The relationship between strength capacity and motor performance in the gymnastic handstand: a machine learning study.

Aim: The aim of the present study was to investigate the relationship between strength capacity and motor performance through machine learning model and linear regression in the gymnastic handstand. Material and methods: Thirty-two university students, 16 of whom were female and 16 were male (24.03 ± 4.74 years of age) participated in the study. The perform the handstand was through absolute error of difference in the three angles produced by the model (video) and the three angles produced by the performer. Four strength tests were conducted: explosive force, maximum force right-hand, maximum force left-hand and resistance force. The machine learning model was then trained using 10 of the folds and cross-validated and a linear regression test was performed using motor performance (absolute error) and strength tests (explosive force, maximum force right-hand, maximum force left-hand and resistance force). Results:The results obtained revealed that the machine learning model indicated low relationship between strength capacity and motor performance. Also, the motor performance was not related to the level of strength capacity. Conclusion: The results of this study gave support to the theory of specific capacities which are based on the perspective of multiple capacities. [ABSTRACT FROM AUTHOR]