The Interactive Effect of Maturity Status and Relative Age on Physical Performance Within the Spanish Volleyball Federation’s Talent Pathway: Analysis by Sex and Playing Position

Abstract

The aim of the present study was to examine the impact of maturation and relative age on the anthropometric variables and physical performance of young elite volleyball players according to sex and playing positions. The sample included 207 girls (13.59 ± 1.74 years) and 59 boys (14.30 ± 1.48 years) who were selected to participate in the 2020–2025 Spanish National Volleyball Programme. Maturity status was estimated using a non-invasive method (percentage of predicted adult height). Relative age was calculated based on date of birth and expressed as decimal age (0–0.99). The physical tests carried out were spike jump reach, vertical jump, 3 × 9, and strength–endurance–agility–coordination (FRAC) tests. The results showed that there was no impact of the interaction between maturity status and relative age on physical performance, except in the 3 × 9 test by boys in the wing-spiker position. Moreover, maturity status had a greater influence on physical test performance than that of relative age. Specifically, maturation served as a statistically significant positive predictor of height in the SJR test for girls who were all-around players, explaining 71.58% of the variance. In addition, an advanced maturity status correlated with better physical performance outcomes, especially in the all-around player and wing-spiker playing positions in boys and the middle-blocker and all-around player playing positions in girls. Coaches and stakeholders should implement strategies to reduce bias, especially regarding maturation, with the aim of retaining the most physically talented late-maturing players, considering differences by playing position and sex.

1. Introduction

Talent identification programmes are strategies frequently employed by sporting institutions with the ultimate aim of providing future players for a high competitive performance [1]. There are many sports organisations (i.e., federations) that develop actions for recruitment, detection, and early talent identification with the aim of achieving a return on the resources invested in the player development process [2]. Some talent identification and development systems seek to achieve this return (e.g., sporting or economic) in the short term, primarily adopting immediate approaches focused on competitive performance. This perspective could lead to the current performance of young players being confused with their future performance [3]. If we also consider the heterogeneity in the application of talent identification programmes [4] and the subjectivity of coaches in the detection of talent—mainly based on their intuition—[5] it is logical to consider the existence of selection bias in youth sport [6].

Two of the most obvious biases with the greatest impact on sporting development are differences in maturation and relative age [7]. Maturation will inevitably modify the anthropometric and/or physical profile of a player, to some extent mediating player selection processes [8]. This process is highly relevant at the transitional ages between childhood and adolescence, which, depending on genetic and environmental factors, are usually between 10 and 15 years for girls and 11 and 16 years for boys [9]. Nevertheless, even in the same age group, maturational differences (skeletal age and somatic maturity) of 5–6 years have been identified between advanced and late-developing athletes [10]. Hence, maturation is a key factor in talent identification and development systems at all competition levels [11].

Relative age expresses the difference in chronological age between players of an age group or competition category according to their birthdate and the cut-off date for the group [12]. The differences that can occur between those born at the beginning of the year (relatively older) and those born at the end of the year (relatively younger) are referred to as ‘relative age effects—RAEs’ [13]. RAEs have been investigated in different sport contexts [14], yielding heterogeneous outcomes in relation to their complex causal factors (i.e., physical, cognitive, emotional, motivational, and social) [15]. Despite variations between individual, environmental, and task constraints and their interactions [16], the disproportionate over-selection of athletes born in the first two quartiles of the selection year appears relatively consistent in a multitude of contexts, especially in male talent systems [17].

Maturation and RAEs have been studied together in the sport context, showing weak relationship levels or small effects on performance variables [18]. This evidence underlines the independent nature of both constructs, which, in addition to operating separately, appear at different ages in the player development process [19]. While RAEs appear in late childhood, persist into adolescence, and diminish with age, differences in maturation do not appear until puberty and increase with age [20]. Thus, a greater presence of RAEs at younger ages shows that their impact on selection processes is due to factors that are aligned more with chronological age than maturation, such as motor or cognitive development and playing experience [21]. This scientific basis therefore makes it possible to rule out biological maturation as a mechanism underpinning RAEs [15]. Indeed, some studies, such as Radnor et al. [22], have shown that within relatively young players belonging to the same age group or cohort, there is a high percentage of early maturers. Contrary examples along the same lines can be found in the literature [23], confirming that RAEs cannot be considered as a proxy indicator of maturity status because an older relative age does not necessarily imply more advanced maturation [24].

Previous research has highlighted both the advantages and disadvantages of early and late maturers, with football being the most widely examined sport [23]. From an anthropometric and physical perspective, the maturational differences are notable, with variability according to sex and an imbalance in favour of male youths [18]. Thus, early-maturing players tend to be taller and heavier with a higher muscle mass [25], which allows them to achieve higher levels of peak force production and absolute muscular strength and power [26]. However, the available scientific evidence does not support any direct relationship between an advanced maturity status and an advanced relative age in relation to physical–anthropometric profile [19,27]. A clear example of this is the study by Parr et al. [28], which found that the mixed impact of maturation–relative age on the physical performance of young football players was small, with maturation being more influential (r = 0.71–0.75; strong) than relative age (r = 0.19–0.23; weak).

The scientific literature on volleyball also agrees in highlighting differences in relation to maturation in young players. For example, various studies by Albaladejo-Saura et al. [29,30,31] have shown that later-maturing players had higher values in terms of body mass, height, and performance in strength tests. Moreover, Grigoletto et al. [32] confirmed that the teams with the best competition performance were composed of early maturers due to advantages associated with their body composition and anthropometric characteristics. Specifically, advanced maturation in volleyball has generally been associated with a greater jumping ability [33], even serving as a predictor—along with age—of physical performance in vertical jump tests [25]. Conversely, it should be noted that advanced maturation has been negatively associated with the sensorimotor ability of volleyball players between the ages of 15 and 17, who have performed worse in agility and coordination tests [34]. In comparison, the impact of relative age on physical performance in volleyball, in line with other studies on other team sports [18,22], has been less than that of maturation [29]. Furthermore, some recent studies, such as that by Ntozis et al. [35], suggest that RAEs are not present in young volleyball players when variable playing positions are considered.

The relevance of certain anthropometric and physical parameters in volleyball (e.g., height), both to the selection process and to competitive performance [36], makes it necessary to investigate the role played by maturation and relative age in relation to the physical–anthropometric profile of young elite players [25,29,31]. Therefore, the present study aimed to continue this line of research based on the RA–maturation–physical performance trinomial in volleyball, examining the impact of maturation and relative age on the anthropometric variables and physical performance of young elite volleyball players according to sex and playing positions.

2. Materials and Methods

2.1. Participants

The sample was drawn from the 2020-25 talent identification programme for the U10-U18 categories organised by the Spanish Volleyball Federation, with fifty-nine male (age = 14.27 ± 1.48 years; height = 170.14 ± 11.21 cm; body mass = 59.80 ± 13.02 kg) and two hundred and seven female volleyball players (age = 13.59 ± 1.74 years; height = 158.90 ± 10.89 cm; body mass = 51.79 ± 10.61 kg). The final sample was obtained after excluding players with missing anthropometric, physical, or maturity data, as well as those with invalid consent forms (n = 255). According to playing position, the distribution was as follows: 39 middle-blockers (MBs), 41 setters (S), 100 wing-spikers (WSs), and 96 all-round players (AAPs). The roles of elite players were adapted from Milic et al. [37]. Thus, versatile players, without specialisation and capable of playing in any position, were redefined as all-around players (AAPs), and opposite players, very rare in these formative categories, were classified as wing-spikers (WSs). Likewise, liberos, due to their original role as receivers, were also considered wing-spikers (WSs). The inclusion criteria were a lack of injuries, not taking energy supplementation, training at least twice a week, and having participated in official competitions during the last season. The fathers, mothers, or legal guardians of the players gave their acceptance to participation in this study by means of informed consent and authorisation, both signed. The project and scientific use of the data were approved by the Ethics Committee of the Universidad Politécnica de Madrid (2020-089/090/091) in compliance with the Declaration of Helsinki.

2.2. Procedures

2.2.1. Physical Fitness Tests

The physical performance tests were part of the National Volleyball Programme 2020-25 (https://www.programa2025.com/, accessed on 21 March 2024) implemented by the Spanish Volleyball Federation. The protocol for each test was described in previous studies [25], with a general standardised 10 min warm-up consisting of continuous running, joint mobility, and flexibility exercises (5 min), followed by a specific warm-up consisting of game actions associated with the physical tests, such as jumps, sprints, or changes in direction (5 min). The order in which the tests were performed was the same for all players, following validated protocols [25]. The tests were supervised by two researchers experienced in the assessment of physical tests in young players, informing and instructing them.

The spike jump reach (SJR) and vertical jump (VJ) tests were used to measure vertical jumping ability by recording the maximum height with an accuracy of ± 1 centimetre (Yardstick; Swift Performance Equipment, Lismore, Australia); the 3 × 9 test was used to assess the speed of movement in combination with the ability to perform 180° changes in direction with an accuracy of ± 0.01 s (photoelectric cell gates; Chrono Jump, Bosco System, Barcelona, Spain); and the FRAC (the Spanish acronym for strength–endurance–agility–coordination) test was used to quantify the agility and coordination of the players on an obstacle course (photoelectric cell gate; Chrono Jump, Bosco System, Barcelona, Spain).

2.2.2. Anthropometric Assessment

Standing height was measured using a stadiometer to the nearest 0.1 cm. (SECA, 216, Vogel & Halke, Hamburg, Germany). Standing reach height was measured using a yardstick with an accuracy of 0.1 cm (yardstick; Swift Performance Equipment, Lismore, Australia). Body mass was measured to the nearest 0.1 kg on a digital scale (SECA, 876, Vogel & Halke, Hamburg, Germany). All measurements were carried out according to the International Standards for Anthropometric Assessment (ISAK) [38] by accredited staff (level 2).

2.2.3. Maturity Status

Maturity status was calculated from the Khamis–Roche equation, which yields the percentage of predicted adult height (%PAH) using a regression formula based on sex-specific coefficients [39]. This method of estimating maturity expresses current height at the time of assessment as a percentage of an individual’s estimated adult stature, attained with an error of approximately 2.2 cm for boys and 1.8 cm for girls. This equation, validated specifically for white Caucasian children between the ages of 4 and 18 years, is as follows:

Predicted adult height equation = β0 + (stature × β1) + (body mass × β2) + (corrected mid-parent stature × β3)

β0–β3 are the sex-specific coefficients for age, stature, body mass, and mid-parent stature (provided for each father and mother), respectively, derived from the Khamis–Roche table. Moreover, a correction factor for self-reported height in females (y = 2.803 + 0.953x) and males (y = 2.316 + 0.955x) must be applied.

2.2.4. Relative Age

Relative age (RA) was calculated based on date of birth and the cut-off date established for international volleyball competitions (1 January). In order to develop a quantitative variable for further statistical analysis, this construct was expressed as a decimal, using the difference between a player’s birthdate and the cut-off date of the selection year, divided by the number of days within the year [24]. Thus, relative age was expressed as a value between 0 and 0.99 years, with these values representing the youngest and oldest players, respectively.

2.3. Data Quality

First, a principal component analysis (PCA) of the SJR, VJ, 3 × 9, and FRAC test results identified a first component that explained 60.40% of the total variance, with very high loadings for the SJR (0.887) and VJ (0.909) tests, indicating that both tests measured the same construct (jumping ability). The 3 × 9 test also showed a high negative loading (−0.818), associated with a speed/agility factor (lower time, better performance), while the FRAC test presented a more moderate loading (−0.036). Second, the internal consistency of the jump battery (SJR + VJ) was acceptable to high (α = 0.764; r = 0.755, p < 0.001), supporting the coherence of both tests in assessing jumping ability. Third, the assessment of criterion validity through multinomial logistic regression showed a significant model fit (χ² = 2.51; p < 0.001; R²N = 0.439), with sex, maturity, and height as predictors of playing position. At the individual level, sex (p < 0.001), maturity (PAH; p = 0.011), and height (p = 0.013) significantly discriminated between playing positions, whereas the VJ (p = 0.098) and 3 × 9 (p = 0.359) tests provided additional but non-significant information for classification. These procedures followed previous methodologies to ensure data quality [40].

2.4. Statistical Analysis

Descriptive statistics for all variables were presented as X ± SD. The assumption of normality was assessed using the Kolmogorov–Smirnov test (>50 participants) and the Shapiro–Wilk test for samples smaller than 50 participants. Homogeneity of variance was assessed using Levene’s test. Pearson’s correlation analysis was carried out to examine the relationship between anthropometric characteristics (height and body mass), physical performance (SJR, VJ, 3 × 9 and FRAC tests), chronological age, relative age, and maturity status. A three-step hierarchical regression analysis was used, controlling for chronological age (i.e., the participant’s age in single year units at the assessment point), to determine the impact of maturity status, relative age, and their interaction on physical performance according to playing position and sex. Prior to computing the interaction term (%PAH × RA), both variables were standardised (z-scores) to reduce multicollinearity and improve coefficient stability [41]. To ensure the validity and reliability of the regression models, assumptions of linearity, independence, homoscedasticity, and normality of the residuals were assessed. All statistical analyses were performed using SPSS v.26 software (IBM Corp., Armonk, NY, USA). The significance was set at p < 0.05.

3. Results

The descriptive statistics for chronological age (CA), maturity status (%PAH), height, body mass, relative age (RA), and physical performance (SJR, VJ, 3 × 9, and FRAC tests) are presented in

Table 1. Descriptive statistics for the volleyball players’ profiles and physical performance according to sex and playing position.

Player Position		MB (n = 6)	S (n = 9)	WS (n = 29)	AAP (n = 15)	Total (n = 59)
Sex	Variables	X ± SD	X ± SD	X ± SD	X ± SD	X ± SD
Boys	CA	15.14 ± 1.01	14.83 ± 0.75	14.62 ± 0.94	13.00 ± 2.03	14.30 ± 1.48
	Height	184.33 ± 5.82	171.78 ± 3.96	172.55 ± 7.86	158.80 ± 11.77	170.14 ± 11.21
	Body mass	80.67 ± 14.45	61.50 ± 9.55	58.73 ± 8.29	48.57 ± 10.78	58.80 ± 13.02
	%PAH	0.96 ± 0.04	0.94 ± 0.02	0.94 ± 0.03	0.86 ± 0.08	0.92 ± 0.06
	RA	0.76 ± 0.22	0.51 ± 0.33	0.56 ± 0.27	0.33 ± 0.31	0.51 ± 0.31
	SJR	302.00 ± 15.49	281.56 ± 14.76	288.76 ± 15.08	256.20 ± 25.04	280.73 ± 23.38
	VJ	60.00 ± 9.84	59.22 ± 14.75	64.34 ± 10.13	51.80 ± 13.63	59.93 ± 12.64
	3 × 9	7.91 ± 0.25	7.22 ± 0.43	7.45 ± 0.63	8.26 ± 0.87	7.67 ± 0.75
	FRAC	25.31 ± 3.90	23.52 ± 1.91	23.89 ± 3.81	24.10 ± 3.03	24.03 ± 3.35
Player Position		MB (n = 33)	S (n = 32)	WS (n = 61)	AAP (n = 81)	Total (n = 207)
Sex	Variables	X ± SD	X ± SD	X ± SD	X ± SD	X ± SD
Girls	CA	14.40 ± 1.25	14.53 ± 1.09	14.40 ± 1.26	12.27 ± 1.61	13.59 ± 1.74
	Height	170.61 ± 4.91	161.22 ± 6.88	160.97 ± 5.51	151.65 ± 11.81	158.90 ± 10.89
	Body mass	61.08 ± 7.53	56.45 ± 10.13	53.20 ± 6.82	45.10 ± 10.08	51.79 ± 10.61
	%PAH	0.94 ± 0.03	0.94 ± 0.03	0.93 ± 0.03	0.86 ± 0.06	0.91 ± 0.06
	RA	0.58 ± 0.31	0.48 ± 0.28	0.49 ± 0.33	0.52 ± 0.27	0.52 ± 0.29
	SJR	265.88 ± 9.56	253.91 ± 9.65	256.44 ± 11.26	237.53 ± 17.81	250.15 ± 17.45
	VJ	45.15 ± 4.86	45.53 ± 7.61	48.72 ± 7.85	38.85 ± 8.63	43.80 ± 8.76
	3 × 9	8.50 ± 0.81	8.43 ± 0.99	8.17 ± 0.69	8.89 ± 0.67	8.55 ± 0.81
	FRAC	25.49 ± 3.89	25.34 ± 3.70	24.34 ± 4.00	25.18 ± 3.57	25.01 ± 3.77

Notes: CA = Chronological Age; %PAH = Percentage of Predicted Adult Height; RA = Relative Age; SJR = Spike Jump Reach; VJ = Vertical Jump; 3 × 9 = 3 × 9 m. Speed; FRAC = Spanish Acronym for Strength–Endurance–Agility–Coordination; MB = Middle-Blocker; S = Setter; WS = Wing-Spiker; APP = All-Around Player.

. Boys presented higher mean values for maturity status than those for girls (0.92% ± 0.06 vs. 0.91% ± 0.06); however, similar mean values associated with RA were identified in both sexes (boys: 0.52 ± 0.30 vs. girls: 0.52 ± 0.29). The highest mean value associated with maturity status was found in MBs, in both boys (0.96%; early maturers) and girls (0.94%, early maturers), and in Ss in girls (0.94%, early maturers). The highest mean RA values were observed in the MB position, both in boys (0.76; Q1—born between January and March) and in girls (0.58; Q2—born between April and June).

The results of the correlational analyses are summarised in

Table 2. Pearson’s correlation among anthropometric attributes, maturity status, relative age, and physical performance in young male and female volleyball players.

Player Position	Variables	Height		Body Mass		%PAH		RA		SJR		VJ		3 × 9		FRAC
		Boys	Girls	Boys	Girls	Boys	Girls	Boys	Girls	Boys	Girls	Boys	Girls	Boys	Girls	Boys	Girls
Middle-Blocker Boys (n = 6) Girls (n = 33)	CA	0.334	0.523	−0.223	0.299	0.762	0.928 ***	0.211	0.357	0.812	0.449	0.866	0.382	0.346	−0.392	−0.466	−0.049
	Height			0.238	0.300	0.399	0.501	−0.400	0.073	0.781	0.803 ***	0.395	0.280	0.009	−0.302	−0.960	−0.159
	Body Mass					−0.081	0.212	0.133	0.271	−0.127	0.143	−0.546	−0.080	0.579	−0.044	−0.234	0.062
	%PAH							0.363	0.350	0.626	0.435	0.627	0.385	0.421	−0.387	−0.384	−0.054
	RA									−0.279	0.035	−0.201	0.125	0.818	−0.122	0.281	−0.221
	SJR											0.858	0.691 ***	0.025	−0.574 **	−0.830	−0.102
	VJ													−0.163	−0.550 *	−0.447	−0.102
	3 × 9															−0.146	−0.107
Setter Boys (n = 9) Girls (n = 32)	CA	0.145	0.174	0.311	0.250	0.878	0.901 ***	−0.049	0.061	0.626	0.306	0.671	0.159	0.169	−0.327	−0.185	−0.185
	Height			0.729	0.607 **	0.528	0.387	−0.302	−0.103	0.332	0.720 ***	−0.025	−0.175	−0.628	0.410	−0.226	−0.169
	Body Mass					0.474	0.395	−0.689	−0.266	0.319	0.249	0.057	−0.252	−0.480	0.508	−0.411	0.009
	%PAH							−0.118	0.067	0.682	0.390	0.553	−0.004	−0.078	−0.062	−0.274	−0.208
	RA									−0.353	−0.001	−0.154	0.020	0.602	−0.198	0.362	−0.045
	SJR											0.920 *	0.420	−0.394	−0.117	−0.361	−0.356
	VJ													−0.135	−0.651 **	−0.255	−0.158
	3 × 9															0.172	−0.106
Wing-Spiker Boys (n = 29) Girls (n = 61)	CA	−0.060	0.386	0.216	0.533 ***	0.819 ***	0.907 ***	−0.032	0.224	0.277	0.223	0.451	0.134	−0.534	−0.350	−0.463	0.057
	Height			0.452	0.563 ***	−0.015	0.451 **	−0.051	0.006	0.772 ***	0.764 ***	0.176	0.315	0.008	−0.456 **	−0.203	0.048
	Body Mass					0.043	0.539 ***	0.002	0.263	0.505	0.496 **	0.077	0.240	−0.242	−0.348	−0.225	−0.046
	%PAH							0.008	0.315	0.241	0.190	0.435	−0.021	−0.433	−0.280	−0.536	0.130
	RA									0.029	−0.082	0.148	−0.209	0.121	−0.015	0.248	−0.051
	SJR											0.702 **	0.784 ***	−0.429	−0.555 ***	−0.515	−0.206
	VJ													−0.645 **	−0.458 **	−0.642 **	−0.332
	3 × 9															0.572 *	−0.062
All-Around Player Boys (n = 15) Girls (n = 81)	CA	0.718	0.776 ***	0.834 **	0.755 ***	0.984 ***	0.963 ***	−0.254	0.067	0.782 *	0.771 ***	0.546	0.277	−0.490	−0.310	0.405	0.015
	Height			0.826 **	0.810 ***	0.776 *	0.870 ***	−0.151	−0.064	0.850 **	0.909 ***	0.363	0.125	−0.356	−0.342	0.347	−0.046
	Body Mass					0.847 **	0.832 ***	−0.080	−0.098	0.728	0.732 ***	0.446	0.080	−0.237	−0.230	0.509	0.001
	%PAH							−0.239	0.020	0.822 **	0.835 ***	0.560	0.241	−0.523	−0.337	0.364	−0.007
	RA									−0.412	−0.006	−0.078	0.096	0.387	−0.026	0.208	0.110
	SJR											0.452	0.469 ***	−0.708	−0.428 **	0.079	−0.170
	VJ													−0.497	−0.332	0.009	−0.178
	3 × 9															0.276	0.108
All Playing Positions Boys (n = 59) Girls (n = 207)	CA	0.585 ***	0.705 ***	0.560 ***	0.686 ***	0.941 ***	0.951 ***	0.113	0.110	0.722 ***	0.710 ***	0.582 ***	0.435 ***	−0.540 ***	−0.446 ***	−0.074	−0.026
	Height			0.738 ***	0.785 ***	0.673 ***	0.810 ***	0.199	−0.005	0.864 ***	0.904 ***	0.365	0.302 ***	−0.331 *	−0.302 ***	−0.018	−0.035
	Body Mass					0.579 ***	0.752 ***	0.182	0.030	0.640 ***	0.698 ***	0.210	0.230 *	−0.252	−0.183	0.021	0.005
	%PAH							0.178	0.089	0.762 ***	0.778 ***	0.569 ***	0.389 ***	−0.547 ***	−0.397 ***	−0.090	−0.016
	RA									0.125	−0.004	0.124	−0.030	0.101	−0.051	0.242	−0.016
	SJR											0.668 ***	0.627 ***	−0.584 ***	−0.474 ***	−0.215	−0.154
	VJ													−0.555 ***	−0.537 ***	−0.348	−0.220
	3 × 9															0.376	0.009

Notes: CA = Chronological Age; %PAH = Percentage of Predicted Adult Height; RA = Relative Age; SJR = Spike Jump Reach; VJ = Vertical Jump; 3 × 9 = 3 × 9 m. Speed; FRAC = Spanish Acronym for Strength–Endurance–Agility–Coordination. * p < 0.05; ** p < 0.01; *** p < 0.001.

. By sex (all playing positions), maturation was positively correlated with the SJR (boys, p < 0.001; girls, p < 0.001) and VJ tests (boys, p < 0.001; girls, p < 0.001) and negatively correlated with the 3 × 9 test (boys, p < 0.001; girls, p < 0.001). No differences were found in the FRAC test (p > 0.05, both). In boys, maturity status was positively with the SJR test in the AAP (p < 0.01). In girls, maturity status was positively correlated with correlated the SJR test in the AAP (p < 0.001). No differences were found by sex or playing position in relation to RA (p > 0.05). The three-step hierarchical regression model predicting physical test performance (VJ, SJR, 3 × 9) is presented in

Table 3. a–d. Hierarchical regression analysis for predictor variables of SJR test performance by sex and playing position.

3a—MB					3b—S
Variables		Model 1	Model 2	Model 3	Variables		Model 1	Model 2	Model 3
		B (SE)	B (SE)	B (SE)			B (SE)	B (SE)	B (SE)
Boys	CA	12.48 (4.49) *	11.00 (5.34)	5.4 (3.70)	Boys	CA	12.28 (5.78)	3.73 (12.38)	14.48 (16.37)
	%PAH		105.08 (139.88)	−355.33 (204.60)		%PAH		288.56 (380.45)	510.91 (882.07)
	RA		−37.28 (16.98)	−978.45 (388.82)		RA		−13.03 (13.83)	−950.78 (933.74)
	Interaction %PAH*RA			216.31 (89.34)		Interaction %PAH*RA			303.33 (302.00)
	R2	0.659	0.900	0.985		R2	0.392	0.548	0.639
	F	7.73	6.00	16.90		F	4.52	2.02	1.77
	F for change in R2	0.659	0.241	0.085		F for change in R2	0.392	0.156	0.910
Girls	CA	3.42 (1.22) **	2.74 (3.35)	2.93 (3.51)	Girls	CA	2.71 (1.54)	−2.16 (3.54)	−2.12 (4.09)
	%PAH		49.93 (143.13)	73 (175.29)		%PAH		214.11 (139.88)	200.56 (233.28)
	RA		−4.62 (5.50)	50.23 (232.43)		RA		−0.95 (5.97)	−16.98 (218.63)
	Interaction %PAH*RA			−17.09 (72.42)		Interaction %PAH*RA			4.51 (61.49)
	R2	0.202	0.223	0.225		R2	0.093	0.164	0.164
	F	7.83	2.77	2.03		F	3.09	1.83	1.32
	F for change in R2	0.490	0.021	0.002		F for change in R2	0.093	0.070	0.000
3c—WS					3d—AAP
Variables		Model 1	Model 2	Model 3	Variables		Model 1	Model 2	Model 3
		B (SE)	B (SE)	B (SE)			B (SE)	B (SE)	B (SE)
Boys	CA	4.45 (2.97)	3.95 (5.37)	4 (5.37)	Boys	CA	9.63 (2.13) **	−12.41 (10.42)	−14.22 (14.26)
	%PAH		17.39 (149.42)	248.06 (271.61)		%PAH		567.39 (272.72)	638.38 (458.24)
	RA		2.01 (10.62)	354.8 (347.16)		RA		−20.13 (12.47)	61.08 (410.30)
	Interaction %PAH*RA			−96.79 (95.20)		Interaction %PAH*RA			−24.86 (125.55)
	R2	0.077	0.079	0.117		R2	0.612	0.756	0.757
	F	2.25	0.71	0.79		F	20.52	11.39	7.81
	F for change in R2	0.077	0.002	0.038		F for change in R2	0.612	0.144	0.001
Girls	CA	2.00 (1.14)	2.14 (2.75)	2.13 (2.77)	Girls	CA	8.52 (0.79) ***	−4.89 (2.53)	−5.27 (2.56)
	%PAH		5.90 (109.10)	37.72 (130.39)		%PAH		355.52 (64.37) ***	325.37 (71.89) ***
	RA		−4.84 (4.70)	61.58 (146.69)		RA		−0.11 (4.14)	−64.16 (67.96)
	Interaction %PAH*RA			−22.04 (48.64)		Interaction %PAH*RA			17.41 (18.43)
	R2	0.050	0.068	0.071		R2	0.595	0.712	0.716
	F	3.09	1.39	1.08		F	116.15	63.61	47.86
	F for change in R2	0.050	0.018	0.003		F for change in R2	0.595	0.117	0.003

Notes: CA = Chronological Age; %PAH = Percentage of Predicted Adult Height; RA = Relative Age; MB = Middle-Blocker; S = Setter; WS = Wing-Spiker; APP = All-Around Player; * p < 0.0.05. ** p < 0.01. *** p < 0.001.

,

Table 4. a–d. Hierarchical regression analysis for predictor variables of VJ test performance by sex and playing position.

4a—MB					4b—S
Variables		Model 1	Model 2	Model 3	Variables		Model 1	Model 2	Model 3
		B (SE)	B (SE)	B (SE)			B (SE)	B (SE)	B (SE)
Boys	CA	8.45 (2.45) *	8.39 (3.23)	8.49 (5.85)	Boys	CA	13.16 (5.49) *	16.53 (13.34)	27.91 (17.71)
	%PAH		31.21 (84.51)	39.57 (323.71)		%PAH		−122 (409.87)	−967.26 (954.69)
	RA		−19.16 (10.26)	−2.06 (615.18)		RA		−6.20 (14.90)	−997.67 (1010.62)
	Interaction %PAH*RA			−3.93 (141.35)		Interaction %PAH*RA			320.71 (326.87)
	R2	0.749	1.909	0.911		R2	0.451	0.475	0.577
	F	11.95	6.70	2.51		F	5.75	1.51	1.36
	F for change in R2	0.749	0.161	1.001		F for change in R2	0.451	0.024	0.102
Girls	CA	1.48 (0.64) *	0.7 (1.78)	0.69 (1.86)	Girls	CA	1.11 (1.26)	6.07 (2.82) *	6.79 (3.26) *
	%PAH		37.05 (75.94)	35.78 (93.09)		%PAH		−217.83 (111.74)	−286.07 (185.63)
	RA		−0.29 (2.92)	−3.3 (123.44)		RA		0.55 (4.77)	−80.17 (173.97)
	Interaction %PAH*RA			0.94 (38.46)		Interaction %PAH*RA			22.71 (48.93)
	R2	0.146	0.153	0.153		R2	0.025	0.142	0.149
	F	5.31	1.75	1.27		F	0.78	1.54	1.18
	F for change in R2	0.146	0.007	0.000		F for change in R2	0.025	0.117	0.007
4c—WS					4d—AAP
Variables		Model 1	Model 2	Model 3	Variables		Model 1	Model 2	Model 3
		B (SE)	B (SE)	B (SE)			B (SE)	B (SE)	B (SE)
Boys	CA	4.87 (1.85) *	3.32 (3.28)	3.32 (3.35)	Boys	CA	3.65 (1.56) *	−0.95 (9.49)	−4.10 (12.92)
	%PAH		54.47 (91.15)	56.36 (169.22)		%PAH		125.59 (248.43)	248.35 (415.29)
	RA		5.79 (6.48)	8.68 (216.29)		RA		2.47 (11.36)	142.92 (371.85)
	Interaction %PAH*RA			−0.80 (59.31)		Interaction %PAH*RA			−43.00 (113.78)
	R2	0.204	0.241	0.241		R2	0.298	0.318	0.327
	F	6.91	2.64	1.90		F	5.51	1.71	1.22
	F for change in R2	0.204	0.037	0.000		F for change in R2	0.298	0.020	0.010
Girls	CA	0.83 (0.81)	5.03 (1.83 **	5.03 (1.84) **	Girls	CA	1.48 (0.58) **	3.04 (2.18)	2.69 (2.21)
	%PAH		−168.42 (101.9) *	−168.05 (86.55)		%PAH		−41.95 (55.49)	−70.07 (61.91)
	RA		−4.07 (3.12)	−3.29 (97.37)		RA		2.08 (3.57)	−57.66 (58.52)
	Interaction %PAH*RA			−0.26 (32.29)		Interaction %PAH*RA			16.23 (15.88)
	R2	0.018	0.158	0.158		R2	0.077	0.090	0.102
	F	1.07	3.57	2.63		F	6.57	2.53	2.16
	F for change in R2	0.018	0.140	0.000		F for change in R2	0.077	0.013	0.012

Notes: CA = Chronological Age; %PAH = Percentage of Predicted Adult Height; RA = Relative Age; MB = Middle-Blocker; S = Setter; WS = Wing-Spiker; APP = All-Around Player; * p < 0.0.05. ** p < 0.01. *** p < 0.001.

and

Table 5. a–d. Hierarchical regression analysis for predictor variables of 3 × 9 test performance by sex and playing position.

5a—MB					5b—S
Variables		Model 1	Model 2	Model 3	Variables		Model 1	Model 2	Model 3
		B (SE)	B (SE)	B (SE)			B (SE)	B (SE)	B (SE)
Boys	CA	0.08 (0.11)	0.04 (0.15)	0.01 (0.26)	Boys	CA	0.1 (0.21)	0.52 (0.36)	0.01 (0.37)
	%PAH		0.00 (3.85)	−2.67 (14.49)		%PAH		−14.07 (11.11)	23.4 (19.98)
	RA		0.87 (0.47)	−4.59 (27.53)		RA		0.73 (0.40)	44.69 (21.15)
	Interaction %PAH*RA			5.72 (28.83)		Interaction %PAH*RA			−47.07 (22.65)
	R2	0.119	0.700	0.711		R2	0.029	0.547	0.782
	F	0.54	1.55	0.61		F	0.21	2.02	3.60
	F for change in R2	0.119	0.580	0.011		F for change in R2	0.029	0.519	0.235
Girls	CA	−0.25 (0.11) *	−0.16 (0.30)	−0.19 (0.31)	Girls	CA	−0.3 (0.16)	−1.31 (0.30) ***	−1.44 (0.34) ***
	%PAH		−4.80 (12.69)	−9.13 (15.49)		%PAH		45.06 (11.81) **	57.08 (19.48) **
	RA		0.07 (0.49)	−10.23 (20.54)		RA		−0.69 (0.50)	13.53 (18.26)
	Interaction %PAH*RA			10.9 (21.74)		Interaction %PAH*RA			−15.26 (19.59)
	R2	0.154	0.158	0.166		R2	0.107	0.433	0.446
	F	5.62	1.82	1.39		F	3.60	7.14	5.43
	F for change in R2	0.154	0.005	0.007		F for change in R2	0.107	0.326	0.012
5c—WS					5d—AAP
Variables		Model 1	Model 2	Model 3	Variables		Model 1	Model 2	Model 3
		B (SE)	B (SE)	B (SE)			B (SE)	B (SE)	B (SE)
Boys	CA	−0.36 (0.11) **	−0.36 (0.20)	−0.36 (0.18)	Boys	CA	−0.21 (0.10)	0.42 (0.58)	0.93 (0.75)
	%PAH		0.04 (5.47)	−16.24 (9.35)		%PAH		−15.83 (15.16)	−35.98 (24.18)
	RA		0.24 (0.39)	−24.66 (11.95) *		RA		0.84 (0.69)	−22.23 (21.65)
	Interaction %PAH*RA			26.37 (12.66) *		Interaction %PAH*RA			28.90 (27.13)
	R2	0.285	0.295	0.403		R2	0.241	0.376	0.440
	F	10.74	3.49	4.06		F	4.12	2.21	1.96
	F for change in R2	0.285	0.011	0.108		F for change in R2	0.241	0.135	0.064
Girls	CA	−0.19 (0.07)	−0.29 (0.16)	−0.29 (0.17)	Girls	CA	−0.13 (0.04) **	0.09 (0.17)	0.08 (0.17)
	%PAH		3.98 (6.50)	3.92 (7.78)		%PAH		−5.77 (4.25)	−6.31 (4.78)
	RA		0.10 (0.28)	−0.05 (8.75)		RA		−0.07 (0.27)	−1.22 (4.51)
	Interaction %PAH*RA			0.15 (9.35)		Interaction %PAH*RA			1.31 (5.18)
	R2	0.123	0.133	0.133		R2	0.096	0.117	0.118
	F	8.24	2.90	2.14		F	8.42	3.42	2.55
	F for change in R2	0.123	0.010	0.000		F for change in R2	0.096	0.021	0.001

Notes: CA = Chronological Age; %PAH = Percentage of Predicted Adult Height; RA = Relative Age; MB = Middle-Blocker; S = Setter; WS = Wing-Spiker; APP = All-Around Player; * p < 0.0.05. ** p < 0.01. *** p < 0.001.

, whereby ‘chronological age’ is entered at step 1 (model 1); ‘maturity status’ and ‘relative age’ are added at step 2 (model 2); and the interaction between ‘maturity status’ and ‘relative age’ is added at step 3 (model 3). The variable ‘CA’ was a predictor of SJR test performance (model 1) in MB boys (p < 0.05) and girls (p < 0.01) and APP boys (p < 0.01) and girls (p < 0.001). Also, ‘CA’ was a predictor of VJ test performance in MB boys (p < 0.05) and girls (p < 0.05), in S boys (p < 0.05), in WS boys (p < 0.05), and APP boys (p < 0.05) and girls (p < 0.001). Moreover, CA was a predictor of 3 × 9 test performance in WS boys (p < 0.05) and AAP girls (p < 0.01).

The main (model 2) and interactive (model 3) models yielded the following results (Table 3, Table 4, Table 5 and

Table 6. a–d. Hierarchical regression analysis for predictor variables of FRAC test performance by sex and playing position.

6a—MB					6b—S
Variables		Model 1	Model 2	Model 3	Variables		Model 1	Model 2	Model 3
		B (SE)	B (SE)	B (SE)			B (SE)	B (SE)	B (SE)
Boys	CA	−1.81 (1.71)	−1.33 (3.30)	1.90 (3.01)	Boys	CA	−0.47 (0.94)	0.42 (2.14)	−0.12 (3.14)
	%PAH		−27.89 (86.31)	237.63 (166.56)		%PAH		−29.64 (65.76)	10.56 (169.16)
	RA		8.13 (10.48)	550.92 (316.53)		RA		1.91 (2.39)	49.06 (179.07)
	Interaction %PAH*RA			−124.75 (72.73)		Interaction %PAH*RA			−50.49 (191.74)
	R2	0.217	0.400	0.848		R2	0.034	0.192	0.205
	F	1.11	0.44	1.39		F	0.25	0.40	0.26
	F for change in R2	0.217	0.182	0.448		F for change in R2	0.034	0.158	0.014
Girls	CA	−0.15 (0.56)	0.18 (1.51)	0.91 (1.47)	Girls	CA	−0.63 (0.61)	0.04 (1.45)	0.93 (1.64)
	%PAH		−3.56 (64.37)	81.91 (73.41)		%PAH		−28.85 (57.34)	−114.38 (93.35)
	RA		−2.96 (2.47)	200.22 (97.34) *		RA		−0.41 (2.45)	−101.58 (87.49)
	Interaction %PAH*RA			−215.11 (103.03)		Interaction %PAH*RA			108.6 (93.87)
	R2	0.002	0.050	0.178		R2	0.034	0.044	0.089
	F	0.08	0.51	1.51		F	1.07	0.43	0.66
	F for change in R2	0.002	0.047	0.128		F for change in R2	0.034	0.010	0.045
6c—WS					6d—AAP
Variables		Model 1	Model 2	Model 3	Variables		Model 1	Model 2	Model 3
		B (SE)	B (SE)	B (SE)			B (SE)	B (SE)	B (SE)
Boys	CA	−1.88 (0.69) **	−0.18 (1.14)	−0.18 (1.16)	Boys	CA	0.60 (0.38)	2.56 (2.11)	0.04 (2.62)
	%PAH		−56.35 (31.67)	−54.83 (58.79)		%PAH		−48.70 (55.29)	49.62 (84.17)
	RA		3.49 (2.25)	5.81 (75.14)		RA		3.40 (2.53)	115.88 (75.36)
	Interaction %PAH*RA			−2.46 (79.55)		Interaction %PAH*RA			−140.99 (94.41)
	R2	0.214	0.351	0.351		R2	0.164	0.316	0.440
	F	7.37	4.51	3.25		F	2.55	1.69	1.97
	F for change in R2	0.214	0.137	0.000		F for change in R2	0.164	0.152	0.125
Girls	CA	0.18 (0.41)	−1.25 (0.99)	−1.24 (0.99)	Girls	CA	0.03 (0.25)	0.53 (0.94)	0.54 (0.96)
	%PAH		64.58 (39.07)	56.73 (46.74)		%PAH		−13.53 (23.88)	−13.00 (26.82)
	RA		−1.59 (1.68)	−17.97 (52.59)		RA		1.32 (1.53)	2.44 (25.35)
	Interaction %PAH*RA			17.51 (56.18)		Interaction %PAH*RA			−1.29 (29.90)
	R2	0.003	0.053	0.055		R2	0.001	0.016	0.016
	F	0.19	1.06	0.81		F	0.02	0.43	0.32
	F for change in R2	0.003	0.050	0.002		F for change in R2	0.001	0.016	0.001

Notes: CA = Chronological Age; %PAH = Percentage of Predicted Adult Height; RA = Relative Age; MB = Middle-Blocker; S = Setter; WS = Wing-Spiker; APP = All-Around Player; * p < 0.0.05. ** p < 0.01. *** p < 0.001.

). In the SJR test, the main (F(_3,77) = 63.61, p < 0.001) and interactive (F(_4,76) = 47.86, p < 0.001) models achieved statistical significance for AAP girls, explaining 71.25% and 71.58% of the variance, respectively. Specifically, maturity status served as a statistically significant positive predictor of height (Table 3d). In the VJ test, the main model (F(_3,57) = 3.57, p < 0.01) achieved statistical significance in WS girls, explaining 15.82% of the variance. Specifically, maturity status served as a statistically significant negative predictor of height while chronological age served as a positive predictor (Table 4c). In the 3 × 9 test, the main (F(_3,28) = 7.14, p < 0.01) and interactive (F(_4,27) = 5.43, p < 0.01) models achieved statistical significance in girls in the S position, explaining 43.34% and 44.59% of the variance, respectively. Specifically, maturity status served as a statistically significant positive predictor of time, as opposed to chronological age, which was a negative predictor (Table 5b). Also, in the 3 × 9 test, the interactive model (F(_4,24) = 4.06, p < 0.001) achieved statistical significance for WS boys, explaining 40.33% of the variance. Specifically, relative age served as a statistically significant negative predictor of time, whereas the interaction between maturity status and relative age served as positive predictor (Table 5c). In the FRAC test, the interactive model (F(_4,28) = 1.51, p < 0.05) achieved statistical significance in girls who were MBs, explaining 17.77% of the variance. Specifically, relative age served as a statistically significant positive predictor of time (Table 6a).

4. Discussion

The purpose of this study was to examine the main and interactive effects of maturity status and relative age on the physical performance in four fitness tests among a sample of 278 young volleyball players within the 2020-25 talent identification programme organised by the Spanish Volleyball Federation. The results showed that there was no impact of the interaction between maturity status and relative age on physical performance, except that in the 3 × 9 test for boys who were WSs. Instead, maturity status had a greater influence on physical test performance than that of relative age. Specifically, maturation served as a statistically significant positive predictor of height in the SJR test for AAP girls, explaining 71.58% of the variance. Furthermore, an advanced maturity status correlated with better physical performance outcomes, except in the FRAC test. Specifically, by sex and playing position, greater maturity was associated with higher jump heights in the SJR test for both boys and girls.

Consistent with the previous literature [23,28], the interaction between relative age and maturity status was not significant, except that in the 3 × 9 test for boys who were WSs, where the early-maturing and relatively older players obtained better marks (lower times) than their counterparts. Similar results were reported by Malina et al. [26], identifying sprint performance differences of 20% between early maturers and their counterparts. Both cases respond to the age period (11–15 years old) in which the differences in body dimensions are greater in boys and therefore maturity status has a greater impact on physical performance. On the other hand, studies such as Albaladejo-Saura et al. [29] showed that an older relative age in young volleyball players (14.17 ± 1.00 years old) corresponded to a higher performance in speed and agility tests, associated with the ability to produce power and strength. Furthermore, considering that relative age was not found to be related to any of the attributes associated with maturation for this playing position, it is more plausible that this specific result was due to individual factors related to growth rather than maturation per se. Nevertheless, this evidence underlines the different nature of both constructs (relative age and maturation), operating at different times in the sport development process [19].

Regardless of sex, positive correlations between height reached and an advanced maturity status were observed. In both sexes, higher spike jump heights (SJR test) were identified in advanced-maturity APPs. A greater height and length of the upper limbs, essential for the volleyball spiking action, would confer a functional advantage to players with more advanced maturity in these playing positions [32]. Interestingly, the predictive results of the SJR test for the AAP position in girls were explained 71.58% by maturity status. Being a sample with an average age of 12.27 ± 1.61 years, it seems logical to connect these results to the chronological age of the players due to the fact that at this development stage, flattening of the maturation rate in girls has not yet occurred, and therefore, the factors associated with growth continuously change, ultimately affecting physical performance [42]. It should be noted that in other playing positions (i.e., setter), this advantage does not seem to be key to talent detection and player selection because performance is more dependent on decision-making [43], although the preference for anthropometrically superior setters (i.e., taller players) is increasing [44]. On the other hand, the superior anthropometric characteristics (i.e., height) of some players may determine their playing position (i.e., middle-blockers), where one’s performance is more dependent on one’s physical condition through actions such as spiking and blocking [30]. In this regard, a recent study [45] showed that the profiles of middle-blockers and opposite players in men’s volleyball exhibited significantly higher anthropometric values (i.e., height, upper limb length, and hand span) compared to those in liberos or setters. However, this top-down approach to sporting talent may overlook the future potential of players who have not yet developed to their ultimate maturity and therefore do not have the necessary opportunities to reach a high level in this position [46].

Likewise, advanced maturation in boys and girls translated into greater heights reached in the VJ test. This finding is closely related to research showing that taller players with a greater muscle mass are able to develop greater power in the lower body [47], which is a crucial aspect for spikes and blocks performed by WS and MB players and for versatility in the offensive and defensive actions of AAPs [48]. Interestingly, the regression related an advanced maturity status to a lower vertical jump height (VJ test) in girls who were WSs. Early-maturing girls in this position may have greater difficulty generating explosive strength for jump take-off due to an increase in the length of the different segments of their bodies. This context, derived from selection biases based on height, could alter coordination and motor control during jump execution, affecting its efficiency [49]. In fact, Bertozzi et al. [33] observed significant differences according to maturity status in young female volleyball players in variables extracted from the CMJ test such as peak power, jump momentum, and concentric and eccentric impulses. Thus, an advanced maturity status could generate imbalances in neuromuscular coordination, compromising the synchronisation and explosiveness necessary for efficient jumping [50]. On the other hand, the increase in oestrogen production in girls at this age tends to favour the greater accumulation of fat mass over muscle mass [51], which makes it difficult to move more body mass, and therefore, lower jump heights are reached. For example, Grigoletto et al. [32] concluded that a greater muscle mass derived from higher anthropometric factors, not transformed into applied strength, did not lead to a greater physical performance in relation to jumping.

In relation to speed ability, no significant regression results were observed. However, correlations were identified between better marks in the 3 × 9 test and an advanced maturity status in both sexes. Numerous studies have shown a direct association between maturation and motor coordination and tactical execution levels [52]. Therefore, an advanced maturity status could imply a favourable selection bias [53], allowing early-maturing players to perform multiple game-specific tasks in formative categories prior to ‘sports specialisation’ [44]. If we add to this the fact that strength also plays a decisive role in achieving high speeds [29], it is logical to think that an advanced maturity status, with higher muscle mass levels and larger bone structures, could translate into better outcomes in speed tests [30]. In fact, studies such as that by Malina et al. [26] identified a 20% difference between early maturers and the rest of their peers. However, as reflected in the results of this study, these differences are not significant when considering the different playing positions in volleyball, where only 44.59% of the variance is explained in the S position for girls and 40.33% in the WS position for boys.

Interestingly, maturity status had no impact on agility and coordination ability (in the FRAC test) in either boys or girls of any position. Although there is no definitive evidence that early/late maturity status correlates with higher or lower levels of agility, the explanatory factors in one direction or the other appear to differ with regard to sex [34]. While for boys, the lack of differences may be due to similar levels of muscle mass [54] and close lower limb length values [55], for girls, the causation is variable. On the one hand, during maturation, some motor skills may deteriorate in a timely manner due to rapid growth of the body’s dimensions, and thus, disordered coordination may result [50]. On the other hand, the nature of the task—planned or not planned—also affects agility and coordination values [56] due, among other factors, to uneven development of these abilities at sensitive sensorimotor ages [34].

This study is not without its limitations. First, the results for the male players should be interpreted with caution due to the small number of cases in certain playing positions. Second, the assessment of the physical performance of the players was carried out through the tests designed by the Spanish Volleyball Federation in its talent detection programme, which have not previously been scientifically validated. Third, the method for estimating maturity status may not be the most appropriate or the gold standard for the non-Caucasian boys and girls included in the sample. Fourth, the use of a cross-sectional design did not allow us to monitor the physical performance of the young players in relation to their maturity status throughout their sport training process. Fifth, the results regarding the sex–playing position subgroups with a case ratio of less than 10 (MB, n = 6 and S, n = 9 in boys) should be interpreted with caution from a statistical perspective due to a higher risk of overfitting in the analysis.

5. Conclusions—Practical Applications

The present study analysed the impact of maturity status, relative age, and their interaction on the physical performance of young volleyball players, considering the playing positions and sex differences. The results showed a lack of interaction between maturity status and relative age in physical performance, with maturation having a greater impact than that of relative age, especially in jumping and speed tests. Furthermore, differences in the performance between boys and girls with regard to maturity status were revealed, indicating in any case that players with a more advanced maturity status (strongly related to height, especially in girls) obtained better outcomes in the tests.

These findings highlight the need to implement different solution strategies since, as noted above (see [15]), the mechanisms underlying selection biases in reference to RAEs and maturation are likely to be different. Understanding the limitations of the present study (cross-sectional and with a limited sample) and considering its specific application to a sports context (i.e., the Spanish Volleyball Federation), professionals and stakeholders should be aware of the need to implement mitigation measures, especially for maturity bias. For example, the alternative grouping strategy known as “bio-banding” could be used to ensure that certain players are not excluded from the Spanish Volleyball Federation’s talent identification and development programme or that their full potential is exploited in other playing positions, regardless of their physical performance. This strategy, introduced at the point of the maximum correlation between chronological age and maturation (late childhood and/or the onset of puberty), could include a greater number of players considered “talented” within the federation system [19].

The lack of a relationship between RAEs and maturity status and the influence of both constructs at different stages of the sport development process are determining aspects for talent development programmes. Therefore, as Sweeney et al. [7] suggest, any assumption of strategies aiming to reduce and/or eliminate selection bias based on RAEs and/or maturation at the macro level (i.e., national federations) must be developed within a bottom-up framework of ‘informed flexibility’, allowing for the removal of barriers to the optimisation and individualisation of sporting talent, both early and late maturers, as well as relatively old and young players.

The practical implications of these results should first of all underline the relevance of maturity status to talent identification and development in volleyball. Thus, stakeholders should be aware that not all young players develop with the same ‘tempo’ and ‘timing’. This suggests that expectations and roles should be adjusted according to maturity status. Late-maturing players may need more specific training to compensate for their disadvantages compared to their more maturely advanced peers. Based on the results of this study, coaches and stakeholders may consider delaying specialisation by playing position, promoting greater exposure to non-specific training that could help preserve the future development potential of late-maturing players.