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

Shanrui Diao1, Tong Zhou1, Yunyun Du2

  • 1School of Intelligent Sports Engineering, Wuhan Sports University, Wuhan, 430079, China.

Scientific Reports
|April 27, 2026
PubMed
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This study developed an interpretable machine learning model to classify 10m air pistol shooting performance. Stable aiming and smaller deviations are key factors for high scores, offering data-driven training feedback.

Area of Science:

  • Sports Science
  • Machine Learning

Background:

  • Precision shooting performance is influenced by complex technical factors.
  • Objective data analysis can enhance training feedback for athletes.

Purpose of the Study:

  • To develop an interpretable machine learning framework for classifying 10m air pistol shooting performance.
  • To identify key technical factors associated with high-ring-value shots using SCATT data.
  • To provide data-driven, individualized feedback for training.

Main Methods:

  • Collected 3,179 shots from an elite shooter using a SCATT laser training system.
  • Extracted eight metrics from aiming-trajectory and process data.
  • Trained an XGBoost classifier with SMOTE-Tomek and Optuna for hyperparameter optimization.
Keywords:
10-meter air pistolMachine learningPersonalised trainingSHAP

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  • Utilized SHAP for model interpretability and feature contribution analysis.

    • Main Results:

    • The optimized XGBoost model achieved an AUC of 0.86 and accuracy of 0.83 on the test set.
    • SHAP analysis identified smaller aiming deviation and stable final-second aiming as crucial for high performance.
    • The model demonstrated effectiveness in classifying performance levels.

    Conclusions:

    • An interpretable machine learning framework can effectively classify shooting performance and identify key technical factors.
    • This approach offers data-driven, individualized feedback for precision shooting training.
    • Further validation with more athletes is recommended to enhance generalizability.