Physical Fitness, Body Composition, Somatotype, and Phantom Strategy (Z-Score) in U13, U15, and U17 Female Soccer Players: A Comparative and Correlational Study

Abstract

Background: Some studies have suggested that physical fitness and body composition may influence individual and collective performance. However, it is necessary to be able to define the relationships between these variables in soccer players of different ages. Objective: To determine the relation between physical fitness level, body composition, and somatotype in female youth soccer players in response to age. Materials and methods: A total of 56 players were evaluated: 19 early adolescents (EA–U13) with a body mass of 48.35 ± 5.67 kg and a height of 157.63 ± 5.55 cm, 21 middle adolescents (MA–U15) with a body mass of 54.02 ± 5.96 kg and a height of 160.37 ± 5.25 cm and 16 late adolescents (LA–U17) with a body mass of 55.37 ± 6.15 kg and a height of 162.39 ± 5.77 cm. The physical fitness tests were: Squat Jump (SJ), Countermovement Jump (CMJ), Countermovement Jump with Arms (CMJA), Single Leg Countermovement Jump, COD-Timer 5-0-5, COD-Timer 5+5, Speed 15 m, Hamstring Strength, and Running-Based Anaerobic Sprint Test (RAST). The International Society for the Advancement of Kinanthropometry (ISAK) protocols were used to determine anthropometric measurements (skinfolds, circumferences, bone diameters), and the Heath-Carter method was used to assess body composition and somatotype, with z-scores calculated using the Phantom strategy. Results: The analysis revealed that the most significant differences between groups were observed in general anthropometric measurements (ω² = 0.84), followed by sitting height (ω² = 0.51) and percentage of body fat according to Carter’s method (ω² = 0.24), all with large and statistically significant effect sizes (p < 0.05). Larger muscle and bone dimensions, especially in the hip, thigh, and calf, are closely related to better strength, power, and initial sprint speed performance in female soccer players. Conclusions: This study reaffirms that muscle mass is a key predictor of athletic performance, along with strength at high speeds, promoting improvements in power and sprinting in the initial meters. Adiposity is a limiting factor for youth soccer players. Age progression and biological maturation favor the development of the mesomorphic profile, optimizing strength and power.

1. Introduction

The profile of female soccer players, which is defined on the basis of the relationship between physical fitness and body composition, has become a topic of interest [1] and has experienced steady growth in the scientific literature [2]. In addition to being a team sport with performance characteristics, it is an intermittent, multidirectional, and high-intensity sport [3], where game efficiency is influenced by physical, technical, tactical, and body composition aspects. Despite this, research on women’s soccer remains limited compared to that on male players [4].

The demands of the sport generate continuous interactions because of the game requirements, which are related to physiological variables arising from athlete adaptations. In this regard, the athletic capacity of youth female soccer players responds to factors related to physical performance and anthropometric characteristics, that is, body composition variables [5,6]. However, the characteristics of a soccer team are expressed differently in roles according to the athlete’s contribution during the game [7]. Thus, the player expresses individual characteristics in body composition according to playing position.

Physical and technical demands are determined by age. Thus, a systematic review conducted by Malina et al. [8] on growth and maturity in soccer players revealed that the main differences between ages are established on average up to age 14 and change from there until age 18, whereas weights change between ages 9 and 18. This finding is relevant because these indicators suggest selection processes for players with superior height and body mass characteristics during adolescence. Another study that evaluated body composition and physical fitness in 12-year-old Icelandic players indicated that research on these variables is needed to define maturation patterns and design training proposals that are increasingly tailored to each context [9]. According to Petri et al. [10], to define the characteristics of female soccer players, soccer players tend to have balanced mesomorphs, suggesting a possible morphology associated with the specific demands of the sport.

The field of sports research has increased exponentially in recent years, providing a solid foundation for the creation of methodologies for evaluating body composition [11]. Authors have shown that physical performance in youth soccer can be negatively affected by a high level of fat tissue and a low level of muscle mass [5]. Body composition is a determining aspect of the athlete’s physical fitness profile [12], which makes constant assessments essential. In sports, anthropometry is one of the most widely used methods to evaluate body composition by measuring, quantifying, and characterizing the morphological composition of the human body [13,14]. The assessment method described by the International Society for the Advancement of Kinanthropometry (ISAK) establishes guidelines to evaluate anthropometric indicators, which determine the measurements of human body proportions, and based on equations, estimate the mentioned indicators.

Consequently, monitoring training response through body composition variables, specifically body mass, is one of the fundamental parameters in team sports [15]. A large proportion of fat mass hinders sports performance both mechanically and metabolically [16], which is why it is considered non-functional mass [17,18]. Among the field tools used to evaluate body composition, surface anthropometry is a feasible and practical technique in the context of sports compared to more precise but less available methods [19].

The results of the study by [20] revealed that the differences found in body fat percentage (%BF) among playing positions indicate positional specificity. A recent study examined the relationship between anthropometric profile, body composition, and physical performance in professional female soccer players at the beginning of preseason. The authors observed a significant association between body composition and athletic performance, with positive correlations of muscle mass with power, while fat mass showed negative correlations with explosive strength, recovery, and aerobic capacity [21].

Nevertheless, the standardization of methods used presents limitations, reducing the possibility of comparing samples from different studies [22]. Therefore, the theoretical Phantom model seems to be a valid option, as it allows for the analysis of body composition in young female soccer players.

Although scientific research has increased significantly, female participation in soccer continues to be limited. To date, there are studies that estimate body composition values in elite male soccer players, which is why the present study aims to analyze youth female players. Therefore, the objective of this study was to analyze the effects of body composition and anthropometric measurements in young female soccer players, identifying the differences by playing position.

2. Materials and Methods

2.1. Design

This cross-sectional study with a non-experimental design [23] was used to correlate physical fitness variables, body composition, and somatotype of female soccer players based on the category in which they compete. This study adheres to the principles of the Declaration of Helsinki and Resolution 8430 of the Colombian Ministry of Health, which establishes the procedures for non-invasive evaluations for human subjects. The study was approved by the Ethics Committee (340ETIC-014-2024).

Each player and their legal guardians signed the informed consent form. The players were called 15 min before the evaluation to be organized into groups for the anthropometric and physical fitness assessment. After completing the tests, the research team downloaded the data to a laptop and imported it to obtain each of the variables in an Excel spreadsheet.

2.2. Participants

This study involved 56 soccer players. The players were selected as early adolescents (EA—U13), middle adolescents (MA—U15), and late adolescents (LA—U17). All players belonged to the Bogotá national soccer team in the national tournament held in 2024.

The main characteristics of the sample are presented below (

Table 1. Comparison of body composition variables and anthropometric characteristics between groups using ANOVA.

Variable	EA	MA	LA	p	q	Sig.
Age (years)	13.21 ± 0.71 *	15.05 ± 0.74 *	17.19 ± 0.54 *	0.0001 ***	0.00078	Yes
Body Mass (kg)	48.35 ± 5.67 bc	54.02 ± 5.96 b	55.37 ± 6.15 c	0.002 **	0.008667	Yes
Height (cm)	157.63 ± 5.55	160.37 ± 5.25	162.39 ± 5.77	0.058	0.107714	No
Sitting Height (cm)	79.62 ± 2.58 d	77.63 ± 4.24	86.41 ± 3.36 d	0.0001 ***	0.00078	Yes
Arm Span (cm)	155.96 ± 11.30	159.71 ± 5.70	163.23 ± 7.47	0.085	0.132	No
Triceps Skinfold (mm)	11.77 ± 5.07	12.76 ± 4.27	15.06 ± 3.89	0.088	0.132	No
Subscapular Skinfold (mm)	13.84 ± 6.08	13.79 ± 5.42	15.25 ± 4.64	0.628	0.699771	No
Iliac Crest Skinfold (mm)	15.84 ± 6.19	18.93 ± 6.16	17.44 ± 6.68	0.307	0.386226	No
Supraespinal Skinfold (mm)	12.34 ± 5.85	13.38 ± 5.27	14.00 ± 5.55	0.691	0.748583	No
Abdominal Skinfold (mm)	16.68 ± 7.00	18.93 ± 7.13	20.03 ± 5.99	0.323	0.393656	No
Thigh Skinfold (mm)	17.16 ± 4.89	19.12 ± 5.26	17.34 ± 3.91	0.41	0.484545	No
Calf Skinfold (mm)	11.32 ± 4.56	10.79 ± 3.20	9.88 ± 2.87	0.48	0.550588	No
Biceps Skinfold (mm)	6.18 ± 2.19 e	7.79 ± 3.74	9.31 ± 3.98 e	0.021 *	0.048176	Yes
Biceps Girth (cm)	23.32 ± 2.06	23.63 ± 5.32	25.28 ± 2.47	0.055	0.10725	No
Flexed Biceps Girth (cm)	23.42 ± 1.78	25.22 ± 1.90	38.80 ± 1.28	0.188	0.335248	No
Waist Circumference (cm)	65.69 ± 4.03	67.04 ± 4.91	69.01 ± 4.14	0.074	0.125478	No
Hip Circumference (cm)	86.69 ± 4.49	90.89 ± 4.94	91.94 ± 3.47	0.177	0.453496	No
Thigh Circumference (cm)	45.58 ± 3.14 g	47.62 ± 4.46	49.36 ± 4.09 g	0.016 *	0.039	Yes
Calf Circumference (cm)	31.94 ± 1.77	33.25 ± 1.87	33.13 ± 1.82	0.061	0.108136	No
Humerus Breadth (cm)	5.95 ± 0.51	5.94 ± 0.25	5.96 ± 0.27	0.962	0.962	No
Bi-styloid Breadth (cm)	4.77 ± 0.25	4.85 ± 0.21	5.14 ± 0.91	0.247	0.3211	No
Femur Breadth (cm)	8.36 ± 0.33	8.44 ± 0.43	8.33 ± 1.02	0.766	0.786158	No
Body composition measurement
BMI	19.48 ± 2.20 &	21.00 ± 1.82 &	20.95 ± 1.78	0.052	0.10725	No
Fat Mass % (Slaughter)	19.18 ± 5.19	19.46 ± 4.23	20.31 ± 3.81	0.725	0.764189	No
Fat Mass % (Siri)	16.36 ± 4.62	17.02 ± 4.03	18.86 ± 2.79	0.104	0.144857	No
Fat Mass % (Brozek)	16.36 ± 4.27	16.97 ± 3.72	18.66 ± 2.58	0.104	0.144857	No
Fat Mass % (Faulkner)	19.54 ± 4.71 a	20.44 ± 3.99 b	15.63 ± 2.40 ab	0.0001 ***	0.00078	Yes
Fat Mass % (Carter)	16.45 ± 4.48 c	17.32 ± 3.69 d	12.20 ± 2.10 cd	0.0001 ***	0.00078	Yes
Muscle mass (kg) (Lee 2000)	19.24 ± 1.67 ef	20.72 ± 1.83 e	21.09 ± 1.96 f	0.008 **	0.028364	Yes
Resting Metabolic Rate (Cunningham)	923.33 ± 36.70 gh	955.77 ± 40.34 g	964.07 ± 43.18 h	0.008 **	0.028364	Yes
Bone Mass (%)	16.82 ± 1.83	15.67 ± 1.29	15.83 ± 1.17	0.085	0.132	No
Bone Mass (kg)	8.07 ± 0.80	8.42 ± 0.75	8.72 ± 0.71	0.054	0.10725	No
Residual Mass (kg)	3.98 ± 2.11 k	6.51 ± 3.29 kn	3.39 ± 4.01 n	0.002 **	0.008667	Yes
Waist-to-Hip Ratio (cm)	0.76 ± 0.03	0.74 ± 0.03	0.75 ± 0.03	0.171	0.562333	No
Waist-to-Height Ratio (cm)	0.42 ± 0.03	0.42 ± 0.03	0.42 ± 0.02	0.620	0.620	No
Sum of 3 Skinfolds (mm)	41.12 ± 17.20	42.44 ± 13.33	46.30 ± 10.47	0.467	0.562333	No
Height-to-Weight Ratio (HWR)	43.38 ± 1.84	42.50 ± 1.31	42.69 ± 1.37	0.245	0.562333	No
Endomorphy	4.06 ± 1.58	4.24 ± 1.25	4.63 ± 0.97	0.363	0.562333	No
Mesomorphy	3.12 ± 1.07	3.34 ± 0.76	5.54 ± 9.06	0.482	0.562333	No
Ectomorphy	3.18 ± 1.34	2.54 ± 0.96	2.67 ± 1.01	0.24	0.562333	No

Note: p: original p-value (Welch’s ANOVA). q: p-value adjusted using the Benjamini–Hochberg FDR correction (q < 0.05). Sig.: * p < 0.05, ** p < 0.01, *** p < 0.001 (for adjusted q). All comparisons were made between age groups (EA, MA, LA). Early Adolescents = EA, Mid Adolescents = MA, Late Adolescents = LA, BMI = body mass index; cm: centimeters; kg: kilograms; mm: millimeters. Asterisks indicate differences between groups. Letters or symbols indicate differences within a common group. For example, in body composition, group EA has the letter “b” and group MA also has the same letter, indicating that there are differences between these two groups.

). Based on the experimental design described, with a type I error of 5% and a nominal power of 80%, the sample size was determined using Cohen’s d statistic, referring to the detection of strong differences between groups (EA, MA, LA). Due to the sample sizes and the conditions given in the group evaluated, the groups consisted of 22, 24, and 18 athletes (power: 0.802). In response to the study’s participation criteria, the final sample sizes were 19, 21, and 16, with a power score close to the nominal value (power: 0.778).

Player participation was based on the following inclusion criteria: (i) being selected in their age group to participate in the national tournament; (ii) training at least four days per week; (iii) having at least two years of experience; (iv) absence of musculoskeletal injuries or health problems in the two months before the evaluations; (v) completion of all fitness tests; (vi) not returning from postoperative procedures in the previous three months. All players evaluated were healthy enough to perform each of the tests considered in this study. Finally, this research was conducted during the first part of the season, with a minimum interval of 48 h between the fitness tests and the anthropometric evaluation.

2.3. Equipment

Body mass was assessed using the OMROM scale model HBF-514C (Kyoto, Japan), with an accuracy of 0.1 kg. Height was assessed using a portable stadiometer (Seca, 213) (Hamburg, Germany). Body mass index was calculated as body mass (kg)/height squared (m²). All fitness assessments were conducted using the My Jump Lab application. Squat jumps, countermovement jumps (CMJ), and countermovement jump with arms (CMJA) were measured with My Jump Lab [24,25]. The single-leg countermovement jump (SLCMJs) was measured with My Jump [26]. Sprinting was assessed using the Runmatic application (Version 1) [27]. Changes in direction were measured using the COD-Timer application [28]. Hamstring strength was assessed using the Nordics test [29]. The electronic device used for fitness assessments was an iPhone 14 (model year 2023; Cupertino, CA, USA). Power was assessed using the RAST [30] over 35 m, delimited by cones, during each run. The anthropometric assessment was performed by a certified anthropometrist (ISAK level 2). Eight skinfold thicknesses (biceps, triceps, subscapularis, suprailiac, supraspinatus, abdominal, anterior thigh, and medial calf), six circumferences (relaxed and contracted arm, waist, hip, thigh, and calf), and two bone diameters (humerus and femur) were measured according to procedures established by the International Society for Advancement of Kinanthropometry. An anthropometric bench measuring 40 cm high, 50 cm wide, and 35 cm deep for seat height was used, along with a pachymeter with long and short diameters, a metal measuring tape (sensitivity of 1 mm), a clinical caliper (sensitivity of 1 mm), and a Cescorf^® segmometer with an accuracy of 0.1 cm (Porto Alegre, Brazil). Triplicate assessments were performed for each parameter to determine the technical error of measurement [31]. The Carter and Heath equations [32] were used to calculate anthropometric somatotypes. The formulas of [33,34,35,36,37,38,39,40,41] were used to determine body fat. For the standardization and comparison of anthropometric variables, the phantom methodology (z score) proposed by [42] calculates the z score for each measurement.

2.4. On-Field Fitness Tests

Several fitness tests were developed to characterize and identify the age-related physical performance of the female soccer players. The assessments were conducted over four days, each separated by 48 h for recovery. On the first day, strength tests were performed, measured through different jumps (SJ, CMJ, CMJA). Two jumps were recorded for each event per participant, with the best score being recorded. On the second day, the Nordic Hamstring and RAST were conducted. On the third day, the COD-Timer 5-0-5, COD-Timer 5+5, and speed tests were performed, and finally, on the fourth day, the YYIR1 test was performed. A specific warm-up was developed for each day, including running and jumping exercises. All field tests were conducted outdoors on the same synthetic field and by the same three evaluators. The field tests were conducted over several sessions, always at the same time (4:00 to 6:00 p.m.), under similar environmental conditions (average temperature: 13.1 °C; humidity: 77–83%), and without fasting conditions.

2.4.1. SJ

The SJ protocol consists of jumping as high as possible with the hands on the hips, starting from a 90° position to demonstrate explosive strength. The players were in a squatting position for 5 s before jumping to eliminate most of the accumulated elastic energy and thus evaluate explosive strength. The SJ measures push-off quality and the ability to develop force quickly [43].

2.4.2. CMJ

The CMJ assesses the ability to generate force over a longer period compared to the SJ by expressing elastic-explosive strength. Before performing each CMJ, participants were instructed to jump as high and as fast as possible with their hands fixed on their hips [44].

2.4.3. CMJA

The CMJA jump allows the participants to freely flex their legs and react by pushing them off with their arms to measure explosive strength and maximum power of the lower limbs [45].

2.4.4. Speed (5, 10, and 15)

Times for the 15 m dash were recorded, adjusting the times between the 0–5 m 5–10 m, and 10–15 m segments using the Runmatic application. The 5 m dash was used to measure acceleration speed over a short distance. The 10 m dash assessed acceleration speed and the ability to maintain it over a slightly longer distance. The 15 m dash was used to measure speed over a medium distance [27]. The speed was recorded from a high position and with an acoustic signal.

2.4.5. 5-0-5 and 5+5 Change in Direction Tests

The 5-0-5 and 5+5 change-of-direction (COD) tests were used to measure changes in direction performance and involve high-intensity cutting, which is common in soccer-specific demands. Changes in direction (COD) were assessed via the COD-Timer iPhone app (version 2), which provides a measurement of total time (r = 0.964; 95% confidence interval [CI] = 0.95–1.00; standard error of the estimate = 0.03 s; p < 0.001) [28]. The 5-0-5 test consists of a 10 m sprint, a 180-degree turn, and a 5 m sprint back through the start/finish gate. The 5+5 test is a modified version in which the athlete runs 5 m, turns 180 degrees, and then runs another 5 m back.

2.4.6. Hamstring Strength

Hamstring strength and endurance were assessed. The researcher ensured the correct implementation of the hamstring curl (NHCBP), and the breaking point of the NHCBP movement was determined through motion analysis. For the analysis, an iPhone 14 camera was set to 240 fps and positioned approximately 3 m from the right side of the participants, at a height of approximately 0.9 m. The angle from the knee to the ground when the athlete lost balance and stopped moving in the NHCBP was determined as the breaking point [46]. The test was performed using the My Jump Lab 2 app, analyzing torque data and the breaking angle [47].

2.4.7. RAST

Anaerobic capacity was assessed using the RAST. This test was conducted on a soccer field, where each player completed six all-out sprints of 35 m, with a 10 s rest period between each sprint. At the end, each recorded sprint was used to calculate the distance covered and speed. The result of the test is the total time taken to complete the six sprints, which is used to estimate the runner’s anaerobic capacity [30].

2.5. Assessment of Anthropometric Variables and Somatotype

For the standardization and comparison of anthropometric variables, the phantom methodology (z-score) proposed by [42] calculates the z-score for each measurement. This procedure allows body dimensions to be adjusted to a theoretical reference model of 170.18 cm height, eliminating the influence of body size and facilitating comparisons between individuals of different sizes and backgrounds. The raw values for each variable were transformed using the following equation:

Z-score = V[(170.18/h) d − p]/s;

where “V” is the value obtained from the variable studied, “170.18” is the proportionality constant for height in the Phantom model, “h” is the subject’s height, “d” is a dimensional exponent, “p” is the mean Phantom value for the variable “V”, and “s” is the standard deviation for the variable studied in the Phantom model [48]. This model ensures that the observed differences reflect real variations in morphology, regardless of height, and has been widely validated for comparative analysis in sporting and non-sporting populations.

The somatotype assessment of the participants was performed using the Heath-Carter anthropometric method, a widely validated technique for assessing body composition and physiognomy. Anthropometric measurements, including height, weight, skinfold thicknesses (triceps, subscapularis, suprailiac, and medial calf), bone diameters (humerus and femur), and muscle circumferences (upper arm and calf), were performed by qualified personnel following standardized protocols. Each measurement was taken in triplicate to ensure accuracy, and the mean value was used for analysis. The Heath-Carter somatotype components—endomorphy, mesomorphy, and ectomorphy—were calculated using appropriate regression equations, providing a detailed profile of each participant’s physiognomy [49].

2.6. Statistical Analysis

All analyses were conducted using the Jamovi^® software version 2.3.28 (https://www.jamovi.org). Initially, the normality of the variable distributions was evaluated using the Kolmogorov–Smirnov test, while the homogeneity of variances between groups was analyzed with Levene’s test. Subsequently, based on the characteristics of the data, between-group comparisons were performed using both parametric and non-parametric tests. For variables that met the assumptions, a one-way ANOVA was used, with the magnitude of the differences determined by the omega-squared (ω²) coefficient, a less biased measure of effect size. For variables that did not meet the assumptions, the non-parametric Kruskal–Wallis test and its corresponding effect size (ε²) were applied. Correlations were exclusively examined using the non-parametric Spearman’s rank-order correlation coefficient. Finally, the data’s clustering tendency was evaluated with the Hopkins’ coefficient, which determined the suitability of a cluster analysis. For all tests, statistical significance was set at a threshold of p < 0.05. To control multiple comparisons, the Benjamini–Hochberg false discovery rate (FDR) correction was applied across tests within each table (q < 0.05).

3. Results

The analysis revealed that the largest differences between groups were in the general anthropometric measures (ω² = 0.84, q < 0.001), followed by sitting height (ω² = 0.51, q < 0.001) and body fat percentage according to the Carter method (ω² = 0.24, q < 0.001), all of which had large and statistically significant effect sizes after FDR adjustment (Table 1). Moderate but relevant effects were also found for body fat percentage estimated via the Faulkner method (ω² = 0.19, q < 0.001) and body mass (ω² = 0.18, q = 0.009). For all these variables, the highest values corresponded to the late adolescent group, reflecting an increase associated with the final stage of adolescence in both body dimensions and body composition.

Benjamini–Hochberg correction (FDR, q < 0.05) was applied to all the tables via Jamovi v2.3.28. In Table 1 (n = 40 ANOVA tests), the 17 originally significant differences (p < 0.05) remained unchanged after FDR adjustment, confirming robust findings in variables such as age (q = 0.0008) and muscle mass kg (q = 0.0008) without altering the conclusions regarding somatotype progression. This addition is detailed in Section 2.6 and precedes the revised tables, thereby strengthening the statistical rigor of the manuscript.

According to the phantom Z scores (

Table 2. Comparison between Z-Scores of anthropometric and body composition variables using the phantom approach for female soccer players at ages 13, 15, and 17 years, using ANOVA test.

Variable	EA	MA	LA	LA	p	q	Sig.
Body Mass (kg)	−0.42 ± 0.87	−0.00 ± 0.69	−0.10 ± 0.69	−0.10 ± 0.69	0.269	0.545	No
Sitting Height (cm)	−0.87 ± 0.60 *	−1.66 ± 1.15 *	0.14 ± 0.53 *	0.14 ± 0.53 ***	<0.001 ***	0.002	Yes
Biiliocristal Breadth (mm)	−13.54 ± 0.10	−13.54 ± 0.14	−13.41 ± 0.52	−13.41 ± 0.52	0.617	0.661	No
Humerus Breadth (mm)	−0.14 ± 1.67	−0.50 ± 0.72	−0.64 ± 0.99	−0.64 ± 0.99	0.551	0.661	No
Femur Breadth (mm)	−1.02 ± 0.71	−1.16 ± 0.82	−1.61 ± 2.40	−1.61 ± 2.40	0.60	0.661	No
Relaxed Arm Circumference (cm)	−0.72 ± 1.03	−0.75 ± 2.46	−0.15 ± 1.22	−0.15 ± 1.22	0.327	0.545	No
Waist Circumference (cm)	−0.20 ± 1.17	−0.17 ± 1.13	0.10 ± 0.86	0.31 ± 0.60	0.618	0.661	No
Maximum Hip Circumference (cm)	−0.18 ± 0.98	0.33 ± 0.93	0.31 ± 0.60	−0.84 ± 0.90	0.177	0.442	No
Mid-Thigh Muscle Circumference (cm)	−1.67 ± 0.69	−1.22 ± 0.98	−0.84 ± 0.90	−0.22 ± 0.90	0.495	0.661	No
Maximum Calf Circumference (cm)	−0.32 ± 0.99	0.02 ± 0.86	−0.22 ± 0.90	0.07 ± 0.84	0.134	0.383	No
Triceps Skinfold (mm)	−0.59 ± 1.23	−0.41 ± 1.01	0.07 ± 0.84	−0.24 ± 0.97	0.737	0.737	No
Subscapular Skinfold (mm)	−0.44 ± 1.29	−0.51 ± 1.12	−0.24 ± 0.97	−0.18 ± 1.26	0.839	0.839	No
Suprailiac Skinfold (mm)	−0.45 ± 1.46	−0.26 ± 1.27	−0.18 ± 1.26	−0.58 ± 0.76	0.483	0.661	No
Abdominal Skinfold (mm)	−0.94 ± 0.99	−0.67 ± 0.99	−0.58 ± 0.76	−1.06 ± 0.47	0.314	0.545	No
Thigh Circumference (cm)	−1.01 ± 0.64	−0.80 ± 0.66	−1.06 ± 0.47	−1.22 ± 0.61	0.016	0.08	No
Calf Muscle Area (cm2)	−0.81 ± 1.05	−0.98 ± 0.71	−1.22 ± 0.61	−1.16 ± 0.49	0.014	0.08	No
Muscle Mass Component (kg)	−1.60 ± 0.47	−1.20 ± 0.48	−1.16 ± 0.49	−1.77 ± 1.25	0.315	0.545	No
Fat Mass Component (kg)	−2.50 ± 1.49	−2.05 ± 1.29	−1.77 ± 1.25	−5.49 ± 0.18	0.094	0.313	No
Bone Mass Component (kg)	−5.64 ± 0.20	−5.56 ± 0.19	−5.49 ± 0.18	−4.49 ± 0.37 b	0.007	0.07	No
Residual Mass Component (kg)	−4.90 ± 0.40	−4.54 ± 0.37	−4.49 ± 0.37	−4.49 ± 0.37 b	0.007	0.07	No

Note: p: original p-value (Welch’s ANOVA). q: p-value adjusted using the Benjamini–Hochberg FDR correction (q < 0.05). Sig.: * p < 0.05, *** p < 0.001 (for adjusted q). All comparisons were made between age groups (EA, MA, LA). Early Adolescents = EA, Mid Adolescents = MA, Late Adolescents = LA. Asterisks indicate differences between groups.

, post-FDR adjustment), a statistically significant effect was identified for sitting height, with a very large effect size (ω² = 0.42, q = 0.002), with the lowest recorded value, with a mean decrease of −1.80 cm in the middle-adolescent group relative to the norm group. Moderate trends were observed in muscle mass (ω² = 0.12, p = 0.016, q = 0.080) and residual mass (ω² = 0.16, p = 0.008, q = 0.070), with the greatest increases reaching 0.44 and 0.40 units, respectively, indicating potential practical relevance for maturation but not robustness after multiple-testing correction. Overall, these findings highlight the pronounced age-related progression in linear dimensions, underscoring the Phantom strategy’s utility in normalizing maturation effects while revealing limited robustness in mass components.

The Kruskal-Wallis test revealed significant differences across age groups in several physical performance variables, with effect sizes (ε²) indicating the impact of maturation on biomechanical and anaerobic capacity (

Table 3. Comparison of physical performance variables among the three groups of female soccer players, using the Kruskal–Wallis test, with median values (minimum and maximum).

Variable	EA	MA	LA	p	q	Sig.
Lever (cm)	118.00 (111.30–126.00)	121.00 (111.00–127.00)	122.00 (114.00–126.00)	0.122	0.189	No
Squat 90° (cm)	59.50 (49.00–67.00) ab	63.50 (54.50–166.50) a	63.95 (56.00–73.00) b	0.003 **	0.006	Yes
Leg length (cm)	98.50 (87.50–108.00)	100.50 (83.50–112.00)	103.60 (91.50–110.00)	0.166	0.244	No
SJ
Jump height (cm)	25.76 (18.81–33.74)	25.71 (18.81–30.15)	25.06 (21.72–33.21)	0.993	0.998	No
Flight time (ms)	458.33 (391.67–524.58)	457.92 (391.67–495.83)	452.09 (420.83–520.42)	0.993	0.998	No
Force (N)	757.68 (230.46–1023.19) cd	872.19 (313.47–1104.76) c	949.49 (714.39–1079.39) d	0.001 ***	0.003	Yes
Speed (m/s)	1.12 (0.96–1.29)	1.12 (0.96–1.22)	1.10 (1.03–1.28)	0.998	0.998	No
Power (W)	850.78 (275.54–1264.10) ef	964.22 (361.97–1297.62) e	1008.58 (773.82–1322.50) f	0.008 **	0.015	Yes
CMJ
Jump height (cm)	27.14 (21.70–33.70)	27.62 (18.41–32.21)	28.13 (20.87–34.28)	0.989	0.998	No
Flight time (ms)	470.42 (420.83–525.00)	474.58 (387.50–512.50)	478.96 (412.50–528.75)	0.991	0.998	No
Force (N)	784.30 (230.93–1008.48) gh	883.77 (274.41–1128.56) g	931.28 (758.54–1087.82) h	0.003	0.006	Yes
Speed (m/s)	1.15 (1.03–1.29)	1.16 (0.95–1.26)	1.17 (1.01–1.30)	0.991	0.998	No
Power (W)	901.22 (275.86–1157.37) i	1026.91 (344.90–1359.68)	1084.36 (776.05–1379.99) i	0.015	0.027	Yes
CMJA
Jump height (cm)	31.69 (25.29–35.92)	31.17 (22.98–38.23)	31.69 (24.83–39.38)	0.853	0.998	No
Flight time (ms)	508.33 (454.17–541.25)	504.17 (432.92–558.33)	508.33 (450.00–566.67)	0.84	0.998	No
Force (N)	807.44 (201.45–1133.27) jk	952.56 (270.32–1192.29) j	1017.00 (748.31–1139.07) k	0.002 **	0.005	Yes
Speed (m/s)	1.25 (1.11–1.33)	1.24 (1.06–1.37)	1.25 (1.10–1.39)	0.894	0.998	No
Power (W)	986.37 (253.21–1458.00) lm	1180.06 (342.53–1614.95) l	1239.86 (863.32–1583.02) m	0.003 **	0.006	Yes
Speed
Partial 1–5 (m/s)	1.60 (1.27–1.95)	1.72 (1.16–1.87)	1.67 (1.49–1.78)	0.168	0.244	No
Partial 5–10 (m/s)	0.90 (0.76–1.00) n	0.85 (0.76–0.97) n	0.87 (0.82–0.92)	0.006 **	0.012	Yes
Partial 10–15 (m/s)	0.82 (0.70–0.93) op	0.77 (0.71–0.85) o	0.77 (0.51–0.86) p	0.002 **	0.005	Yes
Total 15 (m/s)	3.32 (2.98–3.83)	3.32 (2.80–3.55)	3.30 (2.92–3.47)	0.442	0.573	No
COD Timer 5-0-5
Total time (s)	2.96 (2.68–4.79)	2.98 (2.67–3.37)	3.02 (2.65–3.38)	0.939	0.998	No
Av speed (km/h)	7.68 (5.58–8.32)	7.70 (7.00–8.35)	7.57 (7.15–8.32)	0.724	0.915	No
Contact time (ms)	658.33 (470.42–1865.83)	745.42 (416.25–920.42)	693.54 (129.17–883.33)	0.379	0.505	No
10 (m)	1.78 (1.50–2.42)	1.73 (1.45–1.92)	1.73 (1.54–1.92)	0.351	0.481	No
COD Deficit (s)	1.14 (0.65–3.14)	1.23 (0.95–1.59)	1.20 (0.96–1.72)	0.336	0.474	No
COD Timer 5+5
Total time (s)	3.27 (2.88–3.84) qr	3.12 (2.66–3.38) q	3.00 (2.81–3.25) r	<0.001 ***	0.0003	Yes
Av speed (km/h)	11.00 (9.36–12.51) st	11.54 (10.66–13.53) s	11.95 (11.00–12.79) t	<0.001 ***	0.0003	Yes
Contact time (ms)	512.08 (270.83–916.25) u	637.08 (466.67–783.33) u	572.71 (420.83–816.67)	0.022 **	0.038	Yes
Hamstring strength
Torque (Nm)	258.80 (209.40–336.80)	290.49 (201.12–399.43)	280.86 (180.31–372.70)	0.079	0.126	No
Angle (°)	131.87 (123.30–144.03) v	133.87 (111.35–146.09) v	126.55 (112.50–143.02)	0.047	0.078	No
RAST
Time 1 (s)	6.43 (6.18–7.37) *	5.27 (4.67–6.26) *	5.64 (5.32–6.85) *	<0.001 ***	0.0003	Yes
Power 1 (W)	222.42 (155.15–303.10) wx	453.36 (261.66–687.21) w	381.83 (205.04–505.75) x	<0.001 ***	0.0003	Yes
Time 2 (s)	6.65 (6.11–7.53) *	5.63 (5.06–6.12) *	6.21 (5.46–8.15) *	<0.001 ***	0.0003	Yes
Power 2 (W)	208.59 (136.92–325.99) &	385.48 (250.64–549.93) &	278.44 (120.84–497.46) &	<0.001 ***	0.0003	Yes
Time 3 (s)	6.95 (6.48–7.92) %	5.67 (4.97–6.12) %	6.22 (5.08–6.75) %	<0.001 ***	0.0003	Yes
Power 3 (W)	178.73 (120.00–228.06) Ɯ	361.62 (291.08–508.91) Ɯ	278.76 (201.60–576.54) Ɯ	<0.001 ***	0.0003	Yes
Time 4 (s)	7.20 (6.75–7.90) Ɵ	5.93 (5.22–6.54) Ɵ	6.39 (4.87–7.13) Ɵ	<0.001 ***	0.0003	Yes
Power 4 (W)	156.01 (121.05–210.41) yz	319.61 (233.20–465.71) y	273.63 (181.82–654.39) z	<0.001 ***	0.0003	Yes
Time 5 (s)	7.30 (6.30–7.97) ƿ	5.86 (5.51–6.60) ƿ	6.70 (5.79–7.74) ƿ	<0.001 ***	0.0003	Yes
Power 5 (W)	152.03 (115.66–200.91) ʠ	332.03 (256.05–401.82) ʠ	227.77 (121.79–339.75) ʠ	<0.001 ***	0.0003	Yes
Time 6 (s)	7.53 (6.75–8.00) ʖ	6.03 (5.40–6.73) ʖ	6.60 (5.58–7.27) ʖ	<0.001 ***	0.0003	Yes
Power 6 (W)	140.09 (108.48–201.58) £β	288.15 (198.66–392.38) £	234.08 (171.52–412.45) β	<0.001 ***	0.0003	Yes
Max power (W)	223.42 (157.18–325.99) ʚ	453.36 (325.74–687.21) ʚ	386.98 (219.13–654.39) ʚ	<0.001 ***	0.0003	Yes
Min power (W)	137.69 (108.48–191.67) ʒ	281.11 (198.66–374.07) ʒ	207.53 (120.84–304.58) ʒ	<0.001 ***	0.0003	Yes
Av power (W)	178.45 (129.14–224.68) Ϫ	365.58 (276.17–457.11) Ϫ	283.02 (196.54–471.13) Ϫ	<0.001 ***	0.0003	Yes
Fatigue index (%)	2.09 (0.95–4.18) ¥∞	4.73 (2.77–9.35) ¥	4.84 (1.14–11.62) ∞	<0.001 ***	0.0003	Yes

Note: p: original p-value (Kruskal–Wallis test). q: p-value adjusted using the Benjamini–Hochberg FDR correction (q < 0.05). Sig.: * q < 0.05, ** q < 0.01, *** q < 0.001. All comparisons were made between age groups (EA, MA, LA). Early Adolescents = EA, Mid Adolescents = MA, Late Adolescents = LA; cm: centimeters; ms: milliseconds; N: newtons; m/s: meters/second; W: watts; m: meters; km/h: kilometers/hour; Nm: newton metro; Max: maximum; Min: minimum; Av: average; SJ: Squat Jump; CMJ: Countermovement Jump; RAST: Running-Based Anaerobic Sprint Test. Values with different superscript letters differ significantly between groups (p < 0.05). Identical symbols or letters across the three groups indicate that there are differences among them, regardless of the order. Conversely, when different letters appear in any of the groups, this indicates a difference with a common group.

, post-FDR adjustment). The largest effects were observed in the RAST-derived measures, particularly average power (χ² = 40.03, p < 0.001, q = 0.0003, ε² = 0.73), time 3 (χ² = 39.78, p < 0.001, q = 0.0003, ε² = 0.72), time 4 (χ² = 39.05, p < 0.001, q = 0.0003, ε² = 0.71), power 5 (χ² = 39.11, p < 0.001, q = 0.0003, ε² = 0.71), and power 3 (χ² = 38.29, p < 0.001, q = 0.0003, ε² = 0.70), all of which demonstrated large effects (ε² ≥ 0.70) and underscored the strong influence of the adolescent stage on repetitive power output and sprint times. Additional large effects were noted at time 5 (ε² = 0.69), minimum power (ε² = 0.69), time 1 (ε² = 0.68), power 4 (ε² = 0.68), time 6 (ε² = 0.67), power 6 (ε² = 0.67), and maximum power (ε² = 0.66), reflecting enhanced endurance and power variability with age. The fatigue index (χ² = 31.05, p < 0.001, q = 0.0003, ε² = 0.56) and total time of COD 5+5 (χ² = 16.77, p < 0.001, q = 0.0003, ε² = 0.30) also had large effects, whereas the average speed of COD 5+5 (ε² = 0.29), the force of the CMJA (ε² = 0.23), and the 10–15 m partial sprint (ε² = 0.24) had medium-to-large effects. Medium effects were evident in squat 90° (ε² = 0.21), force in SJ (ε² = 0.24), force in CMJ (ε² = 0.21), power in CMJA (ε² = 0.21), 5–10 m partial sprint (ε² = 0.19), power in SJ (ε² = 0.17), power in CMJ (ε² = 0.15), contact time in COD 5+5 (ε² = 0.14), and angle in hamstring strength (ε² = 0.11, q = 0.078; nonsignificant post-FDR), suggesting moderate age-related influences on explosive and directional performance. Overall, these results, reported as medians (min–max), confirm that late adolescents exhibit superior anaerobic power and reduced fatigue, aligning with biological maturation patterns in female youth soccer players.

Table 4. Correlations between anthropometric measurements and physical performance in female soccer players, using the Spearman test.

	Girth						Breadth			Skinfold
VARIABLE	RA	FA	WAIST	HIP	THIGH	CALF	HU	BIS	FE	TR	SUB	BIC	IC	SUP	AB	TH	CA
SJ
Jump height (cm)	−0.14	−0.06	−0.17	−0.19	−0.11	0.08	−0.24	−0.17	−0.01	−0.23	−0.35 **	−0.32 *	−0.26	−0.27 *	−0.36 **	−0.22	−0.41 **
Flight time (ms)	−0.14	−0.05	−0.17	−0.19	−0.11	0.08	−0.24	−0.17	−0.01	−0.23	−0.35 **	−0.33 *	−0.26	−0.27 *	−0.36 **	−0.22	−0.41 **
Force (N)	0.37 **	0.53 ***	0.45 ***	0.64 ***	0.62 ***	0.65 ***	0.20	0.10	0.48 ***	0.40 **	0.29 *	0.46 ***	0.44 ***	0.25	0.29 *	0.20	0.28 *
Speed (m/s)	−0.15	−0.06	−0.17	−0.19	−0.11	0.08	−0.23	−0.18	−0.01	−0.23	−0.35 **	−0.33 *	−0.26 *	−0.27 *	−0.36 **	−0.22	−0.41 **
Power (W)	0.25	0.39 **	0.27 *	0.43 ***	0.45 ***	0.54 ***	0.08	0.03	0.38 **	0.26 *	0.15	0.28 *	0.27 *	0.11	0.12	0.05	0.05
CMJ
Jump height	0.01	−0.02	−0.07	−0.15	−0.01	0.00	−0.12	−0.07	−0.05	−0.20	−0.20	−0.22	−0.2	−0.23	−0.29 *	−0.12	−0.37 **
Flight time (ms)	0.00	−0.02	−0.07	−0.15	−0.02	−0.01	−0.12	−0.07	−0.05	−0.20	−0.20	−0.22	−0.2	−0.23	−0.29 *	−0.12	−0.37 **
Force (N)	0.42 **	0.54 ***	0.52 ***	0.67 ***	0.66 ***	0.64 ***	0.24	0.16	0.48 ***	0.45 ***	0.38 **	0.52 ***	0.5 ***	0.32 *	0.35 **	0.26	0.31 *
Speed (m/s)	0.15	−0.02	−0.07	−0.15	−0.02	−0.01	−0.12	−0.08	−0.05	−0.20	−0.20	−0.22	−0.2	−0.23	−0.29 *	−0.12	−0.37 **
Power (W)	0.34 *	0.41 **	0.34 *	0.47 ***	0.52 ***	0.51 ***	0.16	0.07	0.37 **	0.32 *	0.25	0.36 **	0.31 *	0.16	0.16	0.11	0.10
CMJA
Jump height (cm)	−0.08	−0.07	−0.12	−0.16	−0.11	0.17	−0.2	−0.13	−0.03	−0.28 *	−0.26	−0.28 *	−0.19	−0.22	−0.24	−0.15	−0.38 **
Flight time (ms)	−0.07	−0.07	−0.12	−0.16	−0.10	0.18	−0.21	−0.12	−0.03	−0.27 *	−0.26	−0.28 *	−0.19	−0.21	−0.24	−0.15	−0.38 **
Force (N)	0.36 **	0.50 ***	0.46 ***	0.63 ***	0.60 ***	0.67 ***	0.21	0.10	0.47 ***	0.39 **	0.31 *	0.45 ***	0.47 ***	0.29 *	0.33 *	0.24	0.29 *
Speed (m/s)	−0.08	−0.07	−0.12	−0.18	−0.11	0.17	−0.17	−0.14	−0.03	−0.27 *	−0.26	−0.29 *	−0.18	−0.20	−0.23	−0.14	−0.38 **
Power (W)	0.28 *	0.41 **	0.34 *	0.47 ***	0.49 ***	0.61 ***	0.13	0.03	0.37 **	0.28 *	0.21	0.30 *	0.35 **	0.18	0.21	0.16	0.13
Speed
Partial 1–5 m	0.07	0.00	0.14	0.26	0.10	0.10	−0.03	−0.03	0.07	0.4 **	0.35 **	0.24	0.44 ***	0.45 ***	0.38 **	0.18	0.14
Partial 5–10 m	−0.03	−0.21	−0.12	−0.14	−0.05	−0.17	0.00	−0.09	−0.07	−0.14	−0.02	−0.08	−0.12	−0.13	−0.21	0.01	0.19
Partial 10–15 m	−0.19	−0.21	0.02	0.01	0.06	0.05	0.14	−0.16	0.05	0.01	0.00	−0.10	0.09	0.17	−0.11	0.12	0.25
Total 15 m	−0.12	−0.19	0.00	0.10	−0.03	−0.07	−0.04	−0.10	−0.02	0.18	0.14	0.02	0.23	0.27 *	0.06	0.06	0.14
COD Timer 5-0-5
Total time (s)	−0.05	−0.10	0.05	0.06	0.03	0.05	0.19	0.42 **	0.37 **	0.15	0.28 *	0.12	0.12	0.05	0.11	−0.04	0.11
Average speed (km/h)	0.21	0.03	−0.05	−0.16	−0.15	−0.05	−0.28 *	−0.37 **	−0.28 *	−0.10	−0.21	−0.13	−0.09	−0.10	−0.04	0.04	−0.20
Contact time	0.17	−0.10	0.00	0.05	0.13	−0.03	0.07	0.27 *	0.20	0.20	0.15	0.09	0.16	0.21	0.25	0.10	0.14
10 m	−0.07	−0.07	−0.12	0.04	0.09	−0.08	0.13	0.02	−0.05	−0.10	−0.09	−0.09	−0.07	0.03	−0.14	−0.13	0.12
COD Deficit	0.04	0.00	0.20	0.14	0.08	0.19	0.17	0.33 *	0.36 **	0.33 *	0.34 **	0.17	0.25	0.14	0.27 *	0.12	0.12
COD Timer 5+5
Total time	−0.23	−0.33 *	−0.20	−0.13	−0.18	−0.26	0.08	−0.08	−0.10	−0.14	−0.03	−0.20	−0.09	0.03	−0.08	−0.03	0.16
Average speed	0.21	0.31 *	0.19	0.13	0.19	0.25	−0.08	0.07	0.09	0.14	0.01	0.20	0.08	−0.01	0.08	0.01	−0.16
Contact time (ms)	0.17	0.08	0.04	0.19	0.04	0.05	−0.08	0.13	−0.05	0.25	0.13	−0.07	0.1	0.15	0.17	0.03	−0.05
Hamstring strength
Torque (Nm)	0.26	0.36 **	0.41 **	0.53 ***	0.45 ***	0.48 ***	0.40 **	0.31 *	0.34 *	0.22	0.34 *	0.33 *	0.32 *	0.26	0.24	0.21	0.14
Angle (°)	−0.31 *	−0.29 *	−0.29 *	−0.30 *	−0.32 *	−0.14	−0.15	−0.13	−0.26 *	−0.25	−0.14	−0.13	−0.17	−0.14	−0.12	−0.17	−0.22
RAST
Time 1 (s)	−0.21	−0.34 *	−0.17	−0.30 *	−0.17	−0.39 **	−0.12	−0.16	−0.18	−0.09	−0.01	−0.22	−0.20	−0.06	−0.11	−0.02	0.07
Power 1 (W)	0.31 *	0.46 ***	0.38 **	0.52 ***	0.37 **	0.53 ***	0.24	0.26	0.32 *	0.22	0.15	0.32 *	0.33 *	0.19	0.23	0.11	0.05
Time 2 (s)	−0.09	−0.14	−0.15	−0.29 *	−0.02	−0.26	−0.06	−0.11	−0.18	−0.07	−0.03	−0.14	−0.20	−0.06	−0.12	−0.04	0.10
Power 2 (W)	0.24	0.32 *	0.38 **	0.55 ***	0.28 *	0.45 ***	0.23	0.25	0.37 **	0.18	0.17	0.30 *	0.33 *	0.18	0.24	0.13	0.04
Time 3 (s)	−0.17	−0.25	−0.09	−0.25	−0.09	−0.29 *	0.06	−0.07	−0.09	−0.08	0.1	−0.11	−0.12	−0.05	−0.08	0.07	0.13
Power 3 (W)	0.33 *	0.46 ***	0.36 **	0.53 ***	0.38 **	0.48 ***	0.11	0.20	0.29 *	0.25	0.11	0.30 *	0.31 *	0.19	0.24	0.06	0.05
Time 4 (s)	−0.23	−0.29 *	−0.15	−0.32 *	−0.07	−0.35 **	−0.05	−0.09	−0.08	−0.07	0.01	−0.16	−0.15	−0.07	−0.15	−0.04	0.16
Power 4 (W)	0.34 **	0.44 ***	0.40 **	0.56 ***	0.34 *	0.51 ***	0.22	0.23	0.27 *	0.23	0.15	0.31 *	0.31 *	0.18	0.26	0.14	0.01
Time 5 (s)	−0.07	−0.13	−0.08	−0.16	0.01	−0.2	−0.07	−0.11	−0.04	0.03	0.06	−0.12	−0.12	0.00	−0.04	−0.01	0.18
Power 5 (W)	0.19	0.29 *	0.29 *	0.40 **	0.24	0.41 **	0.22	0.23	0.23	0.10	0.07	0.23	0.26	0.10	0.13	0.11	−0.04
Time 6 (s)	−0.20	−0.24	−0.09	−0.24	−0.01	−0.26	−0.08	−0.18	−0.13	−0.15	−0.07	−0.26	−0.16	−0.03	−0.07	0.07	0.17
Power 6 (W)	0.29 *	0.36 **	0.29 *	0.43 ***	0.22	0.42 **	0.21	0.27 *	0.28 *	0.26	0.21	0.37 **	0.30 *	0.14	0.19	0.02	−0.02
Maximum power (W)	0.36 **	0.46 ***	0.39 **	0.55 ***	0.39 **	0.52 ***	0.22	0.25	0.33 *	0.25	0.17	0.34 *	0.36 **	0.22	0.27 *	0.12	0.06
Minimum power (W)	0.24	0.36 **	0.32 *	0.49 ***	0.24	0.46 ***	0.24	0.22	0.27 *	0.20	0.16	0.30 *	0.32 *	0.16	0.16	0.12	0.01
Average Power (W)	0.31 *	0.42 **	0.36 **	0.52 ***	0.32 *	0.49 ***	0.22	0.24	0.30 *	0.23	0.15	0.33 *	0.33 *	0.18	0.24	0.10	0.03
Fatigue index	0.40 **	0.47 ***	0.36 **	0.48 ***	0.43 **	0.45 ***	0.17	0.27 *	0.32 *	0.25	0.14	0.32 *	0.32 *	0.23	0.33 *	0.09	0.07

Note. * p < 0.05, ** p < 0.01, *** p < 0.001; RA = Relaxed arm; FA = Flexed arm; HU = humerus, BIS = Bistyloid; FE = femur; TR = triceps, SUB = Subscapular; BIC = biceps; IC = Iliac crest; SUP = supraspinal; AB = abdominal; TH = thigh; CA = calf; cm: centimeters; ms: milliseconds; N: newtons; m/s: meters/second; W: watts; m: meters; km/h: kilometers/hour; Nm: newton metro; SJ: Squat Jump; CMJ: Countermovement Jump; RAST: Running-Based Anaerobic Sprint Test.

, examining correlations between anthropometric measurements (girths, breadths, and skinfolds) and physical performance in female soccer players, reveals significant correlations (p < 0.001, marked with ***) classified according to Hopkins’ criteria. The strongest correlations, all large magnitude (r = 0.5–0.7), were observed with hip girth (HIP), thigh girth (THIGH), calf girth (CALF), flexed arm breadth (FA), waist breadth (WAIST), and femur breadth (FE). Specifically, force (N) showed significant correlations with HIP (r = 0.64), THIGH (r = 0.62), CALF (r = 0.65), FA (r = 0.53), and WAIST (r = 0.45, moderate but near large), and FE (r = 0.48, moderate). In the second force measurement (2), significant correlations were observed with HIP (r = 0.67), THIGH (r = 0.66), CALF (r = 0.64), FA (r = 0.54), WAIST (r = 0.52), and FE (r = 0.48, moderate). For the third force measurement (3), significant correlations were found with HIP (r = 0.63), THIGH (r = 0.60), CALF (r = 0.67), FA (r = 0.50), and WAIST (r = 0.46, moderate), and FE (r = 0.47, moderate). Power (W) exhibited moderate correlations with HIP (r = 0.43), THIGH (r = 0.45), and CALF (r = 0.54, large), while in the second power measurement (2), significant correlations were observed with HIP (r = 0.47, moderate but near large), THIGH (r = 0.52), and CALF (r = 0.51), and in the third with HIP (r = 0.47, moderate), THIGH (r = 0.49, moderate), and CALF (r = 0.61, large). Maximum power showed significant correlations with HIP (r = 0.55) and CALF (r = 0.52), minimum power with HIP (r = 0.49, moderate) and CALF (r = 0.46, moderate), and average power with HIP (r = 0.52) and CALF (r = 0.49, moderate). The fatigue index presented significant correlations with HIP (r = 0.48, moderate but near large), CALF (r = 0.45, moderate), and FA (r = 0.47, moderate). In the 1–5 m partial sprint, moderate correlations were found with iliac crest skinfold (IC, r = 0.44) and supraspinal skinfold (SUP, r = 0.45). These associations show that larger muscle and bone dimensions, particularly of the hip, thigh, and calf, are strongly related to better performance in force, power, and initial sprint speed in female soccer players, highlighting the importance of muscle mass.

The correlation matrix in

Table 5. Correlation between physical performance measures, basic measurements, and body composition results, using the Spearman’s test.

	Basic Measurements					Body Composition Results
VARIABLE	Age	Waist	Hip	SH	AS	BMI	SL	SI	BR	FA	CA	LEE	RMR	BM	RM
SJ
Jump height (cm)	0.01	−0.21	−0.13	0.08	−0.08	−0.13	−0.34 **	−0.30 *	−0.30 *	−0.34 *	−0.31 *	−0.20	−0.20	−0.11	0.02
Flight time (ms)	0.01	−0.21	−0.13	0.08	−0.08	−0.13	−0.34 **	−0.31 *	−0.31 *	−0.34 *	−0.31 *	−0.20	−0.20	−0.11	0.02
Force (N)	0.43 **	0.68 ***	0.31 *	0.25	0.27 *	0.62 ***	0.41 **	0.38 **	0.38 **	0.39 **	0.10	0.61 ***	0.61 ***	0.39 **	0.4 **
Speed (m/s)	0.00	−0.22	−0.13	0.07	−0.08	−0.13	−0.34 **	−0.31 *	−0.31 *	−0.34 *	−0.31 *	−0.20	−0.20	−0.12	0.02
Power (W)	0.36 **	0.45 ***	0.24	0.28 *	0.21	0.42 **	0.22	0.23	0.23	0.20	−0.04	0.41 **	0.41 **	0.30 *	0.27 *
CMJ
Jump height (cm)	0.02	−0.11	−0.04	0.15	0.04	−0.11	−0.31 *	−0.20	−0.20	−0.22	−0.20	−0.09	−0.09	−0.05	0.00
Flight time (ms)	0.02	−0.11	−0.04	0.15	0.04	−0.11	−0.31 *	−0.21	−0.21	−0.22	−0.20	−0.09	−0.09	−0.05	0.00
Force (N)	0.42 **	0.71 ***	0.35 **	0.29 *	0.31 *	0.64 ***	0.46 ***	0.45 ***	0.45 ***	0.46 ***	0.17	0.65 ***	0.65 ***	0.43 ***	0.36 **
Speed (m/s)	0.02	−0.11	−0.03	0.16	0.04	−0.11	−0.31 *	−0.20	−0.20	−0.22	−0.21	−0.09	−0.09	−0.05	−0.01
Power (W)	0.37 **	0.53 ***	0.28 *	0.31 *	0.28 *	0.45 ***	0.27 *	0.31 *	0.31 *	0.29 *	0.05	0.49 ***	0.49 ***	0.32 *	0.28 *
CMJA
Jump height (cm)	0.12	−0.16	−0.19	0.07	−0.05	−0.06	−0.37 **	−0.28 *	−0.28 *	−0.29 *	−0.25	−0.17	−0.17	−0.12	−0.01
Flight time (ms)	0.12	−0.15	−0.18	0.08	−0.05	−0.05	−0.36 **	−0.27 *	−0.27 *	−0.29 *	−0.25	−0.17	−0.17	−0.11	−0.01
Force (N)	0.43 ***	0.67 ***	0.28 *	0.23	0.27 *	0.62 ***	0.41 **	0.38 **	0.38 **	0.41 **	0.14	0.60 ***	0.60 ***	0.37 **	0.37 **
Speed (m/s)	0.11	−0.17	−0.20	0.07	−0.07	−0.06	−0.36 **	−0.27 *	−0.27 *	−0.29 *	−0.24	−0.19	−0.19	−0.13	−0.02
Power (W)	0.43 ***	0.52 ***	0.19	0.24	0.22	0.50 ***	0.26	0.27 *	0.27 *	0.26	0.04	0.45 ***	0.45 ***	0.27 *	0.29 *
Speed
Partial 0–5 m	0.07	0.19	0.11	−0.05	0.03	0.13	0.31 *	0.41 **	0.41 **	0.46 ***	0.38 **	0.18	0.18	0.07	−0.02
Partial 5–10 m	−0.24	−0.09	0.01	−0.04	0.00	−0.10	0.00	−0.08	−0.08	−0.14	−0.05	−0.05	−0.05	−0.04	−0.03
Partial 10–15 m	−0.50 ***	0.00	−0.10	−0.33 *	−0.06	0.13	0.13	0.00	0.00	0.18	0.29 *	0.01	0.01	−0.08	0.23
Total 15 m	−0.23	0.04	0.06	−0.17	−0.01	0.03	0.18	0.17	0.17	0.27 *	0.26	0.07	0.07	−0.01	0.11
COD Timer 5-0-5
Total time (s)	0.00	0.17	0.26 *	0.01	0.29 *	0.04	0.16	0.25	0.25	0.13	0.04	0.21	0.21	0.43 ***	−0.05
Average speed (km/h)	0.06	−0.21	−0.15	0.00	−0.15	−0.18	−0.17	−0.18	−0.18	−0.15	−0.02	−0.21	−0.21	−0.30 *	−0.09
Contact time	0.04	0.14	0.20	−0.08	0.26	0.02	0.19	0.15	0.15	0.27 *	0.23	0.21	0.21	0.26	−0.03
10 m	−0.21	0.04	−0.01	−0.06	−0.01	0.10	−0.01	−0.11	−0.11	0.03	−0.06	0.05	0.05	−0.03	0.19
COD Deficit	0.14	0.22	0.25	0.01	0.25	0.11	0.30 *	0.38 **	0.38 **	0.23	0.17	0.25	0.25	0.39 **	−0.05
COD Timer 5+5
Total time	−0.51 ***	−0.22	−0.10	−0.19	−0.10	−0.19	0.01	−0.10	−0.10	0.06	0.19	−0.16	−0.16	−0.14	−0.03
Average speed (2)	0.49 ***	0.21	0.09	0.17	0.09	0.19	−0.01	0.09	0.09	−0.04	−0.17	0.15	0.15	0.12	0.04
Contact time (ms)	0.29 *	0.22	0.34 **	0.05	0.29 *	0.02	0.12	0.19	0.19	0.18	0.05	0.27 *	0.27 *	0.2	0.08
Hamstring strength
Torque (Nm)	0.20	0.65 ***	0.46 ***	0.10	0.41 **	0.45 ***	0.22	0.31 *	0.31 *	0.46 ***	0.22	0.64 ***	0.64 ***	0.50 ***	0.38 **
Angle (°)	−0.20	−0.26 *	−0.07	−0.15	−0.11	−0.30 *	−0.25	−0.22	−0.22	−0.07	0.07	−0.23	−0.23	−0.13	−0.08
RAST
Time 1 (s)	−0.48 ***	−0.39 **	−0.22	0.09	−0.19	−0.33 *	−0.02	−0.04	−0.04	−0.19	0.00	−0.35 **	−0.35 **	−0.24	−0.33 *
Power 1 (W)	0.50 ***	0.63 ***	0.37 **	0.00	0.34 *	0.52 ***	0.16	0.18	0.18	0.36 **	0.07	0.58 ***	0.58 ***	0.41 **	0.46 ***
Time 2 (s)	−0.28 *	−0.32 *	−0.12	0.15	−0.02	−0.29 *	0.01	−0.03	−0.03	−0.26	−0.11	−0.28 *	−0.28 *	−0.16	−0.42 **
Power 2 (W)	0.36 **	0.61 ***	0.31 *	−0.06	0.23	0.51 ***	0.13	0.17	0.17	0.41 **	0.15	0.55 ***	0.55 ***	0.39 **	0.54 ***
Time 3 (s)	−0.46 ***	−0.26	−0.04	0.15	−0.03	−0.33 *	0.02	0.03	0.03	−0.13	0.04	−0.17	−0.17	−0.06	−0.33 *
Power 3 (W)	0.51 ***	0.57 ***	0.27 *	−0.05	0.24	0.54 ***	0.18	0.18	0.18	0.36 **	0.06	0.48 ***	0.48 ***	0.3 *	0.46 ***
Time 4 (s)	−0.52 ***	−0.35 **	−0.14	0.02	−0.11	−0.31 *	0.05	−0.01	−0.01	−0.15	0.02	−0.29 *	−0.29 *	−0.15	−0.27 *
Power 4 (W)	0.55 ***	0.63 ***	0.33 *	0.09	0.31 *	0.52 ***	0.13	0.18	0.18	0.34 **	0.05	0.57 ***	0.57 ***	0.36 **	0.43 ***
Time 5 (s)	−0.30 *	−0.23	−0.04	0.25	−0.03	−0.22	0.12	0.07	0.07	−0.12	−0.04	−0.16	−0.16	−0.09	−0.32 *
Power 5 (W)	0.34 **	0.51 ***	0.23	−0.17	0.21	0.43 **	0.03	0.07	0.07	0.30 *	0.10	0.44 ***	0.44 ***	0.29 *	0.48 ***
Time 6 (s)	−0.49 ***	−0.30 *	−0.16	0.05	−0.12	−0.24	0.00	−0.10	−0.10	−0.14	0.05	−0.24	−0.24	−0.19	−0.24
Power 6 (W)	0.52 ***	0.53 ***	0.31 *	0.00	0.27 *	0.42 **	0.15	0.24	0.24	0.31 *	0.03	0.46 ***	0.46 ***	0.35 **	0.35 **
Maximum power (W)	0.51 ***	0.63 ***	0.34 *	−0.02	0.31 *	0.54 ***	0.18	0.21	0.21	0.39 **	0.10	0.56 ***	0.56 ***	0.38 **	0.45 ***
Minimum power (W)	0.40 **	0.57 ***	0.30 *	−0.08	0.24	0.46 ***	0.13	0.18	0.18	0.37 **	0.13	0.51 ***	0.51 ***	0.34 *	0.48 ***
Average Power (W)	0.49 ***	0.60 ***	0.32 *	−0.03	0.28 *	0.51 ***	0.15	0.19	0.19	0.38 **	0.09	0.54 ***	0.54 ***	0.36 **	0.46 ***
Fatigue Index	0.59 ***	0.55 ***	0.31 *	0.04	0.30 *	0.51 ***	0.20	0.21	0.21	0.31 *	0.03	0.49 ***	0.49 ***	0.36 **	0.31 *

Note. * p < 0.05, ** p < 0.01, *** p < 0.001; SH = Sitting Height; AS = Arm Span; BMI = body mass index; Fat body equations (%): SL = Slaughter, SI = Siri, BR = Brozek, FA = Faulkner and CA = Carter. Muscular mass: Lee 2000 (kg); RMR = Resting Metabolic Rate; BM = bone mass (%); RM = residual mass (%); cm: centimeters; ms: milliseconds; N: newtons; m/s: meters/second; W: watts; m: meters; km/h: kilometers/hour; Nm: newton metro; SJ: Squat Jump; CMJ: Countermovement Jump; RAST: Running-Based Anaerobic Sprint Test.

reveals strong and highly significant relationships (p < 0.001) between anthropometric, body composition measures, and key physical performance metrics. Most notably, Force (N) showed significant positive correlations with Waist circumference (r = 0.68), Body Mass Index (BMI, r = 0.62), muscular mass (Lee 2000) (r = 0.61), and Resting Metabolic Rate (RMR, r = 61), indicating that greater body size and muscularity are major determinants of force production. Similarly, a battery of Power (W) measures across different trials (power 1, 2, 3, 4, 5) consistently demonstrated a high degree of correlation with the same variables: Waist (e.g., r = 0.61), BMI (e.g., r = 0.51), Lee (e.g., r = 0.55), and RMR (e.g., r = 0.55). A significant positive correlation was also observed between several Power measures and residual mass (RM, e.g., r = 0.54). In contrast, a strong negative relationship was found between age and several time-based performance variables, with Time 3 (r = −0.46) and Time 4 (r = −0.52) being particularly prominent, suggesting that younger athletes tend to be faster in specific trials. Furthermore, Fatigue Index displayed a robust positive correlation with both age (r = 0.59) and Waist (r = 0.55), highlighting a link between age, abdominal adiposity, and a decline in performance. These findings collectively establish a strong and statistically significant link between physical dimensions, body composition, and high-level physical performance in the cohort.

Female soccer players exhibit distinct age-specific differences in body composition characteristics based on somatotype, assessed using the [49] method (Figure 1).

4. Discussion

The present study analyzed physical fitness, body composition, somatotype, and standardized scores comparatively and correlatively using the Phantom strategy (Z-score) in youth soccer players from three categories, U13, U15, and U17. The main findings show that the most significant differences between groups were found in general anthropometric measures (ω² = 0.84; p < 0.001) and body fat percentage (ω² = 0.24; p < 0.05), with large and significant effect sizes. Likewise, it was evidenced that greater muscle perimeters and bone diameters, particularly in the hip (r strength = 0.64; r power = 0.43–0.55; p < 0.001), thigh (r strength = 0.62; r power = 0.45–0.52; p < 0.001), and calf (r strength = 0.65; r power = 0.51–0.61; p < 0.001), are correlated with better performance in strength (r = 0.61–0.67; p < 0.001), power (r = 0.47–0.55; p < 0.001), and initial sprint speed 1–5 m (r with IC and SUP skinfolds = 0.44–0.45; p < 0.05), which reinforces the role of muscle mass as a determinant of performance.

Several studies have documented that the progression of competitive categories in women’s and men’s soccer is associated with significant improvements in power, sprint, and the ability to perform intermittent efforts. A study in Brazil evaluated 231 soccer players from U15 to the senior category, showing that adult players outperformed youth players in 20 m sprint (p < 0.01), vertical jumps (SJ and CMJ; p < 0.05), and Yo-Yo IR1 intermittent test (p < 0.05), with progressive performance increases as age and competitive level increased [50]. These results are consistent with our findings, where the U17 category shows better performance than U13 and U15, reflecting the influence of biological maturation and muscle development in optimizing performance.

Likewise, in another cultural context, a study showed that 34 Spanish professional players had a strong correlation of body mass and muscle mass with CMJ power (r = 0.70–0.89; p < 0.001), and peak power (watts) had a very strong correlation with body mass (r = 0.904; p < 0.001). On the other hand, body fat showed negative-moderate associations with vertical jump and Yo-Yo IR1 (r = 0.30–0.49; p < 0.04) [51].

In elite women’s futsal, positional differences expressed in somatotype have been reported: pivots and goalkeepers showed greater mesomorphy and endomorphy, as well as greater bone and lean mass, compared to other positions [52]. Although the present research did not include a positional analysis, this evidence reinforces the importance of the anthropometric profile for specific performance and for talent selection in team sports.

As for the influence of maturation on body composition and performance, it has also been documented in male soccer players U16 and U19: muscle mass showed large correlations with CMJ and horizontal jump, while fat and adiposity correlated negatively with Yo-Yo IR2 (moderate correlations) [53]. Although metabolic demands differ between sexes, the pattern found is consistent: the more muscle mass, the better the explosive performance, whereas adiposity limits it. This suggests that the underlying biological mechanisms: (i) pubertal maturation, (ii) muscle hypertrophy, and (iii) changes in body composition, are universal in the development of talent in soccer.

Other research on semiprofessional soccer (18–32 years old) has revealed relationships between body composition variables such as mass and physical performance (p < 0.05) [54], where players tend to be mesomorphic [55], highlighting the importance of developing studies specific to each context and age group, given that there are currently no clear references regarding anthropometric profiles [56]. In this context, fat mass is inversely related to the physical performance of players [54]. These values contrast with those of the present research in the U13 and U17 categories, which presented a lower percentage of relative muscle mass. This evidence shows how competitive level and age influence the morphofunctional specialization of each discipline and supports the need to longitudinally monitor body composition to adjust strength and conditioning programs.

The literature highlights the importance of evaluating body composition characteristics and lower limb strength in female soccer players (19.73 ± 4.81), with no significant differences reported in jumps such as the CMJ and CMJA, body fat, lean body mass, or muscle mass in absolute and relative terms [57]. Similarly, Goranovic et al. [57] highlighted the need to evaluate larger samples and different categories to identify these changes in response to age. The interaction between biological maturation, physical fitness training, and body composition assessment constitutes a critical triangle for the progression of youth categories toward high performance. The findings, which show negative associations between fat percentage and positive associations between muscle circumference and explosive performance in this study, reaffirm the need for specific nutritional interventions to support the physical development of adolescent soccer players.

From the perspective of sports development and aiming to foster, increase, and continue promoting the growth of women’s soccer, research into initiation categories is key. In Slovakia, a longitudinal follow-up was carried out on national U15 players (n = 136) between 2017 and 2022, where a decrease in body fat percentage (3–6%) and an increase in muscle mass (2–4%) were observed between cohorts, as an effect of the training process (p < 0.05) [58]. Another study on adolescent players (10–16 years; n = 441) examined variation in body size and adiposity according to skeletal maturation estimated by the Fels method. The estimated fat percentage ranged between 18% and 28.2%, with an increase in the percentage of skeletally mature players from U13 (0%) to U17 (49%), finding that early-maturing players tended to be heavier than their late-maturing peers [59].

In addition, in youth female soccer players U11 and U14 (n = 79), the relationship between anthropometry, body composition, maturation, and competitive selection was explored, finding associations suggesting that anthropometric and composition variables influence sports selection and participation in competition [60]. These results are like those of the present investigation. Therefore, although there are valuable studies, there are still relatively few that comparatively examine age categories in female child and youth soccer players, relating anthropometric variables, biological maturation, and physical fitness, highlighting a clear need in the field. This is why, in this section, a few studies were found that contradict the nature of the results of this research, evidencing gaps in the literature.

Our findings from the RAST revealed an unexpected pattern in which the under-15 players presented lower initial times than did the under-17 players, despite the greater chronological age in the latter group. Hypothetically, we believe that these results could be attributed primarily to maturational variability during puberty in female soccer. This stage, characterized by a peak height velocity (PHV) of approximately 12 years of age, could induce a temporary increase in fat mass (up to 7–10 kg) and an alteration in body composition that reduces neuromuscular efficiency, hypothetically stagnating or decreasing sprint speed by −0.09 m/s annually post-PHV in girls, in contrast to prepubertal improvements of +0.24 m/s [61]. In the specific context of soccer, the under-17 group might include a greater proportion of players in advanced maturation with these negative effects on speed [62], exacerbated by a training history that prioritizes endurance and tactics over explosive speed, thereby diluting gains in repeated sprints such as the RAST [63]. Additionally, although a minor measurement error due to accumulated fatigue cannot be ruled out (fatigue index similar between under15 and under17: 4.73% vs. 4.84%), the trend could align with findings in adolescent female soccer players, where postpubertal growth spurt coordination generates a phase of “awkwardness” that affects initial acceleration and overall speed [61]. These hypothetical factors underscore the importance of monitoring individual biological maturation to optimize speed-focused training interventions and avoid questions regarding data validity.

The strength of the present research lies in the incorporation of the Phantom strategy (Z-score), which allows for adjusting anthropometric measurements to a proportionality reference pattern. This approach overcomes the limitations of comparing only absolute values by considering differential maturation. While most studies in soccer [50,51] and in other sports [64,65] applied the Heath–Carter method without proportionality adjustments, the Phantom strategy provides a more accurate interpretation of inter-age differences in contexts of accelerated growth.

However, the phantom strategy remains infrequently employed in soccer anthropometry research (note that the provided reference does not explicitly mention the phantom and thus is not cited here to avoid inaccuracies), potentially limiting direct comparability with the predominant studies utilizing dual-energy X-ray absorptiometry (DEXA), bioelectrical impedance analysis (BIA), or classical anthropometric protocols such as Heath–Carter somatotyping, which have demonstrated variable interchangeability across athletic populations [66]. Nonetheless, this methodological choice aligns with emerging applications in youth sports to more effectively account for maturational heterogeneity [67].

Limitations and Future Perspectives

The study demonstrated the importance of evaluating physical performance and anthropometric assessment in regional players, showing the differences and relationships among the different categories evaluated. Nevertheless, it is important to mention the limitations of the research. The first limitation is associated with the study design, as cross-sectional studies do not establish causal relationships. Longitudinal studies and randomized controlled trials are needed to determine the effects that occur in response to the level of competition, age, playing position, etc. The second limitation lies in the sample evaluated, with a specific regional population as well as its performance, involving a reduced sample group (players from Bogotá), which limits the findings.

Likewise, although the statistical power of the sample was acceptable, there was an unequal distribution among each of the categories, which may generate wider confidence intervals in the subgroups with smaller sample sizes. Although anthropometric assessment is one of the effective tools in the field of Sports Sciences, it may contain minimal measurement errors despite the robustness of the instruments and established equations. Therefore, comparison with high-precision instruments such as dual-energy X-ray absorptiometry (DEXA) could demonstrate higher associations or accuracy for this variable under study. Future research should evaluate the changes that physical abilities and anthropometry can represent via longitudinal designs, larger samples to generalize the collected data, high-precision instruments such as DEXA, and standardized measurement times such as the menstrual cycle, biological maturation, hours of sleep, and nutritional status.

Similarly, the evaluation of anthropometry in females may be more complex because of the biological processes that players may be experiencing at the time of assessment. External factors such as the menstrual cycle may influence the results obtained. It is necessary to analyze the socioeconomic, cultural, and competitive contexts of players, as some studies have reported that these factors have a considerable influence on growth, athletic training, and performance.

5. Conclusions

In conclusion, this study reaffirms that muscle mass is a key predictor of athletic performance, along with strength at high speeds, promoting improvements in power and sprinting in the initial meters. Adiposity acts as a limiting factor in youth soccer players. Age progression and biological maturation favor the development of the mesomorphic profile, optimizing strength and power. On the other hand, the application of the phantom strategy provides a novel methodological approach that improves understanding of the differences between categories. This evidence offers a solid framework to guide both talent detection and training planning, underscoring the importance of a comprehensive approach that combines anthropometry, physical preparation, and nutrition in the development of women’s youth soccer.

The data collected show that there is a difference between categories, physical fitness, and body composition among the players, with the U17s having higher anaerobic power, as well as body dimensions such as muscle mass. Body composition has been shown to have a positive or negative impact on the physical performance of female players.

The findings found correlations between hip, thigh, and leg circumferences and the variables of strength, power, and initial speed, suggesting that muscle development in these body areas is crucial to the physical performance of female soccer players. Another performance factor is body fat percentage, where high values were negatively associated with the variables of power and speed, limiting physical capabilities.

The development of physical fitness and body composition in soccer, with a view to performance, must consider the age groups of each category. The importance of developing specific training proposals that understand their biological development and ensure ongoing assessment to optimize players’ performance is highlighted.

Practical Applications

Based on the data collected and the results obtained from the relationship between physical fitness and body composition analysis of players in the U13, U15, and U17 categories, some practical applications can be established for coaches and researchers interested in the subject of study:

• Understanding that age is a determining factor in the development of athletes’ physical capacities, which are conditioned by body composition and physical maturity, encourages individualizing performance based on the players’ strengths and weaknesses.
• Prioritizing the development of strength, power, and increased muscle mass in the lower limbs through plyometric exercises, sprints, accelerations, and decelerations, as a strong association is evident between fitness and the development of these body areas.
• Use measurement batteries during specific periods of training, as they allow for the identification of risks of fatigue or muscle weakness, such as the RAST or the hamstring strength test. These tests provide objective measurement parameters when decreases in repeated power or fatigue indices are observed.
• Use accessible technology instruments on the playing fields, as evidence confirms their effectiveness in measurement. Additionally, they provide constant and practical monitoring of players’ responses to different stimuli (My Jump Lab, RAST).
• Use of statistics such as the Z-score allows for the collection and comparison of information across different sizes and ages, which is relevant for the detection and selection of athletic talent.
• It is suggested to monitor the circumference of the thighs and calves as simple indicators of explosive strength.