quant-concepts

name	quant_concepts
description	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.
triggers	["brier","log loss","log-loss","logistic regression","xgboost","gradient boosting","overfit","overfitting","calibration curve","regularization","walk-forward"]

Quant concepts — working definitions

Use these to explain your choices and to choose the right tool for the data volume.

Scoring probabilities

• Brier score: mean squared error of prob vs outcome. 0 = oracle; 0.25 = coin flip on a balanced set. Insensitive to tail confidence; easy to interpret.
• Log-loss: negative mean log-likelihood. Punishes confident wrongness brutally (a 0.99 that loses costs ~4.6; a 0.6 that loses costs ~0.9). If log-loss looks much worse than Brier, the model is overconfident in its tails.
• Calibration curve: bucket predictions (e.g. deciles), compare bucket mean prob vs bucket hit rate. The diagonal is calibrated; S-shapes mean over/underconfidence. With < ~200 samples, buckets are noise — say so instead of plotting noise.
• Always score on out-of-sample data; training-set scores are advertising.

Model families (match the tool to the sample size)

• Logistic regression — the default. Linear in log-odds, a handful of parameters, stable on hundreds of events, coefficients are readable ("home advantage = +0.18 log-odds"). Add L2 regularization when features correlate.
• Poisson / normal approximations — for scores and totals: model the scoring process (goals ~ Poisson, points ~ Normal), derive market probs analytically. Few parameters, strong structure — excellent for small samples.
• Gradient boosting (XGBoost/LightGBM) — trees capture interactions and non-linearities, but they overfit small samples enthusiastically and their raw outputs are usually MIScalibrated (recalibrate afterwards — see calibrate_probabilities). Reach for boosting when you have thousands of events and engineered features; never for 50 games. The sandbox has no GPU and may not carry xgboost — sklearn's GradientBoostingClassifier or hand-rolled logistic is the portable default.
• Elo-style ratings — not a model family, a feature factory: a single online rating per team, updated per game, feeds any of the above as a strong feature.

Regularization & validation

• Regularization (L2/shrinkage): pull estimates toward zero/base-rate; the smaller the sample, the harder you shrink. Equivalent intuition: a prior.
• Walk-forward CV: refit at each step, predict the next slice, accumulate out-of-sample scores. Honest for time series where a single split is lucky/unlucky.

Market concepts

• CLV (closing-line value): your price vs the closing price. The close is the market's most informed state; consistently beating it is the strongest evidence of real edge — more reliable than short-run ROI (variance dominates small samples).
• Vig/overround: the book's margin baked into prices; remove it (normalise implied probs) before comparing model vs market — value_finder does this.
• Variance vs edge: positive CLV + negative ROI over 50 bets = variance, keep going; negative CLV + positive ROI = luck, stop. Bet counts under ~100 decide almost nothing at typical edges — say so.