Understanding NHL Win Probability Models

2025-10-26 • NHLForecasts

Machine learning models have revolutionized sports analytics, and NHL predictions are no exception. At NHLForecasts.com, we use a sophisticated blend of logistic regression and gradient boosting to generate accurate win probabilities.

The Challenges of Hockey Prediction

Hockey is notoriously difficult to predict due to:

• High variance: A single goal can swing the game dramatically
• Goaltender impact: Starting goalies have outsized influence on outcomes
• Home ice advantage: Varies significantly by venue
• Parity: The NHL has strong competitive balance

Our Approach

We tackle these challenges with a multi-model ensemble:

1. Logistic Regression: Captures linear relationships and provides interpretable coefficients
2. Gradient Boosting: Handles non-linear interactions between features
3. Blended Predictions: Average of both models (50/50 split) for robustness

Key Features

Our models incorporate:

• Team performance metrics (goals for/against, win rates)
• Home/away splits
• Recent form (weighted recent games)
• Rest advantage
• Venue-specific adjustments

Model Performance

Over the past season, our blended model has achieved:

• Accuracy: ~59.5%
• Brier Score: 0.235 (lower is better)
• Well-calibrated: Predicted probabilities closely match observed frequencies

Calibration Matters

A 70% prediction should win about 70% of the time. We track calibration rigorously and publish our results on the Performance page, broken down by:

• Overall deciles
• Team-specific (home)
• Venue-specific

This transparency helps you understand when to trust our predictions most.

What's Next

We're continuously improving our models by:

• Incorporating real-time goalie assignments
• Adding situational context (back-to-backs, divisional games)
• Developing in-game win probability models

Stay tuned for more insights into our methodology!

← Back to Articles