The Open Chess Plies (OCP)

A large-scale open chess dataset designed for machine learning, chess engine research, NNUE experimentation, and evaluation training.

OCP combines:

• engine self-play with UHO
• Chess960 games
• elite human openings
• curated aggressive and asymmetric opening systems

The goal of the project is to create high-density chess training data with broader structural diversity than traditional engine-only datasets.

Overview

Traditional chess datasets often suffer from one or more of the following issues:

• excessive repetition of equal positions
• overrepresentation of opening theory
• limited structural diversity
• noisy human annotations
• insufficient imbalance coverage

OCP attempts to address these limitations through controlled filtering and hybrid data generation pipelines.

Key Features

• Monthly dataset releases
• Engine-vs-engine self-play
• Chess960 support
• Ply-level extraction
• Evaluation + WDL labels
• Curated opening diversity
• Human + engine hybrid sources

Data Formats

Depending on the release, OCP may provide:

• JSONL
• PGN
• FEN | EVAL | WDL
• BINPACK-compatible formats

Example JSONL Entry

{
  "fen": "3B4/8/bP4p1/K2k4/6P1/8/2P5/8 b - - 2 60",
  "eval": -6,
  "mate": null,
  "wdl": [0.2292, 0.537, 0.2338],
  "white": "Clarity 8.0.0",
  "black": "Berserk 13",
  "tc": "60",
  "depth": 45,
  "gameply": 119,
  "result": "1/2-1/2",
  "event": "OCP-V008",
  "round": "10",
  "date": "2026.05.26",
  "game_id": 548
}

Project Philosophy

OCP is not intended to function as:

• an engine rating list
• a benchmark suite
• a pure opening book

Instead, the project focuses on:

• structured training data generation
• diverse positional coverage
• long-term dataset evolution

The project intentionally includes positions with:

• initiative
• imbalance
• tactical pressure
• asymmetrical structures
• black-side counterplay

rather than only near-equal engine positions.

Chess960

OCP includes Chess960 data in order to reduce overfitting to classical opening theory and increase structural diversity.

Engine Participation

OCP works with a curated ecosystem of open source chess engines.

Participation is selective and inclusion is not guaranteed.

Requirements generally include:

• open source licensing
• UCI compatibility
• stability during large-scale self-play

Intended Use Cases

• NNUE training
• Evaluation research
• Chess representation learning
• Policy/value experiments
• Search/evaluation analysis
• Chess AI prototyping
• Dataset research

Limitations

OCP is an experimental and evolving dataset project.

Potential limitations include:

• evaluation noise
• opening-source imbalance
• duplicated structural motifs
• too few positions

Users are encouraged to apply additional filtering and validation depending on their use case.

Citation

@dataset{ocp_dataset,
  title={The Open Chess Plies (OCP)},
  year={2026},
  note={Large-scale open chess dataset for machine learning and chess engine research}
}