How weighting actually changes the odds
Most people start with a plain list — one option per line, no weighting, every slice the same size. That's the right default for a genuinely even choice: dinner options, whose turn it is, which chore goes to whom. But sometimes you want the odds tilted on purpose. Maybe one restaurant deserves a better shot because it's everyone's second favorite, or you're running a party game where a "wildcard" option should come up less often than the main choices.
That's what the *number syntax is for. Leave an option plain and it gets a default weight of 1. Add *3 after it and its slice — and its probability of being the winner — becomes 3× that of a plain option. This isn't cosmetic: the wheel's canvas actually draws that slice three times wider, and the winner-selection logic uses the same weight when it picks a result.
Pizza, Tacos*3, and Sushi. Total weight = 1 + 3 + 1 = 5. Tacos gets 3⁄5 of the wheel (60%), while Pizza and Sushi each get 1⁄5 (20%). These numbers are just an example to illustrate the math, not a guarantee of any particular spin outcome.| Option | Weight | Approx. win chance |
|---|---|---|
| Pizza | 1 | 20% |
| Tacos*3 | 3 | 60% |
| Sushi | 1 | 20% |
Weighting is also handy for splitting a prize pool unevenly, giving a returning champion a smaller cut of the wheel next time, or just adding one dramatic long-shot option to a game without giving it equal odds to everything else.
Is the spin really random?
It's a fair question, because the animation can make people suspicious — the wheel appears to "slow down" onto a result, and slot-machine software has trained everyone to distrust exactly that kind of visual. On this tool, the winner is actually determined the instant you click Spin: a random number is drawn first, mapped to a slice based on each option's weight, and the spin animation is calculated afterward purely to land the pointer on that already-chosen slice. Nothing about how the wheel visually decelerates changes the outcome.
You don't have to take that on faith. The built-in 10,000-spin fairness test reruns the exact same selection logic ten thousand times in an instant and charts each option's observed share of wins against its mathematically expected share. If you've set Tacos to a 60% weight, the fairness-test chart should show Tacos landing close to 60% of the ten thousand simulated spins — a quick, visual way to confirm the wheel isn't secretly biased toward or against any option.
Using elimination mode without repeats
A plain wheel can land on the same option twice in a row, which is exactly what you want for something like "what's for dinner tonight" but exactly what you don't want for "whose turn is it to take out the trash this week." Elimination mode solves that: check the box, and every time the wheel lands on a winner, that option disappears from the list before the next spin. The remaining options redraw across the full circle, keeping their odds proportional to whatever weights are left.
Example scenario: a household with four chores and four people spins once per person. With elimination mode on, the first spin removes one person from the pool, the second spin only has three people left to choose from, and so on — guaranteeing everyone gets exactly one chore instead of the wheel possibly repeating a name. The same approach works for classroom name-picking, or drawing several distinct winners out of one raffle list.
Combine elimination mode with the winner history toggle to keep a running list of who's already been picked, or with fullscreen presentation mode (and the spacebar shortcut) if you're running the wheel in front of a class or on a livestream.
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Try the free Decision Wheel →Frequently asked questions
How does the decision wheel work, and is it fair?
Can I give one option a better chance of winning?
Is the randomness actually trustworthy? What's the fairness test?
Can I stop the same option from winning twice?
Can I save or share a wheel I made?
Related guides
crypto.getRandomValues is used for winner selection (with a Math.random fallback), weight-based probability follows weight ÷ total weight, and the Fisher–Yates algorithm powers the Shuffle feature. Any numeric examples above (like the Pizza/Tacos/Sushi table) are illustrative only, not a record of a real spin. This article is informational and does not constitute legal advice for regulated drawings, contests, or sweepstakes.