AI Game Review
North Melbourne defied the model's 79% prediction for Richmond — a notable upset. The predicted margin of 3.0 was reasonable against the actual 4-point result. The model went 1/3 on this match. The under 171.5 total call was correct.
Model vs actual outcomes • Post-match analysis
Quarter-by-Quarter Win Probability
AI Win Probability
Richmond
79%
North Melbourne
21%
AI Match Overview
Richmond are clear favourites here at 79%, with our model expecting a comfortable victory over North Melbourne. North Melbourne are stronger on paper across 4 of 7 key factors — including ELO Difference, Midfield ELO and Forward Line ELO — but Richmond counter with Recent Win Rate and Defensive ELO which tips the scales. Recent form favours Richmond with 2 wins from their last 5 compared to 0 for North Melbourne. The margin model predicts Richmond by 3.0 points with a combined total of 161.
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
Richmond to Win @2.50
Lost ✗
Edge
+38.6%
Line / Spread
Richmond +10.5 @1.91
Lost ✗
Edge
+38.6%
Total Points
Under 171.5 @1.91
Winner ✓
Edge
+2.6%
Form & History
| Team | Last 5 | Avg Pts |
|---|---|---|
Richmond | WWLLL | 63.0 |
North Melbourne | LLLLL | 76.2 |
Avg Conceded
89.6
Richmond
105.4
North Melbourne
Avg Margin
-26.6
Richmond
-29.2
North Melbourne
Disposals
337.4
Richmond
347.0
North Melbourne
Inside 50s
50.0
Richmond
50.0
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
79%
Richmond predicted to win by 3 points
Predicted total: 161 · Line: +3.0
Player Work Effort
Per-minute effort vs effectiveness (vs personal average)Team Effort
+0.28
Team Effectiveness
+0.20
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.