Percorrer por autor "Machado, Guilherme"
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- Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic reviewPublication . Teixeira, José Eduardo; Maio, Eduardo; Afonso, Pedro; Encarnação, Samuel; Machado, Guilherme; Morgans, Ryland; Barbosa, Tiago M.; Monteiro, António M.; Forte, Pedro; Ferraz, Ricardo; Branquinho, LuísFootball, as a dynamic and complex sport, demands an understanding of tactical behaviors to excel in training and competition. Artificial intelligence (AI) has evolutionized the tactical performance analysis in football, offering unprecedented data analytics insights for players, coaches, and analysts. This systematic review aims to examine and map out the current state of research on AI-based tactical behavior, collective dynamics, and movement patterns in football. A total of 2,548 articles were identified following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and the Population-Intervention-Comparators-Outcomes framework. By synthesizing findings from 32 studies, this review elucidates the available AI-based techniques to analyze tactical behavior and identify the collective dynamic based on artificial neural networks, deep learning, machine learning, and timeseries techniques. Concretely, the tactical behavior was expressed by spatiotemporal tracking data using convolutional neural networks, recurrent neural networks, variational recurrent neural networks, and variational autoencoders, Delaunay method, player rank, hierarchical clustering, logistic regression, XGBoost, random forest classifier, repeated incremental pruning produce error reduction, principal component analysis, and T-distributed stochastic neighbor embedding. Furthermore, collective dynamics and patterns were mapped by graph metrics such as betweenness centrality, eccentricity, efficiency, vulnerability, clustering coefficient, and page rank, expected possession value, pitch control map classifier, computer vision techniques, expected goals, 3D ball trajectories, dangerousity assessment, pass probability model, and total passes attempted. The performance of technicaltactical key indicators was expressed by team possession, team formation, team strategy, team-space control efficiency, determining team formations, coordination patterns, analyzing player interactions, ball trajectories, and pass effectiveness. In conclusion, the AI-based models can effectively reshape the landscape of spatiotemporal tracking data into training and practice routines with real-time decision-making support, performance prediction, match management, tactical-strategic thinking, and training task design. Nevertheless, there are still challenges for the real practical application of AI-based techniques, as well as ethical regulation and the formation of professional profiles that combine sports science, data analytics, computer science, and coaching expertise.
- Match-to-Match Variation on High-Intensity Demands in a Portuguese Professional Football TeamPublication . Teixeira, José Eduardo; Branquinho, Luís; Leal, Miguel; Morgans, Ryland; Sortwell, Andrew; Barbosa, Tiago M.; Monteiro, A.M.; Afonso, Pedro; Machado, Guilherme; Encarnação, Samuel; Ferraz, Ricardo; Forte, PedroThe aim of this study was to analyze the match-to-match variation in high-intensity demands from one Portuguese professional football team according to playing positions. Twentythree male outfield professional football players were observed during eighteen matches of the Portuguese Second League. Time–motion data were collected using Global Positioning System (GPS) technology. Match running performance was analyzed based on the following three playing positions: defenders (DF), midfielders (MF), and forwards (FW). Repeated measures ANOVA was utilized to compare match running performance within each position role, and seasonal running variation. Practical differences were assessed using the smallest worthwhile change (SWC), coefficient of variation (CV), and twice the coefficient of variation (2CV). Significant differences were found among playing positions in total distance covered (F = 15.45, p < 0.001, η2 = 0.33), average speed (F = 12.79, p < 0.001, η2 = 0.29), high-speed running (F = 16.93, p < 0.001, η2 = 0.36), sprinting (F = 13.49, p < 0.001, η2 = 0.31), accelerations (F = 4.69, p = 0.001, η2 = 0.132), and decelerations (F = 12.21, p < 0.001, η2 = 0.284). The match-to-match running performance encompassed TD (6.59%), AvS (8.67%), HSRr (37.83%), SPR (34.82%), ACC (26.92%), and DEC (27.85%). CV values for total distance covered ranged from 4.87–6.82%, with forwards and midfielders exhibiting the greatest and smallest variation, respectively. Midfielders demonstrated the highest match-to-match variation for all other analyzed variables (8.12–69.17%). All playing positions showed significant variation in high-demanding variables (26.94–37.83%). This study presents the initial analysis of match-tomatch variation in high-intensity demands within a Portuguese professional football team. Thus, the position’s specificity and context can provide a helpful strategy for evaluating match-to-match running performance, and for recommending individualized training exercises based on the peak and high-intensity demands for each player’s role within the game.