Pacing Strategy During 24-Hour Ultramarathon-Distance Running
Bibliographic record
Abstract
Purpose: To describe pacing strategy in a 24-h running race and its interaction with sex, age group, athletes’ performance group, and race edition. Methods: Data from 398 male and 103 female participants of 5 editions were obtained based on a minimum 19.2-h effective-running cutoff. Mean running speed from each hour was normalized to the 24-h mean speed for analyses. Results: Mean overall performance was 135.6 ± 33.0 km with a mean effective-running time of 22.4 ± 1.3 h. Overall data showed a reverse J-shaped pacing strategy, with a significant reduction in speed from the second-to-last to the last hour. Two-way mixed ANOVAs showed significant interactions between racing time and both athlete performance group (F = 7.01, P < .001, ηp2 = .04) and race edition (F = 3.01, P < .001, ηp2 = .02) but not between racing time and either sex (F = 1.57, P = .058, ηp 2 < .01) or age group (F = 1.25, P = .053, ηp2 = .01). Pearson product–moment correlations showed an inverse moderate association between performance and normalized mean running speed in the first 2 h (r = –.58, P < .001) but not in the last 2 h (r = .03, P = .480). Conclusions: While the general behavior represents a rough reverse J-shaped pattern, the fastest runners start at lower relative intensities and display a more even pacing strategy than slower runners. The “herd behavior” seems to interfere with pacing strategy across editions, but not sex or age group of runners.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".