Exercise Physiology Laboratory, Exercise and Sports Science Program, The University of Tulsa, Tulsa, Oklahoma, USA.
J Strength Cond Res. 2012 Jan;26(1):284-92. doi: 10.1519/JSC.0b013e31821d97c0.
The current study purpose examined the vertical height-anthropometry relationship with jump data obtained from an instrumented platform. Our methods required college-aged (n = 177) subjects to make 3 visits to our laboratory to measure the following anthropometric variables: height, body mass, upper arm length (UAL), lower arm length, upper leg length, and lower leg length. Per jump, maximum height was measured in 3 ways: from the subjects' takeoff, hang times, and as they landed on the platform. Standard multivariate regression assessed how well anthropometry predicted the criterion variance per gender (men, women, pooled) and jump height method (takeoff, hang time, landing) combination. Z-scores indicated that small amounts of the total data were outliers. The results showed that the majority of outliers were from jump heights calculated as women landed on the platform. With the genders pooled, anthropometry predicted a significant (p < 0.05) amount of variance from jump heights calculated from both takeoff and hang time. The anthropometry-vertical jump relationship was not significant from heights calculated as subjects landed on the platform, likely due to the female outliers. Yet anthropometric data of men did predict a significant amount of variance from heights calculated when they landed on the platform; univariate correlations of men's data revealed that UAL was the best predictor. It was concluded that the large sample of men's data led to greater data heterogeneity and a higher univariate correlation. Because of our sample size and data heterogeneity, practical applications suggest that coaches may find our results best predict performance for a variety of college-aged athletes and vertical jump enthusiasts.
本研究旨在探讨仪器平台上获取的跳跃数据与垂直高度-人体测量学的关系。我们的方法要求大学生（n = 177）受试者到我们的实验室进行 3 次访问，以测量以下人体测量变量：身高、体重、上臂长（UAL）、前臂长、大腿长和小腿长。在每次跳跃中，通过以下 3 种方式测量最大高度：从受试者的起飞、悬挂时间和他们在平台上的着陆。标准多元回归评估了人体测量学在多大程度上可以预测每个性别（男性、女性、混合）和跳跃高度方法（起飞、悬挂时间、着陆）组合的标准偏差。Z 分数表明，少量数据是异常值。结果表明，大多数异常值来自于女性在平台上着陆时计算的跳跃高度。将性别混合后，人体测量学预测了从起飞和悬挂时间计算出的跳跃高度有显著差异（p < 0.05）。从受试者着陆在平台上计算出的跳跃高度来看，人体测量学与垂直跳跃的关系并不显著，这可能是由于女性异常值所致。然而，男性的人体测量数据确实可以预测从他们着陆在平台上计算出的高度有显著差异；对男性数据的单变量相关性分析表明，UAL 是最好的预测指标。结论是，由于男性数据样本量大，数据异质性更大，单变量相关性更高。由于我们的样本量和数据异质性，实际应用表明，教练可能会发现我们的结果最能预测各种大学生运动员和垂直跳跃爱好者的表现。
