Claude Skills by DanielTomaro13

How to design and read backtests honestly — lookahead/leakage traps, point-in-time discipline, multiple comparisons, CLV vs ROI, sample size.

Verified bookmaker entry points (competition/event ids) for cross-book price lookups, starting with AFL.

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.

How to evaluate and fix probability calibration — holdout discipline, Brier/log-loss reading, Platt-style rescaling.

Cross-bookmaker comparison workflow — find the same selection at every book, surface the best price, and flag value.

DFS lineup workflow — site rules first, projections with stated sources, deterministic optimisation via optimize_lineup, stacking/ownership as explicit judgment.

The general model-building method for ANY market — framing, sample-size discipline, feature selection (user priors + data-driven), baselines, leakage-safe validation, calibration, persistence.

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.

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.

How to read a racecard honestly — barrier/weight/form/scratchings, converting prices to fair probability (overround/vig), cross-book best price, and exotics.

Interpreting captured line movement — steam vs drift, why the close is the benchmark, and how movement context changes a value report.

Remove the bookmaker margin (vig/overround) to estimate fair probabilities and spot value.