ArticleAnnals of operations research2023
Result-based talent identification in road cycling: discovering the next Eddy Merckx.
Article in Annals of operations research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- From Amateur to Professional Cycling: A Case Study on the Training Characteristics of a Zwift Academy Winner.Sports (Basel, Switzerland) · 2025Article
- Talent Identification: Time to Move Forward on Estimation of Potentials? Proposed Explanations and Promising Methods.Sports medicine (Auckland, N.Z.) · 2025Review
- Chilean National Sports Talent Detection System: Influence of Biological Age, Sex, and Geographic Area.Journal of functional morphology and kinesiology · 2024Article
- Pro-cycling team cyclist assignment for an upcoming race.PloS one · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.