Analysis of decision support system of basketball sports competition based on machine learning and IoT

This study develops a machine learning-based decision support system for offensive and defensive tactical analysis in basketball games within an Internet of Things (IoT) environment. By integrating multi-source data collection, spatiotemporal feature encoding, and the Multi-Task Attention Long Short-Term Memory (MT-AttLSTM) model, the system simultaneously addresses offensive-defensive state classification and shooting percentage prediction. The system is evaluated using NBA SportVU tracking data from the 2018-2023 seasons. The results demonstrate that MT-AttLSTM achieves a test set accuracy of 0.897 in offensive-defensive state classification and significantly outperforms traditional baseline models in shooting percentage prediction, as reflected by a lower Mean Absolute Percentage Error (MAPE). These findings highlight the system's high accuracy and strong generalization capability. Moreover, the system facilitates real-time data processing and tactical decision support, offering coaches scientific insights and a technical framework for optimizing game strategies and personalized training.