Applying Machine Learning to Forecast Formula 1 Race Outcomes
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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu |
Master's thesis
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Authors
Date
2023-08-21
Department
Major/Subject
Data Science
Mcode
SCI3115
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
69+3
Series
Abstract
Pit stops are integral to the success of drivers in Formula 1 racing. This thesis aims to develop a predictive model that effectively determines the optimal timing for pit stops during specific laps of Formula 1 races. By employing machine learning algorithms and analyzing historical race data from the 2019 to 2022 seasons, this study creates a reliable system that considers various race factors, including tire degradation, car positions, and overall race dynamics. Three machine learning algorithms, namely Support Vector Machines (SVM), Random Forest, and Artificial Neural Networks are utilized and compared based on performance metrics, primarily the F1 score. The objective is to identify the most suitable algorithm capable of accurately predicting pit stop requirements. The findings of this thesis highlight the challenging nature of pit stop prediction in Formula 1. While the models demonstrate reasonable accuracy in predicting pit stops, achieving precise predictions remains complex due to the multitude of variables and inherent uncertainties involved. The results emphasize the models' potential as valuable decision-support tools rather than standalone predictors, emphasizing the importance of incorporating additional information and expert knowledge into the decision-making process.Description
Supervisor
Jung, AlexThesis advisor
Jung, AlexKeywords
machine learning, data science, artificial neural networks, random forest, predictive model, Formula 1