Random Tools

List Randomizer

Paste a list, choose cleanup and numbering options, shuffle with browser cryptographic randomness, then copy, download, or undo the result.

List — one item per line

Blank lines

Duplicates

Number the shuffled result

ReadySecure Fisher–Yates shuffle using browser Web Crypto.

Core random, wheel & group workflows

Use the broad picker, number, wheel, name, team or dice workflow first. Move to a specialist only when the probability model or output structure genuinely changes.

All 35 random tools

Random Number GeneratorSecure integer draws & unique mode Random PickerPick names or items from a list Spin the WheelVisual choice wheel & winner history Random Name GeneratorGenerate names in local batches Random Team GeneratorSplit a roster into teams Dice RollerCustom dice count & sides Yes or No WheelBinary decision with visible odds Weighted WheelCustom non-negative probability weights

Fairness and randomness boundaries

Random selection can make equal-probability choices from the entered pool, but it cannot prove that the input list itself is appropriate or unbiased. Visual wheel/dice animations are presentation only. Seeded tools remain reproducible pseudo-random workflows rather than cryptographic randomness, and rating-balanced teams use a transparent heuristic rather than an optimal or human-judgment model.

How to use this List Randomizer

Paste one item per line, choose whether to keep blanks and duplicates, optionally number the output, then shuffle, copy, download, or undo the previous shuffle.

Shuffle semantics

Fisher–Yates changes order only. Duplicate lines remain separate entries unless you explicitly remove them, and blank-line handling follows the selected option.

Usage notes optional

Define what “fair” means for the draw

A shuffle gives each ordering a chance when the random source and algorithm are sound, but fairness may also require one entry per person, transparent handling of duplicates, and a rule about redraws. Decide those rules before publishing a result.

Keep reproducibility separate from unpredictability

A seeded shuffle is useful when a result must be repeated for debugging or teaching, while an unpredictable draw is better when participants should not be able to forecast the order. Do not claim cryptographic randomness unless the implementation and threat model actually require and support it.