Principal Component Analysis of Force Plate Variables from Collegiate Athlete Countermovement Jumps

Despite widespread use of countermovement jump (CMJ) assessments in athletic performance testing, research has focused on isolated kinetic metrics without addressing redundancy among variables. This oversight can obscure data interpretation and limit CMJ utility in sports performance analysis. Principal component analysis (PCA) offers a means to distill complex, multivariate data into key performance indicators. PURPOSE: To evaluate PCA outcomes on kinetic variables recorded during CMJ assessments to reduce data dimensionality and identify redundant measures. METHODS: Retrospective analysis of kinetic force data from dual force plate assessments was performed on 7,404 trials from 308 collegiate athletes (male = 127, female = 181) across 16 teams over 28 months. Each athlete performed 28 ± 33 jumps. Variables, selected per literature prevalence, were computed via the force plate system’s software. PCA appropriateness was confirmed using Kaiser-Meyer-Olkin (MSA > .8) and Bartlett’s test (p < .05). Principal components capturing 95% of variance were initially selected; only components with eigenvalues > 1 were retained. Loadings, squared cosine (Cos²) values, and Pearson correlations among variables were examined using R (v4.43) and the factoextra package. RESULTS: Three principal components explained 95% of variance: PC1 accounted for 77.9% (eigenvalue = 9.3), PC2 for 10.7% (eigenvalue = 1.3), and PC3 for 6.4% (eigenvalue = 0.8). Given PC3’s eigenvalue below 1, further analysis focused on PC1 and PC2. Variables with the highest loadings on PC1 were linked to the eccentric–concentric transition, whereas PC2 predominantly comprised measures associated with jump height. Pearson correlations (r > .80) were observed among most top-loaded variables, except Peak Braking Rate of Force Development. CONCLUSION: These findings emphasize that variations during the braking phase parallel propulsive outcomes in CMJ performance, underscoring the need for phase-specific analyses. The heterogeneous athletic sample may explain lower loadings and Cos² values relative to prior studies. Sport-specific evaluations are recommended to optimize CMJ performance metrics for sport science applications.