Time Management Strategies of Rock Climbers in World Cup Bouldering Finals
Bibliographic record
Abstract
Competitive rock climbing recently made its Olympic debut, but minimal published research exists regarding training and competition strategies. Time management strategies define the structured approach climbers take in bouldering competitions to successfully obtain a "top" or a "zone" hold. During finals rounds of the International Federation of Sport Climbing bouldering competitions, climbers are allotted 240 s to complete a boulder. Variables influencing a climber's time management strategies include their work-to-rest intervals, and the frequency of their attempts or rests. Video analysis of International Federation of Sport Climbing competitions was used to collect time management strategy data of professional climbers. Fifty-six boulders (28 female and 28 male boulders) over the 2019 International Federation of Sport Climbing season were analyzed. Time management strategies variables were compared between slab/slab-like and non-slab bouldering styles using generalized estimating equations with significance set to p < 0.05. Additionally, we determined trends in success rates for various styles of boulders. There were no differences in the number of attempts taken per boulder between slab/slab-like and non-slab boulders (3.7 ± 2.3 and 3.8 ± 2.4, p = 0.97), but climbers spent more time actively climbing on slab/slab-like (92 ± 36 s) compared to non-slab boulders (65 ± 26 s, p < 0.001). Trends in the success rate suggest climbers who take more than 6 attempts on any boulder style are unsuccessful. The results of this study provide practical information that can be used by coaches and athletes to guide training and competition strategy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".