Human Performance Laboratory, Coventry University, Coventry, United Kingdom.
J Strength Cond Res. 2013 Mar;27(3):597-603. doi: 10.1519/JSC.0b013e31825d97ac.
Equations, commonly used to predict peak power from jump height, have relied on linear additive models that are biologically unsound beyond the range of observations because of high negative intercept values. This study explored the utility of allometric multiplicative modeling to better predict peak power in adolescent basketball players. Seventy-seven elite junior basketball players (62 adolescent boys, 15 adolescent girls, age = 16.8 ± 0.8 years) performed 3 counter movement jumps (CMJs) on a force platform. Both linear and multiplicative models were then used to determine their efficacy. Four previously published linear equations were significantly associated with actual peak power (all p < 0.01), although here were significant differences between actual and estimated peak power using the SJ and CMJ equations by Sayers (both p < 0.001). Allometric modeling was used to determine an alternative biologically sound equation which was more strongly associated with (r = 0.886, p < 0.001), and not significantly different to (p > 0.05), actual peak power and predicted 77.9% of the variance in actual peak power (adjusted R = 0.779, p < 0.001). Exponents close to 1 for body mass and CMJ height indicated that peak power could also be determined from the product of body mass and CMJ height. This equation was significantly associated (r = 0.871, p < 0.001) with, and not significantly different to, actual peak power (adjusted R = 0.756, p > 0.05) and offered a more accurate estimation of peak power than previously validated linear additive models examined in this study. The allometric model determined from this study or the multiplicative model (body mass × CMJ height) provides biologically sound models to accurately estimate peak power in elite adolescent basketballers that are more accurate than equations based on linear additive models.
方程通常用于根据跳跃高度预测峰值功率，但由于高负截距值，这些方程在观察范围之外是不符合生物学原理的线性加性模型。本研究探索了使用异速乘法模型更好地预测青少年篮球运动员峰值功率的实用性。77 名精英青少年篮球运动员（62 名男青少年，15 名女青少年，年龄=16.8±0.8 岁）在力量平台上进行了 3 次反向运动跳跃（CMJ）。然后使用线性和乘法模型来确定它们的效果。四个先前发表的线性方程与实际峰值功率显著相关（所有 p<0.01），尽管使用 Sayers 的 SJ 和 CMJ 方程，实际峰值功率和估计峰值功率之间存在显著差异（均 p<0.001）。使用异速建模来确定另一个符合生物学原理的方程，该方程与实际峰值功率的相关性更强（r=0.886，p<0.001），并且与实际峰值功率没有显著差异（p>0.05），预测了实际峰值功率的 77.9%（调整后的 R=0.779，p<0.001）。体重和 CMJ 高度的指数接近 1 表明，峰值功率也可以根据体重和 CMJ 高度的乘积来确定。该方程与实际峰值功率显著相关（r=0.871，p<0.001），与实际峰值功率没有显著差异（调整后的 R=0.756，p>0.05），并且比本研究中检查的先前验证的线性加性模型更准确地估计峰值功率。本研究确定的异速模型或乘法模型（体重×CMJ 高度）提供了符合生物学原理的模型，可以准确估计精英青少年篮球运动员的峰值功率，比基于线性加性模型的方程更准确。
