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<p>This pilot study aimed to develop an interpretable machine-learning framework to classify high- versus low-ring-value performance in 10&#xa0;m air pistol shooting and to identify key technical factors relevant to training feedback. A total of 3,179 valid shots were collected from an elite shooter using a SCATT laser training system. Eight SCATT-derived metrics were extracted from aiming-trajectory and process data. An XGBoost classifier was trained with SMOTE–Tomek to mitigate class imbalance and Optuna for hyperparameter optimization. The decision threshold was selected on the training set via cross-validation by maximizing the F1 score. Model interpretability was examined using SHAP to quantify feature contributions. On the held-out test set, the optimized XGBoost model achieved an AUC of 0.86 and an accuracy of 0.83 (F1-optimized threshold = 0.30). SHAP analyses the most influential features, indicating that smaller deviation and more stable final-second aiming were associated with high-ring-value performance. This interpretable classification framework provides data-driven, individualized technical feedback from SCATT data and may support practical decision-making in precision shooting training. Further validation with additional athletes is needed to improve generalizability.</p>

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Personalized training model for 10 m air pistol through machine learning: a pilot study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月27日

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  • Shanrui Diao,
  • Tong Zhou,
  • Yunyun Du

摘要

This pilot study aimed to develop an interpretable machine-learning framework to classify high- versus low-ring-value performance in 10 m air pistol shooting and to identify key technical factors relevant to training feedback. A total of 3,179 valid shots were collected from an elite shooter using a SCATT laser training system. Eight SCATT-derived metrics were extracted from aiming-trajectory and process data. An XGBoost classifier was trained with SMOTE–Tomek to mitigate class imbalance and Optuna for hyperparameter optimization. The decision threshold was selected on the training set via cross-validation by maximizing the F1 score. Model interpretability was examined using SHAP to quantify feature contributions. On the held-out test set, the optimized XGBoost model achieved an AUC of 0.86 and an accuracy of 0.83 (F1-optimized threshold = 0.30). SHAP analyses the most influential features, indicating that smaller deviation and more stable final-second aiming were associated with high-ring-value performance. This interpretable classification framework provides data-driven, individualized technical feedback from SCATT data and may support practical decision-making in precision shooting training. Further validation with additional athletes is needed to improve generalizability.