What should a model output before you bet?
The model needs to produce a probability or a projection that can be converted into a probability. For spreads, that means cover probability. For props, that means probability of going over or under the posted line.
A raw projection is useful, but the bet decision happens in probability space. Sportsbooks price probabilities, not vibes with decimal places.
Workflow: (1) model outputs a probability/projection, (2) devig the market to fair probability, (3) edge = model prob minus no-vig prob, (4) if edge clears the vig threshold, size with fractional Kelly. A practical workflow keeps the math in one order. Price the market first, convert everything to probability, compare against your projection, and only then think about stake size. Reversing that order is how bettors talk themselves into action before they know whether the number is actually playable.
How do you compare the model to the market?
Devig the market to find the fair probability. Then subtract that no-vig probability from your model probability.
If your model gives a side 56% and the no-vig market is 53%, the edge is 3 percentage points. That gap is the signal you evaluate before staking.
Workflow: (1) model outputs a probability/projection, (2) devig the market to fair probability, (3) edge = model prob minus no-vig prob, (4) if edge clears the vig threshold, size with fractional Kelly. A practical workflow keeps the math in one order. Price the market first, convert everything to probability, compare against your projection, and only then think about stake size. Reversing that order is how bettors talk themselves into action before they know whether the number is actually playable.
For product work, keep the loop explicit: use No-Vig Calculator and Kelly Criterion Calculator for the math, then use Model Report Examples to audit the assumptions behind the number.
When is the edge large enough to bet?
The edge must clear the market's vig and your own uncertainty. As a practical filter, edges below about 1% are usually too thin because normal variance and estimation error can erase them.
A tiny edge is not a moral victory. It is a maybe with a receipt printer attached. Sharkie prefers clear disagreement, not decimal dust.
For product work, keep the loop explicit: use No-Vig Calculator and Kelly Criterion Calculator for the math, then use Model Report Examples to audit the assumptions behind the number.
How should you size the bet?
Once the edge passes your threshold, size with fractional Kelly or a conservative staking rule. Full Kelly assumes your probability is exact, which is a bold claim in sports.
The job is to convert model disagreement into disciplined exposure. Prediction first, fair price second, edge third, stake last.
Write the inputs down before the bet: market price, fair probability, model probability, edge threshold, stake fraction, and the reason the number could be wrong. That small audit trail makes it much easier to separate a good losing bet from a bad winning one.
Which tools and guides support this answer?
What else should bettors know?
Can I bet whenever my model disagrees with the book?
No. Disagreement is only the start. You need the model probability to beat the no-vig market by enough to overcome juice and uncertainty.
Why skip bets with tiny edges?
Small edges are highly vulnerable to model error, stale lines, and variance. A clean process should filter out weak signals before money gets involved.
Where do SharkSnip model predictions fit in?
SharkSnip's model-driven predictions can supply the probability side of the workflow for NFL, NBA, MLB, and NHL markets. You still compare them to no-vig market prices before betting.