List Randomiser — Shuffle, Weighted Pick & Team Assigner

Shuffle any list of names, words, items or numbers using the cryptographically secure Fisher-Yates algorithm. Furthermore, the Weighted Pick mode assigns each item a weight — items with higher weights are more likely to be selected, enabling probability-aware draws for prize competitions. The Team Assigner mode splits any list into N equal teams instantly — unique among free list randomisers. Copy or download any output as a text file.

How to use the List Randomiser

Three randomisation modes and their uses

Each mode serves a different randomisation need. Furthermore, choosing the right mode ensures the output matches the fairness or probability requirements of the task.

How weighted randomisation works

Weighted randomisation assigns each item a probability proportional to its weight divided by the total weight of all items. Furthermore, if three prizes have weights 1, 3 and 6, the total weight is 10. The first prize has a 10% chance, the second a 30% chance and the third a 60% chance. Moreover, weights can be any positive number including decimals — which allows fine-grained probability control for complex prize structures.

The Fisher-Yates shuffle algorithm

The Fisher-Yates algorithm produces a perfectly uniform random permutation — every possible ordering of the list is equally likely. Furthermore, simpler naive shuffles (like picking a random position for each element) produce biased results where some orderings are more likely than others.

Team assignment algorithm

The Team Assigner shuffles the full list first using Fisher-Yates, then assigns members to teams by position — person 1 goes to Team 1, person 2 to Team 2 and so on, cycling back to Team 1 when all teams receive one person. Furthermore, this round-robin distribution after a random shuffle produces teams that are balanced in size and randomly composed. Moreover, no team can be significantly larger than another — the size difference between any two teams is at most one person.

Worked example: prize draw with weighted items

An online retailer runs a competition with three prize tiers. They want the main prize drawn rarely, the consolation prize drawn often. Setting up Weighted Pick:

What is list randomisation?

List randomisation rearranges items so chance determines their order rather than any original pattern. Furthermore, it removes human bias from decisions about order, selection and assignment. From seating plans to prize draws, randomisation ensures fairness that deliberate human arrangement fails to match. Moreover, a mathematically correct shuffle — like Fisher-Yates — guarantees that every possible ordering is equally likely.

Weighted randomisation extends basic shuffling — different items can carry different probability weights. Furthermore, this is essential for prize draws where top prizes are intentionally rare. It also models real-world probability distributions in educational and simulation contexts. Moreover, recommendation systems, load balancing and A/B testing all use the weighted approach to control how often each option is selected.

Why team assignment benefits from randomisation

Human team assignment is notoriously biased — people naturally group friends together or consciously separate adversaries. Furthermore, random team assignment eliminates these biases and creates groups that reflect the full range of skills, personalities and backgrounds present in a class or organisation. Moreover, randomly assigned teams consistently produce better cross-functional outcomes in educational research — mixing students who would not naturally work together creates new collaborative relationships.

Why fair randomisation matters

A poorly implemented shuffle produces biased results where some orderings are significantly more likely than others. Furthermore, many simple randomisation approaches — like sorting by a random key generated once per item — introduce systematic bias. The Fisher-Yates algorithm used in this tool is mathematically proven to produce a uniform distribution. Moreover, this fairness is verifiable — the tool uses cryptographic randomness rather than a seeded pseudo-random function.

Teachers use list randomisers for classroom activities — randomising the order students present, pick topics or answer questions. Furthermore, the Team Assigner is particularly valuable because manually dividing a class into groups takes significant time and invariably reflects the teacher's conscious or unconscious biases. Moreover, educational psychology research shows randomly assigned groups produce better engagement and learning outcomes because they prevent the formation of insular social clusters.

List randomisation in A/B testing

Software teams use list randomisation to assign users to test variants. Furthermore, random assignment ensures the control and treatment groups have similar characteristics — any observed difference then reflects the variant rather than the group composition. Moreover, weighted randomisation allows traffic splitting — sending 90% of users to the existing experience and 10% to the new variant — a standard practice in product development.

Frequently asked questions

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