Neural Network-Based Process Analysis in Sport

Neural Network-Based Process Analysis in Sport

Juergen Perl (University of Mainz, Germany)
DOI: 10.4018/978-1-60960-195-9.ch414
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Processes in sport like motions or games are influenced by communication, interaction, adaptation, and spontaneous decisions. Therefore, on the one hand, those processes are often fuzzy and unpredictable and so have not extensively been dealt with, yet. On the other hand, most of those processes structurally are roughly determined by intention, rules, and context conditions and so can be classified by means of information patterns deduced from data models of the processes. Self organizing neural networks of type Kohonen Feature Map (KFM) help for classifying information patterns – either by mapping whole processes to corresponding neurons (see Perl & Lames, 2000; McGarry & Perl, 2004) or by mapping process steps to neurons, which then can be connected by trajectories that can be taken as process patterns for further analyses (see examples below). In any case, the dimension of the original data (i.e. the number of contained attributes) is reduced to the dimension of the representing neuron (normally 2 or 3), which makes it much easier to deal with. Additionally, extensions of the KFM-approach are introduced, which are able to flexibly adjust the net to dynamically changing training situations. Moreover, those extensions allow for simulating adaptation processes like learning or tactical behaviour. Finally, a current project is introduced, where tactical processes in soccer are analysed under the aspect of simulation-based optimization.
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Artificial Neural Networks

Current developments in the fields of Soft Computing and/or Computational Intelligence demonstrate how information patterns can be taken from data collections by means of fuzziness, similarity and learning, which the approach of Artificial Neural Networks gives an impressive example for. In particular self organizing neural networks of type KFM (Kohonen Feature Map) play an important role in aggregating input data to clusters or types by means of a self organized similarity analysis (Kohonen, 1995).

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