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Related Experiment Video

Updated: Jul 23, 2026

Swimming Performance Assessment in Fishes

Swimming Performance Assessment in Fishes

Published on: May 20, 2011

Modeling the training-performance relationship using a mixed model in elite swimmers.

Marta Avalos¹, Philippe Hellard, Jean-Claude Chatard

¹HEUDIASYC Laboratory, UMR CNRS 6599, Compiègne University of Technology, France. avalos@hds.utc.fr

Medicine and Science in Sports and Exercise

|May 17, 2003

Summary

This study modeled swimmer training and performance over three seasons, finding that personalized training schedules based on individual responses to short-, mid-, and long-term training loads improve results.

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Area of Science:

Sports Science
Performance Analysis
Physiology

Background:

Optimizing training loads is crucial for competitive swimmers.
Understanding individual responses to training stimuli is key for performance enhancement.
Previous models like the Banister model have limitations in capturing complex training-performance relationships.

Purpose of the Study:

To model the relationship between training load and performance in competitive swimmers over three seasons.
To identify distinct individual and group responses to training stimuli.
To provide a basis for personalized training prescription.

Main Methods:

A linear mixed model was employed as an alternative to the Banister model.
Training load effects were analyzed across three periods: short-term (2 weeks), mid-term (3-5 weeks), and long-term (6-8 weeks) preceding performance.
Cluster analysis was used to categorize swimmers based on their training response patterns.

Main Results:

Four distinct groups of swimmers were identified based on their response to training periods (long-term, long- and mid-term, short- and mid-term, combined).
Short-term training load negatively impacted performance across all groups, while mid- and long-term loads positively affected performance in most groups.
The influence of mid-term training decreased, and long-term training increased over the three seasons, with significant inter-individual differences observed.

Conclusions:

A significant relationship exists between training load and performance in swimmers, varying individually and over time.
The developed mixed model accurately reflects real-world performance data.
Results support the implementation of personalized training schedules tailored to swimmers' specific responses to different training durations.