ArticleScientific reports2026
Analysis of decision support system of basketball sports competition based on machine learning and IoT.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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