WAFL
Performance

How our tipping heuristics are performing.

Current Season 2026

24 / 24 rounds completed

How profit is calculated: each tip carries a simulated $10 stake. Games with bookmaker odds (The Odds API) settle at the real decimal price for the tipped team; games without an odds snapshot settle at a representative $1.90; drawn games refund the stake. Odds coverage shows the share of tips settled at real prices.

Best Bet

Current Season
Year-to-Date Profit $180.00
Projected Annual Profit $180.00
Projections are based on early season performance and may change as the season progresses.
Accuracy62.5%
Rounds Played24
Avg Profit/Round $7.50
Odds Coverage0%

Weighted Tip

Current Season
Year-to-Date Profit $66.00
Projected Annual Profit $66.00
Projections are based on early season performance and may change as the season progresses.
Accuracy56.3%
Rounds Played24
Avg Profit/Round $2.75
Odds Coverage0%

Boosted Tip

Current Season
Year-to-Date Profit $617.00
Projected Annual Profit $617.00
Projections are based on early season performance and may change as the season progresses.
Accuracy86.5%
Rounds Played24
Avg Profit/Round $25.71
Odds Coverage0%
Individual Model Accuracy
Elo Rating★
62.5%
Accuracy
$180
Profit
Form
61.5%
Accuracy
$154
Profit
Matchup
58.8%
Accuracy
$60
Profit
Value
55.2%
Accuracy
$47
Profit
Home Advantage
53.8%
Accuracy
$20
Profit

Weighted Tip Model

v71
Trained: Tue, 10 Nov, 08:00 amTraining rows: 96Intercept: 4.47R²: 0.1491MAE: 26.9

The Weighted Tip model combines all 5 ML model predictions using a linear regression trained on 96 historical games. Each model contributes a margin weight (influence on score margin) and a confidence weight (influence on confidence). The current model (v71) has R² = 0.149 with intercept 4.47. Most influential models: elo rating diff, form wins diff, home advantage home constant. The model is retrained weekly with updated coefficients.

Model Equation
predicted_margin =4.47+0.000·elo rating diffm+0.000·form wins diffm+0.000·home advantage home constantm+0.000·matchup h2h wins diffm+0.000·value win rate diffm

Each model contributes a margin weight (× its predicted margin toward home) and a confidence weight. Models with larger absolute weights have more influence on the final tip.

Margin Weights

Active Boosted Model (XGBoost)

v57
Trained: Tue, 10 Nov, 08:00 amTraining rows: 96R²: 0.7791MAE: 11.7SHAP base value: 3.96

The Boosted Tip model is a gradient-boosted tree ensemble (XGBoost) trained on 96 historical games to predict the home-team-signed margin from the same model predictions the Weighted Tip model uses. The current model (v57) reaches R² = 0.779 with MAE = 11.7 points and is retrained weekly. Feature importances below are mean |SHAP| values — how much each model's margin and confidence signal moves the prediction, with elo rating diff, value win rate diff, form wins diff the most influential.

SHAP Feature Importance