The aim of this study is to propose a novel data analysis approach, an analysis of characterizing phases (ACP), that detects and examines phases of variance within a sample of curves utilizing the time, magnitude, and magnitude-time domains; and to compare the findings of ACP to discrete point analysis in identifying performance-related factors in vertical jumps. Twenty-five vertical jumps were analyzed. Discrete point analysis identified the initial-to-maximum rate of force development (P = .006) and the time from initial-to-maximum force (P = .047) as performance-related factors. However, due to intersubject variability in the shape of the force curves (ie, non-, uni- and bimodal nature), these variables were judged to be functionally erroneous. In contrast, ACP identified the ability to apply forces for longer (P < .038), generate higher forces (P < .027), and produce a greater rate of force development (P < .003) as performance-related factors. Analysis of characterizing phases showed advantages over discrete point analysis in identifying performance-related factors because it (i) analyses only related phases, (ii) analyses the whole data set, (iii) can identify performance-related factors that occur solely as a phase, (iv) identifies the specific phase over which differences occur, and (v) analyses the time, magnitude and combined magnitude-time domains.
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