Wearable-Derived Sleep and Physiological Metrics Are Associated With Performance in Professional Golfers
Bibliographic record
Abstract
PURPOSE: Elite golf performance hinges on physiological and psychological precision, with success often defined by razor-thin margins. Sleep and cardiac autonomic function, reflected by resting heart rate (RHR) and heart-rate variability (HRV), are indicators of recovery and readiness, yet their role in golf remains understudied. METHODS: We analyzed wearable-derived data from 389 male professional golfers across 521 events, totaling 35,140 nights of monitoring. Key metrics included sleep duration (7.2 [0.7] h), sleep consistency (regularity of sleep/wake times; 69.1% [6.9%]), RHR (55.9 [7.9] beats·min-1), HRV (root mean square of successive differences; 64.2 [28.1] milliseconds), and a composite recovery score (integrating sleep and biometric data; 59.1% [9.9%]). Objective golf performance (total score, great/poor shots, strokes gained) was extracted from a subscription-based database. Models assessed between-persons differences and within-person changes across seasons (using seasonal averages), adjusting for age (34.1 [9.1] y), height (1.81 [0.07] m), and weight (83.2 [10.6] kg). RESULTS: Golfers with longer and more consistent sleep, lower RHR, and higher HRV performed better (P < .05). Between athletes, each additional hour of sleep was associated with a lower score (b = -0.522), as was a 10-percentage-point increase in sleep consistency (b = -0.382), a 1-beat-per-minute lower RHR (b = -0.038), and a 10-percentage-point increase in recovery (b = -0.476). Within athletes, improvements in sleep consistency (b = -0.193 per 10 percentage points), HRV (b = -0.016 per 1 millisecond), and recovery (b = -0.238 per 10 percentage points) were also associated with lower scores (P < .05). CONCLUSIONS: Sleep and cardiac autonomic function were associated with elite golf performance. Both individual differences and within-athlete improvements were linked to better play, highlighting the potential role of sleep, RHR, and HRV in optimizing performance at the highest level of golf.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".