Table 1.
Descriptions and definitions of tactical dimensions and categories (independent variables).
Fig 1.
Graphical representation and real example of the three possible offensive outcomes.
A) No offensive penetration: The team possession does not achieve to disorder and beat the forwards or midfielders’ lines of the opposing team during the offensive sequence. B) Offensive penetration: The team possession achieves to beat the forwards and midfielders’ lines of the opponent and face directly the defensive line during the offensive sequence but the possession ends without creating any scoring opportunity. The player(s) facing the defensive line has/have enough time and space to perform intended actions on the ball at the moment of receiving the ball. C) Scoring opportunity: The team has a clear chance of scoring a goal during the ball possession. This includes all goals, all shots produced inside the score pentagon*, those shots produced outside the score pentagon that pass near the goal (evaluated qualitatively) and all chances of shooting inside the score pentagon (the player is facing the goal, there are not any opponents between him and the goal and he has enough space and time to make a playing decision). * Score pentagon is used as the zone of reference because it selects the space with high shooting angle and a short distance to goal (20 meters or less) which are very important factors to achieve goals [33, 34].
Fig 2.
Hierarchical data structure, in which team possessions are nested in teams.
Table 2.
Descriptive characteristics of the sample.
Table 3.
Baseline model (Intercept) for the prediction of high penetration vs no penetration and scoring opportunity vs no scoring opportunity.
Table 4.
Random effects of team identity on achieving high penetration vs no offensive penetration and scoring opportunity vs no scoring opportunity.
Table 5.
Multilevel binary logistic regression predicting to achieve high penetration vs low penetration (reference category).
Fig 3.
Predicted probabilities to create offensive penetration according to different tactical dimensions after adjusting for the variables included in the multivariate analysis.
Table 6.
Multilevel binary logistic regression predicting to achieve scoring opportunity vs no scoring opportunity (reference category).
Fig 4.
Predicted probabilities to create a scoring opportunity according to the type of attack after adjusting for the variables included in the multivariate analysis.