Project title
Breaking the Surface: A Data-Driven Investigation for Swimming Strategies and Outcomes
Project abstract
Investigating long-term trends and predictive insights in competitive swimming performance through large-scale race analysis. Unlike traditional studies that rely on limited
datasets or self-reports, this research uses publicly available data—such as those from the World Aquatics website—to explore broader questions: How do pacing patterns differ in elite swimmers? Can future Olympic records be predicted? Is there a typical peak age? How do elite swimmers evolve over their careers? And how have race strategies changed over time? By applying advanced statistical methods (e.g., regression models) and machine learning techniques (e.g., time-series forecasting), the project aims to uncover patterns across different strokes, events, and genders. There is also potential to extend the scope to national-level data (e.g., Great Britain/Scotland) and youth development, leveraging resources from the Scottish Institute of Sport. The findings could inform training strategies, career planning, and talent development in swimming.