Jump height is a poor indicator of lower limb maximal power output: theoretical demonstration, experimental evidence and practical solutions

Abstract

KEY POINTS1. Despite a widespread use, we contended that jump height as measured during vertical jump tests is not a good indicator of lower limb power or maximal power output capability.2. We showed this based on several confounding factors: body mass, push-off distance, individual force-velocity profile and optimal force-velocity profile. Some experimental data were also shown and discussed to further illustrate the not very-good correlation between jump height and power output.3. Finally, in order to address this issue, we discussed the possible practical solutions, and advocate for the use of a simple, accurate computation method based on jump height as an input. ABSTRACTLower limb maximal power output (Pmax) is a key physical component of performance in many sports. During squat jump (SJ) and countermovement jump (CMJ) tests, athletes produce high amounts of mechanical work over a short duration to displace their body mass (i.e. the dimension of mechanical power). Thus, jump height has been frequently used by the sports science and medicine communities as an indicator of Pmax. However, in this article, we contended that SJ and CMJ height are in fact poor indicators of Pmax in trained populations.To support our opinion, we first detailed why, theoretically, jump height and Pmax are not fully related. Specifically, we demonstrated that individual body mass, distance of push-off, optimal loading and force-velocity characteristics confound the jump height-Pmax relationship. We also discussed the poor relationship between SJ or CMJ height and Pmax measured with a force plate based on data reported in the literature, which added to our own experimental evidence. Finally, we discussed the limitations of existing practical solutions (regression-based estimation equations and allometric scaling), and advocated using a valid, reliable and simple field-based procedure to compute individual Pmax directly from jump height, body mass and push-off distance. The latter may allow researchers and practitioners to reduce bias in their assessment of Pmax by using jump height as an input with a simple yet accurate computation method, and not as the first/only variable of interest. Morin et al. Pre-Print -2018 -Jump height is a poor indicator of lower limb maximal power output3

Determining strength training needs using the force-velocity profile of elite female handball and volleyball players

“…Being similar in force production, small push-off distance (handball players) may underestimate, while large push-off distance (volleyball players) may overestimate the power capability of an athlete. 23…”

Altitude differentially alters the force‐velocity relationship after 3 weeks of power‐oriented resistance training in elite judokas

“…F‐V imbalance was classified in accordance with the thresholds defined by Jiménez‐Reyes et al (2017) (high force deficit, low force deficit, well‐balanced, low velocity deficit and high velocity deficit). A specific spreadsheet based on the equations proposed by Samozino et al (2008) for squat jump and validated for the countermovement jump by Jiménez‐Reyes et al (2017) was used for all calculations (Morin & Samozino, 2017).…”

Measurement Agreement Between Samozino's Method and Force Plate Force-Velocity Profiles During Barbell and Hexbar Countermovement Jumps

“…Computation of the F-v relationships was established by the equations in the SAM method spreadsheets (39). F-v relationships from force plate data were determined by least-squares linear regressions (56) using the trial at each load which demonstrated the highest jump height as identified in the original research by Samozino (46).…”

“…Mean force, velocity, and power were calculated for all CMJ loading conditions using the force plate method and the SAM method. The SAM method calculations were determined using freely available Microsoft Excel spreadsheets (39). A power analysis (14) was conducted before the study using the following test details: "means: difference between 2 dependent means (matched pairs)," with an effect size of 0.5, alpha of 0.05, and power of 0.8 (41), which suggested the total sample size of the study should include 34 subjects.…”