Differences in the attributes required for success in different sporting events likely contribute to the wide range of peak-performance ages of elite athletes. Understanding the relationships between age of peak competitive performance and event duration should be useful for tracking athlete progression and talent identification.
The age-related progression of elite athletes to their career-best performances can provide benchmarks for talent development. The purpose of this study was to model career performance trajectories of Olympic swimmers to develop these benchmarks. We searched the Web for annual best times of swimmers who were top 16 in pool events at the 2008 or 2012 Olympics, from each swimmer's earliest available competitive performance through to 2012. There were 6959 times in the 13 events for each sex, for 683 swimmers, with 10 ± 3 performances per swimmer (mean ± s). Progression to peak performance was tracked with individual quadratic trajectories derived using a mixed linear model that included adjustments for better performance in Olympic years and for the use of full-body polyurethane swimsuits in 2009. Analysis of residuals revealed appropriate fit of quadratic trends to the data. The trajectories provided estimates of age of peak performance and the duration of the age window of trivial improvement and decline around the peak. Men achieved peak performance later than women (24.2 ± 2.1 vs. 22.5 ± 2.4 years), while peak performance occurred at later ages for the shorter distances for both sexes (∼1.5-2.0 years between sprint and distance-event groups). Men and women had a similar duration in the peak-performance window (2.6 ± 1.5 years) and similar progressions to peak performance over four years (2.4 ± 1.2%) and eight years (9.5 ± 4.8%). These data provide performance targets for swimmers aiming to achieve elite-level performance.
Purpose: In recent years (2011–2016), men’s 800-m championship running performances have required greater speed than previous eras (2000–2009). The “anaerobic speed reserve” (ASR) may be a key differentiator of this performance, but profiles of elite 800-m runners and their relationship to performance time have yet to be determined. Methods: The ASR—determined as the difference between maximal sprint speed (MSS) and predicted maximal aerobic speed (MAS)—of 19 elite 800- and 1500-m runners was assessed using 50-m sprint and 1500-m race performance times. Profiles of 3 athlete subgroups were examined using cluster analysis and the speed reserve ratio (SRR), defined as MSS/MAS. Results: For the same MAS, MSS and ASR showed very large negative (both r = −.74 ± .30, ±90% confidence limits; very likely) relationships with 800-m performance time. In contrast, for the same MSS, ASR and MAS had small negative relationships (both r = −.16 ± .54; possibly) with 800-m performance. ASR, 800-m personal best, and SRR best defined the 3 subgroups along a continuum of 800-m runners, with SRR values as follows: 400–800 m ≥ 1.58, 800 m ≤ 1.57 to ≥ 1.48, and 800–1500 m ≤ 1.47 to ≥ 1.36. Conclusion: MSS had the strongest relationship with 800-m performance, whereby for the same MSS, MAS and ASR showed only small relationships to differences in 800-m time. Furthermore, the findings support the coaching observation of three 800-m subgroups, with the SRR potentially representing a useful and practical tool for identifying an athlete’s 800-m profile. Future investigations should consider the SRR framework and its application for individualized training approaches in this event.
Maintaining workload ratios of 1 to 1.5 may be optimal for athlete preparation in professional basketball. An individualized approach to modeling and monitoring the training load-injury relationship, along with a symptom-based injury-surveillance method, should help coaches and performance staff with individualized training-load planning and prescription and with developing athlete-specific recovery and rehabilitation strategies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.