Analysis of the Relationships Between F1 Qualification and Final Race Results Using Machine Learning Methods on the Example of the Ten Most Popular Circuits of All Time
Name
Sander Valt
Abstract
This bachelor's thesis focuses on the study of the relationship between F1 qualification and final races, using data analysis and machine learning methods. The work examines the data related to ten selected circuits in order to understand how the qualifying results affect the final results of the competition and what patterns and regularities can be found between them. Using a large-scale historical data set thesis investigates the influence of the qualifications victory on the race results, the winning percentages of different starting positions and possible relations between the characteristics of the circuit and the final race results. In the practical part, with the help of machine learning algorithms, an attempt is made to predict the drivers' success in finishing in the top three, related to their qualifying starting position. The characteristic features and peculiarities of the circuits are also analyzed, which may also have an impact on the development of the relationship between the qualification and the final results of the competition. Based on the results of the data analysis of this thesis, it is also possible to predict the results of future races and the factors affecting them.
Graduation Thesis language
Estonian
Graduation Thesis type
Bachelor - Computer Science
Supervisor(s)
Vambola Leping
Defence year
2024