Abstract
BACKGROUND: Force plate-derived metrics are increasingly used to assess performance and monitor musculoskeletal injury risk, yet the mechanisms linking jump-mechanics patterns to injury risk remain unclear, particularly when using proprietary, algorithmically derived risk scores. Clarifying these relationships is important for improving screening practices, program design, and load management in athletic populations. METHODS: A total of 233 collegiate athletes completed countermovement vertical jump (CMVJ) testing on a commercial force plate, which produced 26 force-time variables and proprietary composite metrics. LASSO regression with bootstrapping identified important predictors of CMVJ height and algorithmically derived musculoskeletal injury (AMSKI risk), and Partial Least Squares (PLS) models characterized multivariate patterns across force-time variables. Sex-stratified analyses and post hoc modeling examined potential mechanisms. RESULTS: Greater AMSKI risk was associated with a coordinated pattern of greater concentric output, including greater power, velocity, and impulse, combined with reduced braking capacity. Braking rate of force development ("Load") showed an inverse association with AMSKI risk across sexes, and females in the elevated-risk category displayed significantly reduced braking values. Postural control measures contributed differently by sex. PLS models indicated that both CMVJ height and AMSKI risk reflected interactions among multiple variables, while proprietary composite scores showed inconsistent alignment with mechanistic predictors. CONCLUSIONS: Multivariate force-time profiling offers practical value for identifying athletes whose high-output movement strategies may elevate injury risk when braking control is insufficient. Because proprietary, algorithmically derived risk metrics show inconsistent associations with underlying mechanics, further independent validation is needed before such scores are used in clinical or training decisions.