AI Game Review
Geelong Cats defied the model's 77% prediction for Adelaide Crows, a notable upset. The margin model missed here, predicting 13.6 but the actual margin was 19 points. The game's 219 points came in 41 points higher than the predicted 178. Geelong Cats trailed 72–60 at half-time before staging a second-half comeback to win 100–119. A tough result for the model, all 3 picks missed on this one.
Model vs actual outcomes • Post-match analysis
Quarter-by-Quarter Win Probability
AI Win Probability
Adelaide Crows
77%
Geelong Cats
23%
AI Match Overview
Adelaide Crows are clear favourites here at 77%, with our model expecting a comfortable victory over Geelong Cats. The model sees Adelaide Crows ahead on 6 of 7 key factors including ELO Difference, Midfield ELO and Recent Win Rate. The margin model predicts Adelaide Crows by 13.6 points with a combined total of 178.
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
Adelaide Crows to Win @1.45
Lost ✗
Edge
+7.9%
Line / Spread
Adelaide Crows -14.5 @1.91
Lost ✗
Edge
+7.9%
Total Points
Under 181.5 @1.91
Lost ✗
Edge
+2.6%
Form & History
| Team | Last 5 | Avg Pts |
|---|---|---|
Adelaide Crows | W W W L L | 77.3 |
Geelong Cats | W W W L L | 79.2 |
Avg Conceded
98.1
Adelaide Crows
99.3
Geelong Cats
Avg Margin
28.7
Adelaide Crows
-9.7
Geelong Cats
Disposals
378.0
Adelaide Crows
337.6
Geelong Cats
Inside 50s
50.7
Adelaide Crows
50.5
Geelong Cats
📊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
77%
Adelaide Crows predicted to win by 14 points
Predicted total: 178 · Line: +13.6
Player Work Effort
Per-minute effort vs effectiveness (vs personal average)Team Effort
-0.03
Team Effectiveness
+0.04
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.