Career-Span Analyses of Track Performance: Longitudinal Data Present a More Optimistic View of Age-Related Performance Decline
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Bibliographic record
Abstract
Abstract Sport scientists (Starkes, Weir, Singh, Hodges, & Kerr, 1999 Starkes, J. L., Weir, P. L., Singh, P., Hodges, N. J. and Kerr, T. 1999. Aging and the retention of sport expertise. International Journal of Sport Psychology, 30: 283–301. [Web of Science ®] , [Google Scholar]; Starkes, Weir, & Young, 2003 Starkes, J. L., Weir, P. L. and Young, B. W. 2003. “Retaining expertise: What does it take for older athletes to continue to excel?”. In Advances in research on sport expertise Edited by: Starkes, J. L. and Ericsson, K. A. 251–272. Champaign, IL: Human Kinetics. [Crossref] , [Google Scholar]) have suggested that prolonged training is critical for the maintenance of athletic performance even in the face of predicted age-related decline. This study used polynomial regression analyses to examine the relationship between age and running performance in the 1500 and 10,000 metre events. We compared the age and career-longitudinal performances for 15 male Canadian Masters athletes with a cross-sectional sample of performances at different ages. We hypothesized that the 30 years of uninterrupted training characteristic of this longitudinal sample would moderate the patterns of age-related decline (retention hypothesis); alternatively, the cross-sectional data were expected to demonstrate pronounced age-related decline (quadratic hypothesis). Investigators performed multimodel regression analyses on the age and performance data. Based on the absence (for longitudinal data) or presence (for the cross-sectional data) of significant quadratic components in second-order polynomial models, the authors found support for their respective hypotheses. The longitudinal data showed that running performance declined with age in a more linear fashion than did cross-sectional data. Graphical trends showed that the moderation of age-related decline appeared greater for the longitudinal 10 km performances than for the 1500 m event.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it