Data & Advanced Metrics
Market efficiency, CLV, variance, bankroll sizing, and advanced modeling for sharper decision-making.
Start with a forecast recorded before the outcome, then examine its calibration, uncertainty, and performance on held-out data. These guides distinguish probability points from expected return and win-count variation from bankroll variation. Closing Line Value is a useful price benchmark, but it does not prove an edge or model correctness. Power ratings and Kelly calculations are only as credible as their stated assumptions. Use the decision-psychology guides to record contrary evidence and review the process without treating discipline as a guarantee of profit.
Updated September 22, 2026
Markets and price quality
1. Markets and price quality
Market Efficiency and Information Flow
Evaluate information flow and market-efficiency claims with explicit samples, prices, and forecast uncertainty.
2. Markets and price quality
Closing Line Value (CLV): Comparing Entry and Closing Prices
Learn what Closing Line Value measures, how to compare entry and closing prices across odds formats, and the limitations of interpreting a CLV record.
Modeling
1. Modeling
Building Your Own Forecasting Model
Build a forecasting model with timestamped data, held-out validation, calibration checks, and explicit probability, expected-return, and Kelly assumptions.
2. Modeling
Building Proprietary Power Rankings
How to weight home field, rest, and team strength into a usable market model.
Variance and sizing
1. Variance and sizing
Statistical Variance: Samples, Uncertainty, and Bankroll Swings
Distinguish win-count variance from bankroll-return variance, with explicit payout units, independence assumptions, and limits of CLV and sample-size claims.
2. Variance and sizing
The Kelly Criterion in Sports Betting
A practical framework for bet sizing, bankroll growth, and controlling risk of ruin.
Decision psychology
1. Decision psychology
Identifying and Mitigating Confirmation Bias
How to spot narrative-driven analysis and keep your process anchored to data.