Random Raffle Draw Generator
Draw unique eligible tickets uniformly without replacement; individuals with multiple tickets can win multiple prizes. Caller supplies eligibility. Reproducible PRNG is not secure randomness, audited fairness or legal raffle approval.
Description
A raffle inventory consists of distinct eligible tickets rather than distinct people.
A raffle inventory consists of distinct eligible tickets rather than distinct people.1
Sampling tickets without replacement can still award multiple prizes to one person holding several eligible tickets.
When to use Random Raffle Draw Generator
Use this tool when you need to produce the reproducible draw or model experiment described below. Prepare the seed and the exact inventory or scenario inputs described below. Keep units, reference edition, and sampling convention explicit.
How to use Random Raffle Draw Generator and understand its results
- Prepare the seed and the exact inventory or scenario inputs described below. Keep units, reference edition, and sampling convention explicit.
- Enter seed, count, eligibleTicketIds, following the field table and calculation rules below, then run the tool.
- Read result. Inspect the nested fields in the output table below and keep any missing evidence or selected indices visible.
- Correct any input issues and run again. Before using a successful result, review its units, signs, evidence coverage, and stated model or reference assumptions.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| seed | Nonblank reproducibility seed; default seeded runs restart for each request | string | Required |
| count | Number of winning tickets | safe integer from 1 to 64 | Required |
| eligibleTicketIds | Eligible ticket ids | list of string | Required |
Use a nonempty unique nonblank eligible-ticket list and count in [1,64] no greater than ticket count. Always draw without replacement under the common sampling rules below. Return winners in draw order, undrawn tickets in original order, and the explicit ticket sampling-unit label.2, 3
Use a nonblank seed of at most 256 UTF-16 units and a safe-integer count from 1 to 64. By default the seed is hashed into a deterministic pseudorandom state; each run restarts that state, so identical full inputs and implementation reproduce the result. The SDK may provide context.random, which replaces the seeded source and advances across the request; the seed remains required. Every source value must be finite in [0,1), with a request limit of 100000 source calls. Draw an index as floor(U×remainingSize). With replacement draw from the full inventory each time; without replacement remove each chosen position from an ordered pool and reject counts exceeding the inventory size. Uniform-source draws are over positions, not necessarily distinct values. This is not secure randomness.
winners=sampleDistinct(ticketPositions,count) Read these symbols with the field meanings and branch or reference rules above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Object containing the fields below | object |
| result.winningTicketIds | Winning Ticket Ids | list |
| result.undrawnTicketIds | Undrawn Ticket Ids | list |
| result.samplingUnit | Sampling Unit | string |
{
"input": {
"seed": "tool.io/batch42",
"count": 1,
"eligibleTicketIds": [
"ticket-a",
"ticket-b",
"ticket-c"
]
},
"output": {
"result": {
"winningTicketIds": [
"ticket-c"
],
"undrawnTicketIds": [
"ticket-a",
"ticket-b"
],
"samplingUnit": "eligible-distinct-ticket-not-person"
}
}
}Limitations and assumptions
The default source is deterministic pseudorandomness, not an independently audited or secure draw. Replacement applies to inventory positions; distinct positions may render identically or yield equal answers. Review the supplied inventory or scenario domain independently before interpreting the sample.
Missing required fields, unknown fields, wrong types, and violated calculation rules return input issues. Numeric values must be finite; nonfinite numeric outputs are rejected. Arrays must be dense, with no missing cells. Ordinary string lists allow at most 128 strings of at most 128 UTF-16 units each. Extremely large composite outputs are rejected.
Alternative or Complementary methods
For a related task, use Random Team Assignment Generator: Random team assignment can balance member counts by distributing a random participating order. That size balance differs from balancing skill or inferring membership eligibility. For a complementary step, use Random Urn Sampling Experiment Generator: An urn inventory represents physical items whose labels may repeat. Sampling distinct item positions preserves repeated colors or labels as valid outcomes.
References
-
Random Raffle Draw Generator — tool.io
-
Calculation or validation: random/number — tool.io
Similar or alternative tools
- Random Team Assignment Generator
Random team assignment can balance member counts by distributing a random participating order. That size balance differs from balancing skill or inferring membership eligibility.
- Random Urn Sampling Experiment Generator
An urn inventory represents physical items whose labels may repeat. Sampling distinct item positions preserves repeated colors or labels as valid outcomes.