Result-based talent identification in road cycling: discovering the next Eddy Merckx

David Van Bulck (), Arthur Vande Weghe and Dries Goossens ()
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David Van Bulck: Ghent University
Arthur Vande Weghe: Ghent University
Dries Goossens: Ghent University

Annals of Operations Research, 2023, vol. 325, issue 1, No 23, 539-556

Abstract: Abstract In various sports large amounts of data are nowadays collected and analyzed to help scouts with identifying talented young athletes. In contrast, the literature on result-based talent identification in road cycling is remarkably scarce. The purpose of this paper is to provide insight into the possibilities of the use of publicly available data to discover new talented Under-23 (U23) riders via statistical learning methods (linear regression and random forest techniques). At the same time, we try to find out the main determinants of success for U23 riders in their first years of professional cycling. We collect results for more than 25000 road cycling races from 2007–2018 and consider more than 2500 riders from over 80 countries. We use the data from 2007 to 2017 to train and validate our models, and use the data from 2018 to predict how well U23 riders will perform in their first three elite years. Our results reveal that past U23 race results appear to be important predictors of future cycling performance.

Keywords: Scouting; Talent identification; Professional road cycling; Performance (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10479-021-04280-0

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