Sprint Performance and Mechanical Force-Velocity Profile among Different Maturational Stages in Young Soccer Players

the present study was to determine the in uence of maturation status on the components of the sprint force-velocity (F-V) pro le in young soccer players. Sixty-two young male soccer players from the same professional soccer academy took part in the present study. A cross- sectional design was implemented to compare the main components of the sprint F-V pro le (i.e., maximal theoretical force (F 0), velocity (V 0), power (Pmax), and ratio of horizontal-to-resultant force (RF peak), and decrease in the ratio of horizontal-to-resultant force (DRF)) and sprint performance (5, 20, and 30 m sprint time)among participants' maturation stages (i.e., pre-, mid- and post-peak height velocity (PHV) groups). The results show that the ES of differences in 5 min sprint performance, F 0, and RF peak(i.e., strength- and acceleration-related components of the sprint F-V pro le) were greater between pre- and mid-PHV groups than those between mid- and post-PHV groups (i.e., large and very large effects (1.24 ES 2.42) vs. moderate,

mid- and post-peak height velocity (PHV) groups). The results show that the ES of differences in 5 min sprint performance, F 0, and RF peak(i.e., strength- and acceleration-related components of the sprint F-V pro le) were greater between pre- and mid-PHV groups than those between mid- and post-PHV groups (i.e., large and very large effects (1.24 ES 2.42) vs. moderate, small, and zero effects (0 ES 0.69), respectively). However, the ES of differences in V 0and DRF (i.e., peak speed-related components of the sprint F-V pro le) were greater between mid- and post-PHV groups than those between pre- and mid-PHV groups (i.e., large effects (1.54 ES 1.92) vs. moderate effects ( 0.59 ES 1), respectively). Once the strength development is achieved to a great extent from the pre- to mid-PHV groups, speci c strength training methods may be used for young soccer players to improve their sprint performance. Keywords:physical exercise; performance; football; adolescents; team sports; allometric scaling 1. Introduction Soccer is an acyclic sport, in which low to moderate intensity actions are interchanged with repeated explosive actions during the game [1]. It has been reported that the external load is in uenced by the age of the players; thus, soccer players under 15 years of age (U-15) cover approximately 6 to 8 km and perform ~80 accelerations (>1 m s 2 ) per match with a duration of 2 and 4 s each [2]. Considering that the most decisive actions in soccer occur in areas smaller than 10 m 2 , the ability to accelerate and decelerate can be a fundamental factor in performance achievement [3]. The optimization of sprint performance in young players can be attributed to growth- and maturity-related changes in neuromuscular mechanisms [4] and to the use of different training methods. In this way, sprint training (<30 m with a rest >3 min) [5], strength training [6], and resisted sprint training (RST) [7] are considered the most popular and effective ones. Traditionally, linear sprint performance has been evaluated by the time required to cover a given distance [8]. More recently, Morin et al. [9] recommended the assessment

of different training methods. In this way, sprint training (<30 m with a rest >3 min) [5], strength training [6], and resisted sprint training (RST) [7] are considered the most popular and effective ones. Traditionally, linear sprint performance has been evaluated by the time required to cover a given distance [8]. More recently, Morin et al. [9] recommended the assessment of the entire force-velocity (F-V) spectrum during sprint acceleration (i.e., the horizontal F-V pro le) to obtain more complete and meaningful information on the determinants of linear Int. J. Environ. Res. Public Health2022,19, 1412.

Int. J. Environ. Res. Public Health2022,19, 1412 2 of 11 sprint performance. The pro le is composed of different variables: the maximal theoretical force (F0), velocity (V0), and power (Pmax). These variables can be calculated through linear regression over a distance of 30 m [10]. In addition, the F-V pro le in sprinting includes the percentage of the resultant force that is generated in the horizontal direction [11], with the decrease in the ratio of horizontal-to-resultant force (DRF) and the maximal ratio of horizontal-to-resultant force (RF peak) typically used to assess mechanical effectiveness and sprint performance [12]; their use has been shown to be reliable in adolescents [13]. These components can be expressed as absolute or relative to body dimensions [9], with the latter being commonly used to control for the independent effect of body mass (BM), assuming a linear relationship between size and strength (i.e., watt kg 1 ) [14,15]. This relationship has been widely criticized [14] because it penalizes heavier individuals [15]. Thus, allometric scaling has been suggested to remove the effects of size in the interpretation of performance results and physiological variables [14,16]. It is known that growth and maturation processes have mediating effects on trainability levels and that, depending on the stimulus applied, we can enhance or undermine the effectiveness of training [17]. Biological maturity refers to the time required and the process of change in sexual, somatic, and skeletal factors to reach the adult stage [18]. Maturity offset, de ned as the time before reaching peak height velocity (PHV) [19], and estimated age at PHV (i.e., the difference between chronological age and predicted maturity offset) are widely used as estimates of maturity status [18,19]. According to these concepts, researchers in this eld classi ed youth as pre-PHV (10 to 12.9 years), mid-PHV (13 to 16 years), and post-PHV (16.1 to 18.5 years) [19], while using a band of 0.5 to + 0.5 years to de ne the lower and upper borders delimiting the mid-PHV stage [20]. Recent reviews showed that different maturational stages (pre-, mid-, and post-PHV) lead to speci c physiological and structural changes

as pre-PHV (10 to 12.9 years), mid-PHV (13 to 16 years), and post-PHV (16.1 to 18.5 years) [19], while using a band of 0.5 to + 0.5 years to de ne the lower and upper borders delimiting the mid-PHV stage [20]. Recent reviews showed that different maturational stages (pre-, mid-, and post-PHV) lead to speci c physiological and structural changes [21], and, consequently, different train- ing stimulus should be considered to improve performance optimally at each stage [22–24]. Therefore, the pre-PHV group is characterized by an increased activation of agonist muscles, coactivation of synergist muscles, and modi cation of the activation patterns of antagonist muscles [25]. However, the mid-PHV group also undergoes structural adaptations due to the direct in uence on the metabolic system, which is related to the amount of surrounding androgenic hormones. These hormones have a determinant role in the processes of muscle glycogen synthesis and hypertrophy [26], which generates an improvement in strength capacities and its different expressions (i.e., F0and RF peak) [27]. Finally, the post-PHV group is characterized by an increased muscle mass development, changes in musculotendinous tissue, and limb growth [28], along with an increased ef ciency of the stretch-shortening cycle [29], leading to an improvement in V 0and a smaller decrease in DRF [23]. The training process can be optimized in sprinting when planned according to mat- urational status [30]. Therefore, knowing the athlete's biological age is essential to apply the appropriate training stimulus [31]. Furthermore, knowing the speci c adaptations of the different sprint F-V components occurring across the different maturational stages leading to improvement of sprint performance may allow coaches to implement the most appropriate training stimulus to ensure athletes' long-term development. In addition, this information would be very useful to optimize the talent selection process for soccer players, given that, to date, no previous research has analyzed the aforementioned adap- tations in young soccer players. However, the identi cation of the biological age is not an easy task, as each subject undergoes morphological and neural changes with growth and maturation [18]. Thus, the aim of this research was to determine the

the talent selection process for soccer players, given that, to date, no previous research has analyzed the aforementioned adap- tations in young soccer players. However, the identi cation of the biological age is not an easy task, as each subject undergoes morphological and neural changes with growth and maturation [18]. Thus, the aim of this research was to determine the in uence of the maturation status on the components of the sprint F-V pro le in young soccer players. Given that improvement of F0and RF peakis associated with an increased strength level, we hypothesized that they would develop to a greater extent from the pre- to mid-PHV groups than from the mid- to post-PHV groups. Alternatively, as V0and DRF are determined by maximum sprint speed-related adaptations, we expected them to improve to a greater extent from the mid- to post-PHV groups than from the pre- to mid-PHV groups. Our second hypothesis was that after removing the in uence of BM (allometric scaling) on the

Int. J. Environ. Res. Public Health2022,19, 1412 3 of 11 BM-dependent variables of the F-V pro le (i.e., F0and Pmax), the results would be more homogeneous and similar across maturational stages. 2. Materials and Methods 2.1. Subjects Sixty-two male young soccer players from 3 different maturational status-related categories and the same professional soccer academy took part in this study during the 2019/2020 season. Players undergoing pre-, mid-, and post-PHV maturational stages trained 2 days and 3 h, 3 days and 4.5 h, and 4 days and 6 h per week, respectively. All subjects also participated regularly in one competitive match on the weekend in the rst category of the provincial league in Castellon province (Valencia area, Spain). The subjects declared as not having taken medication, drugs, or dietary supplements that may in uence physical performance. Neither physical limitations nor musculoskeletal injuries that could affect testing were reported for at least six months prior to the test. Subjects agreed to participate in the current research, and parental consents were signed, thereby allowing their participation in the study according to the Declaration of Helsinki. Descriptive characteristics of the subjects are reported in Table. Table 1. Means and standard deviations (SDs) for maturity status, age, height, body mass, body mass index, years of maturity offset, years of peak height velocity, years of experience, sprint performance at 5, 20, and 30 m, and sprint force-velocity (F-V) pro le components. Pre-PHV n = 25 Mid-PHV n = 21 Post-PHV n = 16 Age (years) 11.11 1.04 13.90 0.91 17.14 1.17 Height (cm) 145 5.64 160.62 5.10 173.88 5.65 Body mass (kg) 41.88 5.98 54.71 9.41 68.25 10.12 Body mass index (Kg m 2 ) 19.96 2.92 21.16 3.16 22.55 2.99 Maturity offset (years) 2.22 0.59 0.11 0.68 2.82 0.78 Years at PHV 13.33 0.60 13.78 0.46 14.32 0.73 Experience (years) 3 0.91 5.24 1.41 6.94 2.05 Time 5 m 1.67 0.13 1.52 0.1 1.56 0.1 Time 20 m 4.43 0.33 4 0.23 3.76 0.19 Time 30 m 6.21 0.53 5.59 0.36 5.19 0.28 F 0(N) 241.71 35.35 374.84 69.15 424.09 68.58 F 0(N BM 0.67

0.11 0.68 2.82 0.78 Years at PHV 13.33 0.60 13.78 0.46 14.32 0.73 Experience (years) 3 0.91 5.24 1.41 6.94 2.05 Time 5 m 1.67 0.13 1.52 0.1 1.56 0.1 Time 20 m 4.43 0.33 4 0.23 3.76 0.19 Time 30 m 6.21 0.53 5.59 0.36 5.19 0.28 F 0(N) 241.71 35.35 374.84 69.15 424.09 68.58 F 0(N BM 0.67 ) 19.88 2.58 25.67 3.09 25.02 2.33 V 0 6.05 0.69 6.72 0.6 7.81 0.79 Pmax(W) 364.9 63.25 627. 92 120.26 853.16 193.19 Pmax(N BM 0.67 ) 30.12 5.35 43.08 6.19 50.22 8.23 DRF (%) 0.09 0.02 0.1 0.01 0.08 0.01 RF peak(%) 0.41 0.04 0.46 0.03 0.46 0.03 PHV: peak height velocity; BM: body mass; F0: maximal theoretical force; V0: maximal theoretical velocity; Pmax: maximal power; DRF: decrease in the ratio of horizontal-to-resultant force; RF peak: maximal ratio of horizontal-to-resultant force. 2.2. Design A cross-sectional design was implemented to compare the main components of the force-velocity pro le in sprint (i.e., F0, V0, Pmax, DRF and RF peak) and performance variables (5, 20, and 30 m sprint time) among different maturation statuses in young soccer players, through Samozino's method [11]. These variables were then categorized according to different maturation status to assess the effect of maturity offset on the components of the

Int. J. Environ. Res. Public Health2022,19, 1412 4 of 11 sprint F-V pro le. Where necessary, allometric scaling was used on the BM-dependent variables of the F-V pro le (i.e., F0and Pmax) to adjust the in uence that BM has on the components of the sprint F-V pro le, as outlined in Section. The evaluation was carried out during the competitive period. 2.3. Methodology 2.3.1. Assessment of Maturity Offset Prior to data collection, anthropometric variables were recorded to calculate the maturity offset. Tests were carried out during a single session in the laboratory. BM (Tanita BF-522W, 0.1 kg precision, Japan), height, and seated height (non-commercial portable stadiometer, 0.1 cm precision) were assessed in participants. To measure sitting height, subjects sat on a 42 cm seat, with their buttocks and shoulders against the stadiometer; the height of the seat was subtracted from the overall sitting height value. A practical method for predicting years from PHV as a measure of maturity offset was applied using the following equation [19]. Maturity offset = (9.236 + 0.0002708 Leg Length and Sitting Height interaction) (0.001663 Age and Leg Length interaction) + (0.007216 Age and Sitting Height interaction) + (0.02292 Weight by Height ratio). This equation was previously validated for boys and presents a standard error of the estimate (SEE) of 0.592 years [19]. Therefore, a maturity offset of –1.0 indicates that the player was measured 1 year before his PHV, a maturity offset of 0 indicates that the player was measured at the time of his PHV, and a maturity offset of +1.0 indicates that the athlete was measured 1 year after his PHV [30]. Therefore, the mid-PHV period matches with the estimated “peak growth interval” at age 14 years [18], with the onset of the growth spurt occurring approximately one year later [32], when 94% of subjects reach their maximum height; a greater diversity of maturational stages stabilizes after age 16 [33]. Twenty- ve, 21, and 16 subjectswere allocated in the pre-PHV (< 1 Y-PHV), mid-PHV ( 1 to +1 Y-PHV) and post-PHV (>1 Y-PHV) maturational status groups, respectively. 2.3.2. Sprint Acceleration Test Participants

the growth spurt occurring approximately one year later [32], when 94% of subjects reach their maximum height; a greater diversity of maturational stages stabilizes after age 16 [33]. Twenty- ve, 21, and 16 subjectswere allocated in the pre-PHV (< 1 Y-PHV), mid-PHV ( 1 to +1 Y-PHV) and post-PHV (>1 Y-PHV) maturational status groups, respectively. 2.3.2. Sprint Acceleration Test Participants were instructed to arrive for performance testing in a rested state, having avoided strenuous exercise during the previous 48 h, in fasted state for at least 3 h, and properly hydrated. The test session was performed at 17:00. The trial was conducted in the middle of the playing season. Weather conditions were calm (sunny, wind speed average of 1.4 0.7 m/s, 22 0.8 C, and 43 7.3% humidity). Participants performed a 18 min warm-up consisting of 5 min of jogging, 5 min of lower limb dynamic stretching, and 8 min of progressive sprints (i.e., 30 m at 50%, 70%, and 90% of the subjects' self- perceived maximal velocity) before the sprinting test. Following the warm-up, participants performed 2 maximal effort 30 m sprints, with 5 min rest between trials, on a synthetic outdoor track. The fastest attempt was used for further analyses. Subjects were instructed that no backward movement was allowed prior to the start of the sprint and to begin each sprint at their own convenience to eliminate the in uence of reaction time. Verbal encouragement was given to all subjects to sprint through the 30 m distance. 2.3.3. Sprint Acceleration Test Data Processing Trials were assessed by recording each sprint using an iPhone 11 and MySprint app (Apple Inc., Cupertino, CA, USA). This app has been shown to be valid and reliable in relation to the reference systems (radar gun and timing photocells) [10] and children and adolescents [34]. The start of the sprint was determined as the moment at which the right thumb of the athlete left the ground. Five markers were located in front of the 5, 10, 15, 20, 25, and 30 m distances to ensure that their respective split times were measured correctly. Two

(radar gun and timing photocells) [10] and children and adolescents [34]. The start of the sprint was determined as the moment at which the right thumb of the athlete left the ground. Five markers were located in front of the 5, 10, 15, 20, 25, and 30 m distances to ensure that their respective split times were measured correctly. Two independent observers were asked to select the rst frame in which participants' right thumb left the ground (start of the sprint) and, subsequently, the frame in which the pelvis

Int. J. Environ. Res. Public Health2022,19, 1412 5 of 11 was aligned with the 6 different markers for each of the 124 recorded sprints using the MySprint app [10]. Split time and velocity-time data were used by the MySprint app along with participants' BM and body height as inputs to calculate F0, V0, Pmax, RF peak, and DRF, according to Samozino's method [10,11]. 2.3.4. Allometric Scaling of the Participants in the Study The effect of body size plays a fundamental role in physical performance variables (i.e., strength, speed, and power) [18,33] and can be an explanatory indicator of the variability of the results [14,15]. To account for this effect, the usual practice is to divide the performance variable by body size, which has been strongly discouraged and even more so when dealing with adolescents [14,15]. An alternative is allometric scaling, which has been shown to be an effective method to normalize aerobic capacity [35], jump and sprint power, and maximal oxygen [36,37] and upper body power [38] in young soccer players. The allometric scaling procedure rst raises the body size by a power exponent based on geometric symmetry theory [14]. The procedure used in the present study was described byVanderburgh et al. [16] . The equationy=a x b (y= outcome variable (i.e., F0or V0), x= anthropometric variable (i.e., BM), in whichais the constant multiplier andbis a constant exponent) was transformed into a log-linear model so that linear regression could be used to solve the value ofb(the allometric exponent) for each variable of interest. The relationship between the performance variables (i.e., F0or V0) and body size descriptor (i.e., BM) was described as followinglog y=log a+b log x, whereywas the dependent variable,awas the constant multiplier,bwas the allometric exponent, andxwas the body size descriptor. The allometric exponentb= 2/3 = 0.67 recommended by Jaric et al. [14] for parameters F0and Pmaxwas used in this study. 2.4. Statistical Analyses Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) v. 24.0 (Chicago, IL, USA). Data were tested for normality of distribution and homo- geneity of variances using a Shapiro–Wilk normality test and

This research analyzes how maturation affects sprint performance in young soccer players.