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Optimizing race strategy: a machine learning model for predicting formula 1 pit stop timing
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This thesis explores Formula 1 pit stop strategies through advanced analytics, with a focus on driver
clustering in relation to performance, tactical, and behavioural aspects. Our approach led to the
identification of four distinct driver categories, providing a framework to investigate various pit
stop strategies. By integrating these driver profiles into predictive models, the study delves into the
impact of driver characteristics on team strategy and pit stop efficiency. We introduce a novel
dimension by developing a binary prediction model for pit stop timing, thoroughly evaluated within
a simulation environment. This research contributes to a more refined understanding of strategic
elements in Formula 1, demonstrating the role of tailored analytic methods in optimizing racing
tactics and decision-making processes.
Descrição
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Machine learning Predictive modeling Strategy Pit stop Motorsport Formula 1