Maturity-associated variation within the physique dimension, bodily health, technical effectivity, and network-based centrality measures in younger soccer gamers

Experimental design

On this cross-sectional examine, knowledge assortment befell between September and October 2018 and occurred over 4 days7. On the primary day, anthropometric measurements had been carried out, adopted by the efficiency checks; on the second day, particular expertise checks, after which repeated dash’s capacity take a look at had been carried out; on day three, gamers had their wrists x-rayed; and on the final day, small-sided video games had been carried out (GK + 3 vs. 3 + GK). Gamers had been instructed to recuperate 48 h earlier than the start of information assortment, and the restoration time was 21 h between checks. Concerning the weekly coaching quantity, the U-13 class participated on common in 2 weekly coaching lasting 120 min every. The U-15 class, in flip, skilled on common 5.49 ± 0.49 weekly coaching models, lasting 120 min every.


Eighty-one younger male soccer gamers (14.37 ± 1.12 years; 58.03 ± 10.33 kg; 169.85 ± 10.04 cm) belonging to 2 soccer groups that compete at nationwide and state levels2 participated within the examine. Information assortment occurred throughout the topics’ aggressive interval, by which all athletes had been in the midst of the aggressive interval. The next inclusion standards had been adopted: (I) prepare with one of many chosen groups, (II) take part in official competitions for the membership; and (III) current the free and knowledgeable consent kind signed by dad and mom or guardians, in addition to the assent kind. Topics who offered musculoskeletal accidents throughout the analysis interval had been excluded, and people who had not accomplished all undertaking evaluations had been excluded. The examine was carried out in accordance with related tips and laws, and it was permitted by the Human Ethics Committee of the State College of Londrina (Proc. 2.650.232/2018).


Physique mass, top, and sitting top had been obtained utilizing a digital scale Seca 813®, with a precision of 0.1 kg, and a conveyable stadiometer, Harpenden®, with a precision of 0.1 cm, in line with procedures described within the literature30.

Chronological age and bone age

The chronological age of the gamers was obtained in a centesimal means from the distinction between the date of delivery and the date of the anteroposterior radiograph of the hand and wrist. Gamers had been required to take an anteroposterior radiograph of the hand and wrist in a specialised laboratory to acquire an x-ray of the left hand and wrist. Subsequently, the Tanner-Whitehouse 3 methodology was adopted to categorise 13 bones of the left hand and wrist in line with their stage of growth. Primarily based on these scores, it was used a sex-specific desk to transform scores into bone age31. A radiologist carried out the radiography, and bone age assessments was carried out by a skilled researcher.

To check the reliability of the bone age evaluation, the identical rater randomly reassessed 20 hand and wrist radiographs two weeks after the primary evaluation. The intraclass correlation coefficient discovered was 0.97, and the intra-observer error was 0.26 years. The classification of the maturity standing of younger athletes befell in two phases. Initially, the distinction between the bone age and the chronological age of the soccer gamers was obtained. Primarily based on this distinction, the pattern was divided into early (distinction > 1 12 months), “on time” (distinction ± 1 12 months), and “late” maturity gamers (distinction < − 1 year), according to the criteria suggested by the literature4. Physical fitness The Yo-Yo Intermittent Recovery Test level 1 was used to estimate the individual’s ability to perform intense exercise repeatedly. The objective of the test was to run 20 m from a cadence pre-established by audio, with 10 s of rest every 40 m walked32. The final score was expressed by the maximum distance in meters covered by the athlete. Lower limb muscle power was estimated based on a vertical jump proposed by Bosco, Luhtanen, and Komi33 called Counter Movement Jump (CMJ), performed on a contact platform (Hidrofit®) connected to the computer. The subjects performed 3 jumps, with a one-minute interval between attempts, with only the jump with the best performance being computed. The anaerobic performance of young soccer players was evaluated using the Repeated Sprints Ability (RSA) test proposed by Rampinini et al.34. This protocol consists of performing six runs of 40 m each, separated by 20 s of recovery. The running time was recorded using a set of photocells (Multisprint Full®) connected to the computer. The time spent during the six runs was computed, in seconds, for the athlete’s score. Small-sided games protocol To evaluate the tactical-technical actions, each player was filmed in the game GK + 3 vs. 3 + GK (goalkeeper + 3 players vs. 3 players + goalkeeper), being this a small-sided game in a field of 36 m by 27 m during 2 periods of 4 min each, with a 1-min break. The camcorder (Cassio® model EX-10) was located 6 m above and to one side of the pitch long axis at a distance of 15 m from the pitch. The official rules of themodality were adopted, including the offside rule, as described in Borges et al.2. Social network analysis and technical efficiency From footage of players in the game GK + 3 vs. 3 + GK, an observational protocol35 was used to analyze the interactions carried out in the small-sided games and to observe the technical actions performed by the young soccer players. Regarding social network analysis (SNA), the execution of a pass between two players was adopted as a criterion for interaction between them36. The Social Network Visualizer® software (SocNetV 1.9 © 2005–2015 by Dimitris V., Kalamaras) was used to graphically visualize the interactions between the players and obtain the following information: degree centrality, indicating the number of passes made by the player within the network; closeness centrality, which in sports context may be understood as a measure of the ability of a node (player) to access or send information to other nodes on the network; degree of prestige, concerning the number of passes that the player receives within the network; and proximity prestige, expressing the geodesic distance of other teammates from a specific player, suggesting that a player with high proximity prestige values may receive more passes from teammates in the case of a pass37. The individual technical actions of each subject were obtained from the protocol proposed by Gréhaigne, Mahut, and Fernandes38: conquered ball (CB), which refers to the action of re-conquering the ball through interception, direct recovery over the opponent, or after an unsuccessful shot on goal; offensive balls (OB), considered a pass to a teammate that puts pressure on the other team and, most of the time, leads to a shot on goal; successful kick (successful shot—SS), when the shooting action ends in a goal or ball possession returns to the attacking team; and lost ball (LB), which occurs when the player loses the ball to an opponent without having finished on goal. A specific equation was used from these indicators to obtain the technical efficiency index: (CB + OB + SS)/(10 + LB). The analysis was performed using the Lince® software (version 1.4)39. To assess the quality of the observed data, intra- and inter-rater reliability analyses were used. In this sense, intraclass correlation coefficients above 0.82 were obtained, and both assessments revealed good/excellent reliability. Statistical analysis The relationships of maturity status on indicators of body growth, physical performance, and measures of the centrality of young soccer players were tested using multivariate analysis of variance (MANOVA) after validating the assumptions of data normality and homogeneity of data variances-covariances, applied to the Kolmogorov–Smirnov tests (p > 0.05 for all teams) and M of Field for the needs talked about above. Moreover, the multivariate evaluation of covariance (MANCOVA) was utilized for variables that differed considerably and adjusted for chronological age. The above analyses had been processed utilizing the SPSS software program (v. 23, IBM SPSS, Chicago, IL), contemplating the importance set at 5%.

Pearson’s correlation coefficient and the correlational evaluation of networks had been utilized utilizing the “qgraph” package deal to confirm the diploma of relationship between the set of examine variables primarily based on the maturity standing. Correlational evaluation of networks constitutes a necessary methodological device to research advanced patterns of interplay between variables40, which permits the visualization of how the completely different dimensions that comprise sports activities efficiency work together between the completely different maturity statuses. On this evaluation, a vertex represents every variable, and hyperlinks kind the connection between them (correlation). In offered graphs, inexperienced strains point out constructive correlation, and crimson strains are unfavorable correlations. The illustration of connections with weights was adopted; that’s, the thickness of the connection is related to the energy of the correlation.

The significance of every variable in figuring out the construction of the community was noticed from the next centrality indicators: energy, which is obtained from the sum of the energy of the connections that the vertex receives; betweenness, offering details about the significance of a vertex amongst different vertices, that’s, a vertex with excessive connectivity performs a elementary function within the community, because it represents the shortest path between two different vertices; and closeness, a metric used to quantify the connection of a vertex to all different vertices, the place excessive levels of proximity point out a brief distance between the opposite vertices and symbolizes that any change on this variable rapidly impacts different elements of the network40. This evaluation was processed in R (model 4.0.0) and RStudio (model 1.2.5042) software program.

Author: ZeroToHero