NFL Point Kicker Data Scraper

A sophisticated Python-based web scraping toolkit that extracts and processes NFL kicking statistics from Pro-Football-Reference, focusing on field goal attempts and environmental conditions.

Technologies Used

Python BeautifulSoup4 CSV Web Scraping

Features

Comprehensive Data Collection: Gathers stadium info, roof type, playing surface, and weather conditions
Intelligent Processing: Rate limiting, incremental saving, and error recovery systems
Multiple Output Formats: Specialized CSV files for various analysis scenarios
Environmental Analysis: Detailed weather and facility condition tracking

Project Overview

CSV Output View of the Data

Statistical Analysis and Data Visualization of the Data

Implementation

Processing Pipeline

1. FootballInfoScraper.py      → Initialize and collect URLs
2. FootballGameInfoScraper.py  → Extract game data
3. GameUrlDateSeparator.py     → Process game dates
4. DateToCSVFormat.py          → Standardize formats
5. TotalDataFormatting.py      → Create master dataset
6. SplitDataFormatting.py      → Generate analysis files

Output Files

Complete dataset (2000-2022)
Weather-specific analysis
Dome-specific analysis
Wind chill impact analysis

Technical Highlights

Data Fields Collected

Game dates and locations
Stadium characteristics
Environmental conditions
Kicking performance metrics

Key Features

Automated rate limiting to prevent IP bans
Incremental data saving for reliability
Comprehensive error handling
Multiple analysis-ready output formats

Resources

GitHub Icon GitHub Repository

License

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