KRONECTOR Model Card
Architecture Overview
The core of the KRONECTOR F1 Intelligence terminal is powered by a custom LightGBM Binary Classifier. It is designed to predict the probability of a driver winning a given Formula 1 race based on historical data, pre-race telemetry, and qualifying performance.
- Model Type: LightGBM (Gradient Boosting Framework)
- Objective: Binary Classification (Win = 1, Not Win = 0)
- Evaluation Metric: Log Loss & Area Under ROC Curve (AUC)
- Explainability: SHAP (SHapley Additive exPlanations)
Features
The model digests 25+ features per driver per race, heavily relying on:
- Track Position:
grid_position,pole_conversion_rate - Driver Momentum:
driver_form_last3,championship_standing - Telemetry Data: Era-normalized Sector Times (
sector_1_time_era_norm, etc.) - Experience:
career_race_starts
Accuracy & Metrics (Proofs)
KRONECTOR is rigorously cross-validated against 10+ years of F1 data (2014-2024). Below are the mathematical proofs of the model's accuracy on the latest unseen test set (2023-2024 seasons).
1. ROC AUC (Receiver Operating Characteristic)
The ROC Curve demonstrates the model's ability to distinguish between a race winner and a non-winner. An AUC of 1.0 is perfect. KRONECTOR achieves an impressive ~0.94 AUC, proving it is highly capable of separating true contenders from the rest of the grid.
2. Precision-Recall Curve
Because Formula 1 is highly imbalanced (1 winner vs 19 losers per race), the PR curve is critical. High Area Under the PR Curve means when KRONECTOR predicts a driver will win, it is very rarely wrong.
3. Confusion Matrix
Evaluating the raw accuracy using a 50% probability threshold. This matrix shows the breakdown of True Positives, True Negatives, False Positives, and False Negatives.
4. Global Feature Importance (SHAP)
This chart aggregates the absolute SHAP values across all predictions, revealing the fundamental laws of the model. As expected, Grid Position, Championship Standing, and Driver Form have the largest average impact on predicting race outcomes.
