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Dice Pool Success Probability Calculator

Calculate the probability of rolling at least a certain number of successes with a pool of dice, a mechanic common in tabletop games.

Result

Probability of Success
53.91%
Per-Die Success Chance
33.3%

Uses the binomial probability formula, treating each die as an independent trial with the same per-die success probability. This models the common tabletop mechanic (used in games like World of Darkness and Shadowrun) of rolling a pool of d6s and counting how many meet or exceed a target number.

About the Dice Pool Success

The Dice Pool Success Probability Calculator finds the odds of rolling at least a certain number of successes out of a pool of six-sided dice, a mechanic used in tabletop systems like World of Darkness and Shadowrun. Rather than asking about the total shown on the dice, it answers how many individual dice in the pool are likely to clear a target face value.

How It Works

You enter how many dice are rolled, the minimum face value that counts as a success (for example, 5 means rolls of 5 or 6 both count), and how many successes you need. The calculator first converts the success threshold into a per-die probability, then applies the binomial probability formula across every possible number of successes from the target up to the full dice count, summing them into one overall probability of hitting at least that many successes.

Per-die success probability p = (7 - success threshold) / 6, clamped between 0 and 1. Probability of at least k successes out of n dice = sum from j=k to n of [C(n,j) x p^j x (1-p)^(n-j)], where C(n,j) is the binomial coefficient (n choose j).

Formula & Methodology

The per-die probability comes from counting how many of the 6 faces meet or beat the threshold: a threshold of 5 means faces 5 and 6 succeed, or 2 out of 6 faces, giving (7-5)/6 = 2/6 = 33.3%. From there, the binomial formula adds up the probability of getting exactly the target number of successes, exactly one more, and so on up through rolling successes on every die, since 'at least k successes' covers all of those outcomes together.

Examples

Standard pool roll

Rolling 5 dice where a 5 or 6 counts as a success, and needing at least 2 successes, gives a per-die chance of 33.3% and an overall probability of about 53.91%.

Larger, harder pool

Rolling 10 dice where only a 6 counts as a success, and needing at least 3 successes, gives a per-die chance of about 16.7% and an overall probability of about 22.48%.

Advantages

  • Applies the exact binomial distribution instead of approximating, so the probability accounts for every combination of which specific dice succeed.
  • Separately reports the per-die success chance, making it easy to see how much a tighter or looser success threshold shifts the overall odds.
  • Adapts to the exact rules of different tabletop dice pool systems just by changing the success threshold and dice count entered.

Common Mistakes

  • Assuming probability scales linearly with the number of dice added to a pool, when the binomial relationship means adding dice has diminishing or accelerating effects depending on the threshold.
  • Mixing up the success threshold with the number of successes needed, since one sets the per-die odds and the other sets the target count across the whole pool.
  • Underestimating how much a stricter success threshold, like requiring a 6 instead of a 5 or 6, reduces overall odds, since it cuts the per-die chance roughly in half.

Edge Cases to Watch For

  • A success threshold above 6 would make the raw per-die probability negative, so it's clamped to 0, meaning no die can ever succeed.
  • A success threshold of 1 or lower would push the per-die probability above 1, so it's clamped to 1, meaning every die automatically counts as a success.
  • If the successes needed is greater than the number of dice rolled, the summation has no valid terms to add and the probability of success is 0%.
  • Dice count, success threshold, and successes needed are all rounded to the nearest whole number before the calculation runs.

Common Use Cases

  • Tabletop RPG players estimating their odds of success before making a dice pool roll during a game.
  • Game masters calibrating how difficult a given success threshold and dice pool size should feel for players.
  • Tabletop game designers testing probability curves while building or balancing a new dice pool mechanic.
Written & fact-checked by the Calculateus TeamLast updated August 5, 2026How we verify our formulas

Frequently asked questions

How does this differ from a standard single-roll probability calculation?

A dice pool mechanic asks how many dice out of a whole handful clear a threshold, not what a single roll's exact value is - this requires the binomial distribution, which accounts for all the different combinations of which specific dice succeed and which don't, rather than a simpler single-event probability calculation.

Conclusion

Because dice pool success counting depends on combinations of successes across many independent dice, the binomial formula is the only way to get an exact answer rather than a rough guess. This calculator applies that formula directly so players and designers can see precisely how threshold and pool size interact.