Wheel of Fortune Spinner

A spinning wheel is a visual randomizer where each segment represents an outcome, weighted equally or by segment size. The animation extends the suspense beyond an instant random pick — useful for classroom raffles, team draws, decision-making with a group, or content-creation overlays.

Enter labels separated by lines; the tool builds the wheel and animates a spin. Each segment's selection probability is uniform regardless of label length. Repeated spins are independent; the tool doesn't track or exclude previous results unless explicitly configured.

The mechanics are theatrical, not physical. A pseudorandom number is drawn first, the result computed, and the deceleration animation calculated to land at the right segment. There's no physics simulation — angular momentum, friction, and bounce are visual artifacts to make the result feel earned. The selection probability is uniform across segments by default. To weight one option twice as heavily as another, list it twice; the duplicated segments collectively have double the chance even though each individual segment has the same arc width as the others. Visually larger segments don't get higher probability unless the underlying logic explicitly weights them.

A worked example. A classroom of 25 students with one student to choose for a question. List all 25 names; the wheel divides into 25 equal segments and spins to one. With 25 entries, each has 4 percent probability. Across many spins the same student will be picked once every 25 spins on average, with significant variance — independent trials produce visible clusters and gaps that feel non-random to humans. To exclude already-picked students within a single session (a 'pick without replacement' mode), most wheel tools require manually deleting the entry between spins. Some implementations have an 'eliminate selected' toggle that does this automatically.

Limitations and edge cases worth flagging. Independence is the most counterintuitive property. Each spin is unaware of all previous spins. Getting the same result twice in a row from a 10-option wheel has 10 percent probability — common enough to feel surprising. Three in a row is 1 percent — rare but not implausible. Humans interpret these as 'broken' or 'rigged' but the math is correct; the perception is gambler's fallacy in reverse. For genuinely fair group draws (raffle prizes, lottery seedings), use a system that publishes the algorithm and ideally uses verifiable randomness (commit-reveal schemes, blockchain randomness beacons) so participants can confirm fairness. The visual wheel is presentation; the randomness comes from a standard PRNG that's not cryptographically secure but is statistically sound for any non-adversarial use case.