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Statistical models for classification by handedness of Olympic Trap shooters in digital training services and remote coaching

Author

Listed:
  • Riccardo Zanardelli

    (University of Brescia
    Fabbrica d’Armi Pietro Beretta S.p.A.)

  • Maurizio Carpita

    (University of Brescia)

  • Marica Manisera

    (University of Brescia)

Abstract

In this paper, we address the problem of classification by handedness of Olympic Trap shooters applying statistical methods to newly available data gathered from the field. We assess the performance of binary classification models based on KNN and Binary Regression, with both symmetric and asymmetric link functions, in a context characterized by unbalanced data. Our results show promising classification performance, suitable for first non-critical applications in data driven training services and remote coaching, encouraging further future research.

Suggested Citation

  • Riccardo Zanardelli & Maurizio Carpita & Marica Manisera, 2025. "Statistical models for classification by handedness of Olympic Trap shooters in digital training services and remote coaching," Computational Statistics, Springer, vol. 40(4), pages 1801-1823, April.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:4:d:10.1007_s00180-024-01552-8
    DOI: 10.1007/s00180-024-01552-8
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