INTRODUCTION: Typically, for teaching in alpine skiing biomechanists provide theoretically ideal solutions that are independent of single individuals. Mainly world best athletes take over the role for practically ideal solutions that are hard to be generalized. Despite these alleged contradictions a mixture of both assumptions forms the basis for most teaching approaches that very often lead to numerous repetitions and error avoidance as basic principles. The recognition of high performance athletes on the basis of their biomechanically described movements (Schöllhom & Bauer 1998) as well as the low probability of producing two identical movements leads to a challenge of these approaches. Aims of this study are firstly, to develop a biomechanical deduction tree for alpine skiing and secondly, to suggest an alternative approach that copes with the shortcomings of traditional models on the basis of the deduction tree and principles of self organizing neural nets. METHODS: The chosen method is a deduction tree with nodes on different explanation levels. The nodes are physical and biomechanical variables that have logical influence on skiing performance (Lind & Sanders 1996) and are structured hierarchically. RESULTS: The target level of the resulting DT is formed by the total race time T that can be explained biomechanically by means of the first explanation level of time intervals deltati that can be explained by means of the partial changes in velocity deltavi over a partial distance deltasi on the second explanation level. On the third explanation level variables of influence on Asi and variables of influence on deltavi have to be distinguished. Accelerating and decelerating forces can be identified as variables of influence on deltavi. At this, the pushing and the gravitational forces can be assigned to the first whereas air, adhesion and sliding friction correspond to the later. Variables that Influence deltavi and deltasi can coarsely be assigned to variables of the athlete, the athletes kinematics, the physics of the equipment and the geometry of the environment etc. While the interaction between the first three levels and the top level can be considered as rather deterministic the levels below are rather indeterministic. Sources of this uncertainty can be assigned to interdependencies of variables on the same level as well as their changing behavior over time. DISCUSSION: The DT provides a qualitative model for theoretical dependencies of physical and biomechanical variables that form the basis for numerous quantitative physical and biomechanical calculations. Especially, the indeterministic levels of explanation display numerous possibilities for compensation and therefore include the individuality within the biomechanical approach. CONCLUSION: Instead of becoming a poor copy of a perfect model, a situatively and individually optimized technique should be developed by initiating a self organized optimization by means of differential learning according to Schöllhom (1999) which includes variations in all nodes of the DT and especially on the indeterministic levels.
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