Leveraging graph neural networks and gate recurrent units for accurate and transparent prediction of baseball pitching speed

(P = 0.000, Z = - 2.981, Cohen's d = 2.314) of the LSTM model were significantly lower than those of the GNN-GRU model. Compared to LSTM, the GNN-GRU model achieved better prediction accuracy but was potentially more susceptible to the influence of data variability. Moreover, the GNN-GRU-based model demonstrated the better interpretability and decision transparency.