| --- |
| license: mit |
| tags: |
| - xgboost |
| - lightgbm |
| - sports-prediction |
| - formula1 |
| - tabular |
| - classification |
| - ensemble |
| - optuna |
| language: |
| - en |
| --- |
| |
| # Telemetry Chaos — F1 Race Prediction Model |
|
|
| XGBoost + LightGBM ensemble predicting Formula 1 race winners from 76 seasons of historical data. Tuned with Optuna hyperparameter optimization across 200 trials. Auto-retrains weekly during the active season. |
|
|
| **Live demo:** [telemetrychaos.space](https://telemetrychaos.space) |
|
|
| ## Performance |
|
|
| | Metric | Score | |
| |---|---| |
| | Top-1 Accuracy | 53% | |
| | Top-3 Accuracy | 85% | |
| | Top-5 Accuracy | 96% | |
|
|
| Evaluated on 2024–2025 seasons with time-series split to prevent data leakage. |
|
|
| ## Features (21 per driver per race) |
|
|
| - **Form:** Rolling average points, recent podiums, win streak |
| - **Pace:** Practice session lap time delta vs. teammate and field |
| - **Constructor:** Team rolling performance, reliability score |
| - **Track history:** Driver-specific circuit win rate, podium rate |
| - **Tyre:** Degradation profile, pit stop speed, strategy tendency |
| - **Conditions:** Weather forecast, safety car probability |
| - **Grid:** Starting position, qualifying gap to pole |
|
|
| ## Architecture |
|
|
| ``` |
| XGBoost (GPU) + LightGBM |
| Optuna HPO: 200 trials, TPE sampler |
| Time-series split: train on seasons N-5 to N-1, evaluate on N |
| Final output: softmax win probabilities per driver |
| ``` |
|
|
| ## Dataset |
|
|
| - **Coverage:** 1950–2025, 76 seasons |
| - **Records:** 1,322,914 race records |
| - **Telemetry laps:** 470K+ |
| - **Sources:** FastF1, Jolpica-F1, f1db, Kaggle |
|
|
| ## Usage |
|
|
| ```python |
| import joblib |
| |
| model = joblib.load("f1_ensemble.joblib") |
| # Input: 21-feature vector per driver |
| # Output: win probability (0-1) |
| probs = model.predict_proba(X) |
| ``` |
|
|
| ## Auto-Update Pipeline |
|
|
| During the active F1 season the model retrains weekly: |
| 1. Pull latest race results and telemetry via FastF1 |
| 2. Engineer features for upcoming race grid |
| 3. Retrain ensemble with updated data |
| 4. Publish updated predictions to telemetrychaos.space |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{rubin2026telemetrychaos, |
| author = {Rubin, Theodore}, |
| title = {Telemetry Chaos: F1 Race Prediction with XGBoost/LightGBM Ensemble}, |
| year = {2026}, |
| publisher = {HuggingFace}, |
| url = {https://huggingface.co/datamatters24/f1-race-predictor-model} |
| } |
| ``` |
|
|
