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The aim of this study was to propose a set of network methods to measure the specific properties of a team. These metrics were organised at macro-analysis levels. The interactions between teammates were collected and then processed following the analysis levels herein announced. Overall, 577 offensive plays were analysed from five matches. The network density showed an ambiguous relationship among the team, mainly during the 2nd half. The mean values of density for all matches were 0.48 in the 1st half, 0.32 in the 2nd half and 0.34 for the whole match. The heterogeneity coefficient for the overall matches rounded to 0.47 and it was also observed that this increased in all matches in the 2nd half. The centralisation values showed that there was no 'star topology'. The results suggest that each node (i.e., each player) had nearly the same connectivity, mainly in the 1st half. Nevertheless, the values increased in the 2nd half, showing a decreasing participation of all players at the same level. Briefly, these metrics showed that it is possible to identify how players connect with each other and the kind and strength of the connections between them. In summary, it may be concluded that network metrics can be a powerful tool to help coaches understand team's specific properties and support decision-making to improve the sports training process based on match analysis. [ABSTRACT FROM AUTHOR]

In the last few years, the collective analysis in football teams has been focused on their complex reality. This study aimed to propose 4 collective metrics based on the spatio-temporal relationship, trying to identify differences between the moments with and without ball possession. A U14 7-a-side football match was analysed. Significant differences were found between the moments with and without ball possession regarding the space covered by team A (F(1, 1506)= 8.31, p= 0.004, η²= 0.005, Power= 0.82) and B (F(1, 1506)= 37.66, p= 0.001, η²= 0.024, Power= 1.00), effective area of play on team A (F(1, 1506)= 1343.89, p= 0.001, η²= 0.472, Power= 1.000) and B (F(1, 1506)= 968.50, p= 0.001, η²= 0.391; Power= 1.00), as well as at centroid position for team A (F(1. 1506)= 11.79, p= 0.001, η²= 0.008, Power= 0.93) and B (F(1, 1506)= 9.43, p= 0.001, η²= 0.006, Power= 0.87). As a conclusion, it was possible to analyse the spatio-temporal relationship between teammates, identifying expansion behaviour with ball possession, as well as a centroid progression at the field with the collective metrics selected. [ABSTRACT FROM AUTHOR]

In the last few years, the collective analysis in football teams has been focused on their complex reality. This study aimed to propose 4 collective metrics based on the spatio-temporal relationship, trying to identify differences between the moments with and without ball possession. A U14 7-a-side football match was analysed. Significant differences were found between the moments with and without ball possession regarding the space covered by team A (F(1, 1506)= 8.31, p= 0.004, η²= 0.005, Power= 0.82) and B (F(1, 1506)= 37.66, p= 0.001, η²= 0.024, Power= 1.00), effective area of play on team A (F(1, 1506)= 1343.89, p= 0.001, η²= 0.472, Power= 1.000) and B (F(1, 1506)= 968.50, p= 0.001, η²= 0.391; Power= 1.00), as well as at centroid position for team A (F(1. 1506)= 11.79, p= 0.001, η²= 0.008, Power= 0.93) and B (F(1, 1506)= 9.43, p= 0.001, η²= 0.006, Power= 0.87). As a conclusion, it was possible to analyse the spatio-temporal relationship between teammates, identifying expansion behaviour with ball possession, as well as a centroid progression at the field with the collective metrics selected. [ABSTRACT FROM AUTHOR]

Optimizing athlete's performance is one of the most important and challenging aspects of coaching. Physiological and positional data, often acquired using wearable devices, have been useful to identify patterns, thus leading to a better understanding of the game and, consequently, providing the opportunity to improve the athletic performance. Even though there is a panoply of research in pattern recognition, there is a gap when it comes to non-controlled environments, as during sports training and competition. This research paper combines the use of physiological and positional data as sequential features of different artificial intelligence approaches for action recognition in a real match context, adopting futsal as its case study. The traditional artificial neural networks (ANN) is compared with a deep learning method, Long Short-Term Memory Network, and also with the Dynamic Bayesian Mixture Model, which is an ensemble classification method. The methods were used to process all data sequences, which allowed to determine, based on the balance between precision and recall, that Dynamic Bayesian Mixture Model presents a superior performance, with an F1 score of 80.54% against the 33.31% achieved by the Long Short-Term Memory Network and 14.74% achieved by ANN. [ABSTRACT FROM AUTHOR]