f1-strategy-models

F1 StratLab Strategy Models

The machine learning models behind F1 StratLab, an open-source multi-agent system for Formula 1 race strategy. Six LangGraph sub-agents and a ReAct orchestrator call these models to produce pit-stop recommendations, tire-degradation forecasts, overtake and undercut probabilities, and answers grounded in the FIA regulations.

Links:

• Project: https://f1stratlab.com
• Documentation: https://docs.f1stratlab.com
• Source code: https://github.com/VforVitorio/F1-StratLab
• Dataset: https://huggingface.co/datasets/VforVitorio/f1-strategy-dataset

Models

Training data

Built from telemetry, lap data and race-control messages covering 70 Grand Prix across the 2023 to 2025 seasons, taken from the FastF1 and OpenF1 public APIs. The seasons are not interchangeable: every model trains on 2023 and 2024 and is tested on 2025, which is the holdout the shipped system infers on (train_seasons: [2023, 2024], test_season: 2025 in each model config). Quoting a figure that pools all three is partly the system reading back its own training data. The processed data is published as a companion dataset: VforVitorio/f1-strategy-dataset.

Intended use

Research and educational use for Formula 1 strategy analysis. Not affiliated with Formula 1, the FIA or any team. Predictions are estimates, not guarantees.

Citation

@misc{vegasobral2026f1stratlab,
  author = {Vega, V{\'i}ctor},
  title  = {F1 StratLab: AI Models for Strategy Recommendations in Formula 1 Races},
  year   = {2026},
  note   = {Bachelor's Thesis, Intelligent Systems Engineering, UIE Campus Coru{\~n}a},
  url    = {https://f1stratlab.com}
}

License

Apache 2.0. Author: Víctor Vega (https://github.com/VforVitorio).