Historical data is a valuable asset in sports science, offering insights into athlete development and performance trends. This study explores long-term patterns in competitive swimming by analyzing performance variability over the past 20 years. Instead of clustering similar data points, we grouped entire performance trajectories based on their overall shape, regardless of timing or duration. To model variability, we used a Markov Switching Regression (MSR) model to identify transitions between two volatility regimes: stable (low variance) and unstable (high variance). Only swimmers with at least 50 performances were included to ensure reliable estimates. We then applied the KmlShape clustering algorithm, using Fréchet distance, to group swimmers by the similarity of their volatility profiles and performance patterns. Results showed a link between age, volatility, and competitive outcomes. Swimmers who began earlier tended to have lower volatility and lower performance, while those starting later showed higher volatility and better performance, possibly due to more intense adaptation and pressure in competition. These findings highlight how historical data can inform athlete development. Coaches and analysts can use this approach to understand progression, refine training strategies, and manage performance variability. Future research should expand this framework across sports and demographics for broader applicability.
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