A state-space approach to modeling tire degradation in formula 1 racing

Tire degradation plays a critical role in Formula One race strategy, influencing both lap times and optimal pit-stop decisions. This paper introduces a Bayesian state-space modeling framework for estimating latent degradation dynamics of Formula One tires using publicly available timing data from the FastF1 Python API. Lap times are modeled as a function of fuel mass and latent tire pace, with pit stops represented as structural state resets. Several model extensions are explored, including compound-specific degradation rates, time-varying degradation dynamics, and a skewed-t observation model to account for asymmetric driver errors. While Lewis Hamilton’s performance in a single Grand Prix serves as an illustrative case study, predictive robustness is evaluated across 19 race sessions from the 2025 season using rolling-origin cross-validation. The proposed state-space model is compared to a structurally comparable AR(1) benchmark with stint resets and demonstrates superior performance in the majority of races in terms of both RMSPE and CRPS. Although compound-specific differences are not always statistically distinct, the results show that the state-space approach provides interpretable, probabilistic, and computationally efficient estimates of tire degradation, offering a principled foundation for real-time strategy modeling and performance prediction in Formula One racing.