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

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

Abstract Long short-term memory (LSTM) networks are widely used in biomechanical data analysis but have the significant limitations in interpretability and decision transparency. Combining graph neural networks (GNN) with gate recurrent units (GRU) may offer a better solution. This study proposes an...

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Bibliographic Details
Main Authors: Chen Yang, Pengfei Jin, Yan Chen
Format: Article
Language:English
Published: Nature Portfolio 2025-03-01
Series:Scientific Reports
Subjects:
Graph neural networks
Gate recurrent units
Long short-term memory
Baseball pitching
Baseball biomechanics
Online Access:https://doi.org/10.1038/s41598-025-88284-x

Internet

https://doi.org/10.1038/s41598-025-88284-x

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