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From Data to Podium: A Machine Learning Model for Predicting Formula 1 Compound Decisions
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From Data to Podium: A Machine Learning Model for Predicting Formula 1 Compound Decisions
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Dissertation
From Data to Podium: A Machine Learning Model for Predicting Formula 1 Compound Decisions
2023
Overview
This thesis explores the optimization of Formula 1 pit stop strategies, integrating advanced analytics and machine learning to predict tire compound decisions. A novel aspect of this study is the clustering of driver profiles based on performance, tactical, and behavioral metrics, which provides a deeper understanding of driver characteristics and their impact on race strategy. By analyzing data from the FastF1 API and employing various machine learning techniques, we developed predictive models that not only forecast compound decisions with higher accuracy but also highlight the significance of personalized strategies tailored to different driver clusters. The findings demonstrate the potential of combining driver clustering with predictive analytics to refine pit stop strategies, offering teams a competitive edge through data-driven decision-making.
Publisher
ProQuest Dissertations & Theses
Subject
ISBN
9798311956574
