Explainable Graph-Based Golf Swing Analysis Integrating Club and Body Keypoints for Ball Flight Outcome Prediction

We propose a graph-based, explainable golf swing analysis framework that integrates human body keypoints with golf club keypoints to predict ball flight outcomes. We collected 321 driver swing sequences from six amateur golfers in a controlled studio setting, synchronizing monocular swing videos with TrackMan-derived ball trajectory measurements. Using a unified spatial–temporal graph that jointly models body joints and golf club keypoints, we trained graph neural networks (ST-GCN and STGAT) to perform three prediction tasks: Spin Axis and Launch Direction (classification) and Ball Speed (regression). Model performance was evaluated using AUC and accuracy for classification, and R2 and RMSE for regression. STGAT achieved the best overall performance, reaching an AUC of 0.9188 and an accuracy of 78.33% for Spin Axis classification, an AUC of 0.7599 and an accuracy of 69.81% for Launch Direction classification, and an R2 of 0.6925 with an RMSE of 6.4020 for Ball Speed prediction, outperforming traditional machine learning baselines. Finally, we applied Integrated Gradients to quantify the importance of both body and club keypoints across swing phases, enabling interpretable, phase-specific feedback to support individualized swing refinement.