How to read prediction markets (Kalshi, Polymarket) — price-as-probability, resolution rules before any comparison, fees/spread, and comparing exchange contracts to book odds honestly.
How to read a racecard honestly — barrier/weight/form/scratchings, converting prices to fair probability (overround/vig), cross-book best price, and exotics.
DFS lineup workflow — site rules first, projections with stated sources, deterministic optimisation via optimize_lineup, stacking/ownership as explicit judgment.
How to design and read backtests honestly — lookahead/leakage traps, point-in-time discipline, multiple comparisons, CLV vs ROI, sample size.
Worked example of model_development for match-winner (h2h/moneyline) markets — ratings + logistic regression, home advantage, draw handling.
Worked example of model_development for totals (over/under) markets — scoring-process baseline, pace adjustment, holdout evaluation.
The general model-building method for ANY market — framing, sample-size discipline, feature selection (user priors + data-driven), baselines, leakage-safe validation, calibration, persistence.
Working definitions of the core quant concepts — Brier, log-loss, calibration, logistic regression, gradient boosting/XGBoost, regularization, walk-forward CV, CLV — and when each tool fits.