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This study aimed to analyse the effect of game format and age-group on positioning and displacement of soccer players (age ranging from 6.94 ± 0.7 to 13.46 ± 0.5 years; height ranging from 125.36 ± 6.04 to 159.16 ± 7.78 cm; weight ranging from 27.16 ± 5.75 to 49.89 ± 8.89 kg). Linear and non-linear analyses were used to capture the spatial distribution variability and relative positioning of the players during soccer matches. Variables were assessed using global positioning system technology. Results suggest significant effect of the game formats in spatial distribution variability (η 2 = 0.142, p < 0.001) and relative positioning (η 2 = 0.926; p < 0.001) of the players. The variability decreased and mean covered area increased as game format increased. There also was a significant effect of the age-group in spatial distribution variability (η 2 = 0.120, p < 0.001) and relative positioning (η 2 = 0.405; p < 0.001). The U10 age-group presented significantly higher values than other age-groups (p < 0.001). These findings can provide an opportunity for coaches and governmental bodies to maximise the efficiency of the soccer matches conditions.

The aim of this study was to analyze the effect of different pitch surfaces (artificial turf, natural turf and dirt field) on positioning and displacement of young soccer players (age: 13.4 ± 0.5 yrs; body height: 161.82 ± 7.52 cm; body mass: 50.79 ± 7.22 kg and playing experience: 3.5 ± 1.4 yrs). Data were collected using GPS units which allowed to calculate spatial distribution variability, assessed by measuring entropy of individual distribution maps (ShannEn). Ellipsoidal areas (m2) representing players’ displacement on the pitch, centred on the average players’ positional coordinates, were also calculated, with axes corresponding to the standard deviations of the displacement in the longitudinal and lateral directions. Analysis of variance (ANOVA) was used to evaluate differences between pitch surfaces and across players’ positions. There was significant effect in positioning (η2 = 0.146; p < 0.001) and displacement (η2 = 0.063; p < 0.05) by the players between pitch surfaces. A dirt field condition induced an increase in the players’ movement variability, while players’ displacement was more restricted when playing on artificial turf. Also, there were significant effects on positioning (η2 = 0.496; p < 0.001) and displacement (η2 = 0.339; p < 0.001) across players’ positions. Central midfielders presented the greatest movement variability and displacement while fullbacks showed the lowest variability. Subsequently, the results may contribute to implement strategies that optimise players’ performance in different surface conditions.

-Aims:The aim of this study was to verify how European countries manage the type of game variants and their frequency along youth competitive stages. Methods: Data were collected from the official rules of youth football championships. To identify countries homogenous groups according to their game variants, Two Step Cluster Analysis procedure was used while a nonparametric Kruskal-Wallis test was used to compare the game variants distribution in each Cluster. In order to correlate the game variants with age groups, a Chi-Square independence test and a Spearman ordinal correlation coefficient were used. Results: The results showed there were five clusters with significant differences in their game variants distribution (X 2 kw (4) = 22.149; p<0.001; n = 30) and a significant correlation between age group and game variant (χ 2 (63) = 477.724; p<0.001; n = 30). Specifically, the most used game variants in each age group were the five-a-side (F5) in Under-8; the nine-a-side (F9) in Under-12; the seven-a-side (F7) in Under-9 and Under10; and the eleven-a-side (F11) in and above Under-13. Conclusion: These results may contribute to understand the different country perspectives about the competitive game variants of youth football within the European space and its relation with diverse learning philosophies and pathways.