R. D. L. Torre, Laura Calvet, David López-López, A. Juan, Sara Hatami
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引用次数: 0
Abstract
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.
期刊介绍:
The main objective of the International Journal of Business Analytics (IJBAN) is to advance the next frontier of decision sciences and provide an international forum for practitioners and researchers in business and governmental organizations—as well as information technology professionals, software developers, and vendors—to exchange, share, and present useful and innovative ideas and work. The journal encourages exploration of different models, methods, processes, and principles in profitable and actionable manners.