AI Transparency

• A probability engine. Every output is a distribution across outcomes.
• A blend of statistical models, live market signals, form features and reviewed analyst input.
• A continuously retrained system — new settled results feed the next model version.
• Calibrated: a 60% confidence pick is designed to be correct ~60% of the time.

• A crystal ball. Sport is noisy; upsets happen.
• A guarantee. Confidence is not certainty.
• A substitute for your judgement. You decide whether to place a bet.
• Aware of last-minute private information (undisclosed injuries, off-record decisions).

Prediction analysts research each fixture and submit evidence-backed views. A senior analyst reviews before publication. Approved views become verified training samples, and the model's weight for analyst input grows in leagues where analysts consistently add value.

Visit AI Accuracy & Performance for accuracy and ROI by sport, market and confidence band. Model versions are listed with their calibration score so you can see improvement or regression over time.

Models are re-trained automatically once enough new settled samples are available. Every deployment records a version, and admins can roll back if performance regresses.

The full technical documentation of the running engine — architecture, signals, dynamic weighting, training pipeline, live metrics and limitations — is published in the BettingAI AI Model Card.