ttwpa

design doc for chess opening recommendations

Summary

This project aims to develop a web application that provides personalized chess opening recommendations based on a user's games on Lichess. The system leverages advanced techniques to analyze the complexity and similarity of chess openings, as described in the research paper "Quantifying the Complexity and Similarity of Chess Openings Using Online Chess Community Data." By utilizing data downloaded from the Lichess database, the system offers insights and suggestions to help players enhance their opening repertoire.

High-Level Overview

Frontend: A web interface where users can input their Lichess username and receive opening recommendations.
Backend: A Flask-based server that fetches user games, analyzes openings, and determines recommendations based on the complexity and similarity metrics from the research paper.
Data Source: PGN files from Lichess games.

Complexity and Similarity Metrics

The research paper outlines several methodologies to quantify the complexity and similarity of chess openings.

Complexity Metrics:

Economic Fitness and Complexity (EFC) Algorithm: This algorithm measures the complexity of openings based on the diversity of players who use them and the success they achieve. The idea is that complex openings are used by more diverse and successful players.
Opening Diversity: The number of different continuations from a given opening position. A higher diversity indicates a more complex opening.

Similarity Metrics:

Bipartite Network Projection: Players and openings are represented as a bipartite network. The network is projected onto the opening space to identify similar openings based on common player usage.
Co-occurrence Matrix: A matrix that counts how often pairs of openings are played by the same players. This matrix is used to calculate the similarity between openings.
Filtered Relatedness Network: Uses statistical significance (e.g., z-scores) to filter out spurious co-occurrences and retain meaningful relationships between openings.

Component Level Updates

Frontend

Input Form: A simple form for users to input their Lichess username. Results Display: A section to display recommended openings along with explanations.

Backend

API Endpoints:
/: Serves the homepage with the input form.
/recommend: Handles the recommendation logic and returns the results.

Data Processing:

Fetch Games: Download and parse PGN files from Lichess.
Extract Openings: Identify openings from the PGN data.
Analyze Complexity and Similarity: Implement the techniques from the paper to analyze the complexity and similarity of openings.
Generate Recommendations: Use the analysis to recommend openings to the user.

Data Handling

Data Download: Scripts to download and extract PGN files.
Preprocessing: Scripts to preprocess the data, extract relevant information, and store it in a format suitable for analysis.

Definition of Done

Frontend:

Users can enter their Lichess username and submit the form.
The frontend displays opening recommendations and explanations.

Backend:

The server can fetch and parse PGN data.
Complexity and similarity of openings are analyzed as per the research paper.
The server generates and returns accurate recommendations.

Data Handling:

Successfully download and extract 500 games from April 2024.
Correctly preprocess and store the data for backend use.

Testing:

Unit tests for data fetching, parsing, and recommendation logic.
Integration tests to ensure end-to-end functionality.

Test Plan

Unit Tests:

Test the extraction of openings from PGN data.
Test the analysis functions for complexity and similarity.
Test the recommendation generation logic.

Integration Tests:

Test the end-to-end process from user input to recommendations.
Test the API endpoints for correct responses and error handling.

Outline

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