Predicting fatigue in football matches

• Autor(a) principal: Ferreira, Filipe Correia
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10071/27144
• topic: Physical performance; Player monitoring; Fatigue monitoring; Fatigue identification; Soccer; Football; Predictive models; Fatigue prediction; GPS tracking; GPS positioning; Performance física; Monitorização de atletas; Monitorização de fadiga; Identificação de fadiga; Futebol; Modelos preditivos; Previsão de fadiga; Rastreamento por GPS; Posicionamento GPS

Football coaches must inevitably prepare their strategies not only for the upcoming match but also be prepared to restructure their strategies during the match itself. But there's more than that. The coach's duties extend beyond the four lines on weekends. The coach and his staff must plan a full week of training while considering everyone's current physical capabilities to avoid excessive fatigue or an injury that could prevent the player from being eligible to play for weeks. It is unquestionably in a coach's best interest to keep his players from being hurt or over-exhausted to the point where they are no longer suitable for the upcoming game. Fatigue or injuries are occurrences that can have a negative impact on every club stakeholder. Following investigations conducted by notable researchers like Bangsbo et al. (2006), Krustrup et al. (2010), Mohr et al. (2003), Rampini et al. (2009), Novak et al. (2021), and others, it was possible to achieve highly encouraging results that could actually be useful for coaches by providing them with knowledge of their players' degrees of fatigue in anticipation by developing a predictive model for three different time periods of anticipation (5-minutes; 10-minutes and 20-minutes) based on accurate monitoring of the players' GPS positions. Academically, it is believed that this work will open the door for more research initiatives of this kind as it was among the first, if not the first, to integrate real data to develop a realistic prediction model that could be used to evaluate fatigue.