From Data to Decision: Machine Learning in Football Team Management

Sports and digital technologies have become increasingly intertwined, transforming how athletes train, how teams are managed, and how fans engage with their favourite sports. Data science in sports has emerged as a game-changer, offering insights that enable coaches to make informed decisions, improve player performance, and identify strategies that can tilt the field in their favour, effectively revolutionising traditional approaches to sports analytics and strategy development. This paper explores the integration of technology and football, both on and off the field, to unveil how machine learning and complex data analysis enhance team management and competitive edge. The research provides a case study using machine learning methodology that identifies patterns and strategies among the leading European football teams. Research is based on a dataset from the top five European football leagues during the 2021-2022 season, encompassing metrics such as goals scored, cards received, and possession statistics. Employing cluster analysis, more specifically K-means, the research has been conducted with the conclusion that top teams play with high intensity, an offensive mindset, and a focus on maintaining possession. This study demonstrates the pivotal role of machine learning in extracting actionable insights from European football data, thereby influencing strategic decisions in team management.