Predictive Model for Pitstop Strategy in Formula 1 using Ensemble Learning

Rao, Aniketh Mahesh (2023) Predictive Model for Pitstop Strategy in Formula 1 using Ensemble Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

The research explores the complex world of Formula 1 racing, where winning or losing might depend on split-second decisions made during pit stops. This project investigates how tire deterioration, weather, and sensor data affect pit-stop tactics and race results using advanced analytics and supervised machine learning. The research aims to optimize pit-stop decision-making by building historical data-driven models. One distinctive feature of the model is its adaptability to user input, which enables people to enter their own data or preferences and receive corresponding results from the predictive model. This opens the door to the development of an optimized pit-stop race strategy model. The research uses concepts from ensemble learning to rethink race strategy, driven by the strategic significance of pit stops and the dynamic nature of Formula 1 racing. The all-encompassing model combines a variety of data sources, including as historical race results, telemetry data, weather patterns, and tire performance factors, to offer a flexible tool for optimizing pit stop tactics in a range of racing contexts. The model for predicting pit stops correctly determines the best laps to stop, following the patterns of Formula 1 races. The aforementioned findings demonstrate the model’s resilience and capacity for generalization, highlighting its versatility across various datasets and its possible practical uses in scenarios where comprehensive data may be scarce.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Cosgrave, Noel UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science 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:	21 May 2025 10:44
Last Modified:	21 May 2025 10:44
URI:	https://norma.ncirl.ie/id/eprint/7601

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