Data-driven pit stop decision support for Formula 1 using deep learning models

Abstract

In Formula 1, which is among the most competitive motorsports in the world, the timing of a pit stop can make the difference between winning and losing a race. Conventional methods based on human judgment can be erratic, especially in rapidly changing race conditions. This work proposes a datadriven framework based on deep learning models to predict optimal pit stop timings using raw telemetry data extracted from FastF1 API. To improve the robustness of the models, advanced preprocessing techniques such as normalization, imputation, and class balancing with Synthetic Minority Over-sampling Technique (SMOTE) were implemented. Five different deep learning architectures, including Bi-LSTM, TCN-GRU, GRU, InceptionTime, and CNN-BiLSTM, were trained and evaluated employing precision, recall, and F1-score as metrics. Of these, the Bi-LSTM model achieved the overall best performance which can be explained by its capability to model long-range dependencies in both forward and backward temporal directions. The Bi-LSTM achieved a precision of 0.77, recall of 0.86, and an F1-score of 0.81 on the test set, demonstrating strong predictive accuracy under real-race conditions. Additionally, a historical race visualization interface was developed to visualize the model's predictions.