AI Game Review
Our model correctly predicted St Kilda to win at 65% probability. The predicted margin of 15.2 was reasonable against the actual 9-point result. The game's 147 points came in 24 points lower than the predicted 171. St Kilda led 30–29 at the break and pulled away in the second half to win by 9. 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
St Kilda
65%
North Melbourne
35%
AI Match Overview
St Kilda are clear favourites here at 65%, with our model expecting a comfortable victory over North Melbourne. The model sees St Kilda ahead on 4 of 7 key factors including ELO Difference, Recent Win Rate and Forward Line ELO. St Kilda carry a 160-point ELO rating advantage (1349 vs 1190). Recent form favours St Kilda with 1 wins from their last 5 compared to 0 for North Melbourne. The margin model predicts St Kilda by 15.2 points with a combined total of 171.
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
St Kilda to Win @1.28
Winner ✓
Edge
-12.7%
Line / Spread
St Kilda -24.5 @1.91
Winner ✓
Edge
-12.7%
Total Points
Under 179.5 @1.91
Winner ✓
Edge
+2.6%
Form & History
| Team | Last 5 | Avg Pts |
|---|---|---|
St Kilda | WLLLL | 81.6 |
North Melbourne | LLLLL | 67.0 |
Avg Conceded
94.0
St Kilda
127.4
North Melbourne
Avg Margin
-12.4
St Kilda
-60.4
North Melbourne
Disposals
355.2
St Kilda
334.8
North Melbourne
Inside 50s
50.0
St Kilda
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
65%
St Kilda predicted to win by 15 points
Predicted total: 171 · Line: +15.2
Player Work Effort
Per-minute effort vs effectiveness (vs personal average)Team Effort
-0.44
Team Effectiveness
-0.03
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.