Identification and Prediction of Tennis Players’ Technical and Tactical Behaviors Based on Transformer Model

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.