A machine learning model that predicts the winners of AFL football matches
Correct tips
51
Accuracy
63%
Top percentile of AFL footy tipping
35%
Welcome to the Footy Forest. Here you’ll find predictions, details on how the model is performing, rankings of each team and occasional statistical analyses of AFL footy and other sports.
Home | Away | Relative advantage to the home team1 | Predicted winner | Probability | Margin | |||
---|---|---|---|---|---|---|---|---|
Power | Venue exp | Team rating | Travel | |||||
Fremantle | Collingwood | −25 | 60 | 0 | 75 | Collingwood | 59 | 11 |
St Kilda | Carlton | −16 | −3 | 6 | 0 | Carlton | 56 | 9 |
Melbourne | Hawthorn | −40 | 19 | −5 | 0 | Hawthorn | 74 | 36 |
Essendon | Sydney Swans | −20 | 38 | 30 | 20 | Sydney Swans | 62 | 15 |
Gold Coast Suns | Western Bulldogs | −12 | 2 | 10 | 7 | Western Bulldogs | 52 | 5 |
Port Adelaide | Adelaide Crows | −29 | −6 | 4 | 0 | Adelaide Crows | 70 | 25 |
Richmond | West Coast Eagles | 3 | 89 | 36 | 75 | Richmond | 70 | 17 |
Geelong Cats | GWS Giants | 0 | 61 | 6 | 21 | Geelong Cats | 69 | 15 |
North Melbourne | Brisbane Lions | −43 | 24 | −2 | 33 | Brisbane Lions | 75 | 36 |
1 Relative advantage to the home team scaled from -100 to +100 www.footyforest.com |