Optimal pacing strategy: from theoretical modelling to reality in 1500-m speed skating
Bibliographic record
Abstract
PURPOSE: Athletes are trained to choose the pace which is perceived to be correct during a specific effort, such as the 1500-m speed skating competition. The purpose of the present study was to "override" self-paced (SP) performance by instructing athletes to execute a theoretically optimal pacing profile. METHODS: Seven national-level speed-skaters performed a SP 1500-m which was analysed by obtaining velocity (every 100 m) and body position (every 200 m) with video to calculate total mechanical power output. Together with gross efficiency and aerobic kinetics, obtained in separate trials, data were used to calculate aerobic and anaerobic power output profiles. An energy flow model was applied to SP, simulating a range of pacing strategies, and a theoretically optimal pacing profile was imposed in a second race (IM). RESULTS: Final time for IM was ∼2 s slower than SP. Total power distribution per lap differed, with a higher power over the first 300 m for IM (637.0 (49.4) vs 612.5 (50.0) W). Anaerobic parameters did not differ. The faster first lap resulted in a higher aerodynamic drag coefficient and perhaps a less effective push-off. CONCLUSION: Experienced athletes have a well-developed performance template, and changing pacing strategy towards a theoretically optimal fast start protocol had negative consequences on speed-skating technique and did not result in better performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".