gbechtold / uno-game-analysis

Uno Game Statistical Analysis

Uno Game Statistical Analysis

This project provides a statistical analysis of the popular card game Uno through simulation and data visualization.

Overview

This Python script simulates multiple games of Uno and analyzes the results to provide insights into game dynamics, player advantages, and overall game statistics.

Features

• Simulates thousands of Uno games with a configurable number of players
• Implements a simplified version of Uno game rules
• Tracks and analyzes game statistics, including:
  • Win distribution among players
  • Average number of turns per game
  • Average number of cards played per game
  • Longest winning streak
  • Most frequent winner
• Visualizes results with a bar chart showing the distribution of wins
• Provides detailed text output of game statistics

Requirements

• Python 3.x
• pandas
• matplotlib

Installation

1. Clone this repository: git clone https://github.com/gbechtold/uno-game-analysis.git Copy2. Install the required packages: pip install -r requirements.txt

Usage

Run the script using Python: python uno_analysis.py Copy The script will simulate the specified number of Uno games and output the results both in the console and as a saved PNG image.

Customization

You can modify the following parameters in the script:

• num_games: Number of games to simulate
• num_players: Number of players in each game

Output

The script generates:

1. A console output with detailed game statistics
2. A bar chart saved as 'uno_results.png' showing the distribution of wins among players

Limitations

• This simulation uses simplified Uno rules and random play strategies
• Real-world results may vary due to player skill and strategy

Future Improvements

• Implement more complex Uno rules (e.g., special cards, official scoring)
• Add different player strategies
• Extend analysis to include card usage statistics and game duration

Contributing

Contributions, issues, and feature requests are welcome. Feel free to check issues page if you want to contribute.

License

MIT

Acknowledgements

• This project was inspired by the classic Uno card game
• Thanks to the Python community for providing excellent data analysis and visualization tools