IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i4d10.1007_s00180-024-01552-8.html

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-024-01552-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-024-01552-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Kowarik, Alexander & Templ, Matthias, 2016. "Imputation with the R Package VIM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i07).
    2. Charles I. Jones & Christopher Tonetti, 2020. "Nonrivalry and the Economics of Data," American Economic Review, American Economic Association, vol. 110(9), pages 2819-2858, September.
    3. Desheng Dash Wu & Wolfgang Karl Härdle, 2020. "Service data analytics and business intelligence 2017," Computational Statistics, Springer, vol. 35(2), pages 423-426, June.
    4. Wu, Desheng Dang & Härdle, Wolfgang Karl, 2020. "Service Data Analytics and Business Intelligence," IRTG 1792 Discussion Papers 2020-002, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. de Pedraza, Pablo & Vollbracht, Ian, 2020. "The Semicircular Flow of the Data Economy and the Data Sharing Laffer curve," GLO Discussion Paper Series 515, Global Labor Organization (GLO).
    2. Long Chen & Yadong Huang & Shumiao Ouyang & Wei Xiong, 2021. "The Data Privacy Paradox and Digital Demand," Working Papers 2021-47, Princeton University. Economics Department..
    3. Huang, Zhehao & Dong, Hao & Liu, Zhaofei & Albitar, Khaldoon, 2025. "Unleashing the empowered effect of data resource on inclusive green growth: Based on double machine learning," Economic Analysis and Policy, Elsevier, vol. 85(C), pages 1270-1290.
    4. Feng, Wei & Sun, Shujun & Yuan, Hang, 2023. "Research on the efficiency of factor allocation in the pilot free trade zones," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 727-745.
    5. Xiao, Shengpeng & Wu, Kedong & Xiao, Xiao & Lin, Changqing, 2025. "How do data assets affect firm investment?," International Review of Financial Analysis, Elsevier, vol. 106(C).
    6. Jason Potts & Andrew Torrance & Dietmar Harhoff & Eric von Hippel, 2024. "Profiting from Data Commons: Theory, Evidence, and Strategy Implications," Strategy Science, INFORMS, vol. 9(1), pages 1-17, March.
    7. Wang, Huizong & Hao, Yulong & Fu, Qiang, 2024. "Data factor agglomeration and urban green finance: A quasi-natural experiment based on the National Big Data Comprehensive Pilot Zone," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    8. Maximilian Schaefer, 2025. "When Should we Expect Non-Decreasing Returns from Data in Prediction Tasks?," Papers 2503.03602, arXiv.org.
    9. Daron Acemoglu & Ali Makhdoumi & Azarakhsh Malekian & Asu Ozdaglar, 2022. "Too Much Data: Prices and Inefficiencies in Data Markets," American Economic Journal: Microeconomics, American Economic Association, vol. 14(4), pages 218-256, November.
    10. Henry Webel & Lili Niu & Annelaura Bach Nielsen & Marie Locard-Paulet & Matthias Mann & Lars Juhl Jensen & Simon Rasmussen, 2024. "Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    11. Ehsan Valavi & Joel Hestness & Newsha Ardalani & Marco Iansiti, 2022. "Time and the Value of Data," Papers 2203.09118, arXiv.org.
    12. Wang, Li & Wu, Yuhan & Huang, Zeyu & Wang, Yanan, 2024. "Big data application and corporate investment decisions: Evidence from A-share listed companies in China," International Review of Financial Analysis, Elsevier, vol. 94(C).
    13. Bergemann, Dirk & Ottaviani, Marco, 2021. "Information Markets and Nonmarkets," CEPR Discussion Papers 16459, C.E.P.R. Discussion Papers.
    14. Zhang, Wenkang & Wu, Jing, 2025. "Endogenous growth and data heterogeneity in data economics," Finance Research Letters, Elsevier, vol. 78(C).
    15. Bram Janssens & Matthias Bogaert & Mathijs Maton, 2023. "Predicting the next Pogačar: a data analytical approach to detect young professional cycling talents," Annals of Operations Research, Springer, vol. 325(1), pages 557-588, June.
    16. Chhetri, Netra & Ghimire, Rajiv & Wagner, Melissa & Wang, Meng, 2020. "Global citizen deliberation: Case of world-wide views on climate and energy," Energy Policy, Elsevier, vol. 147(C).
    17. Ieva Burakauskaitė & Andrius Čiginas, 2023. "An Approach to Integrating a Non-Probability Sample in the Population Census," Mathematics, MDPI, vol. 11(8), pages 1-14, April.
    18. Catherine E. Tucker, 2023. "The Economics of Privacy: An Agenda," NBER Chapters, in: The Economics of Privacy, pages 5-20, National Bureau of Economic Research, Inc.
    19. Zhou, Zhongsheng & Li, Zhuo & Du, Shanzhong & Cao, June, 2024. "Robot adoption and enterprise R&D manipulation: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 200(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:40:y:2025:i:4:d:10.1007_s00180-024-01552-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.