Influence of Maturity Status on Kinanthropometric and Physical Fitness Variables in Adolescent Female Volleyball Players

Abstract

The aim of this research was to analyze differences in kinanthropometric characteristics and physical performance in relation to maturity status, as well as to determine if age, maturity offset or kinanthropometric variables could predict better performance in physical fitness tests. A total of 152 female volleyball players (14.16 ± 1.25 years old) underwent a kinanthropometric assessment, followed by a physical fitness assessment composed of different tests. The age at peak height velocity (APHV) was calculated, and the sample was divided according to biological maturation into three groups. Significant differences were observed in kinanthropometric variables (p < 0.001–0.026), with early maturers showing higher values. Age, body mass, Cormic index, relative arm span, ∑8 skinfolds, fat mass, corrected arm and thigh girths, muscle mass and biacromial and biiliocristal breadths were the variables that best predicted performance in the physical tests (p < 0.001–0.024). The more mature players showed higher values in most of the kinanthropometric variables, with the more remarkable differences being in body mass, height, arm span and sitting height, and those related to adiposity and absolute body composition, and with structural variables being the most influential on the physical tests. Age had a determinant influence on the differences found between groups in strength and power-related test performance.

1. Introduction

In the past few decades, there has been a great interest in finding models to identify future sports talent among children and adolescents [1]. These talent identification programs often include the analysis of physical performance and kinanthropometric variables as a result of their influence on elite performance in sports [2,3,4,5,6]. More specifically, volleyball is characterized by explosive actions, which makes the physical condition of great importance in performance [7,8]. Due to the rules of the sport, height, arm span and leg length could allow the differentiation of high-level players [9], together with specific physical abilities such as vertical jumping and coordination in agility tests [10].

However, the characterization of the physical and kinanthropometric requirements of athletes should be approached with caution when extrapolating values to young growing athletes, as biological changes occur during this stage of maturation that may affect these variables [11]. Along this line, many talent identification programs base their selection on size and physique, dividing athletes by age groups, without taking into account that as the maturation process continues, these characteristics could become equalized [2].

Biological maturation seems to have a significant relationship with kinanthropometric and fitness variables in adolescent male athletes, with early maturers showing higher values in kinanthropometric and fitness tests, perhaps as a result of hormonal changes that occur during biological maturation [2,3]. For this reason, much attention has been paid in recent years to the relationship between biological maturation, physical fitness and kinanthropometric characteristics [3]. The age at peak height velocity (APHV) has become one of the most widely used indicators to monitor the gap between the chronological age and biological maturation of individuals [12,13]. A widely used and validated method to assess APHV is based on regression formulae from kinanthropometric variables [13].

Presently, the participation of female athletes in elite sporting events is on the rise [14]. In spite of this, scientific knowledge on the factors that affect the sports performance of women is lower than for men [14]. It is known that the maturation process occurs unevenly between boys and girls, with differences observed in the age of onset of maturation (between 9 and 14.9 years in girls versus between 12 and 15.8 in boys) [12] and in hormone concentrations related to the process of body change [15]. However, studies on the female population are scarce, and there is little evidence on whether this phenomenon also occurs among adolescent girls [16,17], although the study of the influence of biological maturation on kinanthropometric and fitness variables in female athletes could help to clarify key points in the identification of sporting talent [2,3]. Knowing this, it is possible that girls whose maturation process is more advanced would show higher values in kinanthropometric variables, as well as better performance in physical fitness tests compared to their chronological age peers.

Notwithstanding all of the above, there are no studies that have analyzed the differences in kinanthropometric variables and physical fitness in growing volleyball players, neither in male nor in female populations, according to the maturity status classification [3]. Therefore, the aim of the present research was to analyze the differences in kinanthropometric characteristics and performance in physical fitness tests according to biological maturation, as well as to determine if age, biological maturation or kinanthropometric variables could predict better performance in physical fitness tests in adolescent volleyball players.

2. Materials and Methods

2.1. Participants

Sample size calculations were performed using Rstudio software (version 3.15.0, Rstudio Inc., Boston, MA, USA). The significance level was set at α = 0.05. The standard deviation (SD) was set based on the maturity offset (SD = 0.36) [18] from previous studies. With an estimated error (d) of 0.058 years of maturity offset, the estimated sample needed was 30 subjects per group. With the significance level set a priori (α = 0.05), n = 30 participants per group and three groups based on the maturity status, the statistical power to detect large effects in the analysis of variance was 0.93. A total of 152 female volleyball players (14.16 ± 1.25) took part in the study. Prior to the start of the study, coaches, parents and players were informed of the measurement procedures, and an informed consent form was signed by the parents or legal guardians of the underage participants. The inclusion criteria were: (a) volleyball training on a regular basis, at least two days a week, with a minimum weekly training duration of three hours; (b) participating in official volleyball federation competitions during the season in which the measurements were taken; (c) having played volleyball with a volleyball federation license for at least 3 years without interruption; and (d) being between 12 and 15 years old, due to the age range in which APHV takes place [12]. The exclusion criteria were: (a) suffering from an injury that prevented completion of the tests; (b) missing more than 25% of training sessions in the last 3 months [19]; and (c) practicing other sports regularly.

2.2. Procedures

A cross-sectional study was designed in accordance with the STROBE guidelines [20]. The Institutional Ethics Committee reviewed and authorized the protocol designed for data collection in accordance with the World Medical Association Code (Code number: CE061921). The standards of the Declaration of Helsinki were followed during the entire process. The measurements were carried out in the players’ usual training sports hall. Participants were instructed not to perform strenuous physical exercise or eat large amounts of food 24 h prior to the measurements. A socio-demographic and sports questionnaire was carried out first, followed by a kinanthropometric assessment and physical fitness tests.

2.3. Socio-Demographic and Sports Information Questionnaire

An ad hoc questionnaire was used to collect socio-demographic and sports information. Subjects were asked their birthday to calculate their age at the time of the assessment; the days and hours of weekly training; how many training days they had skipped in the last three months; if they had, recently or currently, suffered a sports injury; if they practiced other sports on a regular basis; and the number of years they had been participating in official federated competition. The subjects self-completed the questionnaire under the supervision of a researcher, who previously explained the content of each question.

2.4. Biological Maturation Assessment

Maturity offset was calculated in accordance with the procedures described by Mirwald et al. [13] using the sex-specific formula: −9.37 + 0.0001882 × ((height-sitting height) × sitting height) − 0.0022 × (age × (height − sitting height)) + 0.005841 × (age × sitting height) − 0.002658 × (age × weight) + 0.07693 × (weight/height). The result of the maturity offset equation is expressed in years from the APHV when the result is positive and in years to the APHV when the result is negative. This formula has been used in the adolescent athlete population, showing a high interclass correlation coefficient (ICC = 0.96), as well as a low coefficient of variance percentage (CV% = 0.8) and a low typical error (TE = 0.1) [21]. The result was used to calculate the age at peak height velocity (APHV) for each subject using the following formula: APHV = chronological age − maturity offset result.

The APHV value was then used to classify players into three maturity status groups using the group mean APHV, in line with previous research [22]. Thus, the early maturer group was composed of players whose APHV was −0.5 years or less with respect to the mean; the average maturer group had APHV values of ±0.5 years with respect to the mean; and the late maturer group had APHV values of +0.5 years or more with respect to the mean of the group.

2.5. Kinanthropometric Assessment

Four basic measurements—eight skinfolds; six girths (arm relaxed, flexed and tensed arm, waist, hips, middle thigh and calf); five breadths (biacromial, biiliocristal, humerus, femur and bi-styloid); three lengths (acromiale-radiale, radiale-stylion and stylion-medio dactylion); and a height (ilioespinale)—were measured following the guidelines of the International Society for the Advancement in Kinanthropometry (ISAK) [23]. All measurements were performed by level 2 and 3 kinanthropometrists accredited by the ISAK. The intra- and inter-evaluator technical error of measurement (TEM) were calculated in a sub-sample of 30 participants (age = 14.47 ± 1.10) in the pilot study. For this purpose, a sample with similar characteristics to the study sample was chosen, and the number of participants included was based on previous research [24], being the minimum number of participants indicated to perform the inter- and intra-evaluator error calculations [25]. The intra-evaluator TEM was 0.03% in basic measurements, lengths, heights and girths and 1.07% in skinfolds; the inter-evaluator TEM was 0.05% in basic measurements of lengths, heights and girths and 2.83% in skinfolds.

The following material was used for kinanthropometric assessment: a SECA 862 scale (SECA, Hambourgh, Germany) with an accuracy of 100 g; a SECA stadiometer (SECA, Hambourgh, Germany) and an arm span meter (Smartmet, Jalisco, Mexico) with an accuracy of 0.1 cm; a skinfold caliper (Harpenden, Burguess Hill, UK) with an accuracy of 0.2 mm; and an inextensible tape (Lufkin, Missouri, TX, USA), a segmometer (CESCORF, Porto Alegre, Brazil), an anthropometer (Realmet, Barcelona, Spain) and a small girth sliding caliper (Holtain, Crymych, UK) with 0.01 cm accuracy. All measurements were taken twice. When the difference between the first and second measurements was greater than 5% for the folds or 1% for the other measurements, a third measurement was taken. The final value used for the analysis was the mean between measurements in the case of two attempts and the median in the case of three measurements.

The final values of the kinanthropometric measurements were used to calculate the variables of the body mass index (BMI), fat mass [26], muscle mass [27], bone mass [28], somatotype [29], ∑6 skinfolds [30], ∑8 skinfolds [30,31], Cormic index [(sitting height/height) × 100], relative arm span [(arm span/height) × 100], upper limb length [acromiale − radiale + radiale − stylion + stylion − medio dactylion], corrected girths of the arm [arm relaxed girth − (π × triceps skinfold)], thigh [middle thigh girth − (π × thigh skinfold)] and calf [calf girth − (π × calf skinfold)], the muscle-bone index [muscle mass/bone mass] and waist-to-hip ratio (waist/hip girth).

2.6. Physical Fitness Assessment

The tests were performed in the following order: sit-and-reach test, back scratch test, long jump, medicine ball throw, counter-movement jump (CMJ), 20 m sprint and agility test (9-3-6-3-9). The test assessment was performed according to previously described protocols [32]. The order of the tests was selected according to the recommendations of the National Strength and Conditioning Association (NSCA), which bases its recommendations on the fatigue generated by the different tests, as well as the metabolic pathways required by each of them, leaving a rest between trials of five minutes so that there was the minimum interference possible in the results [33]. In addition, the order followed in the tests has also been used in previous research in similar populations [34,35]. First, before the warm-up, flexibility tests were performed [36]. This was followed by a standardized warm-up, consisting of 10 min of continuous running, followed by joint mobility and familiarization with the physical fitness tests. Two researchers with previous experience in the assessment of physical fitness tests oversaw the familiarization and assessment of these tests, with the same researcher being responsible for each test during all measurements in order to avoid inter-evaluator error in the assessments. Two attempts were made at each test, with a two-minute rest between them, and with the final value being the mean of the two trials.

The sit-and-reach test was performed with the Acuflex Tester III (Novel Products, Rockton, IL, USA); the back scratch test was performed with a millimeter ruler (GIMA, Gessate, Italy); the long jump and medicine ball throw tests were performed with a tape measure (HaeSt, Wolfenbüttel, Germany) with a 0.1 cm accuracy; the CMJ was performed with a force platform (MuscleLab, Stathelle, Norway); the sprint test (20 m) was performed with MySprint (Apple Inc., Cupertino, CA, USA) [37]; and the agility test (9-3-6-3-9) was performed with five photocells (Microgate, Bolzano, Italy).

To perform the sit-and-reach, the participants were asked to sit on the floor, without shoes, with the soles of their feet in contact with the measurement tool and their legs completely stretched. They were asked to move forward slowly while pushing the indicator of the measurement tool until they reached the maximum distance without bending their knees [36].

For the back scratch test, the participants were asked to stand still and to perform a shoulder flexion and internal rotation with one arm, while with the other arm, they performed a shoulder extension with external rotation, trying to put the middle finger together in the back. The distance between the middle fingers or the overlap was registered for both arms [38].

For the long jump, the participants were asked to stand behind the starting line and to perform a horizontal jump with both legs at the same time. Swinging the arms was permitted, and the distance between the starting line and the heel was registered [39].

For the medicine ball throw, the players were asked to stand behind the start line, holding the ball with both hands. They were asked to throw the ball over their heads at the maximum distance possible, and the distance between the start line and the first bounce was registered [11].

For the CMJ, players were asked to stand on the platform with their hands placed on their hips. They were asked to perform a maximum vertical jump without taking their hands off their hips, and the jump height and power were registered [11].

For the sprint, the participants were asked to stand behind the start line and to cover the 20 m distance as fast as possible. The time was registered for each trial [39].

For agility, the participants were asked to run nine, three, six, three and nine meters, changing the direction after stepping on each of the lines marking the distances as fast as possible. The time to complete the test was registered [35].

2.7. Statistical Analysis

The distribution (Kolmogorov–Smirnov test), kurtosis and asymmetry of the variables were calculated. Levene’s test was used to assess the homogeneity of the variances. The variables showed a normal, platykurtic and symmetric distribution (p = 0.07–0.889), and the variances were homogeneous (p = 0.087–0.953). A descriptive analysis of the sample was performed, including the mean and standard deviation. A one-way ANCOVA was performed to compare the differences between the maturity status groups in the kinanthropometric variables and physical fitness tests and to measure the influence of age on the differences. Both the main effects and the interaction between the variables were checked. The effect size was calculated with partial eta squared (ŋ²_p). The Bonferroni post hoc adjustment was used to analyze differences between groups when these differences were significant. The effect sizes for the pairwise comparisons were calculated with Cohen’s D. The significance level was set a priori at p < 0.05. Correlations between age, maturity offset, kinanthropometric and fitness variables were calculated using Pearson’s correlation test. In addition, a linear regression in blocks with entered predictors based on the significance of Pearson’s r was performed with the variables that showed significant correlations in order to find out which variables could predict performance in physical tests. The normality of residuals was checked for each statistical test used. All statistical analyses were performed with SPSS v.23 software (IBM, Armonk, NY, USA).

3. Results

After calculating the APHV, the sample was divided into early maturers (n = 29), average maturers (n = 93) and late maturers (n = 30).

Table 1. Differences according to maturation group, including age covariate main effects and interactions.

Variable	Group (Mean ± SD)			Individual Models
	Early (n = 29)	Average (n = 93)	Late (n = 30)	Maturity Status Group			Age			Maturity Status Group × Age
				F	p-Value	ŋ2p	F	p-Value	ŋ2p	F	p-Value	ŋ2p
Maturity offset (years) *	2.28 ± 0.64	1.83 ± 0.93	1.30 ± 0.62	108.99	<0.001	0.63	1444.24	<0.001	0.92	648.84	<0.001	0.93
APHV (years) *	11.52 ± 0.24	12.34 ± 0.29	13.13 ± 0.27	108.99	<0.001	0.63	44.07	<0.001	0.25	234.25	<0.001	0.83
Body mass (Kg) *	61.73 ± 10.28	56.12 ± 9.70	53.30 ± 7.27	47.55	<0.001	0.42	87.45	<0.001	0.40	37.54	<0.001	0.43
Height (cm) *	163.90 ± 4.85	161.89 ± 6.34	159.42 ± 6.24	84.68	<0.001	0.57	180.25	<0.001	0.58	75.83	<0.001	0.61
Arm span (cm) *	164.16 ± 5.97	162.73 ± 7.69	160.47 ± 7.52	39.46	<0.001	0.38	98.18	<0.001	0.43	40.14	<0.001	0.45
Sitting height (cm) *	85.73 ± 2.99	84.82 ± 3.65	83.26 ± 3.68	55.52	<0.001	0.46	122.22	<0.001	0.49	57.31	<0.001	0.54
Cormic index	52.31 ± 1.12	52.40 ± 1.40	52.24 ± 1.84	0.23	0.799	0.00	0.30	0.586	0.00	0.24	0.867	0.00
Relative arm span (%)	100.16 ± 2.27	100.51 ± 2.41	100.67 ± 3.16	0.21	0.813	0.00	0.18	0.673	0.00	0.37	0.777	0.01
Upper limb length (cm) *	73.42 ± 2.63	72.62 ± 3.36	71.88 ± 3.60	39.30	<0.001	0.38	89.18	<0.001	0.41	33.72	<0.001	0.41
Iliospinale height (cm) *	92.98 ± 2.85	92.27 ± 4.57	90.63 ± 4.47	23.11	<0.001	0.26	47.93	<0.001	0.27	19.83	<0.001	0.29
Biacromial breadth (cm) *	35.81 ± 1.68	34.95 ± 1.87	34.86 ± 2.06	38.61	<0.001	0.37	91.62	<0.001	0.42	32.79	<0.001	0.40
Biiliocristal breadth (cm) *	26.94 ± 2.09	26.25 ± 1.88	25.93 ± 1.56	31.65	<0.001	0.33	75.27	<0.001	0.37	27.48	<0.001	0.36
Femur breadth (cm) *	9.35 ± 0.53	9.05 ± 0.49	8.83 ± 0.39	25.97	<0.001	0.29	30.67	<0.001	0.19	18.97	<0.001	0.28
Humerus breadth (cm) *	6.48 ± 0.38	6.25 ± 0.34	6.12 ± 0.33	17.44	<0.001	0.21	17.23	<0.001	0.12	14.75	<0.001	0.23
Bi-styloid breadth (cm) *	5.05 ± 0.23	4.91 ± 0.26	4.84 ± 0.27	10.26	<0.001	0.14	7.00	0.009	0.05	7.73	<0.001	0.14
Corrected arm girth (cm) *	21.68 ± 2.12	20.43 ± 1.97	20.52 ± 1.80	20.76	<0.001	0.24	37.60	<0.001	0.23	19.80	<0.001	0.29
Corrected thigh girth (cm) *	43.21 ± 4.19	41.49 ± 4.23	40.17 ± 3.19	20.76	<0.001	0.24	37.19	<0.001	0.22	18.47	<0.001	0.27
Corrected leg girth (cm) *	29.51 ± 3.13	29.08 ± 2.67	29.24 ± 2.03	14.21	<0.001	0.18	63.58	<0.001	0.33	24.14	<0.001	0.33
Endomorphy *	4.42 ± 1.42	3.91 ± 1.30	3.51 ± 0.99	4.74	0.010	0.07	3.29	0.072	0.02	3.06	0.030	0.06
Mesomorphy	4.33 ± 1.13	3.89 ± 1.19	3.89 ± 0.87	1.88	0.157	0.03	1.10	0.297	0.01	1.51	0.215	0.03
Ectomorphy*	2.03 ± 1.23	2.59 ± 1.42	2.55 ± 1.05	3.16	0.046	0.05	3.31	0.071	0.03	2.34	0.076	0.05
∑6 Skinfolds (mm) *	97.85 ± 25.86	86.59 ± 25.18	78.29 ± 18.48	7.92	0.001	0.11	5.47	0.021	0.04	4.41	0.005	0.08
∑8 Skinfolds (mm) *	122.79 ± 33.57	108.27 ± 34.09	96.61 ± 23.80	7.31	0.001	0.10	4.63	0.033	0.03	3.82	0.011	0.07
Fat mass percentage (%) *	26.40 ± 5.41	24.53 ± 5.75	22.46 ± 4.64	7.39	0.001	0.10	5.72	0.018	0.04	3.91	0.010	0.07
Muscle mass (%)	30.46 ± 2.18	31.06 ± 2.48	31.54 ± 2.03	0.14	0.872	0.00	1.66	0.200	0.01	2.29	0.081	0.04
Bone mass percentage (%) *	15.84 ± 1.97	16.40 ± 2.04	16.32 ± 1.74	6.33	0.002	0.09	17.79	<0.001	0.12	6.36	<0.001	0.11
Fat mass (Kg) *	16.61 ± 5.50	14.11 ± 5.13	12.12 ± 3.70	21.88	<0.001	0.25	28.87	<0.001	0.18	13.19	<0.001	0.21
Muscle mass (Kg) *	18.80 ± 3.37	17.41 ± 3.24	16.80 ± 2.50	36.71	<0.001	0.36	84.54	<0.001	0.40	38.05	<0.001	0.44
Bone mass (Kg) *	9.61 ± 0.81	9.05 ± 0.94	8.61 ± 0.85	70.39	<0.001	0.52	106.77	<0.001	0.45	53.97	<0.001	0.52
BMI (Kg/m2) *	22.93 ± 3.27	21.34 ± 3.02	20.92 ± 2.23	13.27	<0.001	0.17	20.91	<0.001	0.14	9.65	<0.001	0.16
Waist to hip ratio *	0.75 ± 0.06	0.75 ± 0.06	0.72 ± 0.04	1.13	0.325	0.02	5.86	0.017	0.04	4.72	0.004	0.09
Muscle-bone index *	1.95 ± 0.28	1.92 ± 0.25	1.95 ± 0.21	4.48	0.013	0.06	21.58	<0.001	0.14	8.44	<0.001	0.15
Sit-and-reach test (cm)	5.50 ± 6.02	5.85 ± 8.60	4.81 ± 9.89	0.54	0.584	0.01	0.09	0.760	0.00	0.40	0.752	0.01
Back scratch test (cm)	4.03 ± 4.56	4.44 ± 5.68	4.16 ± 5.36	0.25	0.778	0.00	0.27	0.605	0.00	0.17	0.915	0.00
Long jump (m)	1.59 ± 0.19	1.63 ± 0.21	1.68 ± 0.20	0.06	0.939	0.00	2.51	0.115	0.02	2.65	0.051	0.05
Medicine ball throw (m) *	4.87 ± 0.91	5.00 ± 1.08	5.17 ± 0.97	4.24	0.016	0.06	21.41	<0.001	0.14	10.49	<0.001	0.18
CMJ (cm) *	22.87 ± 3.79	24.84 ± 4.46	25.43 ± 4.26	0.57	0.568	0.01	2.60	0.110	0.02	4.20	0.007	0.08
CMJ power (W) *	635.00 ± 96.98	603.35 ± 111.99	580.89 ± 94.36	40.16	<0.001	0.38	105.66	<0.001	0.45	44.06	<0.001	0.47
20 m sprint (s)	4.24 ± 0.26	4.14 ± 0.31	4.16 ± 0.25	2.09	0.128	0.03	0.00	0.956	0.00	1.88	0.136	0.04
Agility test (s)	9.65 ± 0.73	9.22 ± 1.05	9.23 ± 0.66	1.46	0.237	0.02	1.56	0.214	0.01	0.74	0.528	0.01

*: p < 0.05.

shows the descriptive statistics (mean ± SD) for each group for all of the variables measured, as well as the differences between maturity groups, the main effects of the covariate “age” and the interaction between variables (maturity group × age).

Table 2. Post hoc comparison between groups for the basic, derived and maturation variables.

Test	Group		Model
			Maturity Status Group				Maturity Status Group × Age
			Mean Difference ± SD	p-Value	95% CI	ES	Mean Difference ± SD	p-Value	95% CI	ES
Maturity offset (years)	E	A *	−0.54 ± 0.18	0.011	−0.97 to −0.10	0.53	00.64 ± 0.06	<0.001	0.49 to 0.78	15.30
	E	L *	−0.99 ± 0.22	<0.001	−1.52 to −0.45	0.58	1.25 ± 0.08	<0.001	1.05 to 1.45	23.89
	A	L *	−0.45 ± 0.18	0.037	−0.88 to −0.02	0.68	0.61 ± 0.06	<0.001	0.47 to 0.75	15.18
APHV (years)	E	A *	−0.82 ± 0.06	<0.001	−0.97 to −0.68	0.95	−0.64 ± 0.06	<0.001	−0.78 to −0.49	15.32
	E	L *	−1.61 ± 0.07	<0.001	−1.79 to −1.44	3.13	−1.25 ± 0.08	<0.001	−1.45 to −1.05	23.91
	A	L *	−0.79 ± 0.06	<0.001	−0.93 to −0.64	2.81	−0.61 ± 0.06	<0.001	−0.75 to −0.47	15.18
Body mass (Kg)	E	A *	5.61 ± 1.99	0.017	0.77 to 10.45	0.81	14.13 ± 1.81	<0.001	9.73 to 18.52	11.01
	E	L *	8.42 ± 2.44	0.002	2.50 to 14.35	0.63	24.45 ± 2.55	<0.001	18.27 to 30.64	15.22
	A	L *	2.82 ± 1.973	0.467	−1.96 to 7.59	0.33	10.32 ± 1.75	<0.001	6.09 to 14.56	8.35
Height (cm)	E	A *	2.01 ± 1.29	0.363	−1.11 to 5.14	0.36	8.82 ± 0.95	<0.001	6.51 to 11.13	13.09
	E	L *	4.47 ± 1.58	0.016	0.64 to 8.303	0.37	17.33 ± 1.34	<0.001	14.08 to 20.58	20.53
	A	L *	2.45 ± 1.27	0.167	−0.63 to 5.55	0.39	8.51 ± 0.92	<0.001	6.28 to 10.73	13.10
BMI (Kg/m2)	E	A *	1.58 ± 0.62	0.037	0.07 to 3.09	0.50	3.05 ± 0.68	<0.001	1.40 to 4.69	6.34
	E	L *	2.00 ± 0.76	0.029	0.15 to 3.86	0.34	4.76 ± 0.96	<0.001	2.44 to 7.07	7.90
	A	L *	0.42 ± 0.61	1.000	−1.07 to 1.92	0.16	1.71 ± 0.65	0.030	0.12 to 3.29	3.69
Arm span (cm)	E	A *	1.421 ± 1.563	1.000	−2.37 to 5.21	0.21	8.67 ± 1.34	<0.001	5.42 to 11.93	9.13
	E	L *	3.68 ± 1.92	0.171	−0.96 to 8.33	0.26	17.27 ± 1.89	<0.001	12.69 to 21.85	14.51
	A	L *	2.26 ± 1.55	0.438	−1.48 to 6.00	0.30	8.60 ± 1.29	<0.001	5.46 to 11.73	9.39
Sitting height (cm)	E	A *	0.91 ± 0.75	0.687	−0.91 to 2.73	0.27	4.55 ± 0.61	<0.001	3.08 to 6.02	10.59
	E	L *	2.47 ± 0.92	0.025	0.24 to 4.70	0.35	9.41 ± 0.86	<0.001	7.34 to 11.49	17.49
	A	L *	1.56 ± 0.74	0.113	−0.24 to 3.36	0.43	4.86 ± 0.58	<0.001	3.44 to 6.28	11.74

E: Early maturer group; A: Average maturer group; L: Late maturer group; *: p < 0.05; ES: Effect size.

,

Table 3. Post hoc comparison between groups for the bone and derived variables.

Test	Group			Model
			Maturity Status Group				Maturity Status Group × Age
			Mean Difference ± SD	p-Value	95% CI	ES	Mean Difference ± SD	p-Value	95% CI	ES
Upper limb length (cm)	E	A *	0.81 ± 0.69	0.755	−0.89 to 2.50	0.27	3.77 ± 0.62	<0.001	2.27 to 5.27	6.15
	E	L *	1.54 ± 0.86	0.220	−0.53 to 3.62	0.24	7.21 ± 0.88	<0.001	5.09 to 9.34	11.68
	A	L *	0.74 ± 0.69	0.858	−0.93 to 2.41	0.21	3.44 ± 0.60	<0.001	1.98 to 4.90	7.58
Iliospinale height (cm)	E	A *	0.95 ± 0.91	0.890	−1.25 to 3.15	0.25	4.22 ± 0.90	<0.001	2.04 to 6.40	6.64
	E	L *	2.34 ± 1.11	0.111	−0.35 to 5.04	0.30	8.49 ± 1.26	<0.001	5.43 to 11.56	10.67
	A	L *	1.39 ± 0.90	0.370	−0.78 to 3.57	0.31	4.27 ± 0.87	<0.001	2.18 to 6.37	6.98
Biacromial breadth (cm)	E	A *	0.86 ± 0.40	0.098	−0.11 to 1.83	0.48	2.56 ± 0.36	<0.001	1.68 to 3.44	9.97
	E	L *	0.94 ± 0.49	0.167	−0.24 to 2.13	0.24	4.14 ± 0.51	<0.001	2.90 to 5.38	12.89
	A	L *	0.08 ± 0.39	1.000	−0.87 to 1.04	0.04	1.58 ± 0.35	<0.001	0.74 to 2.43	6.40
Biiliocristal breadth (cm)	E	A *	0.67 ± 0.40	0.270	−0.28 to 1.64	0.34	2.28 ± 0.37	<0.001	1.38 to 3.18	8.67
	E	L *	0.99 ± 0.49	0.125	−0.17 to 2.17	0.26	4.00 ± 0.52	<0.001	2.73 to 5.27	12.13
	A	L *	0.32 ± 0.39	1.000	−0.63 to 1.27	0.19	1.72 ± 0.36	<0.001	0.85 to 2.59	6.78
Femur breadth (cm)	E	A *	0.30 ± 0.10	0.011	0.05 to 0.55	0.59	.061 ± 0.10	<0.001	0.35 to 0.87	8.02
	E	L *	0.52 ± 0.13	<0.001	0.21 to 0.82	0.49	1.09 ± 0.15	<0.001	0.73 to 1.46	11.55
	A	L *	0.22 ± 0.10	0.104	−0.03 to 0.46	0.48	0.49 ± 0.10	<0.001	0.24 to 0.74	6.69
Humerus breadth (cm)	E	A *	0.23 ± 0.07	0.007	0.05 to 0.40	0.63	0.41 ± 0.08	<0.001	0.22 to 0.60	7.38
	E	L *	0.36 ± 0.09	<0.001	0.14 to 0.57	0.45	0.71 ± 0.11	<0.001	0.44 to 0.98	10.15
	A	L *	0.13 ± 0.07	0.225	−0.04 to 0.30	0.39	0.30 ± 0.08	<0.001	0.11 to 0.48	5.54
Bi-styloid breadth (cm)	E	A *	0.15 ± 0.5	0.024	0.01 to 0.28	0.60	0.24 ± 0.06	<0.001	0.09 to 0.39	5.65
	E	L *	0.21 ± 0.06	0.005	0.05 to 0.37	0.39	0.40 ± 0.08	<0.001	0.19 to 0.60	7.46
	A	L *	0.06 ± 0.05	0.675	−0.06 to 0.19	0.25	0.16 ± 0.06	0.022	0.02 to 0.30	3.84
Bone mass percentage (%)	E	A *	−0.56 ± 0.42	0.554	−1.57 to 0.46	0.28	−1.51 ± 0.46	0.004	−2.62 to −0.39	4.64
	E	L *	−0.49 ± 0.51	1.000	−1.73 to 0.75	0.78	−2.25 ± 0.65	0.002	−3.82 to −0.69	5.53
	A	L	0.07 ± 0.41	1.000	−0.93 to 1.07	0.04	−0.75 ± 0.44	0.282	−1.82 to 0.33	2.38
Bone mass (Kg)	E	A *	0.57 ± 0.20	0.011	0.10 to 1.03	0.65	1.45 ± 0.16	<0.001	1.06 to 1.84	12.66
	E	L *	1.00 ± 0.23	<0.001	0.43 to 1.56	0.99	2.68 ± 0.23	<0.001	2.13 to 3.23	18.67
	A	L *	0.43 ± 0.19	0.071	−0.02 to 0.89	0.48	1.23 ± 0.16	<0.001	0.85 to 1.61	11.13

E: Early maturer group; A: Average maturer group; L: Late maturer group; *: p < 0.05; ES: Effect size.

,

Table 4. Post hoc comparison between groups for the soft tissues measured variables.

Test	Group Comparison			Model
			Maturity Status Group				Maturity Status Group × Age
			Mean Difference ± SD	p-Value	95% CI	ES	Mean Difference ± SD	p-Value	95% CI	ES
Corrected arm girth (cm)	E	A *	1.24 ± 0.42	0.010	0.23 to 2.26	0.61	2.70 ± 0.42	<0.001	1.69 to 3.72	9.13
	E	L *	1.15 ± 0.51	0.078	−0.09 to 2.39	0.88	3.86 ± 0.59	<0.001	2.43 to 5.29	10.42
	A	L *	−0.09 ± 0.41	1.000	−1.09 to 0.90	0.05	1.16 ± 0.40	0.014	0.18 to 2.14	4.07
Corrected thigh girth (cm)	E	A *	1.72 ± 0.86	0.140	−0.36 to 3.81	0.41	4.53 ± 0.87	<0.001	2.41 to 6.65	7.33
	E	L *	3.04 ± 1.05	0.013	0.49 to 5.59	0.79	8.35 ± 1.23	<0.001	5.37 to 11.33	10.78
	A	L *	1.31 ± 0.85	0.370	−0.74 to 3.37	0.35	3.82 ± 0.84	<0.001	1.78 to 5.86	6.41
Corrected leg girth (cm)	E	A *	0.43 ± 0.56	1.000	−0.93 to 1.80	0.15	2.57 ± 0.54	<0.001	1.27 to 3.88	6.75
	E	L *	0.27 ± 0.69	1.000	−1.40 to 1.95	0.05	4.37 ± 0.76	<0.001	2.53 to 6.21	9.15
	A	L *	−0.16 ± 0.56	1.000	−1.51 to 1.19	0.07	1.80 ± 0.52	0.002	0.54 to 3.06	4.90
∑6 Skinfolds (mm)	E	A *	11.26 ± 5.14	0.090	−1.18 to 23.70	0.44	16.32 ± 5.89	0.019	2.06 to 30.58	3.92
	E	L *	19.56 ± 6.29	0.007	4.33 to 34.79	0.40	29.06 ± 8.29	0.002	9.00 to 49.13	5.58
	A	L	8.30 ± 5.07	0.312	−3.98 to 20.58	0.38	12.74 ± 5.67	0.078	−0.99 to 26.47	3.18
∑8 Skinfolds (mm)	E	A *	14.52 ± 6.86	0.107	−2.08 to 31.13	0.43	19.27 ± 7.90	0.048	0.13 to 38.40	3.45
	E	L *	26.19 ± 8.40	0.007	5.86 to 46.52	0.41	34.95 ± 11.12	0.006	8.02 to 61.88	5.00
	A	L	11.66 ± 6.77	0.261	−4.73 to 28.05	0.40	15.68 ± 7.61	0.123	−2.74 to 34.10	2.91

E: Early maturer group; A: Average maturer group; L: Late maturer group; *: p < 0.05; ES: Effect size.

,

Table 5. Post hoc comparison between groups for the body composition variables.

Test	Group Comparison			Model
			Maturity Status Group				Maturity Status Group × Age
			Mean Difference ± SD	p-Value	95% CI	ES	Mean difference ± SD	p-Value	95% CI	ES
Fat mass percentage (%)	E	A	1.87 ± 1.17	0.336	−0.96 to 4.69	0.33	3.14 ± 1.33	0.059	−0.09 to 6.37	3.33
	E	L *	3.94 ± 1.43	0.020	0.48 to 7.40	0.36	6.35 ± 1.88	0.003	1.80 to 10.90	5.37
	A	L *	2.07 ± 1.15	0.223	−0.72 to 4.86	0.40	3.21 ± 1.28	0.041	0.10 to 6.32	3.53
Fat mass (Kg)	E	A *	2.50 ± 1.05	0.057	−0.05 to 5.06	0.47	5.27 ± 1.13	<0.001	2.54 to 8.01	6.61
	E	L *	4.50 ± 1.29	0.002	1.37 to 7.62	0.43	9.70 ± 1.59	<0.001	5.86 to 13.55	9.71
	A	L *	1.99 ± 1.04	0.172	−0.53 to 4.51	0.45	4.43 ± 1.09	<0.001	1.80 to 7.06	5.76
Muscle mass (Kg)	E	A	1.38 ± 0.67	0.120	−0.23 to 2.99	0.42	4.31 ± 0.59	<0.001	2.87 to 5.74	10.28
	E	L *	1.99 ± 0.81	0.048	0.01 to 3.97	0.32	7.51 ± 0.83	<0.001	5.49 to 9.53	14.31
	A	L *	0.61 ± 0.66	1.000	−0.98 to 2.20	0.21	3.20 ± 0.57	<0.001	1.82 to 4.58	7.93
Muscle-bone index	E	A *	0.03 ± 0.05	1.000	−0.09 to 0.16	0.13	0.017 ± 0.06	0.008	0.03 to 0.31	4.30
	E	L *	0.003 ± 0.07	1.000	−0.15 to 0.16	0.01	0.026 ± 0.08	0.004	0.07 to 0.46	5.24
	A	L	−0.03 ± 0.05	1.000	−0.16 to 0.10	0.13	0.09 ± 0.05	0.301	−0.04 to 0.22	2.35
Endomorphy	E	A	0.51 ± 0.27	0.193	−0.15 to 1.16	0.37	0.66 ± 0.31	0.112	−0.10 to 1.41	2.97
	E	L *	0.91 ± 0.33	0.020	0.11 to 1.71	0.35	1.20 ± 0.44	0.021	0.14 to 2.27	4.35
	A	L	0.41 ± 0.27	0.383	−0.24 to 1.06	0.35	0.55 ± 0.30	0.210	−0.18 to 1.28	2.58
Ectomorphy	E	A *	−0.56 ± 0.28	0.141	−1.24 to 0.12	0.42	−0.82 ± 0.32	0.036	−1.60 to −0.04	3.60
	E	L	−0.51 ± 0.34	0.397	−1.35 to 0.31	0.22	−1.01 ± 0.45	0.084	−2.10 to 0.09	3.52
	A	L	0.04 ± 0.28	1.000	−0.62 to 0.71	0.03	−0.19 ± 0.31	1.000	−0.94 to 0.57	0.84

E: Early maturer group; A: Average maturer group; L: Late maturer group; *: p < 0.05; ES: Effect size.

and

Table 6. Post hoc comparison between groups for the physical fitness variables.

Test	Group Comparison			Model
			Maturity Status Group				Maturity Status Group × Age
			Mean Difference ± SD	p-Value	95% CI	ES	Mean Difference ± SD	p-Value	95% CI	ES
Medicine ball throw (m)	E	A	−0.12 ± 0.21	1.000	−0.66 to 0.40	0.18	0.48 ± 0.23	0.115	−0.08 to 1.04	2.96
	E	L *	−0.30 ± 0.27	0.804	−0.95 to 0.35	0.16	0.86 ± 0.33	0.026	0.08 to 1.65	4.22
	A	L	−0.17 ± 0.22	1.000	−0.69 to 0.35	0.12	0.38 ± 0.22	0.269	−0.16 to 0.92	2.41
CMJ (cm)	E	A	−1.97 ± 0.92	0.099	−4.19 to 0.25	0.14	−0.82 ± 1.04	1.000	−3.33 to 1.70	1.11
	E	L	−2.56 ± 1.12	0.071	−5.28 to 0.15	0.30	−0.28 ± 1.46	1.000	−3.82 to 3.26	0.30
	A	L	−0.59 ± 0.90	1.000	−2.78 to 1.60	0.48	0.54 ± 1.00	1.000	−1.88 to 2.96	0.76
CMJ power (W)	E	A *	31.65 ± 22.55	0.488	−22.96 to 86.26	0.30	135.49 ± 19.22	<0.001	88.94 to 182.04	9.97
	E	L *	54.11 ± 27.61	0.156	−12.75 to 120.97	0.27	251.07 ± 27.05	<0.001	185.56 to 316.58	14.75
	A	L *	22.46 ± 22.26	0.944	−31.45 to 76.37	0.22	115.58 ± 18.51	<0.001	70.76 to 160.40	8.83

E: Early maturer group; A: Average maturer group; L: Late maturer group; *: p < 0.05; ES: Effect size.

show the comparisons after the Bonferroni adjustment of the three maturity statuses for the kinanthropometric variables and physical fitness tests, respectively.

Pairwise adjustment showed statistical differences between early and average and between early and late maturers in body mass, BMI, limb breadths and bone mass (p < 0.001–0.037) (Table 2 and Table 3). Early maturers showed statistical differences compared to late maturers in height and sitting height, corrected thigh girth, sums of skinfolds, fat mass and percentage, muscle mass and endomorphy (p = 0.007–0.048) (Table 2, Table 3, Table 4 and Table 5). The inclusion of the covariate “age” showed that the differences found between maturity status groups in the basic measurements (p < 0.001–0.030), in bone-related variables (p < 0.001–0.022), in the fat in the muscle and fat-related variables (p < 0.001–0.048) and in body composition (p < 0.001–0.041) were dependent on age (Table 2, Table 3, Table 4 and Table 5).

3.1. Physical Fitness Test Differences between Groups

Significant differences were found between maturity status groups in the medicine ball throw and CMJ power (p < 0.001–0.016). The covariate “age” had a significant effect on the model for the two variables (p < 0.001). The interaction between variables showed that the differences found between maturity status groups were dependent on age in the medicine ball throw, the CMJ and CMJ power (p < 0.001–0.007).

Pairwise adjustment did not show differences between maturity status groups. The results showed a significant effect of age on the differences found in the medicine ball throw between early and late maturers and between the three groups in CMJ power (p < 0.001–0.026). More mature players showed higher values in both tests (Table 6).

3.2. Correlations and Regression Models

Correlations between maturity offset, age and kinanthropometric variables and fitness variables are shown in

Table 7. Correlations between kinanthropometric and physical fitness variables.

	Sit-and-Reach	Back Scratch test	Long Jump	Medicine Ball Throw	CMJ	CMJ Power	20 m Sprint	Agility Test
Maturity offset	r = 0.096; p = 0.238	r = 0.046; p = 0.571	r = 0.221; p = 0.006	r = 0.470; p < 0.001	r = 0.221; p = 0.009	r = 0.629; p < 0.001	r = −0.078; p = 0.337	r = 0.052; p = 0.524
Age	r = 0.043; p = 0.603	r = 0.043; p = 0.600	r = 0.225; p = 0.005	r = 0.370; p < 0.001	r = 0.269; p = 0.001	r = 0.403; p < 0.001	r = −0.106; p = 0.195	r = 0.021; p = 0.802
Body mass	r = 0.161; p = 0.047	r = −0.088; p = 0.281	r = −0.039; p = 0.634	r = 0.448; p < 0.001	r = −0.172; p = 0.034	r = 0.850; p < 0.001	r = 0.238; p = 0.003	r = 0.180; p = 0.027
Height	r = 0.028; p = 0.730	r = 0.115; p = 0.158	r = 0.147; p = 0.070	r = 0.402; p < 0.001	r = 0.097; p = 0.234	r = 0.626; p < 0.001	r = 0.003; p = 0. 966	r = 0.064; p = 0.432
Arm span	r = 0.180; p = 0.026	r = 0.329; p < 0.001	r = 0.200; p = 0.013	r = 0.473; p < 0.001	r = 0.157; p = 0.054	r = 0.594; p < 0.001	r = −0.011; p = 0.892	r = 0.015; p = 0.858
Sitting height	r = 0.196; p = 0.016	r = 0.007; p = 0.931	r = 0.153; p = 0.060	r = 0.390; p < 0.001	r = 0.082; p = 0.318	r = 0.605; p < 0.001	r = −0.095; p = 0.244	r = 0.010; p = 0.906
Cormic index	r = 0.271; p = 0.001	r = −0.144; p = 0.076	r = 0.040; p = 0.624	r = 0.052; p = 0.526	r = −0.001; p = 0.994	r = 0.075; p = 0.358	r = −0.157; p = 0.053	r = −0.080; p = 0.331
Relative arm span	r = 0.281; p < 0.001	r = 0.416; p < 0.001	r = 0.144; p = 0.076	r = 0.243; p = 0.003	r = 0.137; p = 0.093	r = 0.119; p = 0.143	r = −0.024; p = 0.767	r = −0.078; p = 0.341
Upper limb length	r = 0.099; p = 0.223	r = 0.241; p = 0.003	r = 0.157; p = 0.053	r = 0.412; p < 0.001	r = 0.067; p = 0.414	r = 0.564; p < 0.001	r = 0.069; p = 0.399	r = 0.032; p = 0.695
Iliospinale height	r = −0.076; p = 0.354	r = 0.156; p = 0.055	r = 0.172; p = 0.034	r = 0.234; p = 0.004	r = 0.091; p = 0.267	r = 0.478; p < 0.001	r = 0.054; p = 0.512	r = −0.032; p = 0.698
Biacromial breadth	r = 0.360; p < 0.001	r = 0.216; p = 0.008	r = 0.122; p = 0.135	r = 0.560; p < 0.001	r = 0.100; p = 0.221	r = 0.703; p < 0.001	r = 0.089; p = 0.273	r = −0.103; p = 0.207
Biiliocristal breadth	r = 0.048; p = 0.556	r = −0.095; p = 0.243	r = −0.112; p = 0.169	r = 0.333; p < 0.001	r = −0.249; p = 0.002	r = 0.580; p < 0.001	r = 0.210; p = 0.009	r = 0.142; p = 0.081
Corrected arm girth	r = 0.108; p = 0.187	r = −0.166; p = 0.040	r = −0.008; p = 0.924	r = 0.389; p < 0.001	r = −0.032; p = 0.698	r = 0.703; p < 0.001	r = 0.092; p = 0.260	r = 0.129; p = 0.116
Corrected thigh girth	r = 0.173; p = 0.033	r = −0.137; p = 0.093	r = −0.004; p = 0.958	r = 0.381; p < 0.001	r = −0.115; p = 0.157	r = 0.725; p < 0.001	r = 0.136; p = 0.094	r = 0.129; p = 0.115
Corrected leg girth	r = 0.101; p = 0.214	r = 0.048; p = 0.559	r = 0.094; p = 0.247	r = 0.395; p < 0.001	r = 0.066; p = 0.419	r = 0.688; p < 0.001	r = 0.029; p = 0.724	r = 0.085; p = 0.299
∑8 Skinfolds	r = −0.010; p = 0.902	r = −0.222; p = 0.006	r = −0.292; p < 0.001	r = 0.087; p = 0.284	r = −0.396; p < 0.001	r = 0.411; p < 0.001	r = 0.379; p < 0.001	r = 0.237; p = 0.003
Fat mass	r = 0.119; p = 0.143	r = −0.129; p = 0.112	r = −0.174; p = 0.032	r = 0.313; p < 0.001	r = −0.332; p < 0.001	r = 0.632; p < 0.001	r = 0.353; p < 0.001	r = 0.200; p = 0.014
Muscle mass	r = 0.135; p = 0.097	r = −0.096; p = 0.242	r = 0.028; p = 0.734	r = 0.453; p < 0.001	r = −0.052; p = 0.521	r = 0.817; p < 0.001	r = 0.120; p = 0.142	r = 0.143; p = 0.081
Bone mass	r = 0.067; p = 0.409	r = 0.071; p = 0.387	r = 0.115; p = 0.157	r = 0.463; p < 0.001	r = 0.007; p = 0.936	r = 0.694; p < 0.001	r = 0.098; p = 0.232	r = 0.120; p = 0.141
BMI	r = −0.189; p = 0.020	r = −0.169; p = 0.037	r = −0.127; p = 0.118	r = 0.327; p < 0.001	r = −0.260; p = 0.001	r = 0.703; p < 0.001	r = 0.289; p < 0.001	r = 0.181; p = 0.026
Muscle-bone index	r = 0.151; p = 0.063	r = −0.194; p = 0.017	r = −0.050; p = 0.541	r = 0.269; p = 0.001	r = −0.072; p = 0.379	r = 0.590; p < 0.001	r = 0.078; p = 0.342	r = 0.090; p = 0.270

. Both maturity offset and age showed positive low to moderate correlations with the long jump, medicine ball throw, CMJ and CMJ power (r = 0.221–0.629; p < 0.001–0.006). The sit-and-reach test showed positive moderate or low correlations with body mass, sitting height, arm span, Cormic index, relative arm span, biacromial breadth and corrected calf girth (r = 0.161–0.360; p < 0.001–0.026) and a low negative correlation with BMI (r = −0.189; p = 0.020). The back scratch test showed moderate to low positive correlations with arm span, relative arm span and upper limb length (r = 0.241–0.416; p < 0.001–0.003) and low negative correlations with corrected arm girth, ∑8 skinfolds, BMI and muscle-bone index (r = −0.166; −0.222, p = 0.006–0.040). The long jump test showed low positive correlations with arm span and iliospinale height (r = 0.172–0.200; p = 0.013–0.034) and low negative correlations with ∑8 skinfolds and fat mass (kg) (r = −0.174; −0.292, p < 0.001–0.032). The medicine ball throw test showed significant positive low or moderate correlations with all variables (r = 0.234–0.560; p < 0.001–0.004), with the exception of the Cormic index and ∑8 skinfolds. The CMJ showed negative correlations with biiliocristal breadth, ∑8 skinfolds, fat mass (kg) and BMI (r = −0.249; −0.396, p < 0.001–0.002). All kinanthropometric variables showed significant positive moderate and high correlations (r = 0.411–0.850; p < 0.001) with power in the CMJ, with the exception of relative arm span and Cormic index. The 20 m sprint time and agility test time showed low and moderate positive correlations with body mass, biiliocristal breadth, ∑8 skinfolds, fat mass (kg) and BMI (r = 0.180–0.379; p < 0.001–0.027).

The results of linear regression in blocks are shown in

Table 8. Regression models of the performance in different physical fitness tests.

Variable	R2	p-Value	Included Independent Variables	SC	p-Value	Predictive Equation
Sit and reach	0.24	<0.001	Biacromial breadth	0.27	0.001	Sit and reach = −199.309 + 1.183 × biacromial breadth + 1.630 × cormic index + 77.628 × relative arm span
			Cormic index	0.28	<0.001
			Relative arm span	0.24	0.002
Back scratch	0.21	<0.001	Relative arm span	0.40	<0.001	Back scratch = −77.836 + 85.116 × relative arm span − 0.031 × ∑8 Skinfolds
			∑8 Skinfolds	−0.19	0.010
Horizontal jump	0.13	<0.001	Age	0.20	0.010	Horizontal jump = 1.348 + 0.033 × age − 0.002 × ∑8 Skinfolds
			∑8 Skinfolds	−0.27	0.001
Medicine ball throw	0.37	<0.001	Biacromial breadth	0.43	<0.001	Medicine ball throw = −6.818 + 0.232 × biacromial breadth + 0.149 × age + 0.076 × corrected arm girth
			Age	0.18	0.011
			Corrected arm girth	0.15	0.045
CMJ	0.21	<0.001	∑8 Skinfolds	−0.38	<0.001	CMJ = 18.339 − 0.050 × ∑8 Skinfolds + 0.821 × age
			Age	0.24	0.002
CMJ power	0.79	<0.001	Body mass	0.58	<0.001	CMJ power = 33.780 + 6.391 × body mass + 13.701 × biacromial breadth + 18.304 × muscle mass − 7.662 × corrected thigh girth − 10.436 × biileocrestal breadth
			Biacromial breadth	0.24	<0.001
			Muscle mass	0.54	<0.001
			Corrected thigh girth	−0.30	0.010
			Biiliocrestal breadth	−0.18	0.002
Sprint	0.37	<0.001	∑8 Skinfolds	0.37	<0.001	Sprint = 3.809 + 0.003 × ∑8 Skinfolds
Agility test	0.04	0.019	Fat mass	0.20	0.019	Agility test = 8.718 + 0.038 × fat mass

SC: Standardized coefficient.

. In the prediction models for the different physical tests, between 4 and 79% of the variance in the physical fitness test performance could be predicted by age, body mass, bone-related variables (biacromial and biileocrestal breadths, relative arm spam and Cormic index), fat-related variables (∑8 skinfolds, fat mass) and muscle-related variables (corrected arm girth, muscle mass) (p < 0.001–0.024).

4. Discussion

One of the objectives of the present research was to analyze the differences in kinanthropometric variables among female volleyball players according to maturity group. It was observed that players in the early maturer group showed significantly higher values in most of the kinanthropometric and derived variables than players in the average and late maturer groups, and age had a significant influence on the differences found. More specifically, early maturers had significantly higher body mass, height, sitting height and BMI than late maturers and higher body mass and BMI than average maturers. Similar results have been found in previous research, showing that in group comparisons, early maturers showed higher values for these variables [16,17]. During adolescence, there is usually an increase in body size and body mass due to changes in the hormonal environment [12]. Along these lines, sex steroids, the concentrations of which increase during the maturation process [15], play an important role in the accumulation of fat and lean mass [40] and could be the cause of the observed differences in body mass. On the other hand, during puberty, there is an increase in growth hormone (GH) concentration as compared to basal values at earlier stages, especially around the APHV [41]. This hormone has a great influence on height [42], and therefore, changes in GH could be the cause of the observed differences in height and sitting height between early maturers and other volleyball players. Taking into account that volleyball is a sport in which height is an important competitive advantage [9], based on the results of the present research, it could be hypothesized that early maturers would have a competitive advantage during the early stages of the growth period, which could later be neutralized by late or average maturers as they approach adulthood. This is an issue that would need to be considered in volleyball talent identification models.

The results of the present study also showed differences between groups in variables related to adiposity, muscle and bone development. A possible explanation for this could be that female sex hormones that influence pubertal development are closely linked to adipose tissue [43]. Specifically, estrogens are not only produced in the ovary but also synthesized in target tissues (breast, bone or brain) and in peripheral tissues, such as adipose tissue [44]. In this sense, it has been observed that the amount and distribution of adipose tissue present in pre-pubertal stages are associated with the presence of circulating female sex hormones [45]. This could alter the onset of the maturation process in girls, with a relationship between higher adiposity and an earlier onset of maturation being observed [43]. Furthermore, estrogens not only promote the peripheral accumulation of adipose tissue but also play a crucial role in bone formation [46], which could explain the differences found between groups in the bone diameters measured. Regarding the differences in variables related to muscle mass, during adolescence, the increase in muscle mass is associated with increases in the concentrations of growth hormone (GH), insulin-like growth factor 1 (IGF-1) and testosterone [46,47]. These hormones increase in concentration by between 1.5 and 3 times around the APHV with respect to previous basal levels and are therefore determinant in the changes in muscle development during adolescence [46]. However, the increase in these hormones, especially testosterone, is less abrupt and reaches lower concentrations in females than in males [15], which could explain why the changes observed in the parameters associated with muscle development between groups are only found in some of these variables. In fact, when age was introduced to the model, significant differences were found in most of the variables related to muscle development, which supports this hypothesis. Based on the results of the present research, the muscle development parameter could be used with some certainty in the detection of sports talent in the growth stage, as the influence of maturation seems to be limited, as opposed to what is observed with adiposity and bone development. However, since there are no previous studies that have analyzed the influence of maturation on bone and muscle variables in the female population, this is a question that should be further investigated in the future.

Another objective of the present research was to analyze differences in the physical fitness tests of female volleyball players according to their maturity group. Significant differences were found between groups in the medicine ball throw and CMJ power tests. Although there are no previous studies that have analyzed this aspect in female athletes, the results of the present research are in line with those found in male adolescents, in which it was found that the early maturer group had better results than average maturers and late maturers in all tests that were dependent on muscle strength and power [3]. This could be due to the fact that previous studies showed that physical performance in the kinds of specific tests that require the use of muscle strength and power, such as the long jump, medicine ball throw, CMJ, sprint or agility tests, were favored by higher values of muscle mass and lower values of fat mass, together with other factors such as hormonal and neural factors [48,49,50]. In this sense, while in males, early maturers have greater muscle development than average maturers and late maturers [3,51], in the present research carried out with females, it was found that the group of early maturers had greater muscle development but also greater adiposity than average maturers and late maturers. It is therefore possible that the potential advantage of greater muscle development in these tests may be minimized by the increase in fat mass and by the fact that the increase in muscle mass is more gradual than that among late maturers [45]. In spite of these promising results, many questions remain unanswered.

In addition, no differences were found between maturation groups in the results of flexibility tests. While no previous research has looked at this issue in females, research in males found similar results [18,52]. This could be because while some muscle groups tend to shorten due to histological and biomechanical factors over the years [53,54], extensibility seems to be sensitive to changes produced by training, improving it and producing morphological and neurological adaptations [40]. Previous studies have already pointed out that women volleyball players have lower joint ranges as a result of adaptations produced by training [55]. Therefore, training effects could be the reason for the absence of statistical differences in flexibility between the maturation groups.

One interesting finding is that when age was included as a covariate in the analysis of the differences between groups in the physical fitness tests, it was found that early maturers achieved greater distances in the medicine ball throw than late maturers and that there were differences in CMJ power between all groups, with better results in groups whose maturity was more advanced, with no differences in the rest of the physical tests. This could be due to the fact that performance in both physical fitness tests is related to muscle mass [48,49], and in the female population, the increase in muscle mass during adolescence occurs more gradually due to the sustained increase over time of the hormones responsible for its increase [45]. In fact, the results of the present research are in line with previous studies carried out in a female adolescent population, which found that age was a key factor when changes that occurred in sports performance variables at this stage were analyzed [16,17], especially in physical condition variables that were dependent on muscular strength or agility [12,56,57].

Another objective of the present research was to determine if age, maturity offset or kinanthropometric variables could predict better performance in physical fitness tests. It was found that variables related to adiposity, ∑8 skinfolds, BMI and body mass predicted worse sports performance in the jump, sprint, agility and upper limb flexibility tests. This may be due to the fact that in most sports disciplines, low adiposity and weight favor performance, since added weight in the form of adipose tissue can weigh down performance in sports involving constant movement by requiring a greater effort for movement [58,59].

Age was also shown to be a predictor of better performance in the long jump, medicine ball throw and CMJ. As has been found in previous research, age is a key factor in adolescent performance [60]. With advancing age, there is a significant increase in muscle and bone tissue, and it is more marked when there is a systematic practice of physical exercise due to the hormonal changes induced by it, but this also occurs in the absence of exercise, which could explain its influence on the models [12,61]. In addition, it has been observed that the greatest influence of biological maturation on differences in physical performance is found around the APHV [12,60]. The present study included a homogeneous sample that had mostly passed the APHV, which, together with the influence of age on the between-group differences found in the fitness tests, could be the reason why age is better than maturation in predicting physical performance in this sample.

In addition, variables related to bone structure in both longitudinal and transverse planes were found to be key to performance in both flexibility tests and strength tests of both the upper and lower limbs. Previous studies have already pointed out the importance of bone structure in sports performance due to its relationship with the biomechanical parameters of force execution [62,63] and in providing the appropriate environment for better muscle development [64]. In volleyball players, it was observed that there were differences in bone structure when comparing subjects of different competitive levels, with elite players showing higher values in height, upper limb length and bone diameters [65]. Therefore, factors related to bone structure in adolescent athletes could be key in talent identification and the prediction of future performance.

On the other hand, one of the predictors of the power in the CMJ and medicine ball throw was muscle mass. Muscle strength and power production are initially related to neural factors but are later highly correlated with muscle mass gain [66,67]. Muscle mass is a key factor in the improvement of performance in sports where body mass shifts in the horizontal or vertical plane, such as volleyball [59]. However, despite the fact that the present study found that physical performance was dependent on adiposity, muscle development and bone structure, there is a considerable lack of information in the scientific literature on this subject, which is why further research is needed to continue to expand knowledge on the relationship between plastic and structural components and sports performance, both in general physical fitness tests and in sport-specific actions.

As for the limitations of the present study, a cross-sectional design was used, so it only allowed establishing relationships between kinanthropometric variables, physical fitness variables and biological maturation, with the analysis of the progression of these variables not being possible. Another limitation of the present study was the use of an indirect method to estimate the maturity offset, such as a regression formula based on kinanthropometric measurements [13], instead of the gold standard for the assessment of the maturity offset, which is a wrist and hand X-ray analysis. It should be noted that the X-ray method is not without limitations, such as the radiation to which the subjects are exposed, the cost of implementation or the time invested, as well as the difficulty of implementing the method correctly, so non-expert researchers could introduce error in the estimation [68]. Due to these drawbacks, some authors advocate the use of less invasive alternative methods in the adolescent population [21,68]. Among the alternative methods to X-ray assessment that can be used in cross-sectional studies are formulas that estimate the maturity offset based on anthropometric measurements, the most widely used in the adolescent athlete population being that proposed by Mirwald et al. [13], which has been used in both adolescent athletes participating in team sports and in individual sports [3]. Despite being widely used and having been shown to be reliable in populations similar to that in the present study [21], some limitations have been identified in the use of regression formulas for the estimation of the maturity offset. It has been observed that they tend to underestimate the value for early maturers while overestimating it for late maturers [68], that they may introduce some error in the estimation of the maturity offset (0.50–0.59 years) and that the estimation values increase relatively steadily with advancing chronological age [13,69]. Therefore, knowing the limitations of this method, this research used the estimation of the maturity offset as a categorical variable, as recommended in previous research [13,69], included participants with chronological age ranges within the recommended range, and used the chronological age of the participants to control for the effect of differences between groups.

5. Conclusions

Differences influenced by age were found between the different stages of biological maturation in kinanthropometric variables studied in female volleyball players, with the players who matured earlier showing higher values in all kinanthropometric variables, with the differences in basic measurements related to adiposity and absolute body composition, such as bone diameters or corrected perimeters, being particularly relevant. However, in general, no differences were found in performance in physical fitness tests as a function of maturation, with age being a determining factor in this relationship. Finally, age, structural variables related to bone dimensions, both longitudinal and transversal, adiposity and muscle development were factors that predicted performance on the physical fitness tests, with performance on the flexibility tests being more dependent on bone structure, jumping performance being more dependent on both adiposity and bone structure, and sprinting and agility performance being more dependent on adiposity. However, the results of the present research should be interpreted cautiously, because due to the characteristics of the included sample and the limitations of the study, they may only be extended to similar populations. Future lines of research could address the identified limitations by designing a longitudinal study, including kinanthropometric and physical fitness variables in a population with a wider age range to observe the evolution throughout the entire maturation process, and use methods to observe changes in maturation instead of estimating it with regression formulas. The possible practical applications of the present work could be related to the differences found between maturation groups. In this sense, early maturing players show anthropometric and fitness characteristics that could result in a competitive advantage with respect to their peers. This should be taken into account by coaches, since it is possible that they are paying more attention to players who mature earlier when the variables that make them stand out are influenced by age and could become equal to those of players who take longer to mature after adolescence. In addition, variables that are more related to better performance in physical fitness tests were identified. This could help coaches to focus their work on variables that are modifiable with training and that can lead to a significant improvement in performance.