How a Sharp-Consensus Fair Line Is Built (and How to Build One Yourself)

Every other tool hands you a “fair value” and stops there. We’ll do better. This page teaches the actual technique behind a de-vigged, sharp-anchored fair line, worked step by step on a line you already trust, so the number on our board isn’t magic to you. What we keep to ourselves is narrow, and we’ll say so plainly: which lines have earned the right to anchor, and how much each one counts. Give a bettor the method. The anchor is the edge.

What the number is

For each two-way market we publish one fair probability per side. The two sum to 100%, the vig removed, and it’s our best estimate of the true odds. Every +EV flag and every closing-line-value grade on the site compares a posted price to this number. The technique that produces it is below, and you can run it by hand.

Step 1: take the vig out of every book

A posted line isn’t fair. It includes the book’s margin, which is why the two sides add up to more than 100%. So de-vig each book first, recovering the fair probability it implies (the power method corrects favorite-longshot bias). Track how much the proportional, power, and Shin methods disagree, too. That gap is a data-quality signal: a line whose de-vig is ambiguous deserves less trust.

Step 2: one book is not the truth, and neither is a plain average

The classic move is to treat one sharp book (Pinnacle) as the benchmark. That data is legally fraught to source, and any single book is one point of failure that can be stale or shaded. But the naive fix, averaging every book, is worse than it looks. Soft books systematically shade the popular side, so a plain average gets dragged toward the recreational crowd. You want a pool that leans on the lines that price honestly and discounts the rest.

Step 3: anchor on a line you trust

The move that matters is choosing what to anchor on. Pick the sharpest line in the market, a price set where real money trades on both sides and the hold is thin for the right reasons, and let it lead. Treat the rest as context around it. Note the trap: low vig alone does not mean sharp. Soft books compress their hold to 2% to 4% on exactly the high-volume public games they shade hardest, so weighting purely by thin margins hands the shaded crowd the most influence. On this page, take the anchor as given, “here is a line I trust,” and build the fair number around it. (Which lines have earned that trust, and how much each counts, is the part we’ve worked to learn and the part we keep.)

Step 4: don’t let copycats vote twice

Many “different” sportsbooks run the same odds engine under the hood and post identical lines. Counted naively, ten copies of one price look like ten independent opinions and manufacture false confidence. Collapse near-identical lines into a single vote before you pool them, and before you count how many independent opinions you actually have.

Step 5: average in log-odds, around the anchor

Combine the lines in log-odds (logit) space rather than plain probability. This logarithmic pool is the statistically correct way to merge probability estimates (a plain average is provably underconfident and drifts to the middle). Averaging one number and converting back guarantees the two sides sum to 100% with no vig.

Then guard against a shaded majority: clip genuine outliers around the anchor, the line you trust, not around the crowd’s middle. Center the robustness on the crowd and it “corrects” the sharp line as the outlier. Center it on the anchor and the shaded pack gets discounted instead. That’s the whole ballgame on a public game, and it’s why the choice of anchor in Step 3 does the heavy lifting.

Step 6: a confidence number that can’t lie about certainty

The fair line should ship with a trust signal built to resist false precision: how tightly the independent lines agree, and how many independent opinions there really are once copycats are collapsed. When there’s no line in the market worth anchoring on, the honest move is to say so and cap the confidence rather than dressing up a recreational average as truth. We publish that read alongside every fair line.

The part we keep, and why

Everything above is the technique, and you can run it. What we don’t hand over is the anchor itself: which lines have earned the right to lead, and how heavily each counts. That isn’t a fixed list. It’s learned. As line-movement and closing history accumulate, a line earns weight by consistently anticipating the closing number (positive closing line value) and predicting outcomes (lower log-loss), under strict no-lookahead discipline. That earned weighting sharpens quietly as the data grows. The method is open. The anchor is the edge.