Int J Sports Physiol Perform. 2022 Apr 1;17(4):523-529. doi: 10.1123/ijspp.2020-0756. Epub 2021 Dec 30.
The study aimed to identify the variables that differentiate judo athletes at national and regional levels. Multivariable analysis was applied to biomechanical, anthropometric, and Special Judo Fitness Test (SJFT) data.
Forty-two male judo athletes from 2 competitive groups (14 national and 28 state levels) performed the following measurements and tests: (1) skinfold thickness, (2) circumference, (3) bone width, (4) longitudinal length, (5) stabilometric tests, (6) dynamometric tests, and (7) SJFT. The variables with significant differences in the Wilcoxon rank-sum test were used in stepwise logistic regression to select those that better separate the groups. The authors considered models with a maximum of 3 variables to avoid overfitting. They used 7-fold cross validation to calculate optimism-corrected measures of model performance.
The 3 variables that best differentiated the groups were the epicondylar humerus width, the total number of throws on the SJFT, and the stabilometric mean velocity of the center of pressure in the mediolateral direction. The area under the receiver-operating-characteristic curve for the model (based on 7-fold cross validation) was 0.95.
This study suggests that a reduced set of anthropometric, biomechanical, and SJFT variables can differentiate judo athlete's levels.
本研究旨在确定区分国家级和地区级柔道运动员的变量。采用多变量分析对生物力学、人体测量学和特殊柔道体能测试（SJFT）数据进行分析。
来自 2 个竞争组（14 名国家级和 28 名地区级）的 42 名男性柔道运动员进行了以下测量和测试：（1）皮褶厚度，（2）周长，（3）骨宽，（4）长度，（5）平衡测试，（6）测力测试，和（7）SJFT。使用 Wilcoxon 秩和检验对具有显著差异的变量进行逐步逻辑回归，以选择那些能更好地区分组别的变量。作者考虑了最多包含 3 个变量的模型，以避免过度拟合。他们使用 7 倍交叉验证来计算模型性能的优化校正度量。
最佳区分组别的 3 个变量是肱骨外上髁宽度、SJFT 上的总投掷次数和压力中心在横侧向的稳定平均速度。基于 7 倍交叉验证的模型（基于 7 倍交叉验证）的接收者操作特征曲线下面积为 0.95。
本研究表明，一组减少的人体测量学、生物力学和 SJFT 变量可以区分柔道运动员的水平。
