The significance of data analysis in high-performance sports has largely increased in recent years offering opportunities for further exploration using machine learning techniques. As a pioneer work in the academic community, our work showcases the power of data-driven approaches in enhancing performance and decision-making at high-performance sailing events. Specifically, we explore the application of data mining techniques on the dataset collected at a high-performance sailing event in Bermuda in 2021. By analyzing data from Race 4, the study aims to gain valuable insights into the relationship between variables such as wind speed, wind direction, foil usage, and daggerboard adjustments, and their impact on boat speed. Various prediction models, including Gradient Boosting, Random Forest, and a stacked model, were employed and evaluated using performance metrics like R2 score and mean squared error. The results demonstrate the models` ability to accurately predict boat speed. These findings can be utilized to refine race strategies, optimize sail and rudder settings, and improve overall performance in SailGP races. Future plans include collaboration with SailGP to work with larger datasets and integrate the models into live racing scenarios.
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