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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".