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

Riccardo Zanardelli (), Maurizio Carpita () and Marica Manisera ()
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Riccardo Zanardelli: University of Brescia
Maurizio Carpita: University of Brescia
Marica Manisera: University of Brescia

Computational Statistics, 2025, vol. 40, issue 4, No 7, 1823 pages

Abstract: 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.

Keywords: Olympic Trap shooting; Handedness; Classification; KNN; Binary regression (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-024-01552-8

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