Convolutional Neural Networks in Deep Learning for Predicting Basketball Players' Shooting Accuracy

In this study, a dataset encompassing diverse shooting scenarios was meticulously constructed by collecting and analyzing basketball game videos and athletes' personal data. By leveraging this dataset, a specialized convolutional neural network (CNN) model framework was designed to efficiently extract crucial features during the shooting process. Through extensive training and optimization, the model demonstrated outstanding performance in predicting shooting accuracy. When compared with traditional machine-learning models, the proposed convolutional neural network model exhibited a significant improvement in accuracy. Furthermore, this research employed visualization techniques to analyze the importance of features, thereby uncovering the key factors influencing shooting accuracy. These findings not only offer a scientific foundation for personalized training programs for basketball players and the formulation of game strategies, but also open up a novel direction for the application of deep-learning technology in the sports domain.