Jaime E Sampaio1, Carlos Lago2, Bruno Gonçalves3, Victor M Maçãs3, Nuno Leite3. 1. Research Centre for Sports Sciences, Health and Human Development, Vila Real, Portugal; Department of Sports Sciences, Exercise and Health, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal. Electronic address: ajaime@utad.pt. 2. Faculty of Educational Sciences and Sports, University of Vigo, Pontevedra, Spain. 3. Research Centre for Sports Sciences, Health and Human Development, Vila Real, Portugal; Department of Sports Sciences, Exercise and Health, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal.
Abstract
OBJECTIVES: To compare time-motion variables, heart rate and players' tactical behaviour according to game pace (slow, normal or fast), status (winning and losing) and team unbalance (superiority and inferiority) in football 5-a-side small-sided games. To identify the most discriminating variables in classifying performances according to these constraints. DESIGN: Cross-sectional field study. METHODS: The data were gathered using global positioning systems (5 Hz) in 5-a-side small-sided games (7 × 5 min) played by twenty-four footballers. The tactical performance was measured using dynamical positioning variables, processed by non-linear signal processing techniques (approximate entropy). ANOVA models were used to compare between constraints and discriminant analyses to identify the variables that best discriminate between pacing and status × unbalance constraints. RESULTS: The fast paced games had the highest mean speed value, followed by normal and slow paced games (8.2 ± 0.6 km h(-1), 7.8 ± 0.5 km h(-1) and 6.2 ± 0.4 km h(-1), respectively). The stronger predictor variables of pacing were the randomness in distance to team centroid and the distances covered above 13 km h(-1). The results also changed according to game status and team unbalance. The strongest predictor variables were the distance covered below 6.9 km h(-1), distance and randomness to team centroid, with higher values when winning in superiority conditions. CONCLUSIONS: Practice task design manipulating game pace, status and team unbalance significantly influenced the emergent behavioural dynamics. Collective positioning variables were more accurate in discriminating these constraints and, therefore, need to be considered when planning and monitoring performance.
OBJECTIVES: To compare time-motion variables, heart rate and players' tactical behaviour according to game pace (slow, normal or fast), status (winning and losing) and team unbalance (superiority and inferiority) in football 5-a-side small-sided games. To identify the most discriminating variables in classifying performances according to these constraints. DESIGN: Cross-sectional field study. METHODS: The data were gathered using global positioning systems (5 Hz) in 5-a-side small-sided games (7 × 5 min) played by twenty-four footballers. The tactical performance was measured using dynamical positioning variables, processed by non-linear signal processing techniques (approximate entropy). ANOVA models were used to compare between constraints and discriminant analyses to identify the variables that best discriminate between pacing and status × unbalance constraints. RESULTS: The fast paced games had the highest mean speed value, followed by normal and slow paced games (8.2 ± 0.6 km h(-1), 7.8 ± 0.5 km h(-1) and 6.2 ± 0.4 km h(-1), respectively). The stronger predictor variables of pacing were the randomness in distance to team centroid and the distances covered above 13 km h(-1). The results also changed according to game status and team unbalance. The strongest predictor variables were the distance covered below 6.9 km h(-1), distance and randomness to team centroid, with higher values when winning in superiority conditions. CONCLUSIONS: Practice task design manipulating game pace, status and team unbalance significantly influenced the emergent behavioural dynamics. Collective positioning variables were more accurate in discriminating these constraints and, therefore, need to be considered when planning and monitoring performance.
Authors: Diogo Coutinho; Bruno Gonçalves; Hugo Folgado; Bruno Travassos; Sara Santos; Jaime Sampaio Journal: PLoS One Date: 2022-06-24 Impact factor: 3.752
Authors: Luis Branquinho; Ricardo Ferraz; Bruno Travassos; Daniel A Marinho; Mário C Marques Journal: Sports Health Date: 2021-02-23 Impact factor: 3.843