Krzyszkowski, J, Chowning, LD, and Harry, JR. Phase-specific predictors of countermovement jump performance that distinguish good from poor jumpers. J Strength Cond Res 36(5): 1257-1263, 2022-The modified-reactive strength index (RSImod) is commonly examined during the countermovement vertical jump (CMJ) to assess neuromuscular characteristics (i.e., explosiveness, fatigue, adaptation, etc.) of an athlete. However, both phase-specific variables explaining RSImod and corresponding differences between good and poor jumpers are not well understood in trained populations. This study sought to (a) identify predictors of RSImod during the CMJ based on phase-specific temporal and rate of force development (RFD) variables, and (b) identify differences in those predictors between performers with high and low RSImod performances from a sample of collegiate male basketball players (n 5 22; 20 6 2 years; 1.99 6 0.06 month; 93.8 6 7.5 kg). Subjects performed 3 maximal effort CMJ trials while ground reaction force data was recorded using 2 force platforms. Phase-specific temporal and RFD variables were calculated and entered into separate stepwise regression models using backward elimination to identify predictors RSImod. Individuals were then categorized into high (n 5 11; RSImod 5 0.68 6 0.10) and low (n 5 11; RSImod 5 0.48 6 0.04) RSImod groups according to the overall median RSImod (RSImod 5 0.55). Independent t-tests (a 5 0.05) were conducted and supplemented by Cohen's d effect sizes (d $ 1.2, large) to compare groups relative to significant predictors identified by the linear regression models and related variables. The temporal regression model (R 2 5 0.530) retained unloading time and concentric time, whereas the RFD regression model (R 2 5 0.429) retained unloading RFD and braking RFD. The high RSImod group exhibited significantly greater RSImod scores (d 5 2.51, p , 0.001) and jump heights (d 5 1.58, p , 0.001), shorter times to takeoff (d 5 1.27, p 5 0.007) and concentric times (d 5 1.51, p 5 0.002), and a greater braking RFD (d 5 1.41, p 5 0.005) than the low RSImod group. Individuals targeting enhanced CMJ performance may consider exploring strategies or interventions to develop quicker unloading and concentric phases and increasing eccentric RFD abilities.

Countermovement vertical jump (CMVJ) studies using ground reaction force (GRF) data analyze either unfiltered (i.e., raw) or filtered data while providing little-to-no justification for the selected filtering process. Inappropriate filter choices can lead to inaccurate study results and erroneous interpretations. We examined the effects of not filtering GRF data in comparison with filtering data with various objectively and subjectively selected cutoff frequencies. Twenty-one collegiate male basketball players completed 3 maximal-effort CMVJ trials while GRF data were obtained from 2 force platforms. Countermovement vertical jump performance, explosiveness, power output, and neuromuscular function variables were compared among the following methods using one-way repeated-measures analyses of variance (a 5 0.05): no filtering (raw data), a standard 50-Hz cutoff (50 Hz), a visually determined cutoff frequency describing the frequency band containing the majority of the summed (visual inspection 1) or not-summed (visual inspection 2) GRF signal's frequency content, filtering the summed (99% signal power 1) or not-summed (99% signal power 2) GRF using a cutoff frequency retaining 99% of the signal power. The raw data method produced significantly shorter concentric phase times and significantly greater center of mass flight heights (;3%), modified reactive strength indices (RSI MOD ; ;4%), power outputs (;6%), and push-off distances (;4%) than 99% signal power 1 and 2 (p , 0.05). Discrete GRF and phase-specific yank magnitudes were not different among methods (p $ 0.05). Importantly, no differences were detected between the raw data and 50 Hz methods for any variable (p . 0.05). Low-pass filtering is not necessary when analyzing GRF data from the CMVJ. However, a low-pass filter with a 50-Hz cutoff can remove noise without altering results when compared with raw data. Explicit methodological descriptions of filtering processes should always be provided to improve the integrity of future CMVJ analyses, comparisons among various studies' results, or both.