A statistical NRL model: try-scorer, goal-kicking and player-points projections priced against live multi-bookmaker odds to surface value. Educational — not betting advice.
How it works
From Champion Data match stats + the confirmed team lists, we build each player’s recent-form features — shifted one game so a prediction never sees the game it’s predicting.
Gradient-boosted trees predict a try-scorer’s expected tries (Poisson rate), a team’s goal-kicker output (anchored to the named kicker), and stat means for tackles, metres and performance points.
A projection isn’t a bet — we turn it into a distribution: Poisson for tries, a convolution of 4×tries + 2×goals for points, and Normal(mean, σ) for the rest, with σ calibrated from out-of-sample error.
Summing the distribution above/below a line gives the win probability for that market; fair odds = 1 ÷ probability, with no bookmaker margin.
Live odds from 5 books are de-vigged to their true probability. EV = model prob × best price − 1; a Kelly stake sizes the bet to the edge.
Every figure is out-of-sample — trained on earlier seasons, scored on 2023–25: try-model AUC ≈ 0.73, calibrated, and 25–45% lower error than a recent-form baseline.
The short version: trees predict each player’s rate or mean → that becomes a probability distribution → distributions price every market line → compared against de-vigged book odds to surface +EV. See the backtest for how it actually performs.
The model runs in a separate pipeline and refreshes through the day. Figures are for information and entertainment only — not betting advice. Gamble responsibly.