Longitudinal Variations of Body Characteristics in Italian Elite Adolescent Football Players: An Observational Study

Abstract

(1) Background: Body characteristics and physical skills affect field performance, and longitudinal improvements of these features allow one to join elite teams. This pilot study aims to investigate longitudinal changes (30 months) in 24 adolescent football players of an elite Italian club. (2) Methods: Participants were clustered according to their age (U10 = 8, U11 = 11, U12 = 5). Anthropometry and body composition assessments were performed following standardized methods. Countermovement jump (CMJ), maximal speed (15 m), and change of direction (RSA) were tested. The repeated-measures ANOVA was assessed for the interaction effect between time and category. The Pearson correlation product–moment was used to correlate the changes (∆) in physical performance and body characteristics. Also, each delta of performance skill was used as the dependent variable in a multiple linear regression model. (3) Results: Stature, body mass, fat-free mass (FFM), total upper area (TUA), total calf area (TCA), and CMJ improved in all categories (p < 0.05). The lower limb power variability was better explained by humeral diameter and the supraspinal skinfold thickness variation (adj-R² = 0.621 p < 0.001), while both maximal speed and RSA were negatively affected by the calf fat index (p < 0.01). (4) Conclusions: To face the complexity of human physiology and ameliorate the monitoring process in youth, football technicians need deeper insight into how body shapes and performance can vary over growth.

1. Introduction

Football, also known as soccer, is the world’s most popular sport, but most high-profile games are played in Europe [1]. Professional football clubs’ rewards associated with successful investment in youth academies have helped focus attention on talent identification and development models [2,3]. Research and analysis of talent detection and development have grown considerably over the last 20 years [4]. These are the primary goals of football academies, which follow the athletes during their growth and guide them in the transition to the professional level [5,6,7].

The relationship between anthropometrical features and physical performance found its origins in Greek populations, and it is now recognized as Kinanthropometry [8]. To date, many studies have enhanced the relevant contribution of body shapes and proportions in specific performance, leading to the selection of professional players in several sports [9,10]. However, most of the studies related to this topic are based on cross-sectional data, which do not allow for evaluation of the growth trend of a single boy, and nothing is known about everything that happened before. During growth, the relationships between anthropometric and performance characteristics show a higher level of complexity, and evident differences can be found due to the players’ morphological growth and physiological development [11,12]. Thus, cross-sectional studies cannot provide definitive information about cause-and-effect relationships, and longitudinal studies are required.

Few longitudinal reports or well-controlled experimental studies are available on the anthropometric and performance characteristics of football players in the literature, and this limits current knowledge concerning youth football players’ development [6,12,13,14].

For this purpose, a recent systematic review of young football players dealt with synthesizing the available information regarding longitudinal data addressing young football players’ motor performance changes [2]. The review found a series of inconsistencies and gaps in the literature, as the studies tended to use too many different tests to assess physical performance in football, and it highlights the need for coaches and stakeholders to consider players’ physical performance over time whilst considering biological maturation, biological characteristics, and training stimuli.

Despite the large body of scientific evidence on anthropometric and sport physiology characteristics in young football players, to the best of our knowledge, no longitudinal studies regarding young Italian players have been carried out.

Therefore, considering that most professional academies seek to optimize the early detection and physical development of their young players, this study aimed to (1) investigate how anthropometric characteristics and physical performance changed during growth in adolescent Italian football players and (2) investigate how age-related anthropometric and body composition changes affected variations in players’ physical performance.

2. Materials and Methods

2.1. Study Design

This study has an observational (longitudinal) design of 30 months (2019–2021) with two follow-ups (t₀ and t₁, Figure 1). A total of 68 football players (U10 = 26, U11 = 26, and U12 = 16) who belonged to the elite team Bologna Football Club 1909^® were measured in February 2019. Due to the COVID-19 lockdown and because pandemic emergencies limited the possibility of carrying out intermediate evaluations, the follow-up was assessed in September 2021. All players trained four times a week (about 90 min/training day) and played a match over the weekend. These conditions were unaffected by the pandemic emergency.

The parents provided written informed consent before the study began. Players were excluded if they had any health problems that could have interfered with anthropometric measurements or the execution of motor tests.

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the Bioethics Committee of the University of Bologna (approval n. 25027, dated 13 March 2017).

2.2. Anthropometric Measurements

All of the anthropometrical evaluations were conducted at the Sport Sciences Laboratory of Bologna University, with standardized conditions (~20 °C, 30–50% humidity). A trained operator collected all of the anthropometric measurements (stature, sitting height, body mass, lengths, widths, circumferences, and skinfold thicknesses) through standardized procedures [15]. Stature and sitting height were measured to the nearest 0.1 cm with a Raven anthropometer (Raven Equipment Ltd., London, UK), and leg length was derived through the subtraction of sitting height from stature. To evaluate sitting height, a flat anthropometric chair with a known height (40 cm) was used, and trunk height was obtained by subtracting it from sitting height. The participants (barefoot) were measured in a straightened upright position with the head oriented on the Frankfurt plane. Body mass was measured to the nearest 0.1 kg using a weighing scale (Seca Deutschland Medical Measuring Systems and Scale, Hamburg, Germany) with participants dressed in light clothing. Circumferences (relaxed and contracted upper arm, thigh, and calf) were measured to the nearest 0.1 cm with a non-stretchable tape (GPM measuring tape, DKSH, Swiss) and widths (humerus and femur) were measured to the nearest 0.1 cm with a sliding caliper, both at the left side of the body. The upper arm circumference was taken midway between the tip of the acromion and olecranon processes, with the participant’s elbow relaxed along the body side (relaxed evaluation) or flexed 90° with the palm facing upward (contracted evaluation); the thigh circumference was taken at the mid-point between the inguinal fold and the superior rotula point, with the participant in a standing position (thigh muscles relaxed); and the calf circumference was taken at the bulkiest calf point, with participant in a standing position (calf muscles relaxed). The humerus and femoral widths were taken, respectively, between the lateral and medial epicondyles, with participants’ elbows and knees flexed 90°. Skinfold thicknesses (biceps, triceps, subscapular, supraspinal, supra-iliac, thigh, and calf) were measured to the nearest 0.1 cm using a Lange skinfold caliper at the left side of the body (Beta Technology Inc., Houston, TX, USA); triceps and biceps were measured vertically at the mid-point between the acromion process and the olecranon process at the posterior and anterior upper arm face, respectively; subscapular thickness was measured at an angle of 45° to the lateral side of the body, about 2 cm below the tip of the scapula; supraspinal skinfold thickness was measured above the anterior superior iliac spine; supra-iliac thickness was measured above the iliac crest (in the axillary line); the thigh was measured vertically at the mid-point between the inguinal fold and the superior rotula point; and the calf was measured vertically at the bulkiest calf point both medially and laterally.

Each anthropometric measurement was performed three times, and the mean value was gathered.

Body mass index (BMI) was computed as weight (kg)/stature squared (m²). The body fat percentage (%F) was estimated according to Slaughter and colleagues [16]. To complete the body composition assessment, Frisancho’s formulas [17] were also applied to calculate the total area of the upper arm, the calf, and the thigh, the muscle area of the upper arm, the calf, and the thigh, and the fat area of the upper arm, the calf, and the thigh. In addition, the arm fat index, the calf fat index, and the thigh fat index were derived.

2.3. Bioelectric Impedance Analysis (BIA)

Bioelectrical impedance measurements were conducted with a body impedance analyzer (BIA 101 Anniversary, Akern, Florence, Italy) using an electric current at a frequency of 50 kHz.

Measurements were made using four electrical conductors; the subjects were in a supine position with a lower limb angle of 45° compared to the median line of the body and an upper limb angle of 30° from the trunk. After cleansing the skin with alcohol, two Ag/AgCl low-impedance electrodes (Biatrodes Akern Srl, Florence, Italy) were placed on the back of the right hand, and two electrodes were placed on the corresponding foot [18].

To avoid disturbances in fluid distribution, athletes were instructed to abstain from foods and liquids for ≥4 h before the test. Athletes consumed a normal breakfast at 07:00, and the measurements were taken at 11:00.

2.4. Physical Performance Tests

We used the countermovement jump (CMJ) test to assess explosive lower-body power. Before testing, each participant was instructed to start from an upright position, making a rapid downward movement to a knee angle of 90° and simultaneously beginning to push off [19]. The feet’s position coincided with the fitted acromion vertical line, with an extra rotation of, at most, 15°. The hands were maintained on the waist for the entire trial. Two minutes of rest were allowed between the three attempts, and the higher value was gathered.

A 15 m sprint test was performed to evaluate the maximal speed, and the time to cover the desired distance was recorded. All participants wore training clothing and football boots, as previously recommended [20]. Players were positioned behind the start line (0.5 m) and were instructed to perform the sprint with maximal effort after a sound start signal. Each athlete performed three attempts, and the best result was gathered.

To evaluate the change of direction (CoD) ability, an RSA test consisting of six 40 m (20 + 20 m sprints at 180°) shuttle sprints separated by 20 s of passive recovery was assessed, as described by Rampinini and collaborators [21]. The athletes started from a line, sprinted for 20 m, touched a line with a foot, and came back to the starting line as fast as possible. After 20 s of passive recovery, the football players started again. Three trials were assessed for each player. The best time (BT) in a single trial was measured.

Both sprint and CoD times were recorded using photoelectric cells (Fusion Sport Smart Speed Timing Gates, Brisbane, Australia).

All tests were performed under the same environmental features at both baseline and follow-up evaluations: ~18–20 °C, ~40–60% humidity, 10 a.m to 5 p.m. The performance battery was performed on a different day from anthropometry evaluations and had the following hierarchy: (I) CMJ; (II) sprint; and (III) CoD. For CMJ, a rigid court with no inclination was selected, whereas the sprint and CoD were performed on dry grass (synthetic court).

2.5. Statistical Analysis

All of the statistics were assessed using STATA^® software, version 18, Windows edition (StataCorp, College Station, TX, USA).

(a) First hypothesis

The 2019 evaluation was considered as the baseline (t₀) and September 2021 as the follow-up (t₁). For summarizing the variables’ central tendency and dispersion measures, the mean and standard deviation (sd) were computed. The normality of the variables’ residual distribution was previously checked through graphs (box and Q-Q plots) and then confirmed using the Shapiro–Wilk test. The repeated-measures ANOVA was performed to evaluate the time effect and its interaction with the categories. The Snedecor–Fisher test statistic value F with k−1 (number of evaluations) and n-k-1 degrees of freedom and the probability value corrected with the Greenhouse–Geisser epsilon (G-G) were reported. The post hoc test with Bonferroni correction criterium was performed to look for the time effect in each category, and Student’s test (t) and its related probability value (p) were reported.

(b) Second hypothesis

The difference between t₁ and t₀ (∆ = t_1 − t_0) was calculated for every variable. The Pearson product–moment (r) was computed to find any correlation between a variation in physical performance and anthropometric characteristics, and the correlation matrix with the Sidak correction criterium was included; an r < 0.10 was considered a small effect, while an r > 0.5 was considered a large effect [22]. The anthropometrics deltas that showed a rho value higher than 0.499 were considered a potential regressor. The stepwise backward procedure with an entry probability level of 0.07 and a remaining probability level of 0.05 was performed. The variance inflation factor (VIF) was computed to check the multicollinearity, and a mean VIF lower than 5 was considered acceptable [23]. To check for homoskedasticity, Szroeter’s test was computed. The standardized normal probability plot (P-P plot) was computed to control the distribution of the curve of the predicted variable. Also, Cook’s distance was computed to detect any outliers, and the threshold was settled at 4/n [24]. When any outlier was found, a new model without it was computed and compared with the desired model. Both the Akaike Information Criterium (AIC) and the goodness of fit (R²) were confronted, and a better model was provided. The R², the F (k-1, n-k) values, and the regression tables were reported.

3. Results

Figure 1 shows the observation flowchart. Finally, 35.29% of the sample completed all of the evaluations at baseline (t₀) and follow-up (t₁). Only 24 football players (U13 = 8, U14 = 11, and U15 = 5) were able to pass the rigorous selection and remained with the club (Figure 1).

3.1. First Hypothesis

Table 1. Descriptive statistics and repeated-measures ANOVA results.

	2019 (n = 24)						2021 (n = 24)						rm ANOVA		Post hoc with Bonferroni
	U10 (n = 8)		U11 (n = 11)		U12 (n = 5)		U10 → U13		U11 → U14		U12 → U15		All		U10 vs. U13		U11 vs. U14		U12 vs. U15
	Mean	sd	Mean	sd	Mean	sd	Mean	sd	Mean	sd	Mean	sd	F(1, 21)	G-G	t	p	t	p	t	p
age, years	9.94	0.25	10.86	0.25	11.75	0.37	12.41	0.27	13.42	0.25	14.34	0.37	12,249	<0.01 *	65.4	<0.01 *	79.57	<0.01 *	54.22	<0.01 *
body mass, kg	32.39	4.33	37.55	5.24	38.28	0.88	41.75	4.40	52.71	9.08	58.42	3.83	152.61	<0.01 *	4.72	<0.01 *	8.97	<0.01 *	8.03	<0.01 *
stature, cm	138.24	5.64	146.89	6.65	148.40	2.34	150.49	5.97	164.70	9.68	172.60	9.37	257.85	<0.01 *	6.61	<0.01 *	11.27	<0.01 *	10.33	<0.01 *
low limb, cm	68.49	3.49	74.78	4.92	75.04	1.47	74.95	3.30	81.39	5.61	82.90	7.03	79.40	<0.01 *	5.02	<0.01 *	6.02	<0.01 *	4.83	<0.01 *
trunk height, cm	69.75	2.42	72.11	2.67	73.36	2.22	75.54	2.92	83.31	5.75	89.70	4.16	282.50	<0.01 *	5.32	<0.01 *	12.08	<0.01 *	11.88	<0.01 *
BMI, kg/m2	16.89	1.48	17.34	1.60	17.39	0.45	18.42	1.41	19.48	1.49	19.76	2.68	41.20	<0.01 *	2.96	0.110	4.86	<0.01 *	3.64	0.023 *
PHV	−3.53	0.31	−2.82	0.38	−2.40	0.34	−1.84	0.44	−0.34	0.79	0.87	0.63	636.76	<0.01 *	10.46	<0.01 *	17.97	<0.01 *	16.00	<0.01 *
APHV, year	13.47	0.26	13.68	0.45	14.15	0.21	14.26	0.27	13.77	0.65	13.47	0.36	0.46	0.507	4.94	<0.01 *	0.66	0.990	−3.38	0.043 *
waist circ, cm	60.56	2.81	62.62	5.18	62.06	2.11	62.75	2.54	67.92	3.77	67.84	0.86	53.99	<0.01 *	2.21	0.580	6.28	<0.01 *	4.62	<0.01 *
hip circ, cm	70.11	4.46	75.38	4.39	74.10	1.36	75.78	4.45	84.85	5.70	85.38	2.28	175.17	<0.01 *	5.18	<0.01 *	10.15	<0.01 *	8.15	<0.01 *
arm rel circ, cm	19.04	1.58	20.20	2.13	19.78	0.50	20.90	1.16	23.23	1.87	23.10	0.74	108.35	<0.01 *	4.31	<0.01 *	8.21	<0.01 *	6.07	<0.01 *
arm con circ, cm	21.03	2.00	21.96	2.38	21.50	0.77	23.35	1.45	25.55	2.09	25.86	1.05	127.75	<0.01 *	4.67	<0.01 *	8.45	<0.01 *	6.92	<0.01 *
calf circ, cm	28.00	2.05	29.85	1.71	29.80	1.92	30.21	1.91	33.49	2.02	33.56	0.42	108.05	<0.01 *	4.37	<0.01 *	8.41	<0.01 *	5.87	<0.01 *
thigh circ, cm	38.68	2.73	41.02	2.56	41.92	1.24	41.20	2.32	45.54	3.53	46.08	1.52	52.57	<0.01 *	2.98	0.110	6.26	<0.01 *	3.88	0.013 *
humeral d, cm	5.61	0.36	5.66	0.31	5.62	0.24	6.01	0.34	6.29	0.27	6.56	0.11	175.44	<0.01 *	4.91	<0.01 *	9.03	<0.01 *	9.13	<0.001 *
femoral d, cm	8.35	0.35	8.63	0.33	8.62	0.36	8.51	0.29	9.19	0.49	9.32	0.22	47.89	<0.01 *	1.44	0.990	5.85	<0.01 *	4.90	<0.01 *
tric SK, mm	8.19	1.51	8.50	1.58	9.30	1.99	8.81	1.56	7.68	2.09	6.50	1.90	6.87	0.016 *	0.99	0.990	−1.53	0.980	−3.54	0.029 *
bic SK, mm	3.19	1.28	4.23	1.99	4.50	1.32	4.56	1.18	4.45	1.27	3.30	0.27	0.28	0.600	3.32	0.05 *	0.64	0.990	−2.29	0.488
subs SK, mm	4.31	0.70	5.18	1.62	5.10	0.74	5.19	0.53	6.14	0.98	5.90	0.74	9.72	<0.01 *	1.89	0.990	2.42	0.369	1.37	0.990
spinal SK, mm	3.25	1.00	5.05	2.40	5.10	0.82	4.31	0.96	5.36	1.16	5.10	0.65	1.87	0.186	1.92	0.990	0.68	0.999	0.01	0.999
iliac SK, mm	5.38	1.96	7.55	2.77	8.20	2.56	7.56	1.68	7.95	2.09	7.00	1.58	1.04	0.319	2.91	0.124	0.64	0.999	−1.26	0.990
thigh SK, mm	9.50	1.77	10.68	2.55	10.80	2.20	10.69	1.67	9.50	2.62	8.00	1.54	3.11	0.093	1.37	0.990	−1.59	0.990	−2.55	0.282
m calf SK, mm	5.44	1.72	7.00	2.63	7.60	1.47	7.00	1.75	7.45	2.47	6.80	0.84	1.23	0.280	2.60	0.252	0.89	0.999	−1.05	0.999
l calf SK, mm	6.19	1.13	7.50	1.88	8.50	2.06	7.69	1.58	8.41	2.63	6.90	1.43	0.50	0.487	2.39	0.390	1.70	0.990	−2.02	0.851
TUA, cm2	29.01	4.73	32.80	6.58	31.15	1.57	34.85	3.90	43.18	6.84	42.50	2.72	108.16	<0.01 *	4.02	<0.01 *	8.38	<0.01 *	6.17	<0.01 *
UMA, cm2	21.70	3.56	24.74	5.36	22.64	1.60	26.28	3.85	34.73	6.22	35.39	4.22	93.55	<0.01 *	2.95	0.110	7.57	<0.01 *	6.51	<0.01 *
UFA, cm2	7.31	1.69	8.06	1.95	8.51	1.80	8.57	1.45	8.45	2.42	7.11	1.77	0.05	0.819	2.11	0.709	0.77	0.999	−1.85	0.990
UFI, %	25.12	3.54	24.72	3.73	27.25	5.27	24.77	4.27	19.68	5.00	16.94	5.16	26.32	<0.01 *	−0.21	0.999	−3.52	0.03 *	−4.85	<0.01 *
TCA, cm2	62.68	9.09	71.14	8.11	70.90	9.29	72.89	9.13	89.55	10.66	89.64	2.22	105.32	<0.01 *	4.04	<0.01 *	8.54	<0.01 *	5.85	<0.01 *
CMA, cm2	46.43	7.13	51.28	8.02	50.99	6.29	54.26	6.72	66.34	10.25	65.83	3.61	88.89	<0.01 *	3.57	<0.05 *	8.05	<0.01 *	5.35	<0.01 *
CFA, cm2	16.25	4.46	19.86	6.21	19.91	3.76	18.63	4.16	23.21	6.19	23.81	4.05	108.53	<0.01 *	4.69	<0.01 *	7.77	<0.01 *	6.08	<0.01 *
CFI, %	25.81	5.25	27.89	7.93	27.98	3.00	25.44	3.81	26.01	6.22	26.53	4.35	7.88	0.01 *	−0.51	0.999	−3.05	0.092	−1.60	0.999
TTA, cm2	119.55	16.81	134.36	16.76	139.94	8.37	135.45	15.53	165.91	25.87	169.12	11.30	47.22	<0.01 *	2.60	0.250	6.05	<0.01 *	3.77	0.017 *
TMA, cm2	101.79	14.19	113.22	13.02	118.19	6.52	114.38	15.17	144.94	23.85	151.27	13.25	61.75	<0.01 *	2.33	0.446	6.89	<0.01 *	4.84	<0.01 *
TFA, cm2	17.76	3.89	21.14	5.79	21.75	4.64	21.07	3.13	20.97	6.37	17.85	2.97	0.05	0.825	1.79	0.999	−0.11	0.999	−1.67	0.990
TFI, %	14.79	2.28	15.60	3.16	15.49	2.86	15.67	2.62	12.66	3.07	10.64	2.22	11.02	<0.01 *	0.78	0.999	−3.03	0.096	−3.36	0.044 *
BF, %	12.15	2.07	13.31	2.46	14.02	2.67	13.65	1.85	12.45	3.20	8.66	2.90	7.44	0.013 *	1.59	0.990	−1.06	0.990	−4.47	<0.01 *
FM, kg	3.97	1.01	5.05	1.33	5.36	1.02	5.69	0.94	6.51	1.70	5.00	1.49	13.61	<0.01 *	4.10	<0.01 *	4.11	<0.01 *	−0.69	0.990
FFM, kg	28.42	3.56	32.50	4.33	32.92	1.42	36.06	3.96	46.44	8.60	53.42	4.94	150.55	<0.01 *	4.07	<0.01 *	8.69	<0.01 *	8.62	<0.01 *
endomorphy	3.74	0.58	4.28	0.79	4.50	0.72	4.33	0.53	4.39	0.67	4.07	0.49	0.56	0.461	2.90	0.128	0.67	0.990	−1.67	0.990
mesomorphy	4.31	0.65	3.91	0.71	3.58	0.67	3.85	0.63	3.60	0.92	2.87	1.28	9.21	<0.01 *	−1.72	0.990	−1.33	0.990	−2.12	0.690
ectomorphy	3.24	0.86	3.61	0.97	3.65	0.42	3.21	0.92	3.62	0.82	4.01	1.99	0.32	0.578	−0.09	0.990	0.02	0.999	0.87	0.990
R, Ω	616.73	73.08	622.40	55.72	642.80	38.46	590.35	80.15	534.07	49.91	532.92	22.19	54.44	<0.01 *	−1.58	0.990	−6.21	<0.01 *	−5.21	<0.01 *
Xc, Ω	64.69	3.26	67.66	7.56	68.28	3.09	68.25	5.96	58.71	6.61	63.42	4.38	9.28	<0.01 *	1.93	0.990	−5.69	<0.01 *	−2.08	0.746
PhA, °	6.07	0.59	6.25	0.62	6.10	0.31	6.69	1.03	6.25	0.56	6.82	0.66	11.04	<0.01 *	2.80	0.160	−0.01	0.999	2.58	0.260
CMJ, cm	25.08	4.59	26.65	3.50	27.50	3.93	29.09	3.68	32.88	3.39	37.46	6.74	102.20	<0.01 *	3.66	<0.01 *	6.67	<0.01 *	7.19	<0.01 *
sprint, s	3.01	0.12	2.72	0.11	2.64	0.06	2.88	0.07	2.51	0.06	2.36	0.09	70.78	<0.01 *	−3.15	0.073	−6.10	<0.01 *	−5.53	<0.01 *
RSA, s	6.66	0.18	6.25	0.24	6.07	0.24	6.63	0.08	5.81	0.13	5.57	0.19	37.86	<0.01 *	−0.34	0.999	−5.96	<0.01 *	−4.58	<0.01 *

Note: n, sample size; rm, repeated measures; sd, standard deviation; F, Snedecor–Fisher test value; G-G, Greenhouse–Geisser epsilon; t, Student’s test value; p, probability value; BMI, body mass index; PHV, peak height velocity; APHV, age at peak height velocity; circ, circumference; d, diameter; tric, triceps; bic, biceps; subs, subscapular; spinal, supraspinal; iliac, supra-iliac; m, medial; l, lateral; SK, skinfold thickness; R, resistance; Xc, reactance; PhA, phase angle; CMJ, countermovement jump; RSA, repeated sprint ability; *, statistically significant.

shows the descriptive statistics for both 2019 and 2021 in all categories, the repeated-measures ANOVA results, and the interaction effects of time and category for anthropometry, body composition, and physical performance. Generally, all of the players showed significant changes over time, except for most skinfold thicknesses and somatotypes. As regards anthropometry, body mass (Hedges’s g = 1.88 [1.20; 2.55]) and stature (g = 1.74 [1.08; 2.40]) showed larger effect sizes, while FFM and CMJ exhibited the greatest effect on body composition (g = 1.82 [1.15; 2.49]) and physical performance (g = 1.35 [0.72; 1.96]).

As regards the variations in each category, the youngest group had significantly increased arm and calf circumferences and areas, FM (∆ = 7.64 ± 1.90 kg) and FFM (∆ = −1.72 ± 0.92), and lower limb power (∆CMJ = 4.01 ± 2.57 cm), whereas speed did not improve. In the U11 category, players increased their skeletal robustness (humeral and femoral diameters) and all of the mass areas (∆UMA = 9.99 ± 5.84 cm², ∆CMA = 15.06 ± 6.91 cm², ∆TMA= 31.72 ± 19.98 cm²), and both power and speed showed significant improvements (CMJ g = 1.74 [2.69; 0.76], 15 m sprint g = −2.28 [−3.33; −1.20], RSA g = −2.19 [−3.23; −1.13]. Finally, in the eldest category, all of the lengths, circumferences, and diameters increased, whereas no significant changes appeared in skinfold thicknesses (except for triceps, ∆ = −2.8 ± 1.30 mm) and somatotype features.

3.2. Second Hypothesis

Table 2. Pearson product–moment’s correlation matrix of physical performance and body composition variations.

	∆ RSA (s)	∆ Sprint (s)	∆ CMJ (cm)
∆ RSA	1.000
∆ sprint	0.648	1.000
∆ CMJ	−0.509	−0.676	1.000
∆ body mass	−0.516	−0.373	0.546
∆ stature	−0.374	−0.394	0.591
∆ low limb	0.061	−0.157	0.320
∆ trunk height	−0.552	−0.423	0.574
∆ BMI	−0.479	−0.292	0.289
∆ APHV	0.530	0.400	−0.597
∆ waist c	−0.172	−0.022	0.257
∆ hip c	−0.413	−0.221	0.391
∆ arm rel c	−0.277	−0.177	0.441
∆ arm con c	−0.271	−0.094	0.377
∆ calf c	−0.319	−0.311	0.292
∆ thigh c	−0.209	−0.128	0.242
∆ humeral d	−0.432	−0.324	0.672
∆ femoral d	−0.459	−0.277	0.288
∆ tric SK	0.429	0.593	−0.604
∆ bic SK	0.534	0.431	−0.422
∆ subs SK	0.199	0.194	0.156
∆ spinal SK	0.589	0.535	−0.615
∆ iliac SK	0.579	0.557	−0.518
∆ thigh SK	0.470	0.448	−0.255
∆ m calf SK	0.461	0.580	−0.533
∆ l calf SK	0.358	0.439	−0.466
∆ TUA	−0.342	−0.218	0.453
∆ UMA	−0.438	−0.398	0.590
∆ UFA	0.393	0.580	−0.546
∆ UFI	0.416	0.572	−0.625
∆ TCA	−0.327	−0.305	0.276
∆ CMA	−0.375	−0.355	0.317
∆ CFA	−0.011	0.010	0.009
∆ CFI	0.579	0.551	−0.472
∆ TTA	−0.224	−0.123	0.237
∆ TMA	−0.371	−0.259	0.323
∆ TFA	0.425	0.402	−0.235
∆ TFI	0.492	0.483	−0.269
∆ BF	0.511	0.582	−0.535
∆ FM	0.321	0.497	−0.410
∆ FFM	−0.583	−0.472	0.596
∆ endo	0.548	0.643	−0.520
∆ meso	−0.048	0.106	−0.133
∆ ecto	0.189	0.004	0.030
∆ R	0.666	0.488	−0.469
∆ Xc	0.443	0.197	−0.306
∆ PhA	−0.078	−0.223	0.095

Note: BMI, body mass index; PHV, peak height velocity; APHV, age at peak height velocity; c, circumference; d, diameter; tric, triceps; bic, biceps; subs, subscapular; spinal, supraspinal; iliac, supra-iliac; m, medial; l, lateral; SK, skinfold thickness; endo, endomorphic; meso, mesomorphic; ecto, ectomorphic; R, resistance; Xc, reactance; PhA, phase angle; CMJ, countermovement jump; RSA, repeated sprint ability.

shows the correlation matrix between deltas of anthropometric and body composition and physical performance. Generally, variations in body mass, triceps, supraspinal, and supra-iliac skinfold thicknesses, such as the body fat percentage, and the ∆ endomorphic component were highly correlated with all motor tests. The lower limb power variation (∆ CMJ) was strongly and positively correlated with ∆ stature and trunk height, ∆ humeral diameter, and ∆ FFM, whereas negative strong correlations appeared with ∆ APHV, ∆ medial calf skinfold thickness, ∆ UMA, UFA, and UFI. The maximal speed (15 m) change was correlated with ∆ medial calf thickness, ∆ UFA, UFI, and ∆ endomorphic. As regards RSA variation, it also shows a strong direct correlation with ∆ biceps skinfold thickness, ∆ CFI, ∆, and bio-electric resistance, whereas inverse strong correlations appear with ∆ FFM, ∆ trunk height, and ∆ body mass.

Table 3. Multiple regression model for CMJ.

n	F(2, 20)	p	R2	adj-R2	Root MSE
23	19.04	<0.001	0.656	0.621	2.106
Source	SS	df	MS
Model	168.884	2	84.442
Residual	88.710	20	4.436
Total	257.595	22	11.709
CMJ	β	SE	t	p	95% CI
Humeral d	6.353	1.597	3.98	0.001	3.022	9.684
Spinal SK	−0.901	0.307	−2.94	0.008	−1.541	−0.262
Intercept	2.556	1.139	2.24	0.036	0.179	4.933

Note: n, sample size; F, Snedecor–Fisher test value; p, probability value; R², goodness of fit; adj, adjusted; MSE, mean squared error; SS, sum of squares; df, degree of freedom; MS, mean squared; CMJ, countermovement jump; β, regression coefficient; SE, standard error; t, Student’s test value; CI, confidence interval; d, diameter; spinal, supraspinal; SK, skinfold thickness.

,

Table 4. Multiple regression model for 15 m sprint.

n	F(2, 21)	p	R2	adj-R2	Root MSE
24	12.52	<0.001	0.544	0.501	0.089
Source	SS	df	MS
Model	0.199	2	0.099
Residual	0.167	21	0.008
Total	0.367	23	0.016
Sprint	β	SE	t	p	95% CI
CFI	0.028	0.009	3.10	0.005	0.009	0.047
m calf SK	0.034	0.010	3.33	0.003	0.013	0.055
Intercept	−0.185	0.023	−8.04	0.000	−0.232	−0.137

Note: n, sample size; F, Snedecor–Fisher test value; p, probability value; R², goodness of fit; adj, adjusted; MSE, mean squared error; SS, sum of squares; df, degree of freedom; MS, mean squared; β, regression coefficient; SE, standard error; t, Student’s test value; CI, confidence interval; CFI, calf fat index; m, medial; SK, skinfold thickness.

and

Table 5. Multiple regression model for RSA.

n	F(2, 20)	p	R2	adj-R2	Root MSE
23	20.39	<0.001	0.671	0.638	0.188
Source	SS	df	MS
Model	1.436	2	0.718
Residual	0.704	20	0.035
Total	2.141	22	0.097
RSA	β	SE	t	p	95% CI
CFI	0.062	0.022	2.84	0.010	0.016	0.108
R	0.003	0.001	3.72	0.001	0.001	0.005
Intercept	−0.001	0.065	−0.01	0.993	−0.136	0.134

Note: n, sample size; F, Snedecor–Fisher test value; p, probability value; R², goodness of fit; adj, adjusted; MSE, mean squared error; SS, sum of squares; df, degree of freedom; MS, mean squared; RSA, repeated sprint ability; β, regression coefficient; SE, standard error; t, Student’s test value; CI, confidence interval; CFI, calf fat index; R, resistance.

show the multiple regression models for CMJ, the 15 m sprint, and RSA deltas, respectively. For the CMJ model, the average variance inflation factor (VIF) was 1.15, while the probabilities associated with the null hypothesis of homoskedasticity for each regressor were 0.79 and 0.30. One outlier was deleted from the final model (AIC model with 24 players = 110.05 vs. AIC model with 23 players = 101.2), which explained the 62% CMJ variability. As regards sprint, all of the subjects were included, and the average VIF = 1.03. Both of the regressors did not reject the null hypothesis of homoskedasticity (CFI χ² = 0.19, p = 0.67; medial calf SK χ² = 0.25, p = 0.62), and their adjusted goodness of fit was 0.501. Finally, the RSA regression model included two regressors (CFI and R) with 23 subjects (one outlier deleted, ∆ AIC = −40.89). The average VIF was 1.28, and neither of the regressors rejected the hypothesis of homoskedasticity. The adjusted variance explained was 63.8%.

4. Discussion

In this study, we examined the variations in anthropometrical and body composition features and growth-related physical abilities in a sample of young football players engaged in a professional Italian team. In addition, we analyzed the association between physical performance and anthropometrical parameters, and we provided three new regression models to justify players’ power and speed variabilities.

In line with our hypotheses, we highlighted that growth induced many morphological changes that need to be accounted for in juvenile development because they have a noticeable relationship with physical performance. Power, agility, and speed are conditioning abilities vital for football success, and specific body shape proportions could enhance them.

We found that many parameters, such as lengths, circumferences and areas, diameters, and body composition (fat and fat-free mass), change with age. Gravina et al. [25] suggested that body size could be an important factor for progression in a football career between 10 and 14 years of age. As regards stature and body mass, the longitudinal increments we observed were comparable to what was found by Carvalho et al. [26] in their study on Basque football players aged 10–15 years. In contrast, our sample was taller and lighter, with a lower BMI and skinfolds than Spanish players of the same age [25]. The 12-year-old players in the present study were shorter and lighter than the Belgian [27] and German players [28], but the 14-year-old players were taller and heavier than the Belgian players. The height, sitting height, and body mass of the 12-year-olds measured in this study were lower than those measured by Abarghoueinejad and colleagues [12] on a Portuguese sample, while the data for the 14-year-olds were comparable.

As far as body composition is concerned, in our study, the wider improvements appeared in the elder groups between 12 and 14 years, especially for limb circumferences, fat, and muscle areas. When compared with the results of the Portuguese [12] and Czech [29] samples, Italian players exhibited lower levels of body fat percentage. Variations in body composition can be accompanied by shifts in motor dispositions [30], and the present study confirms this aspect. Although the power of the lower limbs (CMJ) increased for all ages, maximal speed (15 m) and change of direction ability (RSA) improved only for the older groups, where bodies with wider levels of FFM were observed. However, the performance results we detected are in accordance with Leyhr and collaborators [30]. The football players of the present study showed better results in the CMJ in comparison with the Portuguese sample [12], while the values were lower than those of the Spanish players of the same age [25]. Physical performance increased considerably in the first year of their promotion [30,31,32,33] and before the players reached the age of peak height velocity [31]. Athletes’ physical or physiological dispositions are expected to suddenly change when they go through puberty during early adolescence [34,35].

Considering the correlations between physical performance and anthropometrical variations, speed and change of direction, computed as the time needed to assess the evaluations, were negatively associated with body mass and stature, which show a positive relationship with power (jump centimeters reached). Although the increase in body mass is expected to reduce maximal speed and CoD ability, its positive influence in reducing time to reach the desired distances is linked to the players’ improvements in muscle and skeletal mass (FFM). In fact, circumferences and diameters exhibited an inverse relationship with speed and CoD and a positive relationship with CMJ, while the skinfold thicknesses, body fat, and BIVA raw values showed the opposite patterns. Our results are in accordance with previous studies that found a negative impact of fat mass on power and speed performances in football players [3,5,25,36]. In addition, the closer the distance between the chronological age and the estimated age at peak height velocity, the better the performance. This indicates a correlation between maturation status and football results [37].

The regression models obtained in this study suggest that different parameters could affect the variabilities of a change in power and/or speed and CoD ability. The variation in centimeters reached during the jump was affected by skeletal size (humeral robustness) and supraspinal skinfold thickness, while the percentage of the calf fat area negatively affects horizontal speed performance (RSA and sprint). According to Gravina [25], sprint time is an important factor associated with selection between the ages of 10 and 14 years. Saward and collaborators [38] affirmed that from age 12, future professionals performed better in a vertical countermovement jump and were faster than future non-professionals, and they improved at a faster rate throughout their development; thus, understanding which are the factors that most influence performance has important practical implications. The quality of both the whole-body mass and its district areas’ results are determinant factors in improving physical performance related to football [39,40]. In addition, the prognostic validity of the motor test battery for adult success has been reported in different studies [28,30,41,42].

The assessment of anthropometrical evaluation and maturity status, and their change in relation to field performance, could help coaches and trainers reduce scouting biases and induce specific physiological adaptations. Although football is one of the most debated sports worldwide, further longitudinal investigations are needed to fill gaps between professional and younger players.

This study includes several limitations: (a) due to the design duration (30 months), many follow-ups could allow for looking at changes over each football season, but COVID-19 forced this design; (b) a greater sample size could improve the results, but many football players dopped out during their growth; (c) all of the training periodization characteristics should be collected and analyzed; and (d) a different design with a randomized control group could exclude the effect of some confounders that should affect adolescent growth.

5. Conclusions

The study provides results concerning changes in anthropometric characteristics, body composition, and physical fitness performance in highly trained young football players over two years. Acquisition of physical features and motor skills during youth are mandatory for juvenile progression in football. To achieve this goal, particular attention should be paid to monitoring body composition parameters related to fat and its influence on speed and power, capabilities strongly affected by age and maturation. Slenderness and muscled calves may prompt speed (wings and middle liner), while leg robustness may improve jump (goalkeeper, defender, and forward). Given the dynamic nature of the talent development process, football practitioners should regularly monitor the progress of anthropometric, body composition, and performance characteristics to improve talent identification and retention policies in youth development programs.