Using multi-criteria decision-making and machine learning for football player selection and performance prediction: a systematic review

• Published in: Data science and management Vol. 7; no. 2; pp. 79 - 88
• Main Authors: Ati, Abdessatar, Bouchet, Patrick, Ben Jeddou, Roukaya
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2024; Elsevier; KeAi Communications Co. Ltd
• Subjects: Computer Science; Football player selection; Humanities and Social Sciences; Machine learning; Managerial-financial and sporting performance; Multi-criteria decision-making; Football player selection; Multi-criteria decision-making; Machine learning; Managerial-financial and sporting performance; managerial-financial and sporting performance; multi-criteria decision-making; machine learning; football player selection
• ISSN: 2666-7649; 2666-7649
• DOI: 10.1016/j.dsm.2023.11.001

Evaluating and selecting players to suit football clubs and decision-makers (coaches, managers, technical, and medical staff) is a difficult process from a managerial-financial and sporting perspective. Football is a highly competitive sport where sponsors and fans are attracted by success. The most successful players, based on their characteristics (criteria and sub-criteria), can influence the outcome of a football game at any given time. Consequently, the D-day of selection should employ a more appropriate approach to human resource management. To effectively address this issue, a detailed study and analysis of the available literature are needed to assist practitioners and professionals in making decisions about football player selection and hiring. Peer-reviewed journals were selected for collecting published papers between 2018 and 2023. A total of 66 relevant articles (journal articles, conference articles, book sections, and review articles) were selected for evaluation and analysis. The purpose of the study is to present a systematic literature review (SLR) on how to solve this problem and organize the published research papers that answer our four research questions.