What is a fair price for a player prop?

What does fair price mean for a player prop?

A fair price is the market price with the sportsbook margin stripped out. For a player prop, that means converting the over and under odds into implied probabilities, removing the vig, and getting the true break-even probability for each side.

The key is distribution, not just a median projection. A receiver projected for 54.5 yards can still have a very different over probability depending on volatility, target share, matchup, game script, and the shape of his yardage outcomes.

Why does vig matter more on props?

Player props often carry more juice than spreads or totals. That extra hold makes a decent-looking projection less useful unless it clears the no-vig price by enough to pay for the tax.

If a book hangs an over at -120 and the under at -110, the listed odds are not the fair market. Sharkie wants the clean probability underneath the price, because that is where the bet either has teeth or gets sent back to the chum bucket.

Prop fair price = the no-vig probability of the over/under given a distribution of outcomes (not a point estimate). The useful way to read this is as a process check, not a promise about a single game. Start with the market baseline, remove the book margin when the question involves odds, and then ask whether the remaining difference is large enough to survive errors in your estimate. If the gap is thin, the disciplined answer is usually to pass or reduce stake size.

How do you compare a projection to the market?

First, devig the prop market. Then convert your projection into a probability of going over or under the posted number. The edge is your probability minus the no-vig market probability.

A point estimate alone is not enough. If your model says 61 yards on a 59.5-yard rushing prop, that is not automatically a bet. You need the probability of clearing 59.5, then you need that probability to beat the fair market by enough to justify staking.

For product work, keep the loop explicit: use No-Vig Calculator and Kelly Criterion Calculator for the math, then use Prop Research Workflow to audit the assumptions behind the number.

When should you skip a player prop?

Skip it when your edge is mostly a rounding error, when the book's juice eats the difference, or when your projection does not account for outcome spread. Props can look tasty because the numbers are player-specific, but small edges vanish quickly after vig.

A good prop process is cold-blooded: devig first, estimate the outcome distribution, compare probabilities, then size only if the edge survives.

That distinction matters because the market can be directionally right and still not offer a bet. SharkSnip pages treat the calculator output as a starting point: the next step is checking model confidence, data freshness, and whether the edge is big enough to bet responsibly.