Successful Pacing Profiles of Olympic and IAAF World Championship Middle-Distance Runners Across Qualifying Rounds and Finals
Bibliographic record
Abstract
PURPOSE: This was the first study to analyze high-resolution pacing data from multiple global championships, allowing for deeper and rigorous analysis of pacing and tactical profiles in elite-standard middle-distance racing. The aim of this study was to analyze successful and unsuccessful middle-distance pacing profiles and variability across qualifying rounds and finals. METHODS: Finishing and 100-m split speeds and season's best times (SB) were collected for 265 men and 218 women competing in 800 m and 1500 m races, with pace variability expressed using coefficient of variation (CV). RESULTS: In both events, successful athletes generally separated themselves from slower athletes in the final 200 m, not by speeding up, but by avoiding slowing compared with competitors. This was despite different pacing profiles between events in the earlier part of the race preceding the endspurt. Approximately 10% of athletes ran SBs, showing a tactical approach to elite-standard middle-distance racing, and possible fatigue across rounds. Men's and women's pacing profiles were remarkably similar within each event, but the previously undescribed seahorse-shaped profile in the 800 m (predominantly positive pacing) differed from the J-shaped negative pacing of the 1500 m. Pacing variability was high compared with world records, especially in the finals (CV: 5.2 - 9.1%), showing that athletes need to be able to vary pace and cope with surges. CONCLUSIONS: Previous studies have focussed more on athletes in finals, but the present study showed that the best athletes had the physiological capacity to vary pace and respond to surges through successive competition rounds.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".