AI Game Review
Our model correctly predicted Adelaide Crows to win at 88% probability. The margin model missed here — predicting 31.5 but the actual margin was 68 points. Adelaide Crows led 14–55 at the break and pulled away in the second half to win by 68. The model went 2/3 on this match. The 1-39 margin band call landed.
Model vs actual outcomes • Post-match analysis
Quarter-by-Quarter Win Probability
AI Win Probability
Richmond
12%
Adelaide Crows
88%
AI Match Overview
Adelaide Crows are clear favourites here at 88%, with our model expecting a comfortable victory over Richmond. 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 582-point ELO rating advantage (1701 vs 1118). Recent form favours Adelaide Crows with 3 wins from their last 5 compared to 0 for Richmond. The margin model predicts Adelaide Crows by 31.5 points with a combined total of 163.
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 @1.10
Winner ✓
Edge
-3.0%
Line / Spread
Adelaide Crows +40.5 @1.91
Winner ✓
Edge
-3.0%
Total Points
Under 168.5 @1.91
Lost ✗
Edge
+2.6%
Form & History
| Team | Last 5 | Avg Pts |
|---|---|---|
Richmond | LLLLL | 60.2 |
Adelaide Crows | WWWLL | 87.8 |
Avg Conceded
90.8
Richmond
58.2
Adelaide Crows
Avg Margin
-30.6
Richmond
29.6
Adelaide Crows
Disposals
329.8
Richmond
339.8
Adelaide Crows
Inside 50s
50.0
Richmond
50.0
Adelaide Crows
📊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 31 points
Predicted total: 163 · Line: -31.5
Player Work Effort
Per-minute effort vs effectiveness (vs personal average)Team Effort
-0.22
Team Effectiveness
-0.29
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.