Published May 20, 2026

How to Devig Sportsbook Odds (Methods and When to Use Each)

Devigging sportsbook odds turns the vig-inflated prices into no-vig probabilities you can actually use. Here are the four methods and when each works best.

Every sportsbook price has a margin baked in. Use those raw numbers as probabilities and you will consistently overstate how likely outcomes are. Devigging is the correction: strip out the margin so the probabilities sum to 100%. That number is what drives every serious +EV calculation, every arb check, and every honest read on whether a price is actually good.

This guide covers what the vig is, why removing it matters, and the four methods most bettors encounter (multiplicative, additive, power, and Shin) with worked examples and guidance on when each is the right tool.

What "vig" actually is

The vig (also called juice, margin, or overround) is the extra percentage a sportsbook adds so the implied probabilities of all outcomes sum to more than 100%. That excess is the book's expected gross revenue on balanced action.

Consider a two-way market priced at -110 on both sides.

Devigging redistributes that 4.76% back out of the prices so the remaining probabilities sum to exactly 1.

Three-way markets work the same way. In a soccer 1X2 market you might see:

Sum: 103.35%. The overround is 3.35%, spread across three outcomes instead of two. Every method below works on three-way markets just as well; you normalize over three probabilities instead of two.

Why devigging matters

Devigging is the bridge between the prices on your screen and any real edge calculation. Without it, every number you compare is systematically biased by the house margin.

It shows up in three places in a typical workflow:

Devigging does not give you a true probability. It gives you what a specific book thinks the probability is after stripping out its margin. That distinction matters for everything that follows.

The four common devigging methods

Each method takes implied probabilities and returns normalized ones that sum to 1. They differ in how the overround gets distributed.

For every example below, work from American odds of -150 / +120 on a two-way market.

(a) Proportional (multiplicative) method

The simplest and most common approach: divide each side's implied probability by the sum of all implied probabilities.

p_true = p_implied / (p_a + p_b)

Applied to our example:

When to use it. The default for most two-way markets. Fast, transparent, and holds up well when the two sides are reasonably close in price.

When it breaks down. It assumes the book spreads its margin in proportion to each side's probability. On very heavy favorites (a -1000 moneyline against a +700 underdog), it slightly understates the favorite's true probability. Heavy favorites are usually a touch more likely than their proportional devig implies.

(b) Additive (equal margin) method

The additive method subtracts an equal share of the overround from each outcome.

p_true = p_implied - overround / n

Where n is the number of outcomes. For our two-way market:

When to use it. Rarely. It assumes the book builds its margin the same way on a 90%/10% market as on a 50%/50% market, which is not how real books price. Worth knowing the method exists, not worth using as a default.

(c) Power method

The power method raises each implied probability to a shared exponent k, where k is chosen so the results sum to 1.

p_a^k + p_b^k = 1, then p_true = p_implied^k

You solve for k numerically. For our -150 / +120 example, k ends up near 1.085:

The gap from multiplicative is small here (56.9% to 57.5%). On a heavier favorite, say -400 against +300, it widens noticeably. The power method pushes probability toward the favorite and away from the longshot, which tends to match empirical results on markets with favorite-longshot bias: heavy moneyline favorites, long parlay legs, some horse racing markets.

When to use it. When prices are lopsided (shorter than about -300, or longer than +500).

When it breaks down. On near-even markets the power method collapses toward multiplicative, so the extra complexity is wasted. It also requires a numerical solver for k, which adds friction.

(d) Shin method

The Shin method, from economist Hyun Song Shin, models a fraction z of bettors as informed insiders. The book widens its prices to hedge against that insider flow, which means the vig is not purely margin. Part of it is a hedge against adverse selection.

You solve for z (typically 0.01 to 0.05) such that the no-vig probabilities sum to 1. For two-way markets:

p_true = (√(z² + 4(1 − z) × p_implied² / sum) − z) / (2(1 − z))

When to use it. Shin is the most rigorous of the four. It produces well-calibrated probabilities across the full favorite-longshot spectrum and tends to beat the others slightly on large historical samples.

When it breaks down. In practice, the complexity rarely pays off versus a simple consensus across many books. Most bettors do not need Shin unless they are building a model from scratch with data to validate it.

Which method is best in practice

There is no single best answer, but there is a sensible default.

The much larger lever is how many books you devig. Averaging a no-vig probability across five books using multiplicative devig will almost always beat using Shin on a single book. Consensus trumps method.

What we use, and why

We use the power method for every market we price. Every no-vig probability in the +EV feed flows through it.

For each market we:

  1. Convert every offered price to an implied probability
  2. Run a binary search to find the exponent k where p_1^k + p_2^k + ... = 1
  3. Renormalize for numerical stability (at the solved k, the sum is already ≈ 1; the division just guarantees it)
  4. Return the resulting vector of no-vig probabilities

Four reasons it is our default over multiplicative, additive, or Shin:

After devigging per book with the power method, we average the no-vig probabilities across books and subtract a standard error buffer before anything gets flagged as +EV. That consensus step is at least as important as the devig method itself. Any single book's devigged number is still one book's opinion.

Devigging three-way markets

Three-way markets (soccer 1X2, futures with draws) use the same four methods. You just normalize over three outcomes instead of two.

Take the earlier 1X2 market at 2.10 / 3.40 / 3.80 decimal, summing to 103.35%.

If a three-way market is lopsided, the power method again does better on the favorite than multiplicative. For balanced 1X2 markets, multiplicative is fine.

A common shortcut when you only care about head-to-head outcomes is Draw No Bet logic: remove the draw and redistribute its probability proportionally to the two remaining outcomes. That is still devigging, just on a restructured market.

Common devigging mistakes

Even with a good method, it is easy to use the resulting numbers wrong.

Final takeaway

Devigging is the step that makes sportsbook prices comparable across books. It is not magic, and no method produces true probabilities. It gives you the book's pricing view with the margin removed.

For most day-to-day betting, the multiplicative method on a consensus of several books is enough. Switch to the power method on heavily lopsided prices, and keep Shin in your back pocket if you are building a model from scratch.

Whichever method you pick, the real edge comes from applying it consistently. The +EV framework shows how devigged probabilities get used in practice, and you can see live edges on the +EV feed.