In this paper, we introduce PingTactics, a multimodal dataset tailored for table tennis action recognition and tactical analysis. Derived from professional table tennis matches, the dataset comprises annotated video clips capturing detailed player actions, positional dynamics, and scoring outcomes. A key feature of PingTactics is its comprehensive temporal annotation framework, which includes sequences of previous and next actions, enabling the study of fine-grained action relationships and their tactical implications. We ensured annotation quality through a semi-automated pipeline that combines machine-assisted pre-labeling with manual refinement by domain experts. To validate the dataset, we conducted extensive experiments using state-of-the-art deep learning models for action recognition, demonstrating PingTactics’ effectiveness for capturing the complex and dynamic action patterns typical of high-stakes matches. Beyond action recognition, PingTactics serves as a foundation for tactical evaluation, as evidenced by our quadrant-based scoring analysis, which reveals interaction patterns, key scoring strategies, and player tendencies. By offering fine-grained action annotations and enabling tactical insights, PingTactics provides a significant resource for advancing research in sports analytics, with implications for intelligent coaching, player performance evaluation, and strategic planning in table tennis and related domains.
Gong et al. (Mon,) studied this question.