Abstract
The analyses of players and teams’ behaviors during the FIFA World Cup may provide a better understanding on how football tactics and strategies have developed in the past few years in elite football. The Social Network Analysis (SNA) has been carried out in the investigations about passing distribution, improving the understanding on how players interact and cooperate during a match. In football official matches, studies have used the SNA as a means of coding players’ cooperation and opposition patterns. However, situational variables such as match status were previously investigated and associated with changes on teams’ dynamics within and/or between matches, but were not considered in studies based on Social Network Analysis. This study aimed to analyze the influence of match status on teams’ cooperation patterns and players’ prominence according to playing positions during 2018 FIFA World Cup. Fourteen matches of the knockout stage were analyzed. Macro and micro network measures were obtained from adjacency matrixes collected for each team, in each match status (winning, drawing, and losing). A one-way ANOVA was used to compare teams’ networks (macro-analysis variables) within each match status, while a two-way ANOVA (match status × playing position) was used to compare the micro-analysis variables. Results showed no differences between match status for macro analysis. Winning situations induced higher prominence in central midfielders (0.107; p = 0.001), wide midfielders (0.093; p = 0.001), and center forward (0.085; p = 0.001), while in losing situations lower prominence levels were observed for goalkeepers (0.044; p = 0.001) and center forward (0.074; p = 0.001). Data revealed that teams do not change macrostructures according to match status. On the other hand, the microstructures showed important adaptations regarding game styles, with changes in players’ behaviors according to playing positions. In general, the levels of centrality and prestige in players of different positions indicated a more direct play style in winning situations and a more build-up style in losing situations. These results allow a better understanding about the influence of match status on players’ and teams’ performance during high-level football competitions and may help coaches to improve athletes’ performance in these situations.
Highlights
Notational match analysis provides valuable information about game dynamics (Hughes and Franks, 1997; Lago, 2009) and may help coaches to optimize training contents (Sarmento et al, 2015)
The comparison between match statuses within each playing position showed that GK (r = 0.286; small-to-medium effect) and centre forward (CF) (r = 0.320; medium-to-large effect) presented significantly higher values in winning situations compared to losing, while central defenders (CD) presented lower values in winning situations in comparison to drawing (r = 0.309 medium-to-large effect) and losing (r = 0.266; small-to-medium effect)
Micro variables can be affected by the differences in passing distribution
Summary
Notational match analysis provides valuable information about game dynamics (Hughes and Franks, 1997; Lago, 2009) and may help coaches to optimize training contents (Sarmento et al, 2015). SNA applied to match analysis uses only the “last snapshot,” that is, the network resulting from the aggregate of all the interactions occurring during the entire match, focusing more on its structure and less on its dynamics (Ramos et al, 2018). Situational variables such as match venue (GarcíaRubio et al, 2015), quality of the opposition (Taylor et al, 2008) and match status (Lago, 2009; Marcelino et al, 2011) were previously investigated and associated with changes on teams’ dynamics within and/or between matches. The SNA may be performed during specific game phases, by including situational variables in the analysis, providing a broader and deeper understanding of players’ dynamics within a game
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