Leveraging Machine Learning for Enhanced Performance in Football
Keywords:
Budget allocation, Clustering, Machine based learning, Network analysis, Ownership percentage, Python, Total pointsAbstract
Data-based decision-making is also becoming a key function in football management, particularly in optimizing player transfers within a budget. This study looks at a data model of player recruitment and team improvement in the context of Chelsea FC. Based on data from Transfermarkt and Understat, the study analyzes factors such as players' market value, player and team performance data, and tactical performance. This case study identifies the use of data integration within contemporary football management and how advice that has been extrapolated from diverse sources is able to inform recruitment, selection of the team, and tactical strategy as a whole. This research adds to a broad base of work examining how clubs are able to enhance their transfer policy through the utilization of machine learning, statistical analysis, and modeling of finances in their scouting and recruitment.
Leveraging data from Transfermarkt and Understat, the study examines parameters like players' market value, player and team performance metrics, and tactical performance. This case study recognizes the application of data integration in modern football management and how recommendations that have been extrapolated from varied sources can be utilized to shape recruitment, team selection, and overall tactical approach. The research contributes to a general body of work on how clubs can improve their transfer policy by making use of machine learning, analysis of stats, and financial modeling in their scouting and recruitment.
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