Running Experience And Training Factors Influence On Boston Marathon Performance
Bibliographic record
Abstract
Various aspects of running training load may influence competitive endurance performance, including training volume, and training behavior changes (TBCs) leading up to races. Beyond training volume, recent research has suggested accounting for run types and cross-training when quantifying volume and TBCs that may influence athlete performance. PURPOSE: Assess the relationship between running experience, running load (volume and run types) and cross-training volume, and TBCs leading up to the 2022 Boston Marathon on race performance. METHODS: Boston Marathon adult registrants were contacted via email one month prior to the race for study survey participation. Athletes reported demographics, experience (N previous marathons), training volume (days, hr, and km/wk), run workouts (run workouts/wk), and cross-training (days/wk) in the past year and 4-months prior to the race. TBCs were calculated by comparing 4-month and year metrics. Official finish places within divisions were obtained through the race website using bib numbers. Separate linear regressions were used to 1) assess effects of running experience, training, and cross-training in the past 4 months on division rankings, and 2) assess effects of changes in TBCs on division rankings (binned: +/- 2 or more activity change). Age, sex, and experience were covariates in both models. RESULTS: There were 919 runners included in analyses (Females: 496, Males: 423). Accounting for covariates, running training and cross-training behaviors in the past year explained 42.3% of the variance in division rankings (p < 0.001). Running 10 or 15+ hr/wk (p-range: 0.006-0.007), average increased km/wk (p < 0.001) and performing 2 or 3 workouts/wk (p-range: <0.001-0.03) were each associated with higher division finish places. TBCs explained 33.5% of the variance in division rankings (p < 0.001); runners who decreased their number of weekly runs (p = 0.05) and workouts (p = 0.05) leading up to the race had higher division rankings compared to athletes who maintained/increased volume. CONCLUSIONS: While habitually higher training exposure contributed to improved marathon performance, notably, reduced training volume/workouts by 2 or more activities in the 4 months prior to the race contributed to more optimal performance.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".