Fetching Out Low Cost and Most Valuable Players

Description

Analyze sports data to identify valuable players based on performance metrics and cost, providing insights for data-driven decision-making in sports management.

Libraries Used

• Pandas
• NumPy
• Matplotlib
• Seaborn

Key Features

• Load and preprocess sports data.
• Analyze performance metrics to determine player value.
• Identify low-cost, high-value players using data analysis techniques.
• Visualize data insights with plots.

Use Case

Assist sports managers in making informed decisions for recruiting and managing players based on their performance and cost-effectiveness.

Installation

# Clone the repository
git clone https://github.com/Ableboy/Value-Player-Scouting.git

# Navigate into the project directory
cd Value-Player-Scouting

# Create a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Contributing

We welcome contributions! Please fork the repository, create a feature branch, and submit a pull request.

License

MIT License