Abstract
The purpose of the present study was to determine a model of anthropometric, physical fitness, and sport-specific performance data in young canoe sprint athletes for the prediction of adult performance level. In a prospective cohort study design, anthropometric (e.g., body height/mass), physical fitness (e.g., muscular endurance, linear speed), and sport-specific performance data (e.g., 250/2000 m on-water trials) of 731 young canoe sprint athletes were obtained between 1992 and 2019. Prediction models comprised random forest classifier, logistic regression, and naive bayes. The comparison of different prediction models showed that random forest classifier outperformed the others. More precisely, the binary outcome of adult performance level (international vs. national) was predicted with an overall accuracy of 95%. The estimated chance of belonging to either the performance level national or international can help coaches and practitioners of canoe sprint athletes for decision-making during the course of long-term athlete development and talent identification.
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References
Bourdon, P.C., et al..: Monitoring athlete training loads: consensus statement. Int. J. Sports Physiol. Performance 12(s2), S2-161–S2-170 (2017). https://doi.org/10.1123/IJSPP.2017-0208. https://journals.humankinetics.com/view/journals/ijspp/12/s2/article-pS2-161.xml
Casalicchio, G.: mlr3gallery: Imbalanced data handling with mlr3 (2020). https://mlr3gallery.mlr-org.com/posts/2020-03-30-imbalanced-data/
Güllich, A.: International medallists’ and non-medallists’ developmental sport activities – a matched-pairs analysis. J. Sports Sci. 35(23), 2281–2288 (2017). https://doi.org/10.1080/02640414.2016.1265662
Lang, M., et al.: mlr3: a modern object-oriented machine learning framework in R. J. Open Source Softw. (2019). https://doi.org/10.21105/joss.01903
Molnar, C., Bischl, B., Casalicchio, G.: iml: an r package for interpretable machine learning. JOSS 3(26), 786 (2018). https://doi.org/10.21105/joss.00786
Pearson, D.T., Naughton, G.A., Torode, M.: Predictability of physiological testing and the role of maturation in talent identification for adolescent team sports. J. Sci. Med. Sport 9(4), 277–287 (2006). https://doi.org/10.1016/j.jsams.2006.05.020
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2020). https://www.R-project.org/
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Saal, C., Helm, N., Prieske, O. (2022). Anthropometry, Physical Fitness, Sport-Specific Performance and the Prediction of Performance Level in Young Canoe Sprint Athletes. In: Baca, A., Exel, J., Lames, M., James, N., Parmar, N. (eds) Proceedings of the 9th International Performance Analysis Workshop and Conference & 5th IACSS Conference. PACSS 2021. Advances in Intelligent Systems and Computing, vol 1426. Springer, Cham. https://doi.org/10.1007/978-3-030-99333-7_11
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DOI: https://doi.org/10.1007/978-3-030-99333-7_11
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