Vlad Adrian Geantă1,2, Pierre Joseph de Hillerin1,3
1National University of Science and Technology Politehnica Bucharest, Pitesti University Center, Doctoral School of Sport Science and Physical Education, Pitesti, Romania
2Aurel Vlaicu University of Arad, Faculty of Physical Education and Sport, Arad, Romania
3Neuromotrica - Information for Sport and Human Performance Ltd., Bucharest, Romania
15-Second Repeated Vertical Jump Models versus Ergometer Approaches for Lower-Limb Average Power Assessment: A Preliminary Exploratory Comparison
Sport Mont 2026, 24(1), Ahead of Print | DOI: 10.26773/smj.260206
Abstract
Lower-limb average power is an important indicator of neuromuscular performance and can be assessed either indirectly through computational models derived from repetitive vertical jumps or directly using ergometers that quantify mechanical output. However, these approaches often yield different values, complicating interpretation and comparison across studies. This preliminary, exploratory within-subject study (n=5) aimed to examine discrepancies between lower- limb average power estimated from computational models applied to 15-second vertical jump tests and that measured during short-duration maximal efforts on cycle- and row-ergometers. Five male sport science university students performed a 15-second repeated vertical jump test assessed using the OptoJump Next system (Microgate, Bolzano, Italy). Average power was calculated using the Bosco, Miron Georgescu (MG), and Miron Georgescu Modified 15-second (MGM-15) formulas. Each participant subsequently completed two 20-second all-out trials on the Concept2 BikeErg and RowErg (Concept2 Inc., Morrisville, Vermont, USA). The analysis revealed a significant main effect of method (F(1.03, 4.12)=77.04, p=0.001, ηp²=0.95). The Bosco and MG equations generated substantially higher power outputs compared with both ergometer assessments (p<0.05). The MGM-15 model produced estimates not statistically different from RowErg (p>0.05), while slightly lower than those recorded on BikeErg (p<0.01). Substantial discrepancies were observed in lower-limb average power values obtained from different computational models applied to repeated vertical jump data. While classical equations produced markedly higher estimates, the MGM-15 formulation yielded power values that were closer to those obtained from direct ergometer measurements, highlighting the influence of computational assumptions on jump-derived power estimates.
Keywords
lower-limb power, ergometer, vertical jump, computational modelling, athlete performance assessment
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