Robotics Laboratory, School of Mathematics, Computer Science and the Environment, Liverpool Hope University, Liverpool, UK
Email: 22004909@hope.ac.uk (A.T.); seccoe@hope.ac.uk (E.L.S.)
*Corresponding author
Manuscript received March 4, 2026; accepted June 2, 2026; published July 31, 2026
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 applied to the data of the 2023 F1 Season. 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 intra-team relative sector differentials. Car performance confounders are partially mitigated by computing intra-team relative metrics throughout the entire feature set. A Variance Inflation Factor (VIF) analysis is applied to identify and remove highly collinear features prior to clustering. 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. A supplementary supervised validation experiment confirms that the engineered feature set captures genuinely driver-discriminative signal, with a Random Forest classifier achieving 78.3% accuracy in predicting driver identity under 5-fold cross-validation. Unsupervised learning can surface interpretable driving style profiles from Formula 1 data, though the explanatory power of these clusters is constrained by data limitations including the small per season sample size, incomplete telemetry coverage and the dominance of car performance over driver skill in raw lap-time metrics.
Keywords—unsupervised learning, clustering, Formula 1 (F1), driving styles, K-Means, gaussian mixture models, telemetry, Principal Component Analysis (PCA), FastF1
[PDF]
Cite: Antony Taylor and Emanuele Lindo Secco, "Can Unsupervised Learning Methods Reveal Distinct Driving Styles Among Formula 1(F1) Drivers?," International Journal of Modeling and Optimization, vol. 16, no. 2, pp. 34-45, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
