AI Game Review
Our model correctly predicted Essendon to win at 82% probability. The margin model missed here — predicting 17.2 but the actual margin was 2 points. Essendon trailed 45–27 at half-time before staging a second-half comeback to win 75–77. A clean sweep — all 3 model picks hit for this match.
Model vs actual outcomes • Post-match analysis
Quarter-by-Quarter Win Probability
AI Win Probability
West Coast Eagles
18%
Essendon
82%
AI Match Overview
Essendon are clear favourites here at 82%, with our model expecting a comfortable victory over West Coast Eagles. The model sees Essendon ahead on 4 of 7 key factors including ELO Difference, Midfield ELO and Recent Win Rate. Recent form favours West Coast Eagles with 3 wins from their last 5 compared to 2 for Essendon. The margin model predicts Essendon by 17.2 points with a combined total of 164.
Generated from model features • Pre-kick-off analysis
Edge Analysis
2 ACTIVE EDGESEach market is predicted by an independent model — H2H, margin, and totals may occasionally disagree.
H2H Recommendation
Essendon to Win @1.33
Winner ✓
Edge
+7.0%
Line / Spread
Essendon +21.5 @1.91
Winner ✓
Edge
+7.0%
Total Points
Under 175.5 @1.91
Winner ✓
Edge
+2.6%
Form & History
| Team | Last 5 | Avg Pts |
|---|---|---|
West Coast Eagles | WWWLL | 86.8 |
Essendon | WWLLL | 84.2 |
Avg Conceded
89.1
West Coast Eagles
97.8
Essendon
Avg Margin
26.6
West Coast Eagles
22.7
Essendon
Disposals
330.9
West Coast Eagles
335.0
Essendon
Inside 50s
48.7
West Coast Eagles
45.2
Essendon
📊Team ELO Ratings
🏈Positional Matchups
Player ELO aggregated by position group — higher = stronger unit
📈Recent Form (Last 5)
🔑Key Prediction Factors
What the model weighted most in this prediction
Model Confidence
82%
Essendon predicted to win by 17 points
Predicted total: 164 · Line: -17.2
Player Work Effort
Per-minute effort vs effectiveness (vs personal average)Team Effort
-0.24
Team Effectiveness
-0.09
Effort = pressure acts + tackles + contested possessions per minute on field, z-scored vs career avg. Effectiveness = disposal efficiency + fantasy/min + score involvements − errors, z-scored vs career avg.
Goal Scorer Predictions
AI-powered goal scorer predictions and player prop markets — built on our 6-model player stats engine.