Performing Meaningful Movement Analysis From Publicly Available Videos Using Free Software – A Case of Acrobatic Sports

Abstract

This paper illustrates how movement analysis could be performed using publicly available videos and freeware to generate meaningful information for sports practitioners and researchers. Using acrobatic sports as a case, we performed kinematic analysis on 206 YouTube videos of high-level competitions in diving and gymnastics using Kinovea. Results revealed good to excellent inter-rater reliability of variables analyzed. Significant differences in angular speed (p < 0.001, η2p = 0.213) and flight time (p < 0.001, η2p = 0.928) were found among eight different events. Divers had longer flight time (p < 0.001, η2p = 0.569) and were somersaulting faster than gymnasts (p = 0.021, η2p = 0.026). Angular speed was higher in tuck than pike somersaults (p < 0.001, η2p = 0.214). Shorter the flight time was significantly correlated with faster angular speed (rho = −0.533, p < 0.001) in gymnastics events. Coaches and scientists can consider applying the proposed method to monitor the athletes’ performance and to identify errors (e.g., insufficient flight time). The kinematics measurements can also be used to guide the transition plan across different apparatus and categories (e.g., 10-m platform to 3-m springboard). In conclusion, the present study highlights the potential of using readily available information and open-source freeware to generate scientific data for sports applications. Such data analysis approach can accommodate a wide range of video qualities, is easily accessible, and not restricted by situations such as social distancing, quarantine, lockdown or other restrictive measures.