In this section
@inproceedings{Todd:2025,
author = {Todd, J and Jiang, J and Russo, A and Winkler, S and Sale, S and McMillan, J and Rago, A},
publisher = {ACM},
title = {Explainable time series prediction of tyre energy in formula one race strategy},
year = {2025}
}
TY - CPAPER
AB - Formula One (F1) race strategy takes place in a high-pressure and fast-paced environment where split-second decisions can drastically affect race results. Two of the core decisions of race strategy are when to make pit stops (i.e. replace the cars’ tyres) and which tyre compounds (hard, medium or soft, in normal conditions) to select. The optimal pit stop decisions can be determined by estimatingthe tyre degradation of these compounds, which in turn can be computed from the energy applied to each tyre, i.e. the tyre energy. In this work, we trained deep learning models, using an F1 team’s historic race data consisting of telemetry, to forecast tyre energies during races. Additionally, we fitted XGBoost, a decision tree-based machine learning algorithm, to the same dataset and compared the results, with both giving impressive performance. Furthermore, weincorporated two different explainable AI methods, namely feature importance and counterfactual explanations, to gain insights into the reasoning behind the forecasts. Our contributions thus result in an explainable, automated method which could assist F1 teams in optimising their race strategy.
AU - Todd,J
AU - Jiang,J
AU - Russo,A
AU - Winkler,S
AU - Sale,S
AU - McMillan,J
AU - Rago,A
PB - ACM
PY - 2025///
TI - Explainable time series prediction of tyre energy in formula one race strategy
ER -
