Merrigan, JJ, Stone, JD, Wagle, JP, Hornsby, WG, Ramadan, J, Joseph, M, and Hagen,
JA. Using random forest regression to determine influential force-time metrics for
countermovement jump height: a technical report. J Strength Cond Res 36(1): 277-283,
2022-The purpose of this study was to indicate the most influential force-time metrics
on countermovement jump (CMJ) height using multiple statistical procedures. Eighty-two
National Collegiate Athletic Association Division I American football players performed
2 maximal-effort, no arm-swing, CMJs on force plates. The average absolute and relative
(i.e., power/body mass) metrics were included as predictor variables, whereas jump
height was the dependent variable within regression models (p < 0.05). Best subsets
regression (8 metrics, R2 = 0.95) included less metrics compared with stepwise regression
(18 metrics, R2 = 0.96), while explaining similar overall variance in jump height
(p = 0.083). Random forest regression (RFR) models included 8 metrics, explained ∼93%
of jump height variance, and were not significantly different than best subsets regression
models (p > 0.05). Players achieved higher CMJs by attaining a deeper, faster, and
more forceful countermovement with lower eccentric-to-concentric force ratios. An
additional RFR was conducted on metrics scaled to body mass and revealed relative
mean and peak concentric power to be the most influential. For exploratory purposes,
additional RFR were run for each positional group and suggested that the most influential
variables may differ across positions. Thus, developing power output capabilities
and providing coaching to improve technique during the countermovement may maximize
jump height capabilities. Scientists and practitioners may use best subsets or RFR
analyses to help identify which force-time metrics are of interest to reduce the selectable
number of multicollinear force-time metrics to monitor. These results may inform their
training programs to maximize individual performance capabilities.