Comparison of discrete-point vs. dimensionality-reduction techniques for describing performance-related aspects of...

The aim of this study was to assess and compare the ability of discrete point analysis (DPA), functional principal component analysis (fPCA) and analysis of characterizing phases (ACP) to describe a dependent variable (jump height) using vertical ground reaction force curves captured during the propulsion phase of a countermovement jump. FPCA and ACP are continuous data analysis techniques that reduce the dimensionality of a data set by identifying phases of variation (key phases), which are used to generate subject scores that describe a subject's behavior. A stepwise multiple regression analysis was used to measure the ability to describe jump height of each data analysis technique. Findings indicated that the order of effectiveness (high to low) across the examined techniques was: ACP (99%), fPCA (78%) and DPA (21%). DPA was outperformed by fPCA and ACP because it can inadvertently compare unrelated features, does not analyze the whole data set and cannot examine important features that occur solely as a phase. ACP outperformed fPCA because it utilizes information within the combined magnitude-time domain, and identifies and examines key phases separately without the deleterious interaction of other key phases.