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Using Supervised Machine Learning to Predict the Final Rankings of the 2021 Formula One Championship

O'Hanlon, Emma (2022) Using Supervised Machine Learning to Predict the Final Rankings of the 2021 Formula One Championship. Masters thesis, Dublin, National College of Ireland.

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Formula One motor car racing is one of the most data-driven sports in the world. Decisions led by data are used to develop every strategy, from tactics for the entire season to in-event racing. It can be challenging for researchers to find the information that these teams are gathering internally in the public domain. The objective of this study is to gather publicly available data and use machine learning to forecast the results of the 2021 Grand Prix Championship. It should further the rapidly expanding field of motorsport forecasting and facilitate a clearer comprehension of how machine learning may be used to forecast sporting outcomes. This will be achieved using both artificial neural networks for regression and a multiple linear regression. Initial feature selection is carried out using the linear regression model and the learnings are then applied to the neural network. The model’s performance metrics are compared, and the results show that with an R2 of 96%, the neural network fared better than the linear regression model overall. However, the accuracy fluctuates as the grid positions of the drivers are ranked. When comparing the results of the top 10 grid positions, the regression model fared better than the neural network.

Item Type: Thesis (Masters)
Iqbal, Zahid
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
G Geography. Anthropology. Recreation > GV Recreation Leisure > Sports
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 23 May 2023 15:53
Last Modified: 23 May 2023 15:53

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