utility calculator
Pick random winners or split any list into fair, balanced teams using an unbiased Fisher-Yates shuffle.
A Fisher-Yates shuffle reorders a name list so every possible ordering is equally likely, then either takes the first N names as winners or deals names round-robin into teams. Splitting 23 people into 4 teams, for example, produces three teams of 6 and one team of 5.
Quick actions: Save inputs, view saved runs, print, share, or export results
Winner
Avery
Not picked (7)
Morgan, Sam, Riley, Casey, Taylor, Alex, Jordan
Tip
Save this page for classroom picks, raffles, giveaways, chore rotations, or splitting a group into teams fairly.
Formula
For i from last index down to 1: swap element[i] with element[random(0..i)]Every permutation of the list is equally likely, unlike naive Math.random()-based sorting, which is a known source of bias.
Step by step
Example
23 ÷ 4 = 5.75, so an even split is impossible. Round-robin dealing produces three teams of 6 and one team of 5 — the fairest possible arrangement, with the randomized shuffle deciding which team gets the extra person.
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Summary
Total Entries
8
Names parsed from your list.
Winners Drawn
1
Unique, non-repeating picks.
Duplicate Names Found
0
None detected.
Guide
Most 'random picker' tools online do exactly one thing — spin a wheel to choose a single winner. Fewer combine that with team-splitting, even though the underlying problem is identical: take a list of names and produce a fair random outcome from it. This calculator does both from the same input list, since the same classroom, workplace, or friend group that needs a random volunteer today often needs to be split into project teams tomorrow.
Not all 'random' code is equally fair. A naive approach — sorting a list by `Math.random()` comparisons — is a well-documented source of bias, because JavaScript's sort isn't guaranteed to call the comparator on every pair equally, which skews results toward certain orderings. This calculator uses the Fisher-Yates shuffle, the standard unbiased algorithm: starting from the last item, it swaps each element with a randomly chosen element at or before its own position, working backward to the front. Every permutation of the list ends up equally likely, which is what 'fair' actually requires for a raffle or team draw.
Splitting N names into K teams evenly only works cleanly when N divides evenly by K — otherwise someone has to decide which teams get an extra person. This calculator shuffles the full list first, then deals names into teams round-robin (team 1, team 2, team 3, team 1, team 2...), which guarantees every team's size differs by at most one person, and which team gets the 'extra' person is itself randomized by the initial shuffle rather than always landing on team 1.
With 20 raffle entries and 1 winner to pick, a fair draw gives each entrant exactly a 1-in-20 (5%) chance. The calculator shuffles all 20 names with Fisher-Yates and takes the first name in the shuffled order as the winner — mathematically identical to drawing one name from a hat, just without needing physical paper.
23 people split into 4 teams doesn't divide evenly (23 ÷ 4 = 5.75), so round-robin dealing produces three teams of 6 and one team of 5 — the fairest possible split, since no valid arrangement of 23 people into 4 non-empty teams can avoid a 1-person size difference. Which specific team ends up with 5 people is randomized along with everything else, so no team is systematically favored across repeated draws.
For a transparent draw, paste the full entry list before drawing (not after seeing who 'should' win) so the process is auditable. If you need to avoid picking the same person twice across multiple rounds, remove each winner from the list before the next draw — this calculator doesn't track picks across separate draws automatically. For contests with legal or promotional requirements, keep in mind that a random-picker tool establishes fairness in the mechanism, but official sweepstakes rules often have their own documentation requirements beyond just 'the draw was random.'
Need a random string instead of a name? The UUID Generator produces cryptographically random unique identifiers, and the Password Calculator generates random secure passwords — both use randomness for a different purpose than picking people, but share the same 'genuinely random, not predictable' requirement.
Fisher-Yates shuffle
The standard unbiased algorithm for randomly reordering a list, where every possible ordering is equally likely.
Round-robin team dealing
Distributing shuffled entries one at a time across teams in rotation, which guarantees team sizes differ by at most one person.
Sampling without replacement
Drawing multiple winners such that no name can be selected more than once in the same draw.