Advancing cricket biomechanics: Neural network based motion prediction for stroke analysis

Cricket stroke analysis plays a critical role in performance evaluation, strategic decision-making, and coaching. Traditional manual techniques are limited in their ability to capture the fine-grained timing and biomechanical nuances of batting movements. This study investigates four advanced neural network architectures—long short-term memory (LSTM), bidirectional LSTM (BiLSTM), Transformer, and a BERT-inspired model—for short-term prediction of cricket stroke dynamics. Using joint-level pose sequences extracted with MediaPipe, the models forecast the next five frames of motion, corresponding to approximately 0.16 s of future movement that is biomechanically relevant for adjustments in bat angle, head stability, and lower-body alignment. Preprocessing involved keypoint scaling, sliding-window generation, and sequence normalization. The models were evaluated across eight stroke types: straight drive, sweep, pull, on drive, flick, cut, cover drive, and back-foot punch. Performance was assessed using standard error metrics and prediction accuracy. The Transformer consistently delivered the best results for most strokes, particularly sweep, flick, and cover drive, achieving low prediction error and strong temporal–spatial alignment. The BERT-inspired model performed competitively on straight drive and on drive, while the BiLSTM model excelled on back-foot punch. Although the LSTM provided reasonable predictions, it was generally outperformed by the more advanced architectures. These findings demonstrate the suitability of Transformer-based models for capturing the complex spatial–temporal patterns inherent in cricket batting mechanics. The proposed framework offers practical implications for real-time coaching, player assessment, and sports science by advancing automated prediction of athletic performance. • Advanced deep learning models for future frame predictions. • Transformer-based approach excels in temporal motion modeling. • Joint-level biomechanics analysis enhances sports analytics. • Optimized preprocessing improves AI-driven stroke accuracy. • Contributions to automated coaching and real-time performance.