Prediction Calibration

Overview

Prediction Calibration compares historical predictions with observed outcomes to measure systematic bias and provide correction guidance.

Use it to answer: “Are we consistently over- or under-estimating, and how should we adjust future scoring inputs?”

What You Get

• Error diagnostics: MAE, RMSE, signed bias, and MAPE (when applicable)
• Calibration adjustments: multiplicative and additive correction factors
• Recommendation mode: additive, multiplicative, or hybrid adjustment guidance
• Calibration bins: where calibration drift changes across predicted ranges
• Confidence diagnostics: whether high-confidence forecasts are truly more accurate
• Segment diagnostics: where certain customer or market segments are over/under-estimated

Typical Workflow

1. Collect realized outcomes for previously scored bets/forecasts.
2. Run Prediction Calibration to quantify bias and reliability.
3. Feed correction factors into bet scoring and prioritization models.
4. Repeat monthly or quarterly to continuously improve forecast quality.

Notes & Limits

• This model diagnoses and corrects predictive bias; it does not estimate causal impact.
• Segment and confidence diagnostics depend on enough historical volume.
• For sparse samples, recommendations default to conservative hybrid guidance.