Modeling Race Dynamics in Formula One : A State-Space Approach for Lap Time Decomposition - Jindrová, Karolína (2025)
This paper proposes a structural time-series methodology for modeling lap-level performance in Formula One. First, the Racing Pace and Conditional Events State-Space Model (R-PACE-SSM) is introduced to capture both observable and latent performance components; it is estimated via maximum likelihood using the Kalman filter and smoother. Second, key contextual variables — including pit stops, blue flag disruptions, and on-track battles — are constructed through fully automated novel algorithms, enabling consistent event detection across drivers. The results confirm that fuel mass, strategic interactions, and blue flag events significantly influence lap times. The proposed methodology adapts flexibly to individual race data and identifies four distinct driver groups based on model complexity. Overall, the R-PACE-SSM proves robust across diverse race situations and offers a unified, interpretable decomposition of performance useful for both retrospective analysis and race strategy.