Multivariate regression modeling of Chinese artistic gymnastic handspring vaulting kinematic performance based on judges scores

Abstract

Introduction: Vault kinematic variables have been found to be strongly correlated with vault difficulty (DV) values and judges' scores. However, the Fédération Internationale de Gymnastique Code of Points (COP) was updated after every Olympic Games rendering previous regression models inadequate. Therefore, the objective of this study was to develop a prediction model for vault performance based on judges' scores. Methods: Handspring vaults (n = 70) were recorded during the Men's Artistic Gymnastic qualifying round of the 2017 China National Artistic Gymnastics Championship using a video camera placed 50 m perpendicular to the vault table. Kinematic data were coded and correlated with judges' official competition final scores (FSs). The vault samples were used to develop a mathematical model (n = 65) and to verify the scores against the predicted model (n = 5). Partial least squares regression was established using the statistical software to calibrate and cross validate the model. Results: The goodness-of-fit of a 3-factor model was utilised (R2 cal = 90.13% and R2 val = 87.30%) and a significant and strong relationship was observed between predicted Y (FS) and reference Y (FS) in both the calibration and validation models (rcal = 0.949, rval = 0.932) with Y-calibration error (RMSEC = 0.1727) and Y-prediction error (RMSEP = 0.1990). Maximum height, 2nd-flight-time and DV were the key variables against FS. Using JSPM, 40% of new samples were within the acceptable range. Conclusion: Kinematic variables and known DV seem adequate to form a JSPM that could offer coaches an alternative scientific approach to monitor vault training.