Meet the author

Hi — I'm Victor Vega Sobral (a.k.a. VforVitorio), a fourth-year Intelligent Systems Engineering student at UIE Campus Coruña and AI & Data Intern at NTT DATA Spain. F1 StratLab is my Final-Degree Project: an open-source multi-agent AI for real-time Formula 1 race strategy, built end-to-end from telemetry ingestion and ML modelling to a LangGraph orchestrator and four operator surfaces. I built it because I wanted to see how far a single thesis could push a digital twin of an F1 race — and because nobody else was going to do it for me.

Where to find me

• GitHub — @VforVitorio
• Project landing — f1stratlab.com
• DeepWiki — F1 StratLab on DeepWiki
• LinkedIn — victorvegasobral
• Hugging Face dataset — f1-strategy-dataset
• Portfolio — victorvegasobral.com

About the project

F1 StratLab is a multi-agent system that turns a live (or replayed) Formula 1 race into actionable strategy. Seven ML models cover the quantitative core — lap-time delta, tire degradation with MC Dropout, overtake probability, safety-car prior, pit duration and undercut success — and feed six LangGraph sub-agents (pace, tire, gap, pit, radio, safety) which a single orchestrator (N31) fuses into a Pydantic-typed decision per lap: action, pace target, risk level, and a plan for the next pit window.

The same engine drives four operator surfaces: a Streamlit dashboard for analysts, a CLI for headless replays, a FastAPI/MCP backend for programmatic access, and a three-window arcade (race replay, strategy dashboard, live telemetry) built in PySide6 + pyglet for the demo experience. The whole stack is open under Apache-2.0 and shipped as wheels and GitHub releases through release-please automation.

This documentation site is the engineering companion to the thesis memoria — every notebook, model, agent and surface is wired into the graph view so you can navigate by topic, by tag, or by cross-reference.

Acknowledgements

• Academic — UIE Campus Coruña, Intelligent Systems Engineering faculty and thesis advisors.
• Open data community — FastF1 and OpenF1 provide the telemetry, lap and timing data this entire project depends on.
• Open-source libraries — LangGraph, LightGBM, XGBoost, PyTorch, Pydantic, FastAPI, Streamlit, PySide6, pyglet, Qdrant, and the Hugging Face ecosystem.
• Reference work — TUMFTM race-simulation for the pit-delta framing, plus the wider F1 analytics community whose public notebooks shaped the early modelling decisions.

No copyright infringement intended. Formula 1, F1, and related marks are trademarks of Formula One Licensing B.V. and are used here for reference only. This project is not affiliated with, endorsed by, or in any way officially connected to Formula 1, the FIA, or any F1 team.