Imposing A Theoretical Optimal Pacing Strategy Compared To Self-paced Competition In 1500-m Speed Skating
Bibliographic record
Abstract
PURPOSE: Humans are well equipped to adequately choose the right pace on a specific effort, such as a 1500-m speed skating. The purpose of the present study was to investigate if a theoretically optimal pacing profile, overriding self-paced strategy, yielded better performance. METHODS: 7 national level speed-skaters performed a self-paced 1500 m and a 1500 m with an imposed pacing strategy. The races were analyzed by obtaining velocity (every 100 m) and body position (every 200 m) to calculate total mechanical power output. Together with gross efficiency and aerobic power output, obtained in separate trials, data were used to calculate anaerobic power output profiles. An energy flow model was applied to the 1500-m self-paced trial and a range of pacing strategies was simulated for each individual by varying the distribution of anaerobic energy (Pan) over time (t). Athletes were instructed to skate more like the theoretically optimal pacing profile and resulting performance was compared to self-paced performance. RESULTS: Confirming previous results, the energy flow model predicted a faster start strategy to be optimal. However, final times of the imposed strategy trials were about 2 s slower than self-paced performance (115.39 ± 4.45 s vs 117.29 ± 3.53 s). Total power distribution per lap differed, with a higher value over the first 300 m for the imposed strategy (637.0 ± 49.4 W vs. 612.5 ± 50.0 W). All parameters of anaerobic power distribution over time, described by a mono-exponential equation, did not differ (self-paced: Pan = 102.2 + 673.0 * e (-0.056 * t) vs. imposed: Pan = 91.6 +710.9 * e (-0.054 * t)). The effort of a faster first lap resulted in a changed skating position. The summation of increased knee- and trunk-angles resulted in a higher aerodynamic drag coefficient throughout the race. CONCLUSION: Without appropriate training, imposing a theoretically optimal pacing profile does not lead to better performance. An imposed fast start has relatively large consequences on speed skating technique, affecting work per stroke and aerodynamics negatively. Since the ability to maintain body position is an important performance determining factor in skating, a predefined movement where body weight has to be carried continuously, it might be beneficial to train this aspect of performance combined with pacing 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 | 0.001 | 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".