Can Unsupervised Learning Methods Reveal Distinct Driving Styles Among Formula 1 Drivers?

Abstract

This paper investigates whether unsupervised machine learning techniques can identify meaningful and interpretable driving style clusters among Formula 1 (F1) drivers, using telemetry and lap-time data extracted via the FastF1 Python library. Four clustering algorithms are implemented and compared: K-Means, Hierarchical Agglomerative Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Gaussian Mixture Models (GMM). A suite of performance features is engineered from raw race telemetry, including relative lap time, lap-time consistency (measured via the Median Absolute Deviation), tyre degradation slope, overtaking efficiency, and sector-specific averages. Car performance confounders are partially mitigated by computing intra-team relative metrics. Principal Component Analysis (PCA) is employed for dimensionality reduction and visualization.

Results suggest that between 3 and 5 statistically meaningful driver clusters emerge, broadly characterized as elite performers, aggressive racers, precision drivers, and solid midfield operators. K-Means with k = 4 and Ward-linkage Hierarchical Clustering yield the most internally consistent groupings, outperforming DBSCAN and GMM on silhouette and Davies-Bouldin scores. Finally, unsupervised learning can surface interpretable driving style taxonomies from Formula 1 data, though the explanatory power of these clusters is constrained by data limitations including incomplete telemetry coverage and the dominance of car performance over driver skill in raw lap-time metrics.