← 研踪 PaperTrailarXiv:2611.15529 · v1
One Hierarchy, Many Sports: Hierarchy-Aligned Multi-Task Learning for Next-Shot Prediction
首次提交 2026-11-11 22:15
版本更新 2026-11-11 22:15
雷达观测 2026-11-13 11:11
摘要
Next-shot prediction in racket sports requires learning from event data with different match structures, recorded attributes, and label vocabularies. Table tennis, badminton, and tennis nevertheless share a hierarchy in which shots form rallies and rallies form higher scoring units. We present MT-HTA, a hierarchy-aligned multi-task Transformer that uses this structure to jointly predict the attributes of the next shot. A temporal Transformer captures event progression, a feature-token path models interactions among semantic fields rather than along the time axis, and task-specific attention retrieves information from hierarchical summaries and player context. The architecture supports three- and four-level hierarchies and sport-dependent task sets. Across four datasets from three sports, the final MT-HTA achieves the highest mean task-averaged F1 among the compared main models. Ablations on table tennis support the combination of hierarchical encoding, task attention, and feature-token interaction. Additional modules alter the balance between classification scores and spatial error rather than improving every metric. These results support a reusable architecture for heterogeneous racket-sport event data. We release the code for reproducibility. https://github.com/gndd1221/For-Sports-event-modeling-with-rally-and-score-hierarchical-A-Multi-Task-Hierarchical-Transformer
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v1 · 2026-11-11 22:15 · 最新观测版本
One Hierarchy, Many Sports: Hierarchy-Aligned Multi-Task Learning for Next-Shot Prediction
Next-shot prediction in racket sports requires learning from event data with different match structures, recorded attributes, and label vocabularies. Table tennis, badminton, and tennis nevertheless share a hierarchy in which shots form rallies and rallies form higher scoring units. We present MT-HTA, a hierarchy-aligned multi-task Transformer that uses this structure to jointly predict the attributes of the next shot. A temporal Transformer captures event progression, a feature-token path models interactions among semantic fields rather than along the time axis, and task-specific attention retrieves information from hierarchical summaries and player context. The architecture supports three- and four-level hierarchies and sport-dependent task sets. Across four datasets from three sports, the final MT-HTA achieves the highest mean task-averaged F1 among the compared main models. Ablations on table tennis support the combination of hierarchical encoding, task attention, and feature-token interaction. Additional modules alter the balance between classification scores and spatial error rather than improving every metric. These results support a reusable architecture for heterogeneous racket-sport event data. We release the code for reproducibility. https://github.com/gndd1221/For-Sports-event-modeling-with-rally-and-score-hierarchical-A-Multi-Task-Hierarchical-Transformer
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