Optimization of Formula 1 Racing Strategies: An Approach Based on Exploratory Analysis and Genetic Algorithms

Eduardo de Lira Brennand; Eugênio Silva; Gabriel Resende Machado

doi:10.5753/jbcs.2026.5833

Authors

Eduardo de Lira Brennand Serra dos Órgãos University Center (UNIFESO) https://orcid.org/0009-0007-8032-6877
Eugênio Silva Serra dos Órgãos University Center (UNIFESO) https://orcid.org/0000-0002-9030-2242
Gabriel Resende Machado Pontifical Catholic University of Rio de Janeiro (PUC-Rio) https://orcid.org/0000-0002-4692-8504

DOI:

https://doi.org/10.5753/jbcs.2026.5833

Keywords:

Genetic Algorithms, Exploratory Data Analysis, Formula 1

Abstract

Formula 1 (F1) is a motorsport that demands high technical and strategic expertise, where tactical decisions can significantly influence driver's performance and race results. This work addresses the challenge of optimizing pit stop strategies and proposes solutions for strategic decisions aimed at minimizing total race time, such as tire compound selection and optimal stint planning, through Exploratory Data Analysis (EDA) and Genetic Algorithms (GAs). The study relies on historical F1 race data obtained through the FastF1 dataset to examine variables such as tire degradation, lap times, and the impact of pit stops on drivers' final positions. Based on the insights from EDA, a GA model was developed to simulate different race strategies and identify the most effective ones, serving as a complementary tool to enhance strategic decision-making. The model offers data-driven insights that can support race strategists in refining and adapting their strategies based on real-time race conditions and expert judgment. The results indicate that the proposed methodology can support teams in designing more efficient strategies, leading to better performance across various circuits.

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