AI Game Review
Our model correctly predicted Geelong Cats to win at 93% probability. The margin model missed here, predicting 58.8 but the actual margin was 39 points. Total score prediction of 164 was close to the actual 167, within 4 points. 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
Richmond
7%
Geelong Cats
93%
AI Match Overview
Geelong Cats are clear favourites here at 93%, with our model expecting a comfortable victory over Richmond. The model sees Geelong Cats ahead on 4 of 7 key factors including ELO Difference, Midfield ELO and Recent Win Rate. Geelong Cats carry a 40-point ELO rating advantage (1513 vs 1473). Recent form favours Richmond with 4 wins from their last 5 compared to 2 for Geelong Cats. The margin model predicts Geelong Cats by 58.8 points with a combined total of 164.
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
Geelong Cats to Win @1.03
Winner ✓
Edge
-3.8%
Line / Spread
Geelong Cats +59.5 @1.91
Winner ✓
Edge
-3.8%
Total Points
Under 177.5 @1.91
Winner ✓
Edge
+2.6%
Form & History
| Team | Last 5 | Avg Pts |
|---|---|---|
Richmond | W W W W L | 90.5 |
Geelong Cats | W W L L L | 88.3 |
Avg Conceded
97.5
Richmond
85.5
Geelong Cats
Avg Margin
24.6
Richmond
29.6
Geelong Cats
Disposals
335.8
Richmond
337.6
Geelong Cats
Inside 50s
51.5
Richmond
42.7
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
93%
Geelong Cats predicted to win by 59 points
Predicted total: 164 · Line: -58.8
Player Work Effort
Per-minute effort vs effectiveness (vs personal average)Team Effort
-0.14
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.