al. Effects of maturation on myotonometric parameters and their predictors of athletic performance in elite youth soccer players. Sci Rep 2024; 14: 12287.

18.

Grosset

J-F

Mora

Lambertz

, et al. Changes in stretch reflexes and muscle stiffness with age in prepubescent children. J Appl Physiol 2007; 102: 2352–2360.

19.

Meyers

Moeskops

Oliver

, et al. Lower-limb stiffness and maximal sprint speed in 11–16-year-old boys. J Strength Cond Res 2019; 33: 1987–1995.

20.

Southey

Willshire

Connick

, et al. Reactive strength index as a key performance indicator in different athlete populations–a systematic review. Sci Sports 2024; 39: 129–143.

21.

Lloyd

Hughes

Williams

. The effects of growth and maturation on leg stiffness and reactive strength Index in youths aged 7-18 years. J Strength Cond Res 2010; 24: 1.

22.

Oliveira

Henrique

Queiroz

, et al. Anthropometric variables, propulsive force and biological maturation: a mediation analysis in young swimmers. Eur J Sport Sci 2021; 21: 507–514.

23.

Abbott

Leong

Gwinn

, et al. The longitudinal mediating influence of maturation on the relationship between strength and performance in male youth swimmers. Int J Sports Physiol Perform 2022; 18: 61–68.

24.

Preacher

Hayes

. SPSS And SAS procedures for estimating indirect effects in simple mediation models. Behav Res Methods Instrum Comput 2004; 36: 717–731.

25.

de Alencar Matos

Rego Barros

Lima

TA,

et al. Understanding the mediating role of anthropometry and body composition in the relationship between biological maturation and sport-specific performance in young swimmers. J Sports Sci 2025; 43(21): 2517–2527.

26.

Sherar

Mirwald

Baxter-Jones

, et al. Prediction of adult height using maturity-based cumulative height velocity curves. J Pediatr 2005; 147: 508–514.

27.

Roso-Moliner

Lozano

Nobari

, et al. Horizontal jump asymmetries are associated with reduced range of motion and vertical jump performance in female soccer players. BMC Sports Sci Med Rehabil 2023; 15: 80.

28.

Fernandez-Santos

Ruiz

Cohen

, et al. Reliability and validity of tests to assess lower-body muscular power in children. J Strength Cond Res 2015; 29: 2277–2285.

29.

Ferguson

Gibson

Weston

, et al. Reliability of measures of lower-body strength and speed in academy male adolescent soccer players. J Strength Cond Res 2022: 10.1519.

30.

Hachana

Chaabène

Nabli

, et al. Test-retest reliability, criterion-related validity, and minimal detectable change of the Illinois agility test in male team sport athletes. J Strength Cond Res 2013; 27: 2752–2759.

31.

Mujika

Santisteban

Impellizzeri

, et al. Fitness determinants of success in men's and women's football. J Sports Sci 2009; 27: 107–114.

32.

Lloyd

Oliver

Hughes

, et al. Reliability and validity of field-based measures of leg stiffness and reactive strength index in youths. J Sports Sci 2009; 27: 1565–1573.

33.

Dalleau

Belli

Viale

, et al. A simple method for field measurements of leg stiffness in hopping. Int J Sports Med 2004; 25: 170–176.

34.

Lloyd

Oliver

Hughes

, et al. The influence of chronological age on periods of accelerated adaptation of stretch-shortening cycle performance in pre and postpubescent boys. J Strength Cond Res 2011; 25: 1889–1897.

35.

Flanagan

Comyns

. The use of contact time and the reactive strength index to optimize fast stretch-shortening cycle training. Strength Cond J 2008; 30: 32–38.

36.

Grazioli

Loturco

Lopez

, et al. Effects of moderate-to-heavy sled training using different magnitudes of velocity loss in professional soccer players. J Strength Cond Res 2023; 37: 629–635.

37.

Cerrah

Şimsek

Soylu

, et al. Developmental differences of kinematic and muscular activation patterns in instep soccer kick. Sports Biomech 2024; 23: 28–43.

38.

Cohen

. Statistical power analysis for the behavioral sciences. Routledge, 2013.

39.

Möck

Hartmann

Wirth

. Vertical jumping performance relates to sprinting performance over short distances and different sections. J Appl Sports Sci 2021; 5: 17–27.

40.

Parr

Winwood

Hodson-Tole

, et al. The main and interactive effects of biological maturity and relative age on physical performance in elite youth soccer players. J Sports Med 2020; 2020: 1957636.

41.

Cinemre

Ünver

Ünlü

, et al. A comparison of drop jump and sprint parameters in youth soccer players. Turkish J Sports Med 2020; 55: 148–155.

42.

Kilci

. Sex-based differences and associations in muscle stiffness and reactive strength index among elite male and female soccer players. Turkish J Sport Exerc 2025; 27: 335–342. Research.

43.

Karakaş

Eryılmaz

Boyraz

, et al. The relationship between lower limb stiffness and running economy in child soccer players. Rev Bras Med Esporte 2022; 29: e2021_0389.

44.

Kubo

Kanehisa

Fukunaga

. Is passive stiffness in human muscles related to the elasticity of tendon structures? Eur J Appl Physiol 2001; 85: 226–232.

45.

Radnor

Oliver

Waugh

, et al. Influence of muscle architecture on maximal rebounding in young boys. J Strength Cond Res 2021; 35: 3378–3385.

46.

Beunen

Malina

Van't Hof

, et al. Adolescent growth and motor performance: A longitudinal study of Belgian boys. Human Kinetics Publishers, 1988.

47.

Yagüe

De La Fuente

. Changes in height and motor performance relative to peak height velocity: a mixed-longitudinal study of Spanish boys and girls. Am J Hum Biol 1998; 10: 647–660.

48.

O’Brien

Reeves

Baltzopoulos

, et al. Muscle–tendon structure and dimensions in adults and children. J Anat 2010; 216: 631–642.

49.

Binzoni

Bianchi

Hanquinet

, et al. Human gastrocnemius medialis pennation angle as a function of age: from newborn to the elderly. J Physiol Anthropol Appl Human Sci 2001; 20: 293–298.

50.

Ozmun

Mikesky

Surburg

. Neuromuscular adaptations following prepubescent strength training. Med Sci Sports Exerc 1994; 26: 510–514.

51.

Jarvis

Turner

Read

, et al. Reactive strength index and its associations with measures of physical and sports performance: a systematic review with meta-analysis. Sports Med 2022; 52: 301–330.

52.

Wild

Bezodis

Blagrove

, et al. A biomechanical comparison of accelerative and maximum velocity sprinting: specific strength training considerations. Prof Strength Cond 2011; 21: 23–37.

53.

Maloney

Richards

Nixon

, et al.

Do stiffness and asymmetries predict change of direction performance?

J Sports Sci 2017; 35: 547–556.

54.

Radnor

Oliver

Waugh

, et al. The influence of growth and maturation on stretch-shortening cycle function in youth. Sports Med 2018; 48: 57–71.

55.

Choo

Novak

Impellizzeri

, et al. Skill acquisition interventions for the learning of sports-related skills: a scoping review of randomised controlled trials. Psychol Sport Exerc 2024; 72: 102615.

56.

Best

Miller

Jones

. Executive functions after age 5: changes and correlates. Dev Rev 2009; 29: 180–200.