Multi-criteria decision-making in soccer: a bibliometric analysis

Abstract

Multi-criteria decision-making (MCDM) methods have been increasingly applied in football to support complex managerial and performance-related decisions; however, a comprehensive mapping of the research landscape is still lacking. This study examines the evolution of MCDM research in football by identifying publication trends, influential authors, leading institutions, collaboration networks, and emerging thematic clusters. A bibliometric analysis was conducted using the Web of Science Core Collection database, from which 26 relevant articles published between 2000 and 2025 were selected according to predefined inclusion criteria. Biblioshiny and VOSviewer software were employed to evaluate publication metrics, citation structures, and keyword co-occurrence patterns. The findings indicate a clear upward trajectory in both publications and citations in recent years, with recurring decision-making problems related to player selection, performance evaluation, and strategic planning. Techniques such as Technique for Order Preference by Similarity to Ideal Solution or TOPSIS and fuzzy Analytic Hierarchy Process or AHP appear most frequently, reflecting a preference for hybrid and uncertainty-based approaches. Collaboration patterns reveal geographically concentrated research hubs alongside emerging international partnerships. Overall, the results provide the first systematic overview of the intellectual and thematic structure of MCDM research in football and highlight the growing integration of artificial intelligence with traditional decision-support methods. These developments offer promising opportunities to enhance evidence-based decision-making processes in player recruitment, team performance assessment, and strategic management within professional football.