Abstract: With the continuous progress of basketball technology and tactics, educators need to adopt new teaching methods to cultivate high-quality athletes who meet the needs of modern basketball development. In basketball teaching, the accuracy of free throw techniques directly affects teaching effectiveness. Therefore, the automated prediction of free throw hits is of great significance for reducing manual labor and improving training efficiency. In order to automatically predict the free throw hits and reduce manual fatigue, the study conducts an in-depth analysis for the criticality of free throw in basketball. In this study, the target detection model of target basketball players is constructed based on YOLOv5 and CBAM, and the basketball free throw hit prediction model is constructed based on the OpenPose algorithm. The main quantitative results showed that the proposed model could accurately recognize the athlete posture in free throw actions and save them as video frames in practical applications. Specifically, when using the free throw keyframe limb angle as features, the model achieved a prediction accuracy of 71% and a recall rate of 86% in internal testing. In external testing, the prediction accuracy was improved to 89% and the recall rate was 77%. In addition, combining the relative position difference and angle characteristics of joint points, the accuracy of internal testing was significantly improved to 80%, and the recall rate was increased to 96%. The accuracy of external testing was improved to 95%, with a recall rate of 75%. The experimental results showed that the various functional modules of the system basically meet the expectations, confirming that the basketball penalty posture analysis and hit probability prediction system based on deep learning can effectively assist basketball teaching and meet the practical teaching application needs. The contribution of the research lies in providing a scientific basketball free throw training tool, which helps coaches and athletes better understand and improve free throw techniques, thereby improving free throw hits accuracy. Meanwhile, this study also provides new theoretical and practical references for the application of deep learning in motor skill analysis and training, which has potential value for updating the basketball education system and reducing teacher workload.

Yuankai Luo, Yan Peng and Juan Yang, “Basketball Free Throw Posture Analysis and Hit Probability Prediction System Based on Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 15(4), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150496

@article{Luo2024,
title = {Basketball Free Throw Posture Analysis and Hit Probability Prediction System Based on Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150496},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150496},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {4},
author = {Yuankai Luo and Yan Peng and Juan Yang}
}