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license: cc0-1.0
task_categories:
- reinforcement-learning
- tabular-classification
language:
- en
tags:
- chess
- opening-theory
- lichess
- elite-games
- gambitflow
size_categories:
- 1M<n<10M
pretty_name: 'GambitFlow: Elite Chess Opening Theory'
---
# ♟️ GambitFlow: Elite Opening Theory (2000+ Elo)
## 📊 Dataset Overview
This dataset serves as the **"Opening Memory"** for the GambitFlow AI project (Synapse-Edge). It contains millions of chess positions extracted exclusively from **Elite Level Games (Elo 2000+)** played on Lichess.
The goal is to provide the AI with Grandmaster-level intuition during the opening phase, preventing early positional disadvantages.
- **Source:** Lichess Standard Rated Games (Jan 2017 - Apr 2024)
- **Filter:** White & Black Elo ≥ 2000
- **Depth:** First 35 plies (moves)
- **Format:** SQLite Database (`.db`)
## 📂 Dataset Structure
The data is stored in a `positions` table with the following schema:
| Column | Type | Description |
| :--- | :--- | :--- |
| `fen` | TEXT (PK) | The board position in Forsyth–Edwards Notation (Cleaned: no move counters). |
| `move_stats` | JSON | Aggregated statistics of moves played in this position by elite players. |
### Example `move_stats` JSON:
```json
{
"e4": 15400,
"d4": 12300,
"Nf3": 5000
}
```
## 🛠️ Usage
```python
import sqlite3
import json
conn = sqlite3.connect("opening_theory_v1.db")
cursor = conn.cursor()
# Get stats for the starting position
start_fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPP1PPP/RNBQKBNR w KQkq - -"
cursor.execute("SELECT move_stats FROM opening_book WHERE fen=?", (start_fen,))
stats = json.loads(cursor.fetchone()[0])
print("Most popular elite move:", max(stats, key=stats.get))
```
## ⚖️ Credits & License
- **Raw Data:** [Lichess Open Database](https://database.lichess.org/)
- **Processed By:** GambitFlow Team
- **License:** CC0 1.0 Universal (Public Domain)
*We gratefully acknowledge Lichess.org for providing open access to millions of chess games.* |