Game creativity analysis using neural networks

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.