Artificial Neural Networks in Sports: New Concepts and Approaches

Artificial neural networks are tools, which - similar to natural neural networks - can learn to recognize and classify patterns, and so can help to optimise context depending acting. These abilities, which are very useful in a lot of technical approaches, seem to be as well useful in particular in analysing and planning tactical patterns in sport games or patterns of learning behaviour in training processes.In a first attempt, in co-operation with LAMES from the University of Rostock, tactical structures in volleyball could successfully be analysed using neural networks.However, the problem is that the special type of network that has to be used for such analyses (i.e. the so called Kohonen Feature Map or KFM) needs a huge amount of data and lacks the necessary dynamic in continuous learning.So in order to describe, analyse, and evaluate continuous learning processes in sports a dynamically controlled network (“DYCON”) has been developed, which consists of a conventional KFM combined with a time-independent neurone-driven control: Each neurone is imbedded in a dynamic performance potential control system, which had been developed for analysis and control of physiological adaptation processes in sport.Two main advantages of DYCON are: Its learning efficiency is very high. In practice, it needs only some hundred data to coin a pattern, where a conventional KFM normally needs about 10.000 to 20.000. Moreover, it can learn continuously and so can recognise and analyse time depending pattern changes.So, DYCON can support the study of processes in sport games in an easier and more efficient way. Moreover, it can help to analyse tactical changes of a team during a season or even during a tournament, as has been done with squash in co-operation with MCGARRY, University of Fredericton. Finally, in a co-operation with RAAB, University of Heidelberg, we try to find out if and how DYCON can be used for analysis and optimisation of training processes in sport.