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

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.