Contract Projections Model
Advanced ML-Driven Contract Value Predictions
Machine learning models trained on historical contract data, with transparent performance tracking and accountability reporting after each free agency period.
Market Inflation
Adjusts for projected salary cap growth and market conditions
Comparable Analysis
Advanced matching algorithm for similar player contracts
ML Baseline
Machine learning model trained on historical contract data
Two-Layer Architecture
Our contract projection model combines machine learning baseline predictions with sophisticated comparable player analysis for maximum accuracy.
Layer 1: ML Baseline Model
Training Features
- • Player performance metrics (PVPI, traditional stats)
- • Age and career stage indicators
- • Position and role classifications
- • Historical market conditions
- • Team cap situation and needs
Market Inflation Adjustment
Adjusts baseline projections based on projected salary cap growth. If the cap was growing 1M when similar players signed, but is projected to grow 15M, the model inflates contract values accordingly.
Layer 2: Comparables Override
Matching Algorithm
- • Performance similarity (PVPI, production)
- • Situational context (age, position, career stage)
- • Market conditions (UFA/RFA, team needs)
- • Contract structure (term, bonuses)
Override Logic
High Confidence (85%+): Use comparable-based projection
Medium Confidence: Weighted average of ML and comparables
Low Confidence: Default to ML baseline
Machine Learning Core
Gradient boosting model trained on historical contract data with feature importance analysis and cross-validation.
Dynamic Market Adjustment
Real-time salary cap projections and market inflation factors ensure projections reflect current economic conditions.
Confidence Scoring
Every projection includes confidence intervals and uncertainty ranges based on comparable quality and data availability.
Model Transparency
All projections are model-generated estimates based on statistical analysis and historical trends. These are not confirmed contract negotiations or insider information, but analytical projections designed to provide data-driven insights into potential contract values.
Comparable Player Analysis
Our sophisticated matching algorithm identifies similar players based on performance, situation, and market context to provide the most accurate contract projections.
Matching Algorithm
Performance Similarity
- • PVPI score comparison
- • Point production rates
- • Advanced metrics
- • Consistency factors
Situational Context
- • Age and career stage
- • Position and role
- • Team context
- • Contract timing
Market Conditions
- • UFA vs RFA status
- • Salary cap environment
- • Team needs
- • Competition level
Contract Structure
- • Term length preference
- • Signing bonus structure
- • Trade protection
- • Performance bonuses
Confidence Scoring System
High Confidence
Multiple strong comparables with similar performance, age, and market conditions. Uses comparable-based projection.
Medium Confidence
Some good comparables but with notable differences. Uses weighted average of ML baseline and comparables.
Low Confidence
Limited or poor-quality comparables available. Defaults to ML baseline with market adjustments.
Performance Tracking & Accountability
Our contract projection models are trained on historical data. We commit to transparent performance tracking and will publish detailed accuracy reports after each free agency period.
Current Status
Our contract projection models have been trained on historical NHL contract data spanning 2010-2024. We will begin live testing during the 2025 free agency period and publish our first performance report at the conclusion of that period.
Performance Tracking Timeline
Model Training
Models trained on historical NHL contract data from 2010-2024
2025 Free Agency
First live testing period - projections will be made for major signings
Performance Report
Detailed accuracy analysis published after free agency concludes
Model Refinement
Continuous improvement based on real-world performance data
Our Reporting Commitments
Transparency
All projection accuracy metrics will be publicly reported
- Mean absolute error
- Prediction accuracy rates
- Confidence score validation
- Market timing analysis
Accountability
We will track and report both successes and failures
- Detailed breakdown of missed projections
- Analysis of model limitations
- Identification of improvement areas
- Honest assessment of market factors
Continuous Improvement
Performance data will drive ongoing model enhancements
- Regular model retraining
- Algorithm refinements
- New data source integration
- Methodology updates
What to Expect in Our Performance Reports
Quantitative Metrics
- • Mean absolute error across all projections
- • Percentage of projections within ±$1M, ±$2M ranges
- • Confidence score accuracy validation
- • Performance by player position and contract type
- • Market timing adjustment effectiveness
Qualitative Analysis
- • Case studies of significant projection misses
- • Analysis of market factors we failed to capture
- • Identification of model blind spots
- • Lessons learned and planned improvements
- • Updated methodology based on real-world results
First Report Timeline: Our inaugural performance report will be published within 30 days of the conclusion of the 2025 NHL free agency period, providing a comprehensive analysis of our model's real-world performance.