Applications of neural networks in training science

Hum Mov Sci. 2012 Apr;31(2):344-59. doi: 10.1016/j.humov.2010.11.004. Epub 2011 Feb 18.


Training science views itself as an integrated and applied science, developing practical measures founded on scientific method. Therefore, it demands consideration of a wide spectrum of approaches and methods. Especially in the field of competitive sports, research questions are usually located in complex environments, so that mainly field studies are drawn upon to obtain broad external validity. Here, the interrelations between different variables or variable sets are mostly of a nonlinear character. In these cases, methods like neural networks, e.g., the pattern recognizing methods of Self-Organizing Kohonen Feature Maps or similar instruments to identify interactions might be successfully applied to analyze data. Following on from a classification of data analysis methods in training-science research, the aim of the contribution is to give examples of varied sports in which network approaches can be effectually used in training science. First, two examples are given in which neural networks are employed for pattern recognition. While one investigation deals with the detection of sporting talent in swimming, the other is located in game sports research, identifying tactical patterns in team handball. The third and last example shows how an artificial neural network can be used to predict competitive performance in swimming.

Publication types

  • Comparative Study

MeSH terms

  • Adolescent
  • Algorithms
  • Aptitude*
  • Athletic Performance
  • Child
  • Competitive Behavior*
  • Female
  • Germany
  • Humans
  • Linear Models
  • Longitudinal Studies
  • Male
  • Motor Skills
  • Neural Networks, Computer*
  • Nonlinear Dynamics
  • Pattern Recognition, Automated*
  • Physical Education and Training*
  • Research
  • Science*
  • Sports / education*
  • Statistics as Topic
  • Swimming / education*