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🏀 NCAA Basketball Player Box Score Scraper

The best NCAA Men's Basketball player box score scraper on Apify — clean and analysis-ready data from 2003 onward

👁 Apify Store
👁 Maintained
👁 Made for Sports Analytics
👁 Data Quality

Stop cleaning messy college basketball data. Get every NCAA Men's Basketball player box score since 2003 - 50+ fields, proper types, zero cleanup required.

🎯 What does this scraper do?

This actor extracts detailed player-level statistics from every NCAA Men's Basketball game played since 2003. It delivers clean, structured data ready for analysis or integration into your applications. No manual cleanup, no parsing nightmares, no messy nested data.

Key Benefits

• ✅ 50+ fields per player - scoring, shooting, rebounds, assists, and more
• ✅ 2003–present - 20+ seasons of historical coverage
• ✅ All Division I teams - 350+ teams across all conferences
• ✅ Clean structure - proper types and descriptive field names
• ✅ Fast & reliable - built-in validation and error handling
• ✅ Export anywhere - JSON, CSV, Excel, XML, and more

💡 No manual cleanup. No parsing nightmares. Just clean data.

📦 What data can you extract?

Every player game returns 50+ structured data points including:

💡 See the full data dictionary for all 50+ fields below.

🚀 How to scrape NCAA basketball player box score data

Quick Start Guide

Extracting NCAA basketball player box score data is simple - no coding required.

1. Create account — Sign up for a free Apify account
2. Open scraper — Go to NCAA Basketball Player Box Score Scraper
3. Select inputs — Choose a team and season year
4. Run — Click Start in the Apify Console
5. Export — Download as JSON, CSV, Excel, XML, or HTML

✨ Try it FREE - No credit card required

📥 Inputs

The scraper requires two simple inputs:

Option A: Use Apify Console (No Code)

The easiest way to get started — just point and click.

1. Open the NCAA Basketball Player Box Score Scraper
2. Enter a season year (defaults to latest)
3. Select a team from the dropdown (e.g., "Kentucky Wildcats", "Duke Blue Devils")
4. Click Start
5. Download results

👁 Apify Console Input UI

💡 Tip: Use the full team name including school and mascot. This ensures you get exactly the team you want — for example, "Kentucky Wildcats" vs "Arizona Wildcats" (there are 10 teams with "Wildcats" as their mascot!).

Option B: Use the API (Programmatic)

For automation, scheduling, or integration into your pipeline:

{
"season":2026,
"team":"Kentucky Wildcats"
}

⚠️ Note: both season and team are required fields.

📤 Output

Results are stored in the Storage tab as a structured dataset. View directly on the platform or download in multiple formats.

Results Preview

Once scraping completes, preview your data immediately in the Output tab.

👁 Apify Output Preview

Organized Views

We've pre-configured logical dataset views to help you explore the data more easily.

👁 Grouped Dataset Views

Sample JSON Output

Each player game returns a clean, flat JSON object with 50+ fields. Field names are descriptive and grouped by prefix (game_, team_, player_, score_, etc.) so you can quickly find what you need.

📖 Data Dictionary

Complete reference for all 50+ fields returned by this scraper. Fields are grouped by prefix for easy navigation.

💡 Note: Shooting percentages are on a 0-100 scale. Stats are null for players who did not play. All missing values are explicit null, not empty strings.

Key Differences from NBA Data

NCAA basketball has some important structural differences from NBA:

🔗 Sportsverse Data Suite

Building something bigger? Our ecosystem covers comprehensive sports data. Check out our other sports scrapers:

🤝 Support & Feedback

Found a bug? Want a new field? Have an idea?

• 🐛 Report issues in the actor comments
• 💡 Suggest features or enhancements
• 📊 Request additional fields or output tweaks

We read every message and aim to respond quickly.

Built with ❤️ and lots of ☕ for the sports analytics community

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