{"id":115,"date":"2026-05-04T19:45:53","date_gmt":"2026-05-04T23:45:53","guid":{"rendered":"https:\/\/simpletool.io\/blog\/?p=115"},"modified":"2026-05-04T19:45:53","modified_gmt":"2026-05-04T23:45:53","slug":"random-name-picker","status":"publish","type":"post","link":"https:\/\/simpletool.io\/blog\/random-name-picker\/","title":{"rendered":"Random Name Picker & List Randomizer Online [2026]"},"content":{"rendered":"
TL;DR:<\/strong> A random name picker (or list randomizer) shuffles a list and either returns the full shuffled order or pulls a single random pick. The fair algorithm is the Fisher\u2013Yates shuffle<\/strong> with a cryptographic random source \u2014 every input has equal probability of every output position. Our free list randomizer<\/a> uses crypto.getRandomValues<\/code> (cryptographically secure) and produces a shareable result URL so participants can verify the draw was honest.<\/div>\n

Picking a winner sounds simple until something goes wrong. A naive shuffle (sort with Math.random() - 0.5<\/code>) is biased \u2014 some positions are 1.5\u20132\u00d7 more likely than others \u2014 and you wouldn’t notice the bias unless you ran 10,000 trials. A weak random source means a savvy participant can, in principle, predict the outcome from prior draws. For a giveaway, raffle, or roommate chore wheel, neither is acceptable.<\/p>\n

Our list randomizer<\/a> uses Fisher\u2013Yates with the browser’s crypto.getRandomValues<\/code> API \u2014 the same RNG used to generate cryptographic keys. The shuffle produces uniformly random results, and a shareable result URL lets you prove which order came out and when. This guide explains the fair-shuffle algorithm, the bias trap to avoid, and the use cases where a list randomizer is exactly the right tool.<\/p>\n

Use cases \u2014 where a randomizer fits<\/h2>\n\n\n\n\n\n\n\n\n\n\n
Use case<\/th>\nWhat you randomize<\/th>\nWhy fair matters<\/th>\n<\/tr>\n<\/thead>\n
Giveaway \/ raffle<\/td>\nList of entrants \u2192 1 winner<\/td>\nLegal in most jurisdictions; bias = liability<\/td>\n<\/tr>\n
Team standup order<\/td>\nList of teammates \u2192 speaking order<\/td>\nSame person speaking last every day = morale issue<\/td>\n<\/tr>\n
A\/B test variant assignment<\/td>\nUser IDs \u2192 variant A or B<\/td>\nBiased assignment ruins the experiment<\/td>\n<\/tr>\n
Tournament bracket seeding<\/td>\nList of players \u2192 bracket position<\/td>\nPredictable seed = exploitable<\/td>\n<\/tr>\n
Classroom cold-call<\/td>\nRoster \u2192 pick one student<\/td>\nEquity \u2014 same students shouldn’t get picked<\/td>\n<\/tr>\n
Chore \/ pairing rotation<\/td>\nList of people \u2192 assignment<\/td>\nReduces “always me” complaints<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

The Fisher\u2013Yates shuffle (and the bias trap to avoid)<\/h2>\n

Fisher\u2013Yates is the canonical unbiased shuffle. Walk the array from the last index down to the first; for each index i<\/code>, pick a random index j<\/code> in [0, i]<\/code> and swap arr[i]<\/code> with arr[j]<\/code>. After one pass, every permutation is equally likely. It’s O(n)<\/code> time and uses O(1)<\/code> extra memory. Our randomizer implements it with crypto.getRandomValues<\/code> for the random index, not Math.random()<\/code>.<\/p>\n

function fairShuffle(arr) {\r\n  const a = [...arr];\r\n  for (let i = a.length - 1; i > 0; i--) {\r\n    const buf = new Uint32Array(1);\r\n    crypto.getRandomValues(buf);\r\n    const j = buf[0] % (i + 1);\r\n    [a[i], a[j]] = [a[j], a[i]];\r\n  }\r\n  return a;\r\n}<\/code><\/pre>\n
The trap:<\/strong> sorting with arr.sort(() => Math.random() - 0.5)<\/code> looks shuffle-ish but is provably biased. Different JS engines use different sort algorithms (V8 uses Timsort, JavaScriptCore uses MergeSort) and the comparator is inconsistent \u2014 the same pair returns different orderings on different calls. Tests show this approach favours the input order; some positions are 2\u00d7 more likely than uniform. Never use sort-with-random for fairness-critical shuffling.<\/p>\n
How to randomize a list in your browser<\/h2>\n
    \n
  1. Open the list randomizer<\/a><\/li>\n
  2. Paste your list (one item per line)<\/li>\n
  3. Pick output: Shuffled order<\/strong>, Pick 1 winner<\/strong>, or Pick N<\/strong><\/li>\n
  4. Optional: set a seed<\/strong> for reproducible results (useful for “audit me later” draws)<\/li>\n
  5. Click Shuffle<\/strong> \u2014 output appears with a shareable URL<\/li>\n
  6. Copy the URL to send participants the verifiable result<\/li>\n<\/ol>\n
    Seeded vs cryptographic shuffles \u2014 when to use which<\/h2>\n
    Default mode uses crypto.getRandomValues<\/code> \u2014 non-deterministic, unpredictable, the right pick for live giveaways and competitions. Seeded mode uses a deterministic PRNG (xoroshiro128+<\/code>) keyed off a string you provide. Same seed plus same input always produces the same output. Use seeded mode when you want to:<\/p>\n