Fantasy Basketball Analytics Pipeline

📊 What Is This?

This project transforms raw fantasy basketball data from a league into actionable insights through an automated data pipeline. Think of it as your fantasy basketball command center - continuously collecting, processing, and analyzing your league's performance data to give you a competitive edge.

🧩 How It Works

The pipeline operates like a well-oiled basketball team, with each component playing a specific role:

Raw Data → Python Scripts → DBT Transformations → Analytics-Ready Data

Key Components

1. Data Collection 🔄

• Automated scripts fetch data from ESPN's fantasy API
• Captures boxscores, player statistics, and team management activities

2. Data Transformation ⚙️

• DBT models clean and restructure raw data
• Implements business logic for fantasy scoring
• Maintains data quality through automated testing
• Creates a consistent, reliable single source of truth

3. Analysis Layer 📈

• Rolling statistical trends (7/15/30 days)
• Category strength analysis
• Matchup projections
• Waiver wire opportunity identification

🚀 Getting Started

Prerequisites

Python 3.10+
PostgreSQL
DBT Core
ESPN Fantasy Basketball account

Quick Setup

1. Clone the repository

   git clone https://github.com/yourusername/fantasy-basketball-pipeline.git
   cd fantasy-basketball-pipeline

2. Install dependencies

   pip install -r requirements.txt

3. Configure credentials

   # Create .env file
   cp example.env .env
   
   # Add your credentials
   # ESPN_S2=your_espn_s2_cookie
   # SWID=your_swid_cookie

4. Initialize the database

   dbt deps
   dbt seed

5. Run the pipeline

   python scripts/run_pipeline.py

📁 Project Structure

fantasy-basketball-pipeline/
├── src/                     # Python data collectors
├── models/                  # DBT transformation models
│   ├── sources/             # Raw data specifications
│   ├── silver/              # Cleaned, typed data
│   └── gold/                # Business-specific views

🔄 Pipeline Overview

The process flows like a fast break:

1. Ingest: Python scripts connect to ESPN's API to extract fresh data
2. Transform: DBT models clean, validate, and restructure the data
3. Analyze: New calculations and aggregations generate insights
4. Deliver: Results are stored in analytics-ready tables

Like any good basketball team, each component has clear responsibilities while working together toward a common goal.

📊 Key Metrics

This pipeline helps you track:

• Player Performance Trends
• Category Strengths: Where your team excels vs. the competition
• Weekly Matchup Analytics: Projected category outcomes
• Transaction Impact Analysis: How roster moves affect team balance

🛠️ Development

Testing

# Run DBT tests
dbt test

Adding New Features

1. Create a feature branch
2. Implement changes
3. Write tests
4. Submit a pull request

🤝 Contributing

Contributions make this project better! Whether it's:

• Adding new analytical models
• Improving data collection efficiency
• Enhancing documentation
• Fixing bugs

Please feel free to fork, modify, and make pull requests.

📝 License

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

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Remember, fantasy basketball is a marathon, not a sprint. This pipeline helps you make data-driven decisions throughout the season, not just on draft day.