Peter Lamb (TU Munich) was the first presenter in the second morning session of Day 2 at the Computer Science in Sport Conference (Special Emphasis:Football) at Schloss Dagstuhl.
Peter discussed Self-Organising Maps (SOMs) and presented Basketball data to exemplify an SOM approach to movement observation and analysis. He developed his discussion with an analysis of golf shots and concluded with a brief exploration of the use of SOMs in football.
Self-organizing maps are a type of artificial neural network useful for visualizing complex human movement coordination. The visualization of the network output can be enhanced by using colour or a third dimension to visualize data clusters, by adding a trajectory to highlight the time-series progression of coordination or by identifying which areas on the output map represent certain critical phases in the movement.
Jurgen Perl presented the second paper in this Dynamical Systems session. He discussed a neural network approach to formations in football and the development of a SOCCER analysis program.
This work is being reported in Grunz, A., Perl, J. & Memmert, D. (2011, accepted) Tactical pattern recognition in soccer games by means of special Self-Organizing Maps. Human Movement Science.
See also this 2009 paper by the same authors.
The abstract for the 2011 paper is:
Increasing amounts of data are collected in sports due to technological progress. From a typical soccer game, for instance, the positions of the 22 players and the ball can be recorded 25 times per second, resulting in approximately 135.000 datasets. Without computational assistance it is almost impossible to extract relevant information from the complete data. This contribution introduces a hierarchical architecture of artificial neural networks to find tactical patterns in those positional data. The results from the classification using the hierarchical setup were compared to the results gained by an expert manually classifying the different categories. Short and long game initiations can be detected with relative high accuracy leading to the conclusion that the hierarchical architecture is capable of recognizing different tactical patterns and variations in these patterns. Remaining problems are discussed and ideas concerning further improvements of classification are indicated.