nfl-data-pipeline 1.1.1

NFL data pipeline combining PFF grades and PFR game data for over/under analysis

nfl-data-pipeline

An end-to-end data pipeline that combines PFF team grades with Pro Football Reference game and betting data, then produces analysis-ready datasets with rolling averages, rankings, and over/under features.

---

Table of Contents

• Features
• Installation
• Prerequisites
• Configuration
• Usage
  • CLI
  • Python API
  • Make Targets
• Pipeline Architecture
• Project Structure
• Development
• Contributing
• Known Limitations
• License

---

Features

• PFF Scraping — Selenium-based scraper for PFF team grades (requires PFF Premium; manual login on first run, cookies cached for subsequent runs)
• PFR Scraping — Proxy-rotated scraper for Pro Football Reference boxscores
• Data Normalization — Standardizes dates and team names across sources
• Dataset Merging — Inner join on date + team columns
• Rolling Averages — Pre-game cumulative stat averages per team per season
• Games Played Tracking — Cumulative games played before each matchup
• Feature Rankings — Per-date rankings across all teams
• CLI + Python API — Run the full pipeline or any individual step

Installation

Install from PyPI:

pip install nfl-data-pipeline

Or install from source with Poetry:

git clone https://github.com/thadhutch/nfl-data-pipeline.git
cd nfl-data-pipeline
poetry install

Prerequisites

Requirement	Why
Python 3.12+	Runtime
Google Chrome	PFF scraper uses Selenium to render client-side data
PFF Premium subscription	Authenticates access to PFF team grades
Rotating proxies (CSV)	PFR rate-limits aggressively; proxies prevent blocks

Configuration

Create a .env file (see .env.example) or export environment variables directly:

cp .env.example .env

Variable	Default	Description
NFL_SEASONS	2025	Comma-separated seasons to scrape from PFF
NFL_START_YEAR	2025	First year for PFR boxscore URL collection
NFL_END_YEAR	2025	Last year for PFR boxscore URL collection
NFL_MAX_WEEK	18	Final week to scrape in the last season
NFL_DATA_DIR	data	Base directory for all output files
NFL_PROXY_FILE	proxies/proxies.csv	Path to proxy list (address:port:user:password per line)

Usage

CLI

The nfl-pipeline command is available after installation.

Note: The first time you run nfl-pipeline scrape pff, a Chrome window will open for you to log in to PFF manually. After login, cookies are saved locally and reused for future runs.

# Full end-to-end pipeline
nfl-pipeline pipeline

# Scrape from a single source
nfl-pipeline scrape pff          # PFF grades (scrape + date parsing + name normalization)
nfl-pipeline scrape pfr          # PFR game data (URLs + scrape + date/name normalization)

# Run all post-processing steps
nfl-pipeline process all

# Run individual processing steps
nfl-pipeline process merge
nfl-pipeline process over-under
nfl-pipeline process averages
nfl-pipeline process games-played
nfl-pipeline process rankings

# Check installed version
nfl-pipeline --version

Python API

Every pipeline step is importable:

import nfl_data_pipeline

# Scraping
nfl_data_pipeline.scrape_pff_data()
nfl_data_pipeline.collect_boxscore_urls()
nfl_data_pipeline.scrape_all_game_info()

# Processing
nfl_data_pipeline.merge_datasets()
nfl_data_pipeline.process_over_under()
nfl_data_pipeline.compute_rolling_averages()
nfl_data_pipeline.add_games_played()
nfl_data_pipeline.compute_rankings()

Or run an entire pipeline at once:

from nfl_data_pipeline.pipeline import (
    run_full_pipeline,
    run_pff_pipeline,
    run_pfr_pipeline,
    run_processing_pipeline,
)

run_full_pipeline()        # end-to-end
run_pff_pipeline()         # PFF scraping chain only
run_pfr_pipeline()         # PFR scraping chain only
run_processing_pipeline()  # post-processing only

Make Targets

A Makefile is included for common development workflows:

make all            # Full pipeline end-to-end
make pff            # PFF scraping + processing chain
make pfr            # PFR scraping + processing chain
make merge          # Merge PFF and PFR data (runs both chains first)
make rankings       # Full postprocessing through rankings
make test           # Run the test suite
make clean          # Remove all generated data files
make dirs           # Create data directory structure

Pipeline Architecture

PFF Scrape              PFR Scrape
    |                       |
    v                       v
Extract Dates          Normalize Dates
    |                       |
    v                       v
Normalize Names        Normalize Names
    |                       |
    +----------+   +--------+
               |   |
               v   v
              Merge
                |
                v
           Over/Under
                |
                v
         Rolling Averages
                |
                v
          Games Played
                |
                v
            Rankings

Output files are written to NFL_DATA_DIR (default: data/):

Stage	Output
PFF scrape	data/pff/raw_team_data.csv
PFF normalized	data/pff/normalized_team_data.csv
PFR URLs	data/pfr/boxscores_urls.txt
PFR normalized	data/pfr/final_pfr_odds.csv
Merged	data/pff_and_pfr_data.csv
Final dataset	data/over-under/v1-dataset-gp-ranked.csv

Project Structure

nfl-data-pipeline/
├── src/nfl_data_pipeline/
│   ├── __init__.py           # Public API re-exports
│   ├── _config.py            # Paths, env vars, logging
│   ├── cli.py                # Click CLI entry point
│   ├── pipeline.py           # Pipeline orchestrators
│   ├── teams.py              # Team name/abbreviation mappings
│   ├── scrapers/
│   │   ├── pff.py            # PFF grades scraper (Selenium)
│   │   ├── pfr.py            # PFR game data scraper
│   │   ├── pfr_urls.py       # PFR boxscore URL collector
│   │   ├── auth.py           # PFF authentication
│   │   └── proxies.py        # Proxy loading utilities
│   ├── parsers/
│   │   ├── pff_dates.py      # PFF date extraction
│   │   ├── pff_teams.py      # PFF team name normalization
│   │   ├── pfr_dates.py      # PFR date normalization
│   │   └── pfr_teams.py      # PFR team name extraction
│   └── processing/
│       ├── merge.py          # Merge PFF + PFR datasets
│       ├── over_under.py     # O/U betting line extraction
│       ├── rolling_averages.py
│       ├── games_played.py
│       └── rankings.py
├── tests/
├── pyproject.toml
├── Makefile
├── LICENSE
└── README.md

Development

# Clone and install with dev dependencies
git clone https://github.com/thadhutch/nfl-data-pipeline.git
cd nfl-data-pipeline
poetry install

# Run the test suite
poetry run pytest -v

# Run a specific test file
poetry run pytest tests/test_rolling_averages.py -v

CI runs automatically on every push to master and on pull requests via GitHub Actions. Releases are published to PyPI through Trusted Publishers.

Contributing

Contributions are welcome! To get started:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-feature)
3. Make your changes and add tests where appropriate
4. Run the test suite (poetry run pytest -v)
5. Commit your changes (git commit -m "Add my feature")
6. Push to your fork (git push origin feature/my-feature)
7. Open a Pull Request

Please make sure all existing tests pass before submitting a PR.

Known Limitations

• PFF scraping is DOM-dependent. The scraper relies on XPath selectors tied to PFF's frontend. If PFF changes their page structure, the selectors in scrapers/pff.py will need updating.
• PFR scraping requires rotating proxies. Without them, requests will be rate-limited and blocked.
• The PFF scraper is slow by design. It drives a real Chrome browser via Selenium because PFF renders data client-side.
• PFF login requires manual interaction on first run. A Chrome window will open for you to log in. Cookies are cached afterward, so subsequent runs are fully automated.
• Data files are not tracked in git. Run the pipeline to generate them, or bring your own data in the expected CSV format.

License

This project is licensed under the MIT License.

---

PyPI · Issues · CI Status