Abstract
Introduction: Network analysis has gained increasing attention, as it provides a framework for identifying both collective and individual behaviours within the football teams.
Objective: This study aimed to analyse the offensive actions that resulted in shots using network analysis in a Portuguese First Division football team during the 2020-2021 season.
Methodology: All 34 matches were coded using Angles® software. Offensive actions were defined as sequences starting with a ball recovery and ending with a shot. Adjacency matrices were constructed for each match, and both macro and micro analytical approaches were employed to examine differences between the two halves of the season.
Results: Findings indicated 914 intra-team interactions, with player 14 (midfielder) and player 2 (forward) as key contributors, particularly in micro network metrics such as degree prestige (passes received) and degree centrality (passes made). Statistical analysis revealed no significant differences in network metrics, including density (W = 95, p = 0.0912) and clustering coefficient (W = 112, p = 0.2689), between the season halves.
Discussion: These findings offer valuable insights for practitioners seeking in recognizing play patterns and optimizing team dynamics. Identifying key players allows coaches to design targeted training exercises, enhance player roles, and better assess opposition threats and vulnerabilities.
Conclusions: Network metrics provides a comprehensive understanding of team dynamics, particularly in identifying key contributors to offensive actions.
Objective: This study aimed to analyse the offensive actions that resulted in shots using network analysis in a Portuguese First Division football team during the 2020-2021 season.
Methodology: All 34 matches were coded using Angles® software. Offensive actions were defined as sequences starting with a ball recovery and ending with a shot. Adjacency matrices were constructed for each match, and both macro and micro analytical approaches were employed to examine differences between the two halves of the season.
Results: Findings indicated 914 intra-team interactions, with player 14 (midfielder) and player 2 (forward) as key contributors, particularly in micro network metrics such as degree prestige (passes received) and degree centrality (passes made). Statistical analysis revealed no significant differences in network metrics, including density (W = 95, p = 0.0912) and clustering coefficient (W = 112, p = 0.2689), between the season halves.
Discussion: These findings offer valuable insights for practitioners seeking in recognizing play patterns and optimizing team dynamics. Identifying key players allows coaches to design targeted training exercises, enhance player roles, and better assess opposition threats and vulnerabilities.
Conclusions: Network metrics provides a comprehensive understanding of team dynamics, particularly in identifying key contributors to offensive actions.
| Original language | English |
|---|---|
| Pages (from-to) | 1045-1055 |
| Number of pages | 11 |
| Journal | Retos |
| Volume | 65 |
| DOIs | |
| Publication status | Published - 24 Feb 2025 |