SportsRef NFL - NFL Data Scraping Library

A comprehensive Python library for scraping NFL data from Pro Football Reference, including schedules, player statistics, draft information, and advanced metrics like ELO ratings.

Features

• NFL Schedule Data: Complete schedules with ELO ratings and playoff information
• Player Statistics: Comprehensive stats for all positions (QB, RB, WR, TE, K, DST)
• Draft Information: Historical NFL draft data with player details
• Team Rosters: Complete team rosters by season
• Depth Charts: Team depth chart information
• Stadium Data: Stadium information including locations and capacities
• Advanced Metrics: ELO ratings for team strength analysis
• Name Utilities: Player name normalization and matching
• Robust Scraping: Cloudflare bypass via FlareSolverr with automatic fallback
• Command Line Interface: Easy-to-use CLI for data extraction

Installation

pip install sportsref-nfl

Cloudflare Bypass (Required)

Pro Football Reference uses Cloudflare protection that blocks automated requests. This library uses FlareSolverr to bypass it. If Docker is installed, the library will automatically start FlareSolverr when needed.

Setup (one-time):

# Install Docker: https://docs.docker.com/get-docker/
# Then pull the FlareSolverr image:
docker pull flaresolverr/flaresolverr:latest

That's it! The library handles starting/stopping the container automatically. You can also manage it manually via the CLI:

sportsref-nfl flaresolverr start    # Start FlareSolverr
sportsref-nfl flaresolverr status   # Check if it's running
sportsref-nfl flaresolverr stop     # Stop FlareSolverr

Note: Without Docker/FlareSolverr, the library falls back to cloudscraper, which may be blocked by Cloudflare depending on the site's current protection level.

Quick Start

Command Line Interface

# Download NFL schedule with ELO ratings
sportsref-nfl schedule --start-year 2020 --end-year 2024 --elo

# Get specific game boxscore
sportsref-nfl boxscore --game-id "202409230buf"

# Download player statistics
sportsref-nfl stats --year 2024 --position QB

# Get draft data
sportsref-nfl draft --year 2024

# Download team rosters
sportsref-nfl rosters --year 2024 --team BUF

Python API

import sportsref_nfl as sr

# Load NFL schedule with ELO ratings
schedule = sr.Schedule(
    start_year=2020, 
    end_year=2024, 
    elo=True
)
print(schedule.schedule.head())

# Get boxscore data
game = sr.Boxscore("202409230buf")
print(f"{game.away_team} {game.away_score} - {game.home_score} {game.home_team}")

# Load player statistics
from sportsref_nfl.data import StatsLoader
stats_loader = StatsLoader()
qb_stats = stats_loader.load_position_stats(2024, "QB")

# Load draft data
from sportsref_nfl.data import DraftLoader
draft_loader = DraftLoader()
draft_2024 = draft_loader.load_draft_data(2024)

Core Classes

Schedule

import sportsref_nfl as sr

# Basic schedule
schedule = sr.Schedule(2020, 2024)

# With ELO ratings and playoffs
schedule = sr.Schedule(
    start_year=2020,
    end_year=2024,
    playoffs=True,
    elo=True,
    verbose=True
)

# Access the data
df = schedule.schedule
print(df.columns)
# ['season', 'week', 'date', 'away_team', 'home_team', 
#  'away_score', 'home_score', 'elo_diff', ...]

Boxscore

import sportsref_nfl as sr

# Load specific game
game = sr.Boxscore("202409230buf")  # Bills playoff game

# Access game data
print(game.date)
print(game.away_team, game.home_team)
print(game.away_score, game.home_score)
print(game.weather)

Data Loaders

from sportsref_nfl.data import (
    StatsLoader, DraftLoader, RosterLoader, 
    DepthChartLoader, StadiumLoader
)

# Player statistics
stats_loader = StatsLoader()
all_stats = stats_loader.load_all_stats(2024)
qb_stats = stats_loader.load_position_stats(2024, "QB")

# Draft data
draft_loader = DraftLoader()
draft_2024 = draft_loader.load_draft_data(2024)

# Team rosters
roster_loader = RosterLoader()
bills_roster = roster_loader.load_team_roster(2024, "BUF")
all_rosters = roster_loader.load_all_rosters(2024)

# Depth charts
depth_loader = DepthChartLoader()
team_depth = depth_loader.load_team_depth_chart(2024, "BUF")

# Stadium information
stadium_loader = StadiumLoader()
stadiums = stadium_loader.load_stadiums()

Name Utilities

from sportsref_nfl.utils import NameUtils

name_utils = NameUtils()

# Normalize player names
normalized = name_utils.normalize_name("Josh Allen")
print(normalized)  # "josh allen"

# Check if names match
is_match = name_utils.names_match("Josh Allen", "J. Allen")
print(is_match)  # True

# Handle common name variations
is_match = name_utils.names_match("Robert Griffin III", "RG3")
print(is_match)  # True

Command Line Reference

Schedule Command

sportsref-nfl schedule --start-year 2020 --end-year 2024 [OPTIONS]

Options:
  --elo               Include ELO calculations
  --playoffs          Include playoff games
  --output PATH       Output CSV file path
  --verbose           Enable verbose output

Boxscore Command

sportsref-nfl boxscore --game-id GAME_ID [OPTIONS]

Options:
  --output PATH       Output CSV file path
  --verbose           Enable verbose output

Stats Command

sportsref-nfl stats --year YEAR [OPTIONS]

Options:
  --position POS      Filter by position (QB/RB/WR/TE/K/DST)
  --output PATH       Output CSV file path
  --verbose           Enable verbose output

Other Commands

# Draft data
sportsref-nfl draft --year 2024

# Team rosters
sportsref-nfl rosters --year 2024 --team BUF

# Depth charts
sportsref-nfl depth-charts --year 2024 --team BUF

# Stadium information
sportsref-nfl stadiums

# Name utilities
sportsref-nfl names --normalize "Josh Allen"
sportsref-nfl names --match "Josh Allen" "J. Allen"

# FlareSolverr management
sportsref-nfl flaresolverr start
sportsref-nfl flaresolverr status
sportsref-nfl flaresolverr stop

ELO Rating System

The library includes an advanced ELO rating system for team strength analysis:

schedule = sr.Schedule(2020, 2024, elo=True)

# ELO data includes:
# - elo_home: Home team ELO rating
# - elo_away: Away team ELO rating  
# - elo_diff: ELO difference (positive favors home team)
# - elo_prob_home: Probability home team wins
# - elo_prob_away: Probability away team wins

elo_data = schedule.schedule[['home_team', 'away_team', 'elo_diff', 'elo_prob_home']]

Error Handling and Retries

The library includes robust error handling with automatic retry and exponential backoff:

1. Cache is checked first for previously fetched pages
2. FlareSolverr is tried (auto-started via Docker if available)
3. cloudscraper is used as a fallback
4. Failed requests are retried with exponential backoff (6s, 12s, 24s)

try:
    schedule = sr.Schedule(2024, 2024)
except Exception as e:
    print(f"Scraping failed: {e}")

Caching

Data is automatically cached to improve performance:

# First call scrapes from web
schedule1 = sr.Schedule(2024, 2024)

# Second call uses cached data
schedule2 = sr.Schedule(2024, 2024)  # Much faster

# Force refresh
schedule3 = sr.Schedule(2024, 2024, force_refresh=True)

Development

git clone https://github.com/tefirman/sportsref-nfl.git
cd sportsref-nfl
pip install -e ".[dev]"

# Install pre-commit hooks (optional but recommended)
pre-commit install

A Makefile is provided for common development tasks:

make lint       # Run ruff linter and format checks
make typecheck  # Run mypy type checking
make test       # Run pytest with coverage
make check      # Run all of the above
make format     # Auto-fix linting issues and reformat code

Dependencies

• requests>=2.25.0 - HTTP requests
• beautifulsoup4>=4.9.0 - HTML parsing
• pandas>=1.3.0 - Data manipulation
• numpy>=1.20.0 - Numerical computations
• geopy>=2.1.0 - Geographic calculations
• cloudscraper>=1.2.0 - Fallback Cloudflare bypass

Optional (recommended):

• Docker + FlareSolverr - Primary Cloudflare bypass

Rate Limiting

Please be respectful of Pro Football Reference's servers:

• Built-in delays between requests
• Automatic retry with exponential backoff
• Caching to reduce redundant requests

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please read CONTRIBUTING.md for guidelines.

Support

• GitHub Issues: https://github.com/tefirman/sportsref-nfl/issues
• Documentation: https://sportsref-nfl.readthedocs.io/
• Email: tefirman@gmail.com