How to Calculate Implied Probability on Player Props

How Do You Convert American Odds to Implied Probability?

The formula is different depending on whether the odds are negative or positive, but both versions are straightforward.

For negative odds (the favoured side): Divide the absolute value of the odds by the absolute value plus 100.

At -125: 125 divided by 225 equals 55.6%. The book is implying this outcome wins 55.6% of the time.

At -110: 110 divided by 210 equals 52.4%.

At -130: 130 divided by 230 equals 56.5%.

For positive odds (the underdog side): Divide 100 by the odds plus 100.

At +105: 100 divided by 205 equals 48.8%.

At +120: 100 divided by 220 equals 45.5%.

At +150: 100 divided by 250 equals 40%.

Once you have both implied probabilities for a prop, add them together. They'll always sum to more than 100%. The excess is the book's margin, also called the vig or juice.

Read More: What Is Juice on Player Prop Bets?

Want to see which players are trending before you bet? Visit our Player Props page to track prop trends, streaks, and key stats all in one place.

What Does the Combined Implied Probability Tell You?

A concrete example using a real prop structure:

Over 24.5 points at +105, Under 24.5 points at -125.

Over +105: 100 divided by 205 equals 48.8%. Under -125: 125 divided by 225 equals 55.6%.

Combined: 48.8 plus 55.6 equals 104.4%.

The extra 4.4% above 100% is the book's hold on this prop. To find what the book's model genuinely thinks about each outcome, remove the vig by dividing each implied probability by the total.

Fair Over probability: 48.8 divided by 104.4 equals approximately 46.7%. Fair Under probability: 55.6 divided by 104.4 equals approximately 53.3%.

So the book's true view on this prop, after stripping out the margin, is that the Under hits about 53% of the time and the Over hits about 47% of the time. Both sides are being sold to you at prices that include a 4.4% house edge built in.

This is useful for two reasons. It tells you what the book genuinely thinks. And it tells you exactly how much edge your own projection needs to overcome before the bet becomes positive expected value.

Read More: How to Spot Value in Player Prop Bets

How Do You Use Implied Probability to Find Value?

Once you have the book's implied probability for each side, compare it to your own projected probability for the same outcome.

Your projection says the player goes Over 24.5 points 54% of the time. The book's implied probability for the Over is 48.8%. Your estimate is 5.2 percentage points above what the book is implying.

That gap is your potential edge. Whether it's large enough to act on depends on how confident you are in the projection and how much of that gap survives after accounting for vig.

A practical threshold: most sharp prop bettors look for a minimum of 3 to 5 percentage points of edge above the implied probability before committing to a bet. Below 3 points, the edge may fall within normal projection error. Above 5 points, the edge is large enough to be meaningful across a reasonable sample of similar bets.

The other direction works the same way. If your projection says the player goes Over only 44% of the time and the book is implying 48.8%, the Under at -125 is the value side despite looking like the expensive option. The implied probability, not the price alone, determines which side has value.

Read More: Real Player Prop Examples Using Trends and Data

Before placing a prop, check the bigger picture. Our Player Props page shows player trends and streak data so you can spot patterns that matter.

How Does Line Shopping Connect to Implied Probability?

Finding the best available price on a prop isn't just about saving a few cents on the juice. It directly changes the implied probability you're betting against, which changes whether the bet has positive expected value at all.

A practical example:

Your projection gives the Over a 52% true probability.

Book A: Over at -115, implying 53.5%. Your 52% projection is below the implied probability. This bet has negative expected value.

Book B: Over at -105, implying 51.2%. Your 52% projection is above the implied probability. This bet has positive expected value.

Same projection. Same outcome. Different books. One bet is a loser, one is a winner, purely because of the price difference.

This is why accessing multiple sportsbooks and checking the implied probability at the best available price is part of the basic analytical process for prop betting, not an optional extra.

Looking for an edge in the prop market? Head to our Player Props page to view player prop trends and streaks across multiple sportsbooks in one easy hub.

FAQ

Do you need to memorise the formulas or is there a shortcut?

A few common reference points are worth remembering. -110 implies 52.4%, -120 implies 54.5%, -130 implies 56.5%, +100 implies 50%, +110 implies 47.6%, +120 implies 45.5%. For anything else, the formula takes about 10 seconds on a phone calculator. Most prop betting platforms also display implied probability directly alongside the odds.

What's a typical combined implied probability range for player props?

Standard spread bets usually have a combined implied probability around 104.5 to 105%. Player props commonly run 105 to 108%, meaning the book holds a larger margin. Niche or obscure props can run even higher. The higher the combined implied probability, the larger the edge your projection needs to show before the bet is worth taking.

Should you bet both sides of a prop if your projection is close to the line?

No. If your projection doesn't clearly favour either side, there's no edge on the bet. Betting both sides just guarantees paying vig twice with no expected value on either side. Pass on props where your projection closely matches the book's line and focus on markets where a meaningful gap exists.

How often do projections need to be accurate for this approach to work long-term?

The break-even accuracy rate depends on the odds you're betting at. At -110, you need to be right 52.4% of the time just to break even before considering edge quality. If your projections consistently identify outcomes that happen more often than the implied probability suggests, across a large sample, the approach produces positive expected value over time.