Comparisons of Countermovement Jump Force-Time Characteristics Among National Collegiate Athletic Association Division I American Football Athletes: Use of Principal Component Analysis

Abstract 

Abstract Merrigan, JJ, Rentz, LE, Hornsby, WG, Wagle, JP, Stone, JD, Smith, HT, Galster, SM, Joseph, M, and Hagen, JA. Comparisons of countermovement jump force-time characteristics among NCAA Division I American football athletes: use of principal component analysis. J Strength Cond Res 36(2): 411–419, 2022—This study aimed to reduce the dimensionality of countermovement jump (CMJ) force-time characteristics and evaluate differences among positional groups (skills, hybrid, linemen, and specialists) within National Collegiate Athletic Association (NCAA) division I American football. Eighty-two football athletes performed 2 maximal effort, no arm-swing, CMJs on force plates. The average absolute and relative (e.g., power/body mass) metrics were analyzed using analysis of variance and principal component analysis procedures ( p \textless 0.05). Linemen had the heaviest body mass and produced greater absolute forces than hybrid and skills but had lower propulsive abilities demonstrated by longer propulsive phase durations and greater eccentric to concentric mean force ratios. Skills and hybrid produced the most relative concentric and eccentric forces and power, as well as modified reactive strength indexes (RSI MOD ). Skills (46.7 ± 4.6 cm) achieved the highest jump height compared with hybrid (42.8 ± 5.5 cm), specialists (38.7 ± 4.0 cm), and linemen (34.1 ± 5.3 cm). Four principal components explained 89.5% of the variance in force-time metrics. Dimensions were described as the (a) explosive transferability to concentric power (RSI MOD , concentric power, and eccentric to concentric forces) (b) powerful eccentric loading (eccentric power and velocity), (c) countermovement strategy (depth and duration), and (d) jump height and power. The many positional differences in CMJ force-time characteristics may inform strength and conditioning program designs tailored to each position and identify important explanatory metrics to routinely monitor by position. The overwhelming number of force-time metrics to select from may be reduced using principal component analysis methods, although practitioners should still consider the various metrics applicability and reliability. 

Last updated on 09/18/2023