Influence of Aerobic Power on Youth Players' Tactical Behavior and Network Properties during Football Small-Sided Games

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

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.

Fitness improvements of young soccer players after high volume or small sided games interventions

The main goal was to determine anaerobic and aerobic improvement of young soccer players after six-week high volume (HVT) or small sided games (SSG) training intervention. One hundred and one highly trained youth soccer players (16.2 ± 1.3 years) were divided into SSG (n = 51) and HVT groups (n = 50) and according to age into an under sixteen subgroup (U16), under seventeen subgroup (U17), and under nineteen subgroup (U19). The performance was assessed by Yo-Yo intermittent test, Repeated sprint ability test (RSA), and K-test before and after both training interventions. For U16 the SSG group recorded significant improvements in the K-test (0.64 ± 0.56 s; p = .04) and RSA (0.15 ± 0.43 s; p = .01). For U19 the SSG group recorded the same improvements, in the K-test (0.43 ± 0.57 s; p = .007), RSA (0.21 ± 0.22 s; p = .048), and Yo-Yo test (127.25 ± 17.87; p = .049). HVT improved aerobic performance when the Yo-Yo test was significantly better after intervention at U17 (199.00 ± 111.83 m; p = .030), U19 (88.40 ± 66.38 m; p = .049). In total, the HVT group spent 621 min (56.45 ± 5.01 min) of aerobic training and the Small sided game group spent 291 min (26.45 ± 8.61 min) of small sided games focused on aerobic performance. This study showed that both SSG and HVT training interventions were effective for aerobic improvement for the U19 category, but not for younger players. SSG was identified to be more appropriate to fitness development of soccer players.

The effect of bio-banding on academy soccer player passing networks: Implications of relative pitch size

The primary aims of this study were to examine the effects of bio-banding players on passing networks created during 4v4 small-sided games (SSGs), while also examining the interaction of pitch size using passing network analysis compared to a coach-based scoring system of player performance. Using a repeated measures design, 32 players from two English Championship soccer clubs contested mixed maturity and bio-banded SSGs. Each week, a different pitch size was used: Week 1) small (36.1 m2 per player); week 2) medium (72.0 m2 per player); week 3) large (108.8 m2 per player); and week 4) expansive (144.50 m2 per player). All players contested 12 maturity (mis)matched and 12 mixed maturity SSGs. Technical-tactical outcome measures were collected automatically using a foot-mounted device containing an inertial measurement unit (IMU) and the Game Technical Scoring Chart (GTSC) was used to subjectively quantify the technical performance of players. Passing data collected from the IMUs were used to construct passing networks. Mixed effect models were used with statistical inferences made using generalized likelihood ratio tests, accompanied by Cohen's local f2 to quantify the effect magnitude of each independent variable (game type, pitch size and maturation). Consistent trends were identified with mean values for all passing network and coach-based scoring metrics indicating better performance and more effective collective behaviours for early compared with late maturation players. Network metrics established differences (f2 = 0.00 to 0.05) primarily for early maturation players indicating that they became more integral to passing and team dynamics when playing in a mixed-maturation team. However, coach-based scoring was unable to identify differences across bio-banding game types (f2 = 0.00 to 0.02). Pitch size had the largest effect on metrics captured at the team level (f2 = 0.24 to 0.27) with smaller pitch areas leading to increased technical actions. The results of this study suggest that the use of passing networks may provide additional insight into the effects of interventions such as bio-banding and that the number of early-maturing players should be considered when using mixed-maturity playing formats to help to minimize late-maturing players over-relying on their early-maturing counterparts during match-play.

Passing Networks and Tactical Action in Football: A Systematic Review

The aim of this study is to examine the most significant literature on network analyses and factors associated with tactical action in football. A systematic review was conducted on Web of Science, taking into account the PRISMA guidelines using the keyword “network”, associated with “football” or “soccer”. The search yielded 162 articles, 24 of which met the inclusion criteria. Significant results: (a) 50% of the studies ratify the importance of network structures, quantifying and comparing properties to determine the applicability of the results instead of analyzing them separately; (b) 12.5% analyze the process of offensive sequences and communication between teammates by means of goals scored; (c) the studies mainly identify a balance in the processes of passing networks; (d) the variables allowed for the interpretation of analyses of grouping metrics, centralization, density and heterogeneity in connections between players of the same team. Finally, a systematic analysis provides a functional understanding of knowledge that will help improve the performance of players and choose the most appropriate response within the circumstances of the game.