Chess Opening Analysis by ELO

This project focuses on analyzing chess opening strategies and their relationship with players' ELO ratings. By examining chess games played by both white and black pieces, this analysis identifies trends and insights into opening choices, game outcomes, and the influence of ELO on strategic decisions.

Objective

The primary objective of this project is to explore how players with different ELO ratings approach chess openings. By analyzing a dataset of chess games, we categorize players into different skill levels (Beginner, Intermediate, Advanced, Expert) based on their ELO ratings. The goal is to identify common opening strategies and understand how skill level impacts the choice of opening moves.

Dataset

The dataset used in this project contains a collection of chess games, including the following key attributes:

• ELO ratings of the white and black players
• Game outcome (win, loss, draw)
• Opening move choices for both players
• Time controls and game duration
• ELO category of players (Beginner, Intermediate, Advanced, Expert)

Analysis

• ELO Segmentation: Players are divided into four categories based on their ELO ratings:

  • Beginner: ELO < 1200
  • Intermediate: ELO between 1200 and 1600
  • Advanced: ELO between 1600 and 2000
  • Expert: ELO > 2000

• Opening Strategies: The project visualizes the opening strategies used by different ELO categories and examines whether certain openings are more effective at specific skill levels.

• Player Distribution: The project also explores the distribution of players by ELO category and piece color (white vs black).

Visualizations

The analysis includes the following visualizations:

• Distribution of players by ELO category and piece color
• Popular openings by ELO category
• Analysis of opening effectiveness based on game outcomes

Tools & Libraries Used

• Pandas for data manipulation and analysis
• Seaborn & Matplotlib for data visualization
• Jupyter Notebooks for interactive analysis and visualization

How to Run

1. Clone the repository:

   git clone https://github.com/yourusername/Chess-Opening-Analysis.git

2. Install the required dependencies:

   pip install -r requirements.txt

3. Open the Jupyter Notebook and run the analysis.

Conclusion

This project provides valuable insights into chess opening strategies based on player skill levels. By understanding these trends, chess players can refine their opening choices and improve their overall performance. Further research could explore deeper relationships between opening strategies and match outcomes, potentially using machine learning for predictive modeling.