Chess Multiverse Opening Intelligence Database (20M Lichess Subset)

🚀 Live Interactive Tool

Explore the dataset visually without writing any code. The full 20M+ game database powers the Opening Analytics Module on the Chess Multiverse platform: 👉 Launch the Opening Intelligence Platform

📊 Dataset Overview

The Chess Multiverse Opening Intelligence Database is a high-fidelity, open-source dataset containing statistical performance metrics for 15,013 distinct chess opening lines.

The data was extracted, filtered, and processed from a subset of 20 million standard-rated games played on Lichess during the 2024 calendar year. Processed by Chess Multiverse Lab v1.1 (Stable), this dataset strips out engine evaluation noise to focus exclusively on human-vs-human practical outcomes across various ELO brackets.

📁 File Specifications

• Filename: openings_2024_12_depth5_20M.json
• Format: JSON (Array of Objects)
• Record Count: 15,013
• Data Vintage: 2024

🗄️ Data Schema (Data Dictionary)

Each object in the JSON array represents a specific opening sequence and contains the following key-value pairs:

📄 Sample Data

Below is a brief sample of the JSON structure demonstrating how the data is natively formatted:

[
  {
    "eco": "C44",
    "opening": "Scotch Game: Lolli Variation",
    "moves": "e2e4 e7e5 g1f3 b8c6 d2d4 e5d4 f3d4 c6d4 d1d4 d7d6",
    "games": 22326,
    "white_win_rate": 0.527,
    "black_win_rate": 0.422,
    "draw_rate": 0.051,
    "avg_rating": 1507
  },
  {
    "eco": "B32",
    "opening": "Sicilian Defense: Löwenthal Variation",
    "moves": "e2e4 c7c5 g1f3 b8c6 d2d4 c5d4 f3d4 e7e5 d4c6 b7c6",
    "games": 17417,
    "white_win_rate": 0.476,
    "black_win_rate": 0.482,
    "draw_rate": 0.042,
    "avg_rating": 1746
  }
]

🧪 Methodology & Processing

To ensure the highest level of statistical integrity for academic and analytical use:

1. Game Selection: Only standard-rated, human-vs-human Lichess games were included. Bullet, hyper-bullet, and variant games were excluded to maintain opening phase validity.
2. Depth Parsing: Opening lines were mapped up to an average depth of 5 full moves (10 ply), capturing key middle-game transitions.
3. Statistical Aggregation: Win/loss/draw rates are strictly empirical, reflecting the practical edge in human play rather than perfect engine evaluations.

💻 Usage & Implementation

This JSON dataset is lightweight and structured for immediate parsing in standard data science pipelines.

Python Example (Pandas):

import pandas as pd
import json

# Load the dataset
with open('openings_2024_12_depth5_20M.json', 'r') as file:
    data = json.load(file)

# Convert to DataFrame
df = pd.DataFrame(data)

# Find the highest winning openings for Black with at least 5,000 games played
solid_black_lines = df[(df['games'] >= 5000)].sort_values(by='black_win_rate', ascending=False)
print(solid_black_lines.head())

🤝 Contributing

Contributions to improve the dataset, refine parsing algorithms, or build new UI modules are welcome!

• Fork the repository.
• Create your feature branch (git checkout -b feature/DataRefinement).
• Commit your changes (git commit -m 'Add specific refinement').
• Push to the branch (git push origin feature/DataRefinement).
• Open a Pull Request.

If you spot a statistical anomaly or an incorrect ECO classification, please open an Issue with the relevant UCI string.

📝 Citation & Academic Use

If you use this dataset in data science projects, chess engine development, or statistical research, please cite it using the following DOI:

APA Format:

BibTeX:

@dataset{varshney_2026_chess_db,
  author       = {Varshney, Sparsh},
  title        = {Chess Multiverse Opening Intelligence Database (20M Lichess subset)},
  month        = {March},
  year         = {2026},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.19307100},
  url          = {[https://doi.org/10.5281/zenodo.19307100](https://doi.org/10.5281/zenodo.19307100)}
}

👨‍🔬 Lead Researcher & Principal Developer

Sparsh Varshney • Founder • Data Scientist • Researcher

Sparsh is a medical researcher and open-source data scientist currently pursuing a Bachelor of Ayurvedic Medicine and Surgery (BAMS) at Uttarakhand Ayurved University. By bridging rigorous academic research methodologies with modern web development and AI, Sparsh builds high-fidelity datasets and analytical tools. He is the founder of Chess Multiverse and Amidha Ayurveda, developing specialized data platforms across multiple disciplines.

• About the Creator
• LinkedIn
• ORCID
• GitHub

⚖️ License

This dataset is published under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license. You are free to share and adapt the material for any purpose, even commercially, provided you give appropriate credit and distribute your contributions under the same license.