Data Analytics for Predicting F1 Race Outcomes

Oliver Carter


Supervised by Oktay Karakus; Moderated by Carolina Fuentes Toro

Implement a machine learning approach in Python to determine to what extent the results of the 2021 Formula One season can be accurately predicted, based on factors that decided outcomes of the previous year. In order to achieve this, various features of the sport that will be explored to determine how statistically significant they are. Such features will include, but are by no means limited to, starting grid position from qualifying, car constructor, driver and weather conditions.

Various Python libraries will be implemented including Pandas and matplotlib to maximise the potential for data manipulation and visualisation, Meteostat to collect weather data. Ergast API will be used to extract historical Formula One data, from the 2014-2022 season.

The potential applications of this project include being used by avid F1 fans to perhaps better understand factors that influence race wins, and to be able to witness this visualized using various data visualisation techniques.

Final Report (09/01/2023) [Zip Archive]

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