The Lab

Betting math and market mechanics, explained from the ground up — the same concepts the model runs on. No picks, no hype. Start at Foundations and work down; each level assumes the one before it.

Foundations

What a betting line really says A moneyline isn't a prediction — it's a price. Learning to read -157 as a probability is the first skill in evaluating any betting market.	2026-07-25
The vig — what betting actually costs The bookmaker's fee is invisible, automatic, and the reason most bettors lose. Here's how to see it and what it does to a season.	2026-07-25

Our method

Why "no pick" is a feature
Most days the model flags nothing. That silence is the most honest output a betting model can produce — here's the math behind it.

2026-07-25

Questions we haven't answered yet

Every article starts as a question we couldn't answer honestly in one sentence. These are still open — not knowing yet is part of the method. Send yours through the feedback link below.

Does closing-line value actually predict long-term profit, or is it just the market grading our homework?
How many graded games until a win rate means anything? (235 games in — what can we actually claim?)
Why does the model's "every lean" record lose while its calibration holds? Is that proof the market is efficient, or proof we're missing something?
When a prediction market (Kalshi, Polymarket) disagrees with every sportsbook, who's right more often?
Is a 60% favorite at -157 a better or worse bet than a 40% underdog at +150 — and why does everyone get this backwards?
What does the vig actually cost a bettor over a season of flat betting?
Do line moves from open to close carry information, or is it noise chasing steam?
Why do the books disagree with each other at all, and is the softest line always the best bet?

Model output is informational and entertainment content, not betting or financial advice. If you bet, bet what you can afford to lose.