Improving sport horse selection using machine learning: Early prediction of elite-level show jumping performance

Marco Zanchi, Clara Bordin, Michela Ablondi, Vittoria Asti, Andrea Summer, Emanuela Valle and Laura Ozella

PLOS ONE, 2026, vol. 21, issue 9, 1-17

Abstract: Early identification of sport potential in show jumping horses remains challenging because competition outcomes vary widely, and elite-level performance develops after considerable time. In this study, we evaluated the applicability of machine learning approaches to competition records from horses identified through FEI participation, complemented by national-level records from Sweden, Belgium and France, with the aim of identifying horses likely to reach elite performance. Performance data from Fédération Équestre Internationale (FEI) competitions were analysed using an Extreme Gradient Boosting (XGBoost) classifier trained on longitudinal competition histories recorded before horses reached ten years of age. Animals were classified as champions if they achieved at least one clear round at 160 cm, while non-champions were defined as those that completed their competitive career without reaching this level. Model performance was assessed using nested cross-validation and compared with two single-rule-based classifiers based on maximum obstacle height thresholds. XGBoost outperformed baseline approaches across all evaluation metrics, achieving an average area under the ROC curve of 83.5% and an average precision of 80.0%. Analysis of probability trajectories showed that predictive information accumulates progressively over a horse’s career, with increasing separation between champion and non-champion predictions as competition history expands. Model performance improved substantially from approximately seven years of age onward. Permutation importance and SHapley Additive exPlanations (SHAP) analyses revealed that obstacle difficulty, performance consistency and age-related progression were among the most influential predictors. Overall, these findings demonstrate that interpretable machine learning models can extract meaningful predictive signals from early competition trajectories and support evidence-based talent identification in show jumping, while maintaining attention to performance development and horse welfare.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0357799

DOI: 10.1371/journal.pone.0357799

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