Implementation of K-Means Clustering in Mapping Driver Performance and Consistency Characteristics in the 2026 Formula 1 New Regulation Era
DOI:
https://doi.org/10.21460/jutei.2026.101.472Keywords:
clustering, K-Means, Formula 1 2026, Sports Analytics, FastF1, Pre-season Testing, Machine LearningAbstract
The 2026 Formula 1 season introduces a radical regulatory transition, rendering historical performance data obsolete. This study addresses the "cold-start" problem in sports analytics by implementing the K-Means clustering algorithm to map competitive hierarchies during the 2026 Bahrain pre-season tests. The analysis is exclusively based on four key performance features: Fastest Lap, Average Lap Time, Standard Deviation (consistency), and Total Laps (reliability), extracted via the FastF1 API. A total of 3,624 telemetry data rows were processed and normalized using StandardScaler. The Elbow Method identified K=4 as the optimal cluster configuration. Although the Silhouette Coefficient of 0.350 reflects the inherent "noise" and "sandbagging" strategies of F1 testing, the model successfully differentiated four distinct performance tiers: Top-Tier Leaders, Stable Midfielders, Reliability-Focused Testers, and Technical Anomalies (Strugglers). The findings provide an objective, data-driven framework for interpreting competitive strength without relying on subjective media reports, proving that unsupervised learning can extract meaningful patterns from unlabelled telemetry data in highly volatile regulatory environments.
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