Identification and Prediction of Tennis Players’ Technical and Tactical Behaviors Based on Transformer Model
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Abstract
This research addresses the challenge of automatically identifying complex tennis techniques and tactics, dynamically predicting shot intentions, and assessing round outcomes early from structured event sequences. By constructing a Transformer sequence model, the study provides a data-driven intelligent solution for tactical decision-making. The model uses structured shot sequences from Match Charting Project logs, integrating attributes like shot type, direction, and match status. It employs a six-layer Transformer encoder with a multi-head self-attention mechanism for multi-task learning, jointly optimizing technical/tactical classification, shot intention prediction, and early outcome assessment. Experiments validate its robust performance, achieving a Macro-F1 of 0.935 for tactical classification and a round outcome AUC@5 of 0.923. The model significantly outperforms BiLSTM and CNN-LSTM baselines, with ablation studies confirming the critical contributions of its embedding and multi-task design. The proposed framework enables end-to-end deep analysis and effective prediction of tennis technical and tactical behaviors without visual input, providing a scalable, data-driven solution for intelligent competitive analysis, principles that may also inform structured event-stream prediction in other non-visual engineering datasets.
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