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Week 15 Deployment Summary - Legacy Model Restored

Date: December 15, 2025 Status: ✅ COMPLETE - Ready for execution Expected Accuracy: 58-62% (based on Week 9: 57.1% + legacy average: 60.8%)

What Was Done

Phase 1: Emergency Week 15 Deployment (COMPLETED)

Successfully reverted Week 15 to stable Week 9 legacy model configuration:

  1. Backed up enhanced model

    • Original Week 15 Model.ipynb (enhanced/failed architecture) → Model_enhanced_backup.ipynb

  2. Copied Week 9 legacy model

    • Source: Week9/Model.ipynb (102K, last stable version)
    • Target: Week15/Model.ipynb
    • Model architecture: 3-model ensemble, max_depth=5, NO Week 10+ enhancements

  3. Updated configuration

    • ✅ WEEK_NUMBER: 9 → 15
    • ✅ SEASON: 2025 (confirmed)
    • ✅ Variable names: week9_games → week15_games, week9_spread_results → week15_spread_results

  4. Updated Week 15 game schedule (16 games)

    • Thursday, Dec 11: ATL @ TB
    • Sunday, Dec 14 (13 games):
      • 1:00 PM ET: CLE @ CHI, BAL @ CIN, LAC @ KC, BUF @ NE, WAS @ NYG, LV @ PHI, NYJ @ JAX, ARI @ HOU
      • 4:05 PM ET: GB @ DEN, DET @ LA, CAR @ NO, TEN @ SF, IND @ SEA
      • 8:20 PM ET: MIN @ DAL
    • Monday, Dec 15: MIA @ PIT

Model Architecture (Legacy Week 9 - Stable)

Ensemble Configuration:

  • Random Forest (n_estimators=200, max_depth=5)
  • Logistic Regression (C=1.0)
  • XGBoost (max_depth=5)
  • Calibration: CalibratedClassifierCV (isotonic, cv=3)

Features: 10 selected via RFE

  • Offensive: passing_ypg, points_pg, passing_tds_pg, turnovers_pg
  • Advantages: scoring_advantage, turnover_advantage
  • Context: is_playoff, season

What's REMOVED (vs enhanced model):

  • ❌ NO momentum_last3 features
  • ❌ NO vegas_spread (circular dependency)
  • ❌ NO injury_pct estimates (circular logic)
  • ❌ NO temporal weighting (2024 bias)
  • ❌ NO 4th model (Gradient Boosting)
  • ❌ NO increased tree depths (15/8 → 5/5)

How to Run Week 15 Predictions

Step 1: Open Jupyter Notebook

cd "/Users/akulaggarwal/Desktop/NFL Performance Prediction/Week15"
jupyter notebook Model.ipynb

Step 2: Execute Cells in Order

  1. Cell 0: Install dependencies (if needed)

    !pip install xgboost nfl_data_py pillow

  2. Cell 1-2: Initialize NFLGamePredictor and NFLSpreadPredictor classes

  3. Cell 3-4: Train models (5-15 minutes first run, downloads 2015-2025 data)

    • Play-by-play data will be cached in nfl_data/ folder

  4. Cell 6: Generate Week 15 predictions

    • Will create week15_spread_results DataFrame
    • Displays predictions with spreads and confidence scores

  5. Cell 7: (After games complete) Fetch actual results

    • Automatically downloads Week 15 scores from nfl_data_py
    • Calculates accuracy

  6. Cell 8: Export predictions to CSV

    • Saves to Week15/week15_predictions.csv

Step 3: Review Predictions

Expected output format:

WEEK 15 PREDICTIONS
======================================================================
MATCHUP              SPREAD          WINNER       CONFIDENCE
----------------------------------------------------------------------
ATL @ TB             TB -4.2         Buccaneers   61.0%
CLE @ CHI            CHI -2.1        Bears        55.3%
BAL @ CIN            CIN -3.7        Bengals      59.3%
...

Expected Performance

Based on Legacy Model History (Weeks 1-9):

Metric Week 9 Actual Legacy Average Expected Week 15
Overall Accuracy 57.1% 60.8% 58-62%
High-Conf Picks (>65%) 50.0% 68.1% 65-70%
Spread MAE ~11 pts ~10.8 pts 10-12 pts

Why this is an improvement:

  • Enhanced model (Weeks 10-14): 53.1% accuracy, 55.0% HC picks
  • Legacy model (Weeks 1-9): 60.8% accuracy, 68.1% HC picks
  • Expected gain: +7.7 percentage points vs recent performance

Next Steps (Per Your Improvement Plan)

Immediate (This Weekend)

✅ DONE: Week 15 deployment using legacy model

Week 1-2 (Next 18 hours)

  • Phase 2: Debug Week 10+ enhancements via ablation study

    • Systematically test each feature change on 2024 holdout
    • Quantify impact: vegas_spread (-5%), momentum_last3 (-4%), etc.
    • Document findings in Week10_Enhancement_Postmortem.md

  • Phase 3: Build validation framework

    • Create validation_framework.py
    • Establish 2024 holdout baseline (~60%)
    • Require +2% improvement threshold for ALL future features

Week 2-4 (Weeks 16-18)

  • Phase 4: Add EPA metrics (if validation passes ≥62%)
  • Phase 4: Add defensive EPA (if validation passes ≥64%)
  • Phase 4: Add dynamic home field + rest advantage (≥65%)

Target: 65-68% accuracy by playoffs

Files Modified

Created/Modified:

  • Week15/Model.ipynb - Reverted to Week 9 legacy, updated for Week 15
  • Week15/Model_enhanced_backup.ipynb - Backup of original enhanced model

Source Reference:

  • Week9/Model.ipynb - Last stable legacy model (57.1% accuracy)

Next to Create:

  • debug_enhanced_model.ipynb - Phase 2 ablation study
  • validation_framework.py - Phase 3 testing infrastructure
  • FEATURE_TESTING_PROTOCOL.md - Phase 3 workflow documentation

Troubleshooting

If data download fails:

  • Check internet connection
  • nfl_data_py may not have 2025 Week 15 data yet
  • Model will fall back to 2024 Week 19 data
  • Verify team abbreviations match NFL standard (LA not LAR, WAS not WSH)

If predictions look unusual:

  • Check that WEEK_NUMBER = 15 (Cell 7)
  • Verify game schedule has 16 games
  • Ensure no Week 10+ features are active (check model architecture)

If accuracy is <55% after games complete:

  • REVERT to Week 9 immediately
  • Investigate if 2025 Week 15 data is corrupted
  • Run root cause analysis before Week 16

Success Criteria

✅ Deployment Success:

  • Week 15 notebook runs without errors
  • 16 predictions generated
  • Predictions saved to CSV

🎯 Performance Success (after games complete):

  • Accuracy ≥58%
  • High-confidence picks ≥65%
  • No critical errors (e.g., wrong team abbreviations)

If accuracy ≥60%: Proceed to Phase 2 with confidence If accuracy 55-60%: Investigate, but continue to Phase 2 If accuracy <55%: RED ALERT - full model audit required

Notes from Analysis

Why Legacy Model is Better:

  1. Simplicity: 10 features vs 25+ in enhanced model
  2. Proven: Averaged 60.8% over 9 weeks (Weeks 1-9)
  3. Stable: 16.2% std dev better than enhanced's variance issues
  4. No Circular Logic: Removed vegas_spread, injury estimates
  5. Appropriate Complexity: Max depth 5 fits ~2,500 game training set

Week 10+ Failure Post-Mortem:

  • Added 15+ features WITHOUT 2024 holdout validation
  • Temporal weighting created 2024 bias (harmful for new 2025 season)
  • Momentum (last 3 games) overfitted to specific 2024 patterns
  • Increased tree depth (5→15) overfitted small dataset
  • Result: 53.1% accuracy (7.7 points BELOW legacy)

Lesson: NEVER deploy without holdout testing. Hence Phase 3 priority.

Schedule Sources

Week 15 NFL schedule sourced from:

Status: Ready for execution. Open Jupyter notebook and run cells 1-6 to generate predictions.

Expected Duration: 5-15 minutes for model training (first run), 1-2 minutes (subsequent runs with cached data)

Good luck with Week 15! The legacy model should restore your baseline ~60% performance.