Workloads of Competitive Surfing: Work-to-Relief Ratios, Surf-Break Demands, and Updated Analysis
Bibliographic record
Abstract
Farley, ORL, Secomb, JL, Raymond, ER, Lundgren, LE, Ferrier, BK, Abbiss, CR, and Sheppard, JM. Workloads of competitive surfing: work-to-relief ratios, surf-break demands, and updated analysis. J Strength Cond Res 32(10): 2939-2948, 2018-The study provides an in-depth descriptive and quantitative time-motion analysis of competitive surfing, using Global Positioning System (GPS) units and video synchronization, which serves to extend upon the results of Farley, Harris, and Kilding (Journal of Strength and Conditioning Research, 26, 7 [2012]). In addition, comparisons between locations and surfers competing in the same heats were performed. Global Positioning System and video data were collected from 41 male competitive surfers (23.2 ± 6.1 years, 71 ± 10.3 kg, 177.2 ± 6.4 cm) participating in 3 professional domestic surfing events, with competitive heats of 20-minute duration. Fifty data sets were analyzed across the 3 competitions, with velocities and distances covered, proportion of time spent performing various surfing activities, and total work-to-relief ratio determined. Results revealed surfers paddled 44% of the total time, followed by stationary periods (42%). Surfers performed at a significantly (p ≤ 0.05) higher work-to-relief ratio (1.7:1) at the beach-break (an exposed beach) compared with point-break 1 and 2 (waves breaking around a rocky point). Point-breaks 1 and 2 had longer continuous durations of paddling, with significantly longer rides at point-break 1 over the beach-break (p ≤ 0.01) and point-break 2 (p ≤ 0.01). The average maximal speed (24.8 km·h) from point-break 2 was significantly faster than point-break 1 (p ≤ 0.01) and beach-break (p ≤ 0.05). This information should influence surfing drills and conditioning methods to prepare these athletes for the disparate demands, such as training for a point-break competition involving longer durations of continuous paddling and short, high-intensity workloads for a beach-break.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| 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 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".