A State-Space Approach to Modeling Tire Degradation in Formula 1 Racing

TL;DR

This paper presents a Bayesian state-space model for estimating tire degradation in Formula 1, improving prediction accuracy and interpretability over traditional methods, with potential applications in race strategy and performance analysis.

Contribution

It introduces a novel Bayesian state-space framework for modeling tire degradation using publicly available data, incorporating extensions like compound-specific rates and skewed error models.

Findings

Superior predictive performance over ARIMA baseline

Model extensions improve fit and interpretability

Framework applicable to real-time race strategy analysis

Abstract

Tire degradation plays a critical role in Formula 1 race strategy, influencing both lap times and optimal pit-stop decisions. This paper introduces a Bayesian state-space modeling framework for estimating the latent degradation dynamics of Formula 1 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 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. Using Lewis Hamilton's performance in the 2025 Austrian Grand Prix as a case study, the proposed framework demonstrates superior predictive performance over an ARIMA(2,1,2) baseline, particularly under the skewed t specification. Although compound-specific degradation differences were not statistically distinct, the results show that the state-space approach provides interpretable, probabilistic, and computationally efficient estimates of tire degradation. This framework can be generalized to multi-race or multi-driver analyses, offering a foundation for real-time strategy modeling and performance prediction in Formula 1 racing.