Combined effect of game position and body size on network-based centrality measures performed by young soccer players in small-sided games

Abstract

This study verified the effects of body size and game position on interactions performed by young soccer players in small-sided games (SSG). The sample consisted of 81 Brazilian soccer players (14.4 ± 1.1 years of age). Height, body mass, and trunk-cephalic height were measured. SSG was applied in the GK + 3v3 + GK format, and Social Network Analyses were carried out through filming the games to obtain the following prominence indicators: degree centrality, closeness centrality, degree prestige, and proximity prestige, in addition to network intensity and number of goals scored. Factorial ANCOVA (bone age as covariate) was used to test the effects of game position, body size, and respective interaction on centrality measurements (p < 0.05). Similarity between game positions in body size indicators (p > 0.05) was observed. The game position affected degree centrality (p = 0.01, η2 = 0.16), closeness centrality (p = 0.01, η2 = 0.11), and network intensity (p = 0.02, η2 = 0.09), in which midfielders presented the highest network prominence values when compared to defenders and forwards. In conclusion, midfielders are players with high interaction patterns in the main offensive plays, which behavior is independent of body size.

Using Social Network Analysis to Study Relationships Between Young Soccer Players’ Bone Age, Body Size, and the Centrality of Their Pass Interactions

“…After constructing adjacency matrices, we entered data into the Social Network Visualizer® software (SocNetV 1.9 (C) 2005–2015 by Dimitris V., Kalamaras) for visualization and graph analysis. We obtained and entered the following information: (a) degree of centrality, which can be interpreted in the sporting context as the number of connections made by the player within the network (Clemente et al, 2015); (b) closeness centrality, which is an index of the node’s (player) capacity to access or send information to other nodes in the network—or, in this sporting context also an index of proximity, defined by the players’ passes with other teammates (Clemente et al, 2015); (c) degree of prestige, which refers to the number of passes that the player receives within the network, with high prestige values indicating the receipt of many links from other players; and (d) proximity prestige, which in the sporting context can be interpreted as the distance of other teammates from a certain player, suggesting that a player with high proximity prestige values may receive more passes (Borges et al, 2022; Clemente, Martins et al, 2016).…”