Rewrite card in plain prose

README.md

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----
-license: apache-2.0
-language:
-- en
-tags:
-- formula-1
-- motorsport
-- race-strategy
-- multi-agent
-- langgraph
-- xgboost
-- lightgbm
-- temporal-convolutional-network
-- sports-analytics
----
-
-# F1 StratLab — Strategy Models
-
-**F1 StratLab** is an open-source multi-agent AI system for real-time Formula 1 race
-strategy. This repository holds the seven machine learning models that power it. They
-are orchestrated by six [LangGraph](https://github.com/langchain-ai/langgraph)
-sub-agents and a ReAct orchestrator to produce pit-stop recommendations, tire-degradation
-forecasts, overtake/undercut probabilities, and regulation-cited answers — end-to-end in
-under one second.
-
-- 🌐 Website: 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
-- 📄 Paper (TFG, work in progress): https://github.com/VforVitorio/F1-StratLab/blob/main/documents/docs_legacy_strat_manager/F1_Strategy_Manager_AI.pdf
-
-## Models
-
-| Task | Algorithm | Headline metric |
-|---|---|---|
-| Lap-time prediction | XGBoost | MAE 0.392 s |
-| Tire degradation | Temporal Convolutional Network + Monte Carlo Dropout | P10/P50/P90 quantiles, tire-cliff & pit-window detection |
-| Overtake probability | LightGBM | AUC-ROC 0.876 |
-| Safety-car probability | LightGBM | classifier |
-| Pit-stop duration | HistGradientBoosting (quantile) | MAE 0.487 s |
-| Undercut success | LightGBM (binary) | AUC-ROC 0.771 |
-| Team-radio NLP | Whisper + RoBERTa + SetFit + BERT-large | 4-stage pipeline |
-
-## Training data
-
-All models are trained on telemetry, lap data, and race-control messages from **71 Formula 1
-Grand Prix across the 2023–2025 seasons**, sourced from the [FastF1](https://docs.fastf1.dev)
-and OpenF1 public APIs. See the companion dataset:
-[`VforVitorio/f1-strategy-dataset`](https://huggingface.co/datasets/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
-
-```bibtex
-@misc{vegasobral2026f1stratlab,
-  author = {Vega Sobral, 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. Built by Víctor Vega Sobral ([VforVitorio](https://github.com/VforVitorio)).
+---
+license: apache-2.0
+language:
+- en
+tags:
+- formula-1
+- motorsport
+- race-strategy
+- multi-agent
+- langgraph
+- xgboost
+- lightgbm
+- temporal-convolutional-network
+- sports-analytics
+---
+
+# 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
+
+| Task | Algorithm | Metric |
+|---|---|---|
+| Lap-time prediction | XGBoost | MAE 0.392 s |
+| Tire degradation | TCN with Monte Carlo Dropout | P10/P50/P90 quantiles, pit-window detection |
+| Overtake probability | LightGBM | AUC-ROC 0.876 |
+| Safety-car probability | LightGBM | classifier |
+| Pit-stop duration | HistGradientBoosting (quantile) | MAE 0.487 s |
+| Undercut success | LightGBM (binary) | AUC-ROC 0.771 |
+| Team-radio NLP | Whisper, RoBERTa, SetFit, BERT-large | 4-stage pipeline |
+
+## Training data
+
+Trained on telemetry, lap data and race-control messages from 71 Grand Prix across the
+2023 to 2025 seasons, taken from the FastF1 and OpenF1 public APIs. 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
+
+```bibtex
+@misc{vegasobral2026f1stratlab,
+  author = {Vega Sobral, 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 Sobral (https://github.com/VforVitorio).