Essential Skills for Superior Wave-Riding Performance: A Systematic Review
Bibliographic record
Abstract
Forsyth, JR, Riddiford-Harland, DL, Whitting, JW, Sheppard, JM, and Steele, JR. Essential skills for superior wave-riding performance: A systematic review. J Strength Cond Res 34(10): 3003-3011, 2020-To successfully and safely perform surfing maneuvers, surfers and their coaches need to know how to perform each maneuver correctly. Although some components of the sport are well understood, evidence-based recommendations in the scientific literature on how to perform surfing skills are sparse. The aim of this article was to systematically review the body of literature pertaining to discrete wave-riding skills and characteristics that are associated with the ability of surfers to successfully perform them. Searches of PubMed, SCOPUS, SPORTDiscus with Full-text, and Web of Science were undertaken in January 2019, to identify the most appropriate literature, with secondary searches of reference lists used to create a greater pool of possible articles. The review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P). Ten studies deemed appropriate for review captured data from 299 surfers, who were predominantly competitive (78.3%) and male (58.2%). The average Down and Black Quality Index of the articles was 76.3 ± 8.4%, with these articles focusing on the "pop-up" and landing skills. Performance indicators, such as isometric push-up peak forces, force-plate derived and in-water time to pop-up, relative peak forces generated when landing and time-to-stabilization, were all shown to be related to the physical characteristics of surfers and could affect the ability of surfers to successfully ride a wave. Findings from the studies included in this review suggest that the pop-up and landing exhibit trainable qualities that coaches and athletes can use to improve surfing performance.
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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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".