Using unsupervised machine learning to characterize recovery patterns in elite canoe-kayak athletes across the Olympic training year.

Background: Continuous wearable monitoring generates high-volume data, yet methods to translate these streams into actionable recovery insights for elite athletes remain scarce. This study applied a multi-layer, unsupervised machine-learning pipeline to characterize nightly recovery states and season-long physiological phenotypes In Olympic-level French canoe-kayak paddlers. Methods: Seventeen national-team athletes (9 women) were followed for 5,855 nights (≈12 months). Internal load-heart rate, heart-rate 5ariability (HRV), respiratory rate and 30 sleep-architecture variables-was captured with thoracic belts and validated smart rings; external load was logged via an online training platform. After data standardization and validation using multiple indices, K-means clustering was performed. Results: A four-cluster night typology (K0-K3) emerged (Silhouette = 0.52). Sleep quantity and fragmentation indices-time in bed, total sleep duration, light-sleep duration, efficiency, phase count and transitions-explained up to 79 % of between-cluster variance (η 2 ≥ 0.70). Nocturnal respiratory rate contributed an additional 15%, whereas HR/HRV each accounted for ≤4%. Forty-one percent of nights were classed as "optimized recovery" (K3), characterized by long, uninterrupted sleep and low respiratory rate. Athlete-level clustering yielded four profiles (A0-A3). Notably, the highest-performing cluster (A3) paradoxically combined slightly reduced sleep efficiency (85.9%) with superior cardiac-autonomic markers (HR: 46 bpm, HRV: 117 ms), suggesting that robust vagal tone may compensate for sub-optimal sleep quality-a finding that challenges conventional recovery paradigms. Conclusion: Integrated sleep architecture is the dominant discriminator of nightly recovery state, while elevated respiratory rate flags residual metabolic strain. Stable season-long physiological signatures align closely with competitive success, underscoring the value of personalized, ML-driven recovery monitoring in high-performance sport. Athlete profile reveals that exceptional cardiac-autonomic tone can compensate for sub-optimal sleep efficiency in elite performers, suggesting that vagal dominance may be more critical than perfect sleep architecture for competitive success.