hs-scraper-toolkit 1.0.1

A comprehensive toolkit for scraping high school data

hs-scraper-toolkit

A comprehensive toolkit for scraping high school sports data from various athletic websites including MaxPreps and Athletic.net.

Features

• MaxPreps Roster Scraping: Extract detailed roster information including player names, numbers, positions, grades, and more
• Athletic.net Track & Field Data: Scrape athlete rosters and event schedules for track & field and cross country
• Flexible Filtering: Filter data by sport, gender, season, and competition level
• Easy Integration: Simple Python classes with pandas DataFrame outputs

Installation

# Install from local directory
pip install .

# Install with development dependencies
pip install -e ".[dev]"

Dependencies

• beautifulsoup4>=4.9.0 - HTML parsing
• requests>=2.25.0 - HTTP requests
• pandas>=1.3.0 - Data manipulation
• selenium>=4.0.0 - Web automation (for Athletic.net)

Quick Start

MaxPreps Roster Scraping

from hs_scraper_toolkit.Athletics.MaxPrepRoster import MaxPrepRoster

# Initialize scraper with team URL
scraper = MaxPrepRoster("https://www.maxpreps.com/il/chicago/northside-mustangs")

# Scrape all available sports
roster_data = scraper.scrape()

# Filter by specific criteria
basketball_data = scraper.scrape(
    sports=['basketball'],
    genders=['boys'],
    seasons=['winter'],
    levels=['varsity']
)

print(f"Found {len(roster_data)} athletes")
print(roster_data.head())

Athletic.net Track & Field Scraping

from hs_scraper_toolkit.Athletics.AthleticNetTrackField import AthleticNetTrackField

# Initialize scraper with team URL
scraper = AthleticNetTrackField("https://www.athletic.net/team/19718")

# Scrape athlete rosters
athletes = scraper.scrape_athletes(['cross-country', 'track-and-field-outdoor'])

# Scrape event schedules
events = scraper.scrape_events(['cross-country'], [2024, 2025])

print(f"Found {len(athletes)} athletes")
print(f"Found {len(events)} events")

Athletics Module

MaxPrepRoster

Scrapes roster data from MaxPreps team pages.

Supported Data:

• Athlete names and jersey numbers
• Sports, seasons, and competition levels
• Player positions and grade levels
• Gender categories

Supported Sports: All sports available on MaxPreps (basketball, football, soccer, etc.)

AthleticNetTrackField

Scrapes track & field and cross country data from Athletic.net using Selenium WebDriver.

Supported Data:

• Athlete rosters with names and gender
• Event schedules with dates and locations
• Meet information and venues

Supported Sports:

• Cross Country (cross-country)
• Outdoor Track & Field (track-and-field-outdoor)
• Indoor Track & Field (track-and-field-indoor)

Requirements:

• ChromeDriver must be installed and accessible in PATH
• Stable internet connection (scraping may take several minutes)

Data Output

Both scrapers return pandas DataFrames with standardized column structures:

Athlete Data Columns

• name: Athlete name
• number: Jersey number (0 for Athletic.net)
• sport: Sport type
• season: Season (fall/winter/spring)
• level: Competition level (varsity/jv/freshman)
• gender: Gender (boys/girls)
• grade: Grade level (9/10/11/12 or N/A)
• position: Player position (N/A for track/field)

Event Data Columns (Athletic.net only)

• name: Event/meet name
• date: Event date
• time: Event time
• gender: Gender category
• sport: Sport type
• level: Competition level
• opponent: Opposing teams
• location: Event venue
• home: Home event indicator

Examples

See the example/main.py file for comprehensive usage examples.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Support

For issues, questions, or contributions, please visit the GitHub repository.