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J Sports Sci. 2009 Jan 15;27(2):139-49. doi: 10.1080/02640410802442007.

Game creativity analysis using neural networks.

Author information

1
Department of Sport Sciences, University of Heidelberg, Heidelberg, Germany. daniel.memmert@urz.uni-heidelberg.de

Abstract

Experts in ball games are characterized by extraordinary creative behaviour. This article outlines a framework for analysing types of individual development of creative performance based on neural networks. Therefore, two kinds of sport-specific training programme for the learning of game creativity in real field contexts were investigated. Two training groups (soccer, n=20; field hockey, n=17) but not a control group (n=18) improved with respect to three measuring points (P < 0.001), although no difference could be established between the two training groups (P=0.212). By using neural networks it is now possible to distinguish between five types of learning behaviour in the development of performance, the most striking ones being what we call "up-down" and "down-up". In the field hockey group in particular, an up-down fluctuation process was identified, whereby creative performance increases initially, but at the end is worse than in the middle of the training programme. The reverse down-up fluctuation process was identified mainly in the soccer group. The results are discussed with regard to recent training explanation models, such as the super-compensation theory, with a view to further development of neural network applications.

PMID:
19058086
DOI:
10.1080/02640410802442007
[Indexed for MEDLINE]

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