Understanding successful and unsuccessful landings of aerial maneuver variations in professional surfing
Bibliographic record
Abstract
Although performing aerial maneuvers can increase wave score and winning potential in competitive surfing, the critical features underlying successful aerial performance have not been systematically investigated. This study aimed to analyze highly skilled aerial maneuver performance and to identify the critical features associated with successful or unsuccessful landing. Using video recordings of the World Surf League's Championship Tour, every aerial performed during the quarterfinal, semifinal, and final heats from the 11 events in the 2015 season was viewed. From this, 121 aerials were identified with the Frontside Air (n = 15) and Frontside Air Reverse (n = 67) being selected to be qualitatively assessed. Using chi‐squared analyses, a series of key critical features, including landing over the center of the surfboard (FS Air χ2 = 14.00, FS Air Reverse χ2 = 26.61; P < .001) and landing with the lead ankle in dorsiflexion (FS Air χ2 = 3.90, FS Air Reverse χ2 = 13.64; P < .05), were found to be associated with successful landings. These critical features help surfers land in a stable position, while maintaining contact with the surfboard. The results of this study provide coaches with evidence to adjust the technique of their athletes to improve their winning potential.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".