Freestyle master’s swimming: Nationality, sex, and performance trends in World Aquatics competitions (1986–2024)
Bibliographic record
Abstract
BACKGROUND: In sports science, freestyle swimming has been thoroughly studied for particular performance-related factors. Nonetheless, it is unknown what countries the top freestyle swimmers are from, especially not for age group swimmers. In addition, the existing research on the performance of master freestyle swimmers has yet to confirm that male swimmers achieve faster times than their female counterparts across all age groups and distances. The current study looked into the nationalities and sexes of the top freestyle swimmers in each age group in World Aquatics competitions for the 50m, 100m, 200m, 400m, and 800m events from 1986 to 2024. METHODS: The data (derived from (www.worldaquatics.com/masters/archives/masters-archives) were presented using mean, standard deviation, maximum and minimum values, and/or confidence intervals. The year of competition, age, age group, stroke, distance, and first and last names of each swimmer were noted. The nations were then divided into six groups: one group comprising all other countries and the top five nations with the greatest number of appearances in the top 10 fastest freestyle swimming times by distance each year. RESULTS: In freestyle swimming, most swimmers (30.6%) competed in the 50m event (n = 25,094, 10,909 female and 14,185 male), followed by the 100m event (25.6%, n = 20,961, 8,796 female and 12,165 male), the 200m event (17.4%, n = 14,309, 6,729 female and 7,580 male), the 400m event (13.4%, n = 10,956, 5,363 female and 5,593 male), and the 800m event (12.6%, n = 10,317, 5,179 female and 5,138 male). The results from the generalized linear models indicate that sex, age group, and the interaction between sex and age group all had significant effects on the 50m, 100m, 200m, 400m, and 800m races. Specifically, for the 50m races, the effect of sex was significant (x2 (1) = 3451.941, p < 0.001), as was the effect of age group (χ2 (13) = 19,295.169, p < 0.001), and the interaction between sex and age group (χ 2 (13) = 654.671, p < 0.001). CONCLUSION: The USA demonstrates quantitative dominance by contributing the greatest number of top 10 performers. Additionally, the study highlights significant sex-based performance differences, with males generally outperforming females in all age categories. This study comprehensively analyzes the performance trends observed in freestyle master swimming for nearly four decades.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".