Sports Medicine

, Volume 38, Issue 9, pp 703–714 | Cite as

Talent Identification and Development Programmes in Sport

Current Models and Future Directions
  • Roel VaeyensEmail author
  • Matthieu Lenoir
  • A. Mark Williams
  • Renaat M. Philippaerts
Leading Article


Many children strive to attain excellence in sport. However, although talent identification and development programmes have gained popularity in recent decades, there remains a lack of consensus in relation to how talent should be defined or identified and there is no uniformly accepted theoretical framework to guide current practice. The success rates of talent identification and development programmes have rarely been assessed and the validity of the models applied remains highly debated. This article provides an overview of current knowledge in this area with special focus on problems associated with the identification of gifted adolescents. There is a growing agreement that traditional cross-sectional talent identification models are likely to exclude many, especially late maturing, ‘promising’ children from development programmes due to the dynamic and multidimensional nature of sport talent. A conceptual framework that acknowledges both genetic and environmental influences and considers the dynamic and multidimensional nature of sport talent is presented. The relevance of this model is highlighted and recommendations for future work provided. It is advocated that talent identification and development programmes should be dynamic and interconnected taking into consideration maturity status and the potential to develop rather than to exclude children at an early age. Finally, more representative realworld tasks should be developed and employed in a multidimensional design to increase the efficacy of talent identification and development programmes.


Multidimensional Nature Senior Level Talent Development Talent Identification Youth Soccer Player 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank Dr Aaron Coutts, Professor Robert M. Malina and Professor Thomas Reilly for providing comments on earlier drafts of the paper. No sources of funding were used to assist in the preparation of this article. The authors have no conflicts of interest that are directly relevant to the content of this article.


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Copyright information

© Adis Data Information BV 2008

Authors and Affiliations

  • Roel Vaeyens
    • 1
    • 2
    Email author
  • Matthieu Lenoir
    • 1
  • A. Mark Williams
    • 2
  • Renaat M. Philippaerts
    • 1
  1. 1.Department of Movement and Sports Sciences, Faculty of Medicine and Health SciencesGhent UniversityBelgium
  2. 2.Research Institute for Sport and Exercise SciencesLiverpool John Moores UniversityUK

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