International Journal of Science, Engineering and TechnologyAuthors: Sahil Jeetendra Vyas, Soham Krishna Morlikar, Dr. Jasbir Kaur, Assistant Professor Suraj Kanal, Assistant Professor Sandhya Thakkar
Abstract: Formula 1 is one of the most technologically advanced motorsports in the world, generating enormous volumes of performance data throughout every race weekend. Accurate prediction of race outcomes remains a challenging task because race performance is influenced by numerous dynamic factors including qualifying pace, tyre degradation, weather conditions, pit-stop strategy, mechanical reliability, safety car deployments, and driver skill. Since most telemetry collected by Formula 1 teams is proprietary, researchers must rely on publicly available datasets to develop reproducible prediction models. This paper presents a lightweight machine learning framework for predicting Formula 1 race finishing times and projected finishing positions using publicly available qualifying data obtained through the FastF1 API. Unlike existing approaches that require extensive telemetry or large feature sets, the proposed framework intentionally utilizes qualifying lap time as the primary predictive feature in order to investigate its standalone predictive capability. A Gradient Boosting Regressor is employed to estimate race finishing times after preprocessing qualifying and race timing data into numerical representations. Predicted race times are subsequently ranked to generate projected finishing positions. Model performance is evaluated using Mean Absolute Error (MAE), while an interactive Streamlit dashboard provides an intuitive visualization interface for exploring prediction results and driver rankings. Experimental evaluation demonstrates that qualifying performance contains substantial predictive information regarding race outcomes while maintaining low computational complexity and complete repro-ducibility using publicly accessible data. The proposed methodology establishes a transparent baseline framework for future Formula 1 race prediction research and provides a foundation for incorporating additional race variables in future studies.
