---
license: cc0-1.0
---

# GambitFlow Opening Database

A curated, high-quality chess opening theory database in SQLite format, designed for chess engines and analysis tools. This dataset contains aggregated move statistics derived from thousands of recent, high-rated (2600+ ELO) grandmaster-level games.

The goal of this database is to provide a powerful, statistically-backed foundation for understanding modern opening theory, without relying on traditional, human-annotated opening books.

## How to Use

The database is a single SQLite file (`opening_theory.db`). You can download it directly from the "Files" tab or use the `huggingface_hub` library to access it programmatically.

Here is a Python example to query the database for the starting position:

```python
import sqlite3
import json
from huggingface_hub import hf_hub_download

# 1. Download the database file
db_path = hf_hub_download(
    repo_id="GambitFlow/Opening-Database",
    filename="opening_theory.db",
    repo_type="dataset"
)

# 2. Connect to the database
conn = sqlite3.connect(db_path)
cursor = conn.cursor()

# 3. Query a position (FEN)
# The FEN should be "canonical" (position, turn, castling, en passant)
start_fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq -"

cursor.execute("SELECT move_data FROM openings WHERE fen = ?", (start_fen,))
row = cursor.fetchone()

if row:
    # 4. Parse the JSON data
    moves_data = json.loads(row)
    
    # Sort moves by frequency
    sorted_moves = sorted(
        moves_data.items(), 
        key=lambda item: item['frequency'], 
        reverse=True
    )
    
    print(f"Top moves for FEN: {start_fen}\n")
    for move, data in sorted_moves[:5]:
        print(f"- Move: {move}")
        print(f"  Frequency: {data['frequency']:,}")
        print(f"  Avg Score: {data.get('avg_score', 0.0):.3f}\n")

conn.close()
```

## Data Collection and Processing

This dataset was meticulously created through a multi-stage pipeline to ensure the highest quality and statistical relevance.

**1. Data Source:**
The foundation of this dataset is the **Lichess Elite Database**, a filtered collection of games from high-rated players, originally curated by [Nikonoel](https://database.nikonoel.fr/). We used multiple monthly PGN files from the 2023-2024 period to capture modern theory.

**2. Filtering Criteria:**
Only games meeting the following strict criteria were processed:
*   **Minimum ELO:** Both players had a rating of **2600 or higher**.
*   **Maximum Depth:** Only the first **25 moves** of each game were analyzed to focus on opening theory.

**3. Processing Pipeline:**
*   **Distributed Processing:** The PGN files were processed in parallel across multiple sessions to handle the large volume of games efficiently.
*   **Perspective-Aware Scoring:** Each move's quality was scored from the perspective of the player whose turn it was. A win scored `1.0`, a loss `0.0`, and a draw `0.5`.
*   **Aggregation:** For each unique board position (FEN), statistics for every move played were aggregated, including frequency and the sum of scores.
*   **Merging:** The databases from the distributed sessions were merged into a single master file. This process recalculated weighted averages for ELO and move scores, ensuring statistical accuracy.

## Dataset Structure

The database contains a single table named `openings`.

**File:** `opening_theory.db`
**Table:** `openings`

| Column        | Type    | Description                                                 |
|---------------|---------|-------------------------------------------------------------|
| `fen`         | TEXT    | The canonical board position (FEN string, Primary Key).     |
| `move_data`   | TEXT    | A JSON object containing statistics for each move played.   |
| `total_games` | INTEGER | The total number of times this position was seen.           |
| `avg_elo`     | INTEGER | The average ELO of players who reached this position.       |

### `move_data` JSON Structure

The `move_data` column contains a JSON string with the following structure:

```json
{
  "e4": {
    "frequency": 153944,
    "score_sum": 76972.0,
    "avg_score": 0.5
  },
  "d4": {
    "frequency": 65604,
    "score_sum": 32802.0,
    "avg_score": 0.5
  }
}
```
*   **`frequency`**: The number of times this move was played from the given position.
*   **`score_sum`**: The sum of all perspective-aware scores for this move.
*   **`avg_score`**: The average score, calculated as `score_sum / frequency`.

## Citation

If you use this dataset in your work, please consider citing it:

```bibtex
@dataset{gambitflow_opening_database_2024,
  author       = {GambitFlow Project},
  title        = {GambitFlow Opening Database},
  year         = {2024},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/GambitFlow/Opening-Database}
}
```