Predicting Competitive Dynamics in Formula 1: Modelling the Impact of FIA Aerodynamic Regulations Using Machine Learning and Simulation

Stancill, Nicholas (2025) Predicting Competitive Dynamics in Formula 1: Modelling the Impact of FIA Aerodynamic Regulations Using Machine Learning and Simulation. Masters thesis, Dublin, National College of Ireland.

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Abstract

This thesis investigates the impact of FIA aerodynamic regulation changes on race competitiveness in Formula One from 1990 to 2024, focusing on the introduction of Drag Reduction System (DRS) in 2011 and the return of ground-effect aerodynamics in 2022. Using a combination of statistical analysis, Random Forest modelling, and Monte Carlo simulation, the study evaluates how these interventions influenced lap-time variability and driver consistency as proxies for competitiveness. Results show that DRS significantly reduced race variability, enhancing overtaking and midfield engagement, while the ground-effect regulations have yet to demonstrate measurable improvements. Predictive modelling achieved R² values above 0.70, indicating strong explanatory power for features such as season, era, and mean lap time. Simulations suggest that circuit-specific DRS deployment may offer greater gains than blanket regulations. Overall, the findings advocate for a more data-driven, adaptive approach to motorsport regulation, supporting evidence-based policymaking in preparation for the upcoming 2026 Formula One rule changes.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Milosavljevic, Vladimir UNSPECIFIED
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics 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:	Ciara O'Brien
Date Deposited:	03 Jul 2026 11:07
Last Modified:	03 Jul 2026 11:07
URI:	https://norma.ncirl.ie/id/eprint/9464

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