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 6 of 7 key factors including ELO Difference, Midfield ELO and Recent Win Rate. Geelong Cats carry a 642-point ELO rating advantage (1760 vs 1118). Recent form favours Geelong Cats with 5 wins from their last 5 compared to 1 for Richmond. 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 | WLLLL | 64.8 |
Geelong Cats | WWWWW | 127.2 |
Avg Conceded
89.4
Richmond
65.8
Geelong Cats
Avg Margin
-24.6
Richmond
61.4
Geelong Cats
Disposals
336.4
Richmond
367.6
Geelong Cats
Inside 50s
50.0
Richmond
50.0
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.