Metadata-Version: 2.4
Name: confpickem
Version: 0.1.0
Summary: Simulation-based tool to analyze Confidence Pick Em pools
Project-URL: Repository, https://github.com/tefirman/confpickem
Project-URL: Documentation, https://github.com/tefirman/confpickem#readme
Project-URL: Bug Tracker, https://github.com/tefirman/confpickem/issues
Author-email: Taylor Firman <tefirman@gmail.com>
License: MIT
License-File: LICENSE
Keywords: confidence-pool,fantasy-sports,football,nfl,pick-em
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: End Users/Desktop
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Games/Entertainment
Requires-Python: >=3.8
Requires-Dist: beautifulsoup4>=4.9.0
Requires-Dist: numpy>=1.19.0
Requires-Dist: pandas>=1.2.0
Requires-Dist: requests>=2.25.0
Requires-Dist: scipy>=1.6.0
Provides-Extra: dev
Requires-Dist: black>=21.0.0; extra == 'dev'
Requires-Dist: flake8>=3.9.0; extra == 'dev'
Requires-Dist: mypy>=0.900; extra == 'dev'
Requires-Dist: pytest-cov>=2.0.0; extra == 'dev'
Requires-Dist: pytest>=6.0.0; extra == 'dev'
Description-Content-Type: text/markdown

# confpickem - Yahoo NFL Confidence Pick'em Analyzer

A Python package for analyzing and optimizing picks for Yahoo NFL Confidence Pick'em pools. This package provides tools for:

- Scraping Yahoo Pick'em league data
- Analyzing pick distributions and trends
- Simulating outcomes and optimizing picks
- Evaluating different picking strategies

## Installation

You can install the package using pip:

```bash
pip install confpickem
```

## Quick Start

```python
from confpickem import YahooPickEm, ConfidencePickEmSimulator, run_simulation

# Initialize scraper with your league info
yahoo = YahooPickEm(
    week=1,
    league_id=YOUR_LEAGUE_ID,
    cookies_file='cookies.txt'
)

# Run simulation with actual picks
simulator, stats = run_simulation(yahoo)

# Print expected points and win percentages
print("\nExpected Points by Player:")
print(stats['expected_points'])
print("\nWin Percentages:")
print(stats['win_pct'])
```

## Features

### Yahoo Data Scraping
- Scrape pick distributions and crowd confidence levels
- Track actual picks and results from your league
- Cache responses to avoid excessive requests

### Simulation and Analysis
- Monte Carlo simulation of game outcomes
- Player skill modeling and analysis
- Pick optimization algorithms
- Risk/reward analysis

### Strategy Optimization
- Evaluate different picking strategies
- Optimize confidence point assignments
- Analyze pick correlations and game importance

## Dependencies

- Python ≥ 3.8
- requests
- pandas 
- numpy
- beautifulsoup4
- scipy

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.