James Lachlan P, Suppiah Haresh, McGuigan Michael R, Carey David L
Int J Sports Physiol Perform. 2021 Jul 1;16(7):1052-1055. doi: 10.1123/ijspp.2020-0606. Epub 2021 Mar 1.
Dozens of variables can be derived from the countermovement jump (CMJ). However, this does not guarantee an increase in useful information because many of the variables are highly correlated. Furthermore, practitioners should seek to find the simplest solution to performance testing and reporting challenges. The purpose of this investigation was to show how to apply dimensionality reduction to CMJ data with a view to offer practitioners solutions to aid applications in high-performance settings.
The data were collected from 3 cohorts using 3 different devices. Dimensionality reduction was undertaken on the extracted variables by way of principal component analysis and maximum likelihood factor analysis.
Over 90% of the variance in each CMJ data set could be explained in 3 or 4 principal components. Similarly, 2 to 3 factors could successfully explain the CMJ.
The application of dimensional reduction through principal component analysis and factor analysis allowed for the identification of key variables that strongly contributed to distinct aspects of jump performance. Practitioners and scientists can consider the information derived from these procedures in several ways to streamline the transfer of CMJ test information.
从反向移动跳（CMJ）中可以得出数十个变量。然而，这并不能保证有用信息的增加，因为许多变量高度相关。此外，从业者应寻求找到性能测试和报告挑战的最简单解决方案。本研究的目的是展示如何将降维应用于CMJ数据，以期为从业者提供解决方案，以帮助在高性能环境中的应用。
使用3种不同设备从3个队列中收集数据。通过主成分分析和最大似然因子分析对提取的变量进行降维。
每个CMJ数据集中超过90%的方差可以由3或4个主成分解释。同样，2到3个因子可以成功解释CMJ。
通过主成分分析和因子分析应用降维，可以识别对跳跃性能的不同方面有强烈贡献的关键变量。从业者和科学家可以通过多种方式考虑从这些程序中得出的信息，以简化CMJ测试信息的传递。
