Overview
This Python application scrapes comprehensive NFL statistics from Pro-Football-Reference.com and implements a custom algorithm to predict team scoring performance. The tool analyzes whether teams consistently outperform or underperform their expected scoring against specific opponents.
The project combines web scraping, data analysis, and predictive modeling to provide insights into team performance patterns.
Technical Implementation
- Web Scraping: Automated data collection from Pro-Football-Reference using BeautifulSoup and lxml parsers
- Data Processing: pandas-based data cleaning and structuring for analysis
- Predictive Algorithm: Custom model that rates teams based on historical scoring vs. expectations
- Automation: Scheduled scraping and analysis with automatic result tracking
- Output: Excel integration for result visualization and historical tracking
Key Challenges & Solutions
- Data Consistency: Handling variations in website structure and ensuring reliable data extraction
- Algorithm Design: Developing a scoring model that accounts for opponent strength and situational factors
- Performance: Optimizing scraping speed while respecting website rate limits
- Accuracy Tracking: Implementing automated validation against actual game results
Results & Impact
The tool successfully tracked 2023 season results with measurable prediction accuracy. It provides valuable insights for sports analysis and demonstrates practical application of data science techniques.
This project showcases the ability to build end-to-end data pipelines from raw web data to actionable predictions.