Advanced search
1 file | 824.49 KB

Predictive models reduce talent development costs in female gymnastics

(2017) JOURNAL OF SPORTS SCIENCES. 35(8). p.806-811
Author
Organization
Abstract
This retrospective study focuses on the comparison of different predictive models based on the results of a talent identification test battery for female gymnasts. We studied to what extent these models have the potential to optimise selection procedures, and at the same time reduce talent development costs in female artistic gymnastics. The dropout rate of 243 female elite gymnasts was investigated, 5 years past talent selection, using linear (discriminant analysis) and non-linear predictive models (Kohonen feature maps and multilayer perceptron). The coaches classified 51.9% of the participants correct. Discriminant analysis improved the correct classification to 71.6% while the non-linear technique of Kohonen feature maps reached 73.7% correctness. Application of the multilayer perceptron even classified 79.8% of the gymnasts correctly. The combination of different predictive models for talent selection can avoid deselection of high-potential female gymnasts. The selection procedure based upon the different statistical analyses results in decrease of 33.3% of cost because the pool of selected athletes can be reduced to 92 instead of 138 gymnasts (as selected by the coaches). Reduction of the costs allows the limited resources to be fully invested in the high-potential athletes.
Keywords
dropout, multilayer perceptron, artificial neural networks, Artistic gymnastics, Kohonen Feature Maps, talent identification, PSYCHOLOGICAL CHARACTERISTICS, SPORT, IDENTIFICATION, PERFORMANCE, ELITE, CHAMPIONS, STRENGTH, HANDBALL

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 824.49 KB

Citation

Please use this url to cite or link to this publication:

Chicago
Pion, Johan, Andreas Hohmann, Tianbiao Liu, Matthieu Lenoir, and Veerle Segers. 2017. “Predictive Models Reduce Talent Development Costs in Female Gymnastics.” Journal of Sports Sciences 35 (8): 806–811.
APA
Pion, J., Hohmann, A., Liu, T., Lenoir, M., & Segers, V. (2017). Predictive models reduce talent development costs in female gymnastics. JOURNAL OF SPORTS SCIENCES, 35(8), 806–811.
Vancouver
1.
Pion J, Hohmann A, Liu T, Lenoir M, Segers V. Predictive models reduce talent development costs in female gymnastics. JOURNAL OF SPORTS SCIENCES. 2017;35(8):806–11.
MLA
Pion, Johan et al. “Predictive Models Reduce Talent Development Costs in Female Gymnastics.” JOURNAL OF SPORTS SCIENCES 35.8 (2017): 806–811. Print.
@article{7262815,
  abstract     = {This retrospective study focuses on the comparison of different predictive models based on the results of a talent identification test battery for female gymnasts. We studied to what extent these models have the potential to optimise selection procedures, and at the same time reduce talent development costs in female artistic gymnastics. The dropout rate of 243 female elite gymnasts was investigated, 5 years past talent selection, using linear (discriminant analysis) and non-linear predictive models (Kohonen feature maps and multilayer perceptron). The coaches classified 51.9% of the participants correct. Discriminant analysis improved the correct classification to 71.6% while the non-linear technique of Kohonen feature maps reached 73.7% correctness. Application of the multilayer perceptron even classified 79.8% of the gymnasts correctly. The combination of different predictive models for talent selection can avoid deselection of high-potential female gymnasts. The selection procedure based upon the different statistical analyses results in decrease of 33.3% of cost because the pool of selected athletes can be reduced to 92 instead of 138 gymnasts (as selected by the coaches). Reduction of the costs allows the limited resources to be fully invested in the high-potential athletes.},
  author       = {Pion, Johan and Hohmann, Andreas and Liu, Tianbiao and Lenoir, Matthieu and Segers, Veerle},
  issn         = {0264-0414},
  journal      = {JOURNAL OF SPORTS SCIENCES},
  keywords     = {dropout,multilayer perceptron,artificial neural networks,Artistic gymnastics,Kohonen Feature Maps,talent identification,PSYCHOLOGICAL CHARACTERISTICS,SPORT,IDENTIFICATION,PERFORMANCE,ELITE,CHAMPIONS,STRENGTH,HANDBALL},
  language     = {eng},
  number       = {8},
  pages        = {806--811},
  title        = {Predictive models reduce talent development costs in female gymnastics},
  url          = {http://dx.doi.org/10.1080/02640414.2016.1192669},
  volume       = {35},
  year         = {2017},
}

Altmetric
View in Altmetric
Web of Science
Times cited: