The association of origin and environmental conditions with performance in professional IRONMAN triathletes
Bibliographic record
Abstract
We have (i) little knowledge about where the fastest professional IRONMAN triathletes originate from and where the fastest races take place and (ii) we have no knowledge of the optimal weather conditions for an IRONMAN triathlon. The aims of the present study were, therefore, (i) to investigate the origin and the fastest IRONMAN race courses for professional triathletes and (ii) to evaluate the best environmental conditions (i.e. water and air temperatures and type of race course) for the fastest IRONMAN race times in professional IRONMAN triathletes. Data of all professional female and male IRONMAN triathletes competing between 2002 and 2022 in all IRONMAN races held worldwide were collected. A total of 6,943 finishers´ records (4,162 from men and 2,781 from women) from 58 different countries participating in 54 different event locations between 2002 and 2022 were considered. Data was analyzed using descriptive statistics and machine learning (ML) regression models. The models considered gender, country of origin, event location, water, and air temperature as independent variables to predict the final race time. Three different ML models were built and evaluated, based on three algorithms, in order of growing complexity and predictive power: Decision Tree Regressor, Random Forest Regressor, and XG Boost Regressor. Most of the athletes originated from the USA (1786), followed by athletes from Germany (674), Canada (426), Australia (396), United Kingdom (342), France (325), and Switzerland (276). Most of the athletes competed in IRONMAN Hawaii (925), IRONMAN Florida (563), IRONMAN Austria (452), IRONMAN France (354), IRONMAN Wisconsin (330), IRONMAN Lanzarote (322) and IRONMAN Texas (313). The Decision Tree and the XG Boost models were the best performing models (r$^{2}$ = 0.48) and rated the relative feature importances in the order gender, country of origin, water temperature, air temperature and event location. Men were on average ~ 0.8 h faster than women. Switzerland had the fastest and Japan and Slovakia the slowest athletes. IRONMAN Brazil Florianopolis, IRONMAN Barcelona, and IRONMAN Louisville hold the fastest races. Optimal water temperature was over 22 °C and optimal air temperature between 19 and 26 °C. Between 2002 and 2022, most professional IRONMAN triathletes originated from the USA, and most professional IRONMAN triathletes competed in IRONMAN Hawaii. The fastest athletes originated from Switzerland, the fastest race courses were IRONMAN Brazil Florianopolis, IRONMAN Barcelona, and IRONMAN Louisville. The fastest race times were achieved in water temperature warmer than 22 °C and air temperature between 19 and 26 °C.
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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 | 0.002 |
| 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) | 0.002 | 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".