Torre Martínez, María del Rocío de laCalvet, Laura O.López López, DavidJuan, Ángel A.Hatami, Sara2023-02-142023-02-142021de la Torre, R., Calvet, L. O., Lopez-Lopez, D., Juan, A. A., & Hatami, S. (2021). Business analytics in sport talent acquisition: Methods, experiences, and open research opportunities. International Journal of Business Analytics, 9(1), 1-20. https://doi.org/10.4018/IJBAN.2904062334-454710.4018/IJBAN.290406https://academica-e.unavarra.es/handle/2454/44685Recruitment of young talented players is a critical activity for most professional teams in different sports such as football, soccer, basketball, baseball, cycling, etc. In the past, the selection of the most promising players was done just by relying on the experts' opinions but without systematic data support. Nowadays, the existence of large amounts of data and powerful analytical tools have raised the interest in making informed decisions based on data analysis and data-driven methods. Hence, most professional clubs are integrating data scientists to support managers with data-intensive methods and techniques that can identify the best candidates and predict their future evolution. This paper reviews existing work on the use of data analytics, artificial intelligence, and machine learning methods in talent acquisition. A numerical case study, based on real-life data, is also included to illustrate some of the potential applications of business analytics in sport talent acquisition. In addition, research trends, challenges, and open lines are also identified and discussed.application/pdfengThis article published as an Open Access article distributed under the terms of the Creative Commons Attribution License which permits unrestricted use, distribution, and production in any medium, provided the author of the original work and original publication source are properly credited.Business AnalyticsMachine LearningSportsTalent Acquisitioninfo:eu-repo/semantics/article2023-02-14info:eu-repo/semantics/openAccess