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Use of Self Organizing Maps in Technique Analysis Bartlett, Roger Lamb, Peter Robbins, Anthony Artificial neural networks basketball shooting movement coordination movement variability self-organizing maps. This study looked at the coordination patterns of four participants performing three different basketball shots from different distances. The shots selected were the three-point shot, the free throw shot and the hook shot; the latter was included to encourage a phase transition between shots. We hypothesised lower variability between the three-point and free throw shots compared to the hook shot. The study uses Self-Organizing Maps (SOM) to expose the non-linearity of the movement and to try to explain more specifically what it is about the coordination patterns that make them different or similar. The SOM proved to draw the researcher's attention to aspects of the movement that were not obvious from a visual analysis of the original movement either viewed from video or as computer animation. A speculative link between the observational learning literature on the importance of the kinematics of distal segments in skill acquisition and the visual information a coach or analyst may rely on for qualitative technique analysis was made. Although making the distinction between the three shooting conditions was meant to be a trivial exercise, in many cases for this dataset the SOM output and the natural inclination of the movement analyst did not agree: the SOM may provide a more objective method for explaining movement patterning. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Roger Bartlett and Peter Lamb and Anthony Robbins 2008 Is Part Of Dagstuhl Seminar Proceedings, Volume 8372, Computer Science in Sport - Mission and Methods (2008) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagSemProc.08372.8 urn:nbn:de:0030-drops-17738 https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08372.8 eng https://creativecommons.org/licenses/by/4.0/legalcode