Analysing the Most Influential Factor in Formula One: A Deep Learning Approach for Predicting Driver and Team Ranks

Surendran, Aswin (2023) Analysing the Most Influential Factor in Formula One: A Deep Learning Approach for Predicting Driver and Team Ranks. Masters thesis, Dublin, National College of Ireland.

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Abstract

This project focuses on time series prediction of Formula One race outcomes for both driver and teams by employing a deep learning learning model approach. By closely examining key variables such as Track, car number (No), Driver, Team, Starting Grid(Starting position), Laps, Fastest Lap and Year. The study uncover the important variable influencing final race ranks and uses deep learning models for prediction, mainly Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) for the race outcomes. After running 50 epochs in the training process to prevent overfitting and to minimize validation loss. The analysis compares the performance of LSTM and GRU models based on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) .The LSTM model demonstrates superior accuracy and precision in MSE and RMSE, while the GRU model excels in MAE, indicating more accurate predictions based on absolute differences. This comprehensive evaluation provides valuable insights for optimizing predictions in Formula One racing, considering both driver and team positions. The study identifies Lap, starting position, and Track as the most impactful factors when determining the end position determination. While making significant strides, future research could explore additional variables include weather conditions, driver dynamics, track crashes urging future research to refine models for more comprehensive Formula One race outcome predictions.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Jilani, Musfira UNSPECIFIED
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 H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Motor Industry
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	23 May 2025 14:21
Last Modified:	23 May 2025 14:21
URI:	https://norma.ncirl.ie/id/eprint/7624

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