build-a-h2h-model

name	build_a_h2h_model
description	Worked example of model_development for match-winner (h2h/moneyline) markets — ratings + logistic regression, home advantage, draw handling.
triggers	["h2h model","head to head model","moneyline model","winner model","match winner model"]

Build a head-to-head (winner) model — worked example

Follow model_development for the method; this is the h2h instantiation.

Recipe (run_python, one script)

1. Data: results for the competition, as many seasons as the regime allows (count events: §2 of model_development). Print what you kept and dropped.
2. Ratings as the core feature: maintain an Elo-style rating per team — r_new = r_old + K * (outcome - expected), expected = 1/(1+10^(-(r_a-r_b)/400)). Tune K on the training slice only (typical 16–32). Ratings compress a team's whole history into one number — ideal for small samples.
3. Logistic regression on top: features = rating difference, home indicator, rest-days difference if available, plus AT MOST one or two user-valued stats (ask — see model_development §3). Fit on train, freeze, predict holdout.
4. Draws (football, etc.): model as multinomial (home/draw/away) or fit P(draw) separately as a function of rating closeness — never silently ignore the draw in a 3-way market.
5. Calibrate + persist: calibration_metrics on the holdout → save_model (params: K, coefficients, feature list, seasons used) → record_predictions with honest predicted_at.

Sanity anchors

• Home advantage exists in every league; if your fitted home coefficient is negative, suspect a data bug before a discovery.
• Compare against the market baseline: vig-removed closing probs. Beating Elo is easy; beating the close is the bar.