confpickem 0.2.1

Simulation-based tool to analyze Confidence Pick Em pools

confpickem - NFL Confidence Pick'em Analyzer A Python package for analyzing and optimizing picks for 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:

pip install confpickem

Quick Start

Command Line Interface (Recommended)

The easiest way to use confpickem is through the command-line tools:

# Optimize your picks for mid-week with live odds
confpickem --week 10 --mode midweek --live-odds

# Check win probabilities for all players
confpickem-win-probability --week 10 --live-odds

# Update player skills from historical data
confpickem-player-skills update --weeks 3,4,5,6,7,8,9 --week 10

Installation: Install the package to get these commands:

pip install -e .   # From project root

See the CLI Documentation for full details.

Python API

You can also use the package programmatically:

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

🎯 Unified CLI Tools

• optimize.py - Comprehensive pick optimization with live odds support
• win_probability.py - Monte Carlo win probability calculator
• player_skills.py - Historical performance analysis and skill modeling

📊 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 and game importance analysis

🔴 Live Vegas Odds Integration

• Real-time betting line integration via The Odds API
• More accurate win probabilities than Yahoo spreads
• Automatic fallback to Yahoo data when API unavailable

🧠 Strategy Optimization

• Evaluate different picking strategies
• Optimize confidence point assignments
• Mid-week re-optimization with completed game results
• Fast mode for quick decisions (~85% accuracy, 10x speed)

Dependencies

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

Documentation

• CLI Tools Guide - Comprehensive guide for command-line tools
• CLI Tools (in package) - Detailed CLI documentation
• GitHub Repository - Source code and issues

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.