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probability-calibration

Calibrate sports prediction model probabilities using logistic or isotonic regression.

2|1|Updated May 1, 2026
Search Tags:#probability#regression#calibration#model validation#sports modeling#Brier score
One-click install
npx skills add https://github.com/PuckAPI/claude-sports-analytics --skill probability-calibration
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: probability-calibration
Source: https://github.com/PuckAPI/claude-sports-analytics/tree/main/skills/probability-calibration
Command: npx skills add https://github.com/PuckAPI/claude-sports-analytics --skill probability-calibration

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scikit-learn, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill verifies and corrects the alignment between predicted win probabilities and actual outcomes, enhancing the reliability of sports models.

Core Features & Use Cases

  • Calibration Verification: Generates reliability diagrams to assess how well the model's probabilities reflect real-world results.
  • Score Calculation: Computes Brier scores and calibration errors to quantify probability quality.
  • Model Adjustment: Applies Platt scaling or isotonic regression to improve calibration based on historical data.
  • Use Case: A sports analyst has a trained model and wants to verify if its predicted probabilities are trustworthy before placing bets or making in-game decisions, ensuring more accurate expected value calculations.

Quick Start

Use the probability calibration skill to evaluate the calibration of your model predictions and apply corrections if necessary.

Frequently Asked Questions about probability-calibration

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I verify if my sports prediction model probabilities are accurate?▼

To verify sports prediction model probabilities, generate reliability diagrams and compute Brier scores to quantify the alignment between predicted win probabilities and actual observed outcomes.

What's the best way to calibrate model probabilities using scikit-learn?▼

Calibrate model probabilities using scikit-learn by applying Platt scaling through logistic regression or isotonic regression adjustments, which corrects predicted probabilities based on historical game data.

When do I need probability calibration for sports betting models?▼

Probability calibration is needed when predicted win probabilities diverge from actual outcomes, ensuring trustworthy expected value calculations before placing bets or making strategic in-game decisions.

Does probability calibration support both isotonic and logistic regression adjustments?▼

Yes, probability calibration supports both isotonic and logistic regression adjustments, allowing you to apply Platt scaling or non-parametric corrections to improve model probability reliability.

How is Brier score used to evaluate sports model calibration?▼

Brier score evaluates sports model calibration by measuring the mean squared difference between predicted probabilities and actual outcomes, quantifying the overall quality of probability forecasts.

Can I use this probability calibration skill with any trained regression model?▼

Yes, you can use this skill with any trained model that outputs win probabilities, as it analyzes calibration curves and applies regression adjustments based on historical game results.

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