Scoring analysis of the men’s 2014, 2015 and 2016 world championship tour of surfing: the importance of aerial manoeuvres in competitive surfing
Bibliographic record
Abstract
The aim of this study was to investigate the impact of aerial manoeuvres on scoring in professional surfing. 23,631 waves were analysed for the number and types of aerial manoeuvres performed from the 2014, 2015 and 2016 Men's World Championship Tour. Additionally, the awarded score, timing and order of the aerial was also analysed. Descriptive statistics and Two Way ANOVA's were performed with Sidak Multiple Comparisons Post Hoc analysis. Results were a significantly higher score being awarded (P ≤ 0.0001) when including an aerial in competition across all three seasons. In 2015 surfers were awarded a significantly larger score when performing an air reverse, compared to 2014 (P = 0.0002) and 2016 (P = 0.0057). Surfers were also awarded a higher score for the full rotation aerial in 2015 compared to 2014 (P = 0.0177). In 2015 surfers performing forehand aerials were awarded a greater score than in 2016 (P = 0.0113). The timing of the aerial and score awarded was significantly greater in 2015 as opposed to 2014 when the aerial was their final manoeuvre (P < 0.0001) and when surfers timed the aerial performance early within the heat (P = 0.0027). If a surfer incorporates an aerial manoeuvre during competition, generally speaking, they will be awarded a significantly higher score.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".