Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/code-jl/nfl-kicker-predictor

Last synced: 3 days ago
JSON representation

Host: GitHub
URL: https://github.com/code-jl/nfl-kicker-predictor
Owner: Code-JL
License: mit
Created: 2025-01-05T20:26:28.000Z (15 days ago)
Default Branch: main
Last Pushed: 2025-01-05T20:27:35.000Z (15 days ago)
Last Synced: 2025-01-05T21:24:20.741Z (15 days ago)
Language: Python
Size: 0 Bytes
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # NFL Kicker Predictor

A sophisticated Python application that provides real-time NFL kicker statistics and performance analysis with an intuitive graphical interface.

## Features

- **Real-time Data**: Scrapes live NFL kicker statistics from ESPN

- **Offline Mode**: Load previously saved kicker data for offline analysis

- **Statistical Analysis**: Track and analyze key performance metrics including:

  - Career & season field goal percentages

  - Average attempts per game

  - Average successful kicks per game

  - Last game performance

- **Visual Performance Indicators**: Color-coded projections comparing historical averages

- **Data Persistence**: Save kicker statistics locally for future reference

## Installation

1. Clone the repository: ```bash git clone https://github.com/yourusername/NFL-Kicker-Predictor.git ```

2. Install required dependencies: ```bash pip install requests beautifulsoup4 tkinter ```

## Usage

1. Run the main application: ```bash python Kicker.py ```

2. Select data source:

   - **From Network**: Fetches real-time data from ESPN

   - **From Save**: Loads previously saved local data

4. Choose league (NFL currently supported)

5. Browse kickers and view their statistics

6. Enter projections to compare against historical performance:

   - Green: Projection below historical average

   - Red: Projection above historical average

## Project Structure

- `Classes/`: Core class definitions

- `Gui/`: User interface implementation

- `Local/`: Local data handling

- `Save/`: Data persistence operations

- `Scrape/`: Web scraping functionality

## Technical Details

- Built with Python 3.x

- Uses BeautifulSoup4 for web scraping

- Tkinter for GUI implementation

- Modular architecture for easy maintenance and expansion

## Contributing

1. Fork the repository

2. Create your feature branch (`git checkout -b feature/AmazingFeature`)

3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)

4. Push to the branch (`git push origin feature/AmazingFeature`)

5. Open a Pull Request

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Acknowledgments

- Data sourced from ESPN's NFL statistics

- Built with inspiration from sports analytics and statistical modeling

--- *Note: This project is for educational and analytical purposes only. All NFL data is property of their respective owners.*