Multi-attribute decision-making methods for sports performance evaluation: a review

Abstract Accepted by: Phil Scarf Multi-attribute decision-making (MADM) techniques are particularly relevant for sports performance analysis, as evaluating athletes or teams often requires weighing multiple and sometimes conflicting attributes. This study concentrates on MADM within the larger discipline of multi-criteria decision-making as it specifically applies to discrete performance variables. We examine the general characteristics of publications identified using the ‘Preferred Reporting Items for Systematic Reviews and Meta-Analyses’ method, as well as their methodological types, application domains and sports branches. Results show that the Analytic Hierarchy Process, TOPSIS, Grey Relational Analysis and PROMETHEE are the most frequently adopted methods, primarily employed for player selection and performance evaluation. Soccer, basketball and baseball are the sports in which these methods are most commonly applied. Key methodological challenges include weighting and normalization of diverse criteria, managing subjectivity and uncertainty and integrating quantitative and qualitative attributes. Emerging hybrid and fuzzy approaches demonstrate potential to overcome these limitations. This review provides coaches, managers and decision-makers with practical guidance on selecting appropriate MADM techniques, enabling systematic evaluation of diverse and often conflicting factors, including both quantitative statistics and qualitative expert judgements. Overall, the study enhances understanding of MADM applications in sports and establishes a foundation for future research in this domain.