Random Round-Robin Schedule Generator

Description

Shuffle participants and circle-method rounds into a single all-pairs schedule with explicit Gregorian dates, odd-field byes and combination-verified coverage; no same-round clashes, home/away balance or federation ordering guarantee.

A workflow or scheduling fixture makes ordering, dependencies, capacity, and timing explicit. Returning both the constructed records and their checks helps consumers distinguish a valid arrangement from a simulated failure case.

When to use this tool

Use this generator to create reproducible round robin schedule cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

Supply participants, startDate, roundIntervalDays in the field shapes below. Set seed when you need an explicit replay.
Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
Read shuffledParticipants, rounds, totalMatches. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Shuffle participants and use the circle rotation method, adding a bye slot for odd counts. Verify each unordered pair occurs exactly once, shuffle constructed rounds, and assign dates by startDate plus round index times interval days.^[1]

Delegated operations used for this result: combinations. Their calculation or parsing conventions apply to the corresponding result fields.^[2]

Input field	Meaning and units	Accepted input
participants	Participants	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; minimum items 2; maximum items 32.
startDate	Start Date	Required. Text; minimum UTF-16 length 10; maximum UTF-16 length 10.
roundIntervalDays	Round Interval Days	Required. Safe integer; minimum 0.
seed	Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.	Optional. Safe integer.

Output field	Interpretation	Returned shape
shuffledParticipants	Shuffled Participants	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; minimum items 2; maximum items 32.
rounds	Rounds	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 31.
rounds[].roundNumber	Round Number	Required. Safe integer; minimum 1; maximum 31.
rounds[].constructionRound	Construction Round	Required. Safe integer; minimum 1; maximum 31.
rounds[].date	Date	Required. Text; minimum UTF-16 length 10; maximum UTF-16 length 10.
rounds[].matches	Matches	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 16.
rounds[].matches[].first	First	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
rounds[].matches[].second	Second	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
rounds[].bye	Bye	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128. or Null.
totalMatches	Total Matches	Required. Safe integer; minimum 1; maximum 496.
randomness	Replay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source.	Required. Object; no unknown keys.
randomness.seed	Optional numeric replay seed; normalization and precedence are explained below.	Required. Safe integer. or Null.
randomness.source	Source	Required. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json

{
  "input": {
    "participants": [
      "Ada",
      "Grace",
      "Lin",
      "Hedy"
    ],
    "startDate": "2026-10-19",
    "roundIntervalDays": 7,
    "seed": 42
  },
  "output": {
    "shuffledParticipants": [
      "Ada",
      "Hedy",
      "Grace",
      "Lin"
    ],
    "rounds": [
      {
        "constructionRound": 2,
        "matches": [
          {
            "first": "Ada",
            "second": "Grace"
          },
          {
            "first": "Lin",
            "second": "Hedy"
          }
        ],
        "bye": null,
        "roundNumber": 1,
        "date": "2026-10-19"
      },
      {
        "constructionRound": 1,
        "matches": [
          {
            "first": "Ada",
            "second": "Lin"
          },
          {
            "first": "Hedy",
            "second": "Grace"
          }
        ],
        "bye": null,
        "roundNumber": 2,
        "date": "2026-10-26"
      },
      {
        "constructionRound": 3,
        "matches": [
          {
            "first": "Ada",
            "second": "Hedy"
          },
          {
            "first": "Grace",
            "second": "Lin"
          }
        ],
        "bye": null,
        "roundNumber": 3,
        "date": "2026-11-02"
      }
    ],
    "totalMatches": 6,
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

Dates are plain calendar dates rather than timed events. A bye is null for even participant counts. constructionRound differs from displayed roundNumber after shuffling; venue and home/away fairness are not optimized.
A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

Tool behavior and example: Random Round-Robin Schedule Generator — tool.io
Calculation or validation: list/combinations — tool.io

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