Sports betting models that publish a fair price

A sports betting model should output a price, not a pick. Give it a game and it should tell you the probability of each outcome and the fair odds that go with it. Then you compare that fair price with what the books are offering and decide for yourself. quant+ models work that way: each one publishes a fair spread, total and moneyline (or, for props, the probability of the over at any line), and its scorecard shows how well those prices have held up against the closing line.

If a model can't show you its calibration and how it does against the close, you're being asked to trust it on faith.

What a model output should look like

Here is what the quant+ game models produce for every game they cover:

• Team ratings (overall, offense and defense).
• A predicted margin and total.
• Fair prices: the spread, total and moneyline probabilities those predictions imply.

Prop models produce the probability of the over at any line, so a 6.5 strikeout prop and a 7.5 alternate get their own fair prices instead of one guess at the median.

On each game page, the model's fair price sits in its own column next to the board. The cell turns green when the best available price is better than the model's fair price. That's the whole output. No "best bet of the day", no confidence stars, no units.

How a model price turns into an edge

Take a moneyline where the model says the home side wins 55 percent of the time. Here is the arithmetic (an illustration, not a result).

Step	Model view	Market view
Fair win probability	55.00%	50.00%
Fair American odds	about -122	+100
Best price available	-110	-110
EV per 100 staked at -110	0.55 × 90.91 − 0.45 × 100 = +5.00	0.50 × 90.91 − 0.50 × 100 = −4.55

Same bet, two opposite answers. Everything depends on whose probability is closer to the truth. Markets are hard to beat as forecasters: a PLOS ONE study found betting odds predicted results better than various quantitative models. So when a model disagrees with the market by 5 points, the default assumption should be that the model is wrong, until its track record says otherwise.

Calibration matters more than accuracy

Accuracy asks how often the model picked the winner. Calibration asks whether its probabilities mean what they say. The scikit-learn documentation puts it simply: among the games a well-calibrated model gives a probability close to 0.8, about 80% should actually win.

For betting, calibration is the one that pays. A model that picks winners often but calls every favourite 75 percent will keep telling you short prices are good value. Walsh and Joshi tested this directly on NBA seasons with published odds and found that choosing models by calibration rather than accuracy led to greater returns in their betting experiments (Machine Learning with Applications, 2024).

A quick check you can run on any model: take the 200 games it priced near 60 percent. About 120 should have won. If 100 won, the model is overconfident there, and every "edge" it shows in that range is inflated.

What the quant+ model scorecard shows

Every model has a detail page with:

• Margin error and moneyline Brier score (the average squared gap between the forecast probability and what happened; lower is better).
• Beats the close: how its prices compared with the closing line.
• Walk-forward by season: each season scored by a version trained only on earlier seasons.
• Calibration charts for moneyline, spread and total.
• Against the spread and totals results on games where the model disagreed with the close, shown with a 95 percent interval and the break-even line.
• Team ratings and a versions table, so you can see what changed between releases.

Prop models are graded on held-out games against a naive baseline: the player's last-ten average. If a prop model can't beat that, it shouldn't be trusted on anything.

We publish these numbers even when they aren't flattering. A model that roughly matches the close is still useful as a second opinion and a sanity check. It is not a reason to bet more.

Using a model that's less accurate than the market

A model doesn't have to beat the market everywhere to be useful. Hubáček and Šír showed that a model can be less accurate than the market and still find value, if its errors are decorrelated from market prices. The reverse warning matters too: a model that mostly copies the market adds nothing, because it will agree with the books exactly where they're right and wrong.

In practice:

1. Start with the market's fair price. The +EV scanner does this for every line.
2. Use the model as a filter, not a trigger. A price that is good against the market and the model is more interesting than one that is good against only one of them.
3. Check the scorecard for that market. A model calibrated on NFL sides may be weak on totals.
4. Log every bet and watch your closing line value. If your model-backed bets don't beat the close over a few hundred bets, the model isn't helping.

Coverage and limits

Leagues: NFL, college football, NBA, college basketball, MLB and NHL. Game models go live league by league once each one has enough graded results to publish a fair scorecard; NFL comes first. Prop models cover the main stat markets (yards and receptions in football, points, rebounds and assists in basketball, strikeouts and total bases in baseball, shots on goal in hockey).

What the models don't do: they don't pick bets, they don't place bets and they don't connect to any sportsbook account. They can be wrong for weeks at a time, and injury news can make a pre-game price stale within minutes. Read the risk disclosure before you use any of it.

If you want to test a rule built on model output before using it, the backtest lab runs it on past seasons with no look-ahead. The quant+ guides cover fair odds and closing line value first.

Start free to open the model pages, or see the terminal on the home page.

Quant+ is analytics for informational purposes. 21+. If you or someone you know has a gambling problem, call 1-800-GAMBLER.