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Going Deeper

How the Iceberg Simulation Predicts a Game

How the Iceberg Simulation Predicts a Game

A headline pick gives you one number: this team wins 58% of the time. Useful — but it's the tip of the iceberg. Underneath is the full range of ways the game could actually unfold, and that's what our Iceberg Simulation surfaces.

What "Monte Carlo" means

A Monte Carlo simulation answers questions about an uncertain event by playing it out at random, thousands of times, and tallying the results. Instead of trying to calculate one perfect answer, you let chance do the work: simulate the game 10,000 times, see how often each thing happens, and those frequencies become your probabilities. The name comes from the casino — it's literally rolling dice, just very fast.

How we run it

For each game, the Iceberg engine starts from the consensus and the market lines, derives each team's expected scoring, then plays the game out 10,000 times using a shape that fits the sport:

Each simulated game produces a final score. After 10,000 of them, we count.

What the distribution tells you

A single win probability can't show you these, but the simulation can:

Why a range beats a point

Two games can both have a 9.5-run total projection, but one might be a tight pitchers' duel (most outcomes near 8–10) and the other a volatile slugfest (4 to 14). A point estimate hides that; the distribution shows it. For totals and props especially, knowing the shape of likely outcomes is often more valuable than the single most likely number.

The Iceberg is our own model — so it doesn't get a vote in the independent consensus (that would double-count) — but it's free on every game page, and like every model here, it's graded against real results so you can watch how it does.


FreezyPicks aggregates independent models, sharp-money data, and our own Iceberg simulation into free, graded picks — for entertainment, not betting advice. See today's picks or the full disclaimer. 21+ and where legal.

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