Olympic cycle periodicity in women’s long and triple jumping performance between 1996 and 2019
Bibliographic record
Abstract
Performance variability is present in a series of competition results in athletics. Some of the variability is random and some can be attributed to factors such as the environment and changes in the level of physical, mental, and technical states of the athlete. Changes in the state of the athlete may be due to the competition schedule. It has been shown that there is periodicity in performance aligned with the seasonal competition schedule in athletics and with the Olympic cycle in pooled athletics data dating from 1896 to 2008. We investigated whether Olympic cycle periodicity was present in modern era long and triple jumping in elite men and women. Top 50 performances per year in the horizontal jumps in men and women from 1996 to 2019 were used. Each performance was normalized to the best result from the previous Olympic year. Two-way ANOVAs revealed significantly lower mean normalized performances in top ten women compared to top ten men (p < 0.001) in both jumps. In both jumps, ten top-performing women also showed decreases between the Olympic year mean normalized performances and the 1st year following (Long Jump: p = 0.022, Triple Jump: p = 0.008). In triple jump, the decrease in performance was also found in the second year following the Olympics. Performances deciles ranked from 11th to 50th place showed a similar pattern in women's triple jump but only for ranks 11 to 20 in the women's long jump. The findings suggest that periodicity driven by the Olympic cycle exists in women's long and triple jump at the elite level.
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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 | 0.001 | 0.001 |
| 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".