A Hidden Markov Analysis of Wet-Weather Tyre Crossover in Formula One
Abstract
Detecting a wet-weather regime and deciding when to replace slick tyres are distinct tasks. This study examines their relationship through a retrospective analysis of the 2024 British Grand Prix. A three-state hidden Markov model (HMM) represents normalized lap time and precipitation, while an entry rule requires both a high filtered Wet-state probability and sufficient pace deterioration. The analysis includes 642 car-laps and 33 complete lap-bin observations within the available precipitation record. Each completed-lap signal takes effect on the following lap. The combined rule enters on lap 27, matching the team-weighted modal reference; removing the pace or state condition advances entry to laps 25 and 20, respectively. The combined entry is retained in all 10 team-exclusion refits, but refitting a specification with larger off-diagonal transition probabilities produces entry on lap 20. These comparisons illustrate how state inference can contextualize a performance criterion while exposing dependence on model assumptions. Because estimation and rule development use the same event, the correspondence establishes neither prospective accuracy nor a race-time advantage.
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BibTeX
@misc{lin2026hidden,
title = {{A Hidden Markov Analysis of Wet-Weather Tyre Crossover in Formula One}},
author = {Bo-Yi Lin and Michka Sharpe},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.14538},
eprint = {2611.14538},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.14538},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A Hidden Markov Analysis of Wet-Weather Tyre Crossover in Formula One
AU - Bo-Yi Lin
AU - Michka Sharpe
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.14538
UR - https://arxiv.org/abs/2611.14538
AB - Detecting a wet-weather regime and deciding when to replace slick tyres are distinct tasks. This study examines their relationship through a retrospective analysis of the 2024 British Grand Prix. A three-state hidden Markov model (HMM) represents normalized lap time and precipitation, while an entry rule requires both a high filtered Wet-state probability and sufficient pace deterioration. The analysis includes 642 car-laps and 33 complete lap-bin observations within the available precipitation record. Each completed-lap signal takes effect on the following lap. The combined rule enters on lap 27, matching the team-weighted modal reference; removing the pace or state condition advances entry to laps 25 and 20, respectively. The combined entry is retained in all 10 team-exclusion refits, but refitting a specification with larger off-diagonal transition probabilities produces entry on lap 20. These comparisons illustrate how state inference can contextualize a performance criterion while exposing dependence on model assumptions. Because estimation and rule development use the same event, the correspondence establishes neither prospective accuracy nor a race-time advantage.
ER -