How Big Data can help football clubs achieve competitive advantage

• Autor(a) principal: Henriques, Francisco Jorge Bettencourt
• Data de Publicação: 2018
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.14/25412
• topic: Big Data; Competitive advantage, football clubs, injury, prevention, noncontact, decision-making; Football clubs; Injury; Prevention; Noncontact; Decision-making; Vantagem competitiva; Clubes de futebol; Lesões; Prevenção; Não-contacto; Tomada de decisão; Domínio/Área Científica::Ciências Sociais::Economia e Gestão

Integrated in a naturally competitive environment, football clubs will thrive based on the ability to generate competitive advantage. With the industry experiencing technological advancements that permit the dissection of athlete and team performances through modern tracking tools, the data inherent to the activity is increasingly relevant to model the approach towards competition. This thesis sets out to explore how Big Data can be advantageous for football clubs’ performance, by extending the availability of the best talent in the team with enhanced injury preventive mechanisms. It also analyses how the decision-making process of the players can be bettered to extract the most value from every game situation. The connection of secondary data sources, both empirical and theoretical, hints on how football clubs can mobilize specialized resources to deliver a differentiating, performance-enhancing protocol. Despite denser competitive calendars, overall injury rates have declined in elite European football clubs between 2001 and 2017, suggesting that improved training methods helped control the occurrence of preventable injuries. In addition, an increasingly faster game promoted the need of having quick-thinking athletes, able to anticipate in-game scenarios and comply with the tactical demands of competition. With injury linked to poorer performance and decision-making associated with a player’s ability to perform at high-level, the Big Data applications explored in this thesis are reinforced as they have the potential to promote the marginal gains that unbalance a low-scoring sport.