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Chess tournament pairing engines for Python — FIDE Dutch System (A.7-conformant, with Baku Acceleration), FIDE Berger round-robin, and a simple casual Swiss engine.

Project description

caissify-pairings

Chess tournament pairing engines for Python. Ships three engines:

  • dutch — the FIDE Dutch System (C.04.3), cross-validated against the FIDE-endorsed reference bbpPairings to full A.7 conformance. Use this for rated Swiss tournaments. Optional Baku Acceleration (FIDE C.04.5.1) via accelerated=True for large opens.
  • round_robinFIDE Berger Tables (FIDE Handbook §C.05). Every player meets every other player; supports single and double round-robin. Verified to match the published FIDE tables exactly.
  • casual — a small, deterministic Swiss engine for club nights and non-rated events. Simpler, more readable, no FIDE guarantees.
Engine Use case FIDE compliance Complexity
dutch Rated Swiss / official tournaments A.7 — 0 discrepancies on 70k rounds; Baku C.04.5.1 acceleration available High (full C.04.3)
round_robin Round-robin / Scheveningen / club leagues Berger Tables verified vs FIDE Handbook §C.05 Low
casual Club nights, ladders, non-rated Swiss (not the goal) Low

What you get

  • A programmable pairing engine — use it as a library, call it from a web service, or wrap it in a tournament-director UI.
  • TRF16 parser and writer.
  • A Free Pairings Checker (FPC) — validate a TRF file against the engine.
  • A Random Tournament Generator (RTG) — generate simulated tournaments for testing.
  • A simple JSON-over-stdin CLI.

FIDE A.7 conformance

The FIDE Swiss Pairings Programs Commission evaluates endorsed pairing software using the A.7 test: a program is expected to produce pairings that match an already-endorsed program on at least 4990 of 5000 random tournaments (≤ 10 discrepancies).

Measured against bbpPairings (FIDE-endorsed, C++) using our Random Tournament Generator + Free Pairings Checker pipeline:

Benchmark Rounds checked Discrepancies FIDE target Result
5000 × 20-player / 9-round 45,000 0 ≤ 10 ✅ PASS
5000 × 10-player / 5-round 25,000 0 ≤ 10 ✅ PASS

See doc/ENGINE_STATUS.md for the full story and doc/DIVERGENCE_TESTING.md for the methodology.

Note. A.7 conformance is a technical criterion. caissify-pairings is not yet FIDE-endorsed — endorsement is a separate administrative process with the FIDE SPP Commission.

Installation

pip install caissify-pairings

Requires Python 3.10+. The only runtime dependency is networkx (for maximum-weight matching).

Quick start — library

from caissify_pairings import generate_pairings

players = [
    {"id": 1, "name": "Carlsen",  "score": 2.0, "rating": 2830,
     "starting_number": 1, "color_hist": ["white", "black"],
     "float_history": [], "bye_count": 0},
    {"id": 2, "name": "Firouzja", "score": 2.0, "rating": 2785,
     "starting_number": 2, "color_hist": ["black", "white"],
     "float_history": [], "bye_count": 0},
    {"id": 3, "name": "Ding",     "score": 1.5, "rating": 2780,
     "starting_number": 3, "color_hist": ["white", "black"],
     "float_history": [], "bye_count": 0},
    {"id": 4, "name": "Nepo",     "score": 1.5, "rating": 2775,
     "starting_number": 4, "color_hist": ["black", "white"],
     "float_history": [], "bye_count": 0},
]

pairings = generate_pairings(
    system="dutch",
    players=players,
    previous_pairings={(1, 3), (2, 4)},
    round_number=3,
    total_rounds=9,
)

for p in pairings:
    print(f"Table {p['table']}: {p['white_id']} vs {p['black_id']}")

Command-line usage

# Pair a round from a JSON state on stdin, write pairings to stdout
caissify-pairings < tournament_state.json

# Validate a FIDE TRF file against the Dutch engine
caissify-pairings-check tournament.trf

# Generate random tournaments for testing
caissify-pairings-rtg --players 20 --rounds 9 -n 100 -o ./output/

Input JSON schema

{
  "system": "dutch",
  "players": [
    {
      "id": 1,
      "name": "Player Name",
      "score": 0.0,
      "rating": 2400,
      "starting_number": 1,
      "color_hist": [],
      "float_history": [],
      "bye_count": 0
    }
  ],
  "previous_pairings": [[1, 3], [2, 4]],
  "round_number": 1,
  "total_rounds": 9,
  "bye_value": 1.0,
  "max_byes_per_player": 1
}

Output JSON schema

[
  {"white_id": 1, "black_id": 4, "table": 1},
  {"white_id": 3, "black_id": 2, "table": 2}
]

What works today

  • FIDE Dutch System (C.04.3, Feb 2026 spec) — full A.7 conformance against bbpPairings on 20p/9r and 10p/5r benchmarks (see above).
  • FIDE Berger round-robin — opt-in via system="round_robin", matches the published FIDE Berger tables exactly.
  • Casual Swiss engine — opt-in via system="casual" for club-level events where FIDE conformance is not required.
  • Full TRF16 round-trip parsing/writing.
  • Free Pairings Checker (FPC) for validating existing TRF files.
  • Random Tournament Generator (RTG) for test corpora.
  • Pure-Python, single runtime dependency (networkx).

Baku Acceleration — quick start

Baku Acceleration (FIDE Handbook §C.04.5.1) is an opt-in modifier on the Dutch engine. For rounds 1 and 2 the top half of the field (by initial pairing number / rating) gets a +1 virtual point added to its score for pairing purposes only. The result: top-half plays top-half and bottom-half plays bottom-half early, spreading the field — exactly what large opens need.

from caissify_pairings import generate_pairings

pairings = generate_pairings(
    system="dutch",
    players=players,            # any list of player dicts
    previous_pairings=set(),
    round_number=1,
    total_rounds=9,
    accelerated=True,           # ← opt in to Baku
)

What you can rely on:

  • The virtual point is applied only for rounds 1 and 2; round 3 onwards is a normal Dutch pairing on real scores.
  • Real score, color_hist, etc. are never modified — only the internal pairing-time score is inflated, on a private copy.
  • For odd player counts the extra slot goes to the top half (FIDE convention — ceiling division).
  • Output shape is unchanged (white_id, black_id, table, …).

To generate accelerated TRF fixtures end-to-end (e.g. for cross-validating against bbpPairings --accelerated), the RTG exposes the same flag:

caissify-pairings-rtg --players 100 --rounds 9 -n 50 --accelerated -o ./baku_fixtures/

Round-robin — quick start

from caissify_pairings import generate_pairings

# Single round-robin: pair round 1 of an 8-player event.
pairings = generate_pairings(
    system="round_robin",
    players=players,            # any 8 players (list of dicts)
    previous_pairings=set(),    # ignored — RR is deterministic
    round_number=1,
    total_rounds=7,             # n - 1 for n=8
)

# Double round-robin: 14 rounds, each pair meets twice with reversed colours.
pairings = generate_pairings(
    system="round_robin",
    players=players,
    previous_pairings=set(),
    round_number=8,             # first round of cycle 2
    total_rounds=14,
    cycles=2,
)

Need the full schedule up front (e.g. to print all rounds at once)?

from caissify_pairings.engines.round_robin import berger_schedule

# Returns 7 rounds × (n/2) pairs as (white_pairing_no, black_pairing_no).
all_rounds = berger_schedule(8)

Pairing numbers are taken from each player's starting_number (ascending). Odd player counts get one bye per round (each player byes exactly once over a single cycle).

Casual engine — quick start

from caissify_pairings import generate_pairings

pairings = generate_pairings(
    system="casual",
    players=players,
    previous_pairings=set(),
    round_number=1,
    total_rounds=5,
    bye_type="F",            # "F" full-point, "H" half-point, "U" PAB, …
    max_byes_per_player=1,   # each player gets at most one bye
)

The casual engine follows a very small rulebook:

  1. Players sorted by (-score, -rating, id).
  2. Round 1 uses the Dutch half-split (top half vs bottom half).
  3. Later rounds pair greedily within score groups; unmatched players float down one bracket.
  4. Odd fields award one bye to the lowest-scored eligible player.
  5. Colours: perfect alternation > avoid a 3-in-a-row streak > minimise colour imbalance.

It never mutates your input dicts. Use dutch if you need FIDE A.7 behaviour — the two engines share the exact same input/output contract, so switching is a one-line change.

What does not work yet

Being honest up front — these are known limitations you will hit if your use-case is beyond them:

  • Swiss systems other than Dutch are not implemented yet. No Accelerated Dutch (Baku), Dubov, Burstein, or Monrad generators yet. Baku Acceleration is the next planned addition.
  • Large-tournament fixture match rates are lower against pre-recorded bbpPairings outputs for 40+ players (e.g. 40p/9r reports ~11% exact pair agreement on a handful of fixtures). The 5000-tournament A.7 conformance benchmark tops out at 20p/9r, where the engine is at 0 discrepancies; at larger scales tie-breaking divergences are expected.
  • No tournament-director UI. This is an engine / library, not a standalone product.
  • No FIDE endorsement. A.7 conformance is met technically; endorsement is a separate process that has not been pursued.
  • API is not stabilised. Version 0.x may introduce breaking changes. API will be frozen at 1.0.0.

If any of these block you, please open an issue — priorities are driven by real user needs.

Using an engine directly

from caissify_pairings.engines.dutch import dutch_pairings

pairings = dutch_pairings(
    players=players,
    previous_pairings=set(),
    round_number=1,
    total_rounds=9,
)

Extending

New pairing systems plug in via a small registry.

from caissify_pairings.base import BasePairingEngine

class MyEngine(BasePairingEngine):
    name = "my_system"

    def generate_pairings(self) -> list[dict]:
        ...

Register it in caissify_pairings/engines/__init__.py to expose it through the top-level generate_pairings(system="my_system", ...) call.

Testing

# Fast suite (skip @slow 5000-tournament benchmarks)
pytest -m "not slow"

# Full suite including FIDE A.7 benchmarks (takes ~25 min)
pytest

Release process (maintainers)

Secrets live in a git-ignored .env at the repo root.

cp .env.example .env
# edit .env and set PYPI_API_TOKEN=pypi-...

Then, after bumping the version in pyproject.toml, updating CHANGELOG.md, committing, and tagging vX.Y.Z:

scripts/release.sh --dry-run   # build + twine check only
scripts/release.sh --test      # upload to TestPyPI
scripts/release.sh             # upload to PyPI

The script builds an sdist + wheel in an isolated venv, runs twine check, uploads to the chosen index, and then verifies by installing the published version in a throwaway venv.

Never commit .env. Rotate your PyPI token if it has ever been shared or exposed.

Acknowledgements

Cross-validated against the FIDE-endorsed reference implementations:

  • bbpPairings by Bierema Boyzvoort Media Foundation — Apache-2.0.
  • JaVaFo by Roberto Ricca.

Thanks to their authors for making these tools freely available.

License

MIT — see LICENSE.

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