AI Game Review
Our model correctly predicted Adelaide Crows to win at 88% probability. The predicted margin of 29.1 was reasonable against the actual 36-point result. Adelaide Crows led 53–35 at the break and pulled away in the second half to win by 36. 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
Adelaide Crows
88%
North Melbourne
12%
AI Match Overview
Adelaide Crows are clear favourites here at 88%, with our model expecting a comfortable victory over North Melbourne. The model sees Adelaide Crows ahead on 5 of 7 key factors including ELO Difference, Midfield ELO and Recent Win Rate. Adelaide Crows carry a 29-point ELO rating advantage (1491 vs 1461). The margin model predicts Adelaide Crows by 29.1 points with a combined total of 180.
Generated from model features • Pre-kick-off analysis
Edge Analysis
Each market is predicted by an independent model, H2H, margin, and totals may occasionally disagree.
H2H Recommendation
Adelaide Crows to Win @1.14
Winner ✓
Edge
+0.7%
Line / Spread
Adelaide Crows -41.5 @1.91
Winner ✓
Edge
+0.7%
Total Points
Under 192.5 @1.91
Winner ✓
Edge
+2.6%
Form & History
| Team | Last 5 | Avg Pts |
|---|---|---|
Adelaide Crows | W W W W L | 74.4 |
North Melbourne | W W W W L | 91.6 |
Avg Conceded
70.7
Adelaide Crows
91.4
North Melbourne
Avg Margin
-2.2
Adelaide Crows
8.9
North Melbourne
Disposals
352.2
Adelaide Crows
369.9
North Melbourne
Inside 50s
46.1
Adelaide Crows
55.4
North Melbourne
📊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
88%
Adelaide Crows predicted to win by 29 points
Predicted total: 180 · Line: +29.1
Player Work Effort
Per-minute effort vs effectiveness (vs personal average)Team Effort
-0.17
Team Effectiveness
-0.01
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.