Business Analytics in Sport Talent Acquisition: Methods, Experiences, and Open Research Opportunities

Laura O. Calvet, Maria del Rocio De La Torre Martinez, Sara Hatami, Angel A. Juan, David Lopez Lopez

Producció científica: Article en revista no indexadaArticle

Resum

Recruitment 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' opinion, but without a 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.
Idioma originalAnglès
Pàgines1-20
Nombre de pàgines20
Volum9
Núm.1
Publicació especialitzadaInternational Journal of Business Analytics
DOIs
Estat de la publicacióPublicada - 2022
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