Movement data is high-dimensional but often redundant, meaning there is certainly a lower dimensional subspace that spans most of the body configurations within an action performance. We propose that one such representation can be achieved through a decomposition method that explores the existence of key configurations and temporal correlations of those configurations that are typical of action matrices. The approach is compatible with computational models of motor synergies based on matrix factorizations, and it builds upon a method that was earlier proposed in the context of biological motion perception. Our experiments show that vertical jump trials collected from children and young adults can be consistently reconstructed from the resulting representation. We also observe that a subset of that same representation suggests differences among populations of jumpers based on their trials, which serves to illustrate the potential of the method as a tool to analyze both actions and actors.