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Abstract
Because of the complexity of sports movements, biomechanical analyses contain many kinematical or dynamical parameters and characteristic curves. Principal component analysis (PCA) is a technique for simplifying a dataset by reducing multidimensional datasets to lower dimensions for analysis. The purpose of this article is the presentation of several studies which used the PCA to solve various problems in the movement science in sports. In particular, we interpret the number of the components or also named components with relatively high eigenvalues as the number of degrees of freedom. For cyclic and automated movements, the first PCA component is dominant. The PCA was successfully applied to gait analyses in rehabilitation and in triathlon as well as in riding. Phase plots could be used to quantify the variability of the movement coordination.