Pricing

Rating

Developer

Actor stats

Categories

Share

👁 ParseForge Banner

🏈 nflverse NFL Data Releases Scraper

🚀 Export every nflverse NFL data release in seconds. Play-by-play from 1999, schedules, rosters, snap counts, injuries, depth charts, contracts, combine, Next Gen Stats and more across 24 dataset families. CSV, Parquet, RDS, qs.

🕒 Last updated: 2026-05-21 · 📊 19 fields per release · 🏈 24 dataset families · 📅 1999 to current season · 🔗 every download link, every format

The nflverse Data Scraper indexes the community-maintained nflverse release feed and returns one record per release, including release metadata (tag, name, publish date, author, description, prerelease flag), aggregate stats (asset count, total size, total downloads, list of seasons covered, list of file formats), and the complete asset manifest (file name, size, season, team, format, direct download link). nflverse powers the R packages nflreadr, nflfastR, and nflseedR, and is the de-facto open data backbone for modern football analytics.

Coverage spans 24 dataset families including play-by-play (1999-present), play-by-play participation, schedules, rosters, weekly rosters, players, player stats, weekly player stats, team stats, depth charts, snap counts, injuries, officials, draft picks, combine, contracts, FTN charting, ESPN data, PFR advanced stats, Next Gen Stats, trades, and teams.

📋 What the nflverse Scraper does

Three filtering workflows in a single run:

• 🏈 Pick a dataset family. One of 24 nflverse release tags (pbp, schedules, rosters, injuries, snap_counts, nextgen_stats, etc.).
• 📅 Filter by season. Restrict the asset list to a single NFL season year (1999-present where data exists).
• 🗂️ Include or exclude the asset manifest. Pull just release metadata, or the full file list with download links.

Each record exposes the release tag, name, ID, publish and create timestamps, prerelease / draft flags, author handle, release description, asset count for the filter, the sorted list of seasons present, the list of file formats (CSV, CSV.GZ, Parquet, RDS, qs), total asset size in bytes, cumulative download count, the GitHub release URL, and (optionally) a full asset array with per-file metadata.

💡 Why it matters: building an NFL analytics pipeline yourself means watching a community release feed, parsing season-naming conventions across 24 different tag families, and refreshing weekly. This Actor returns a clean index with every download link on tap.

🎬 Full Demo

🚧 Coming soon: a 3-minute walkthrough showing how to load weekly play-by-play and snap counts into a fantasy model.

⚙️ Input

Example: full play-by-play release index with every season and every download link.

{
"maxItems":1,
"dataset":"pbp",
"includeAssets":true
}

Example: snap counts for 2024 only.

{
"maxItems":1,
"dataset":"snap_counts",
"season":2024
}

⚠️ Good to Know: nflverse releases are produced and maintained by a community of volunteers. Files are updated through the season as new game data arrives. Always confirm the publish date and asset list match the analysis window you need.

📊 Output

Each release record contains 19 fields. Download the dataset as CSV, Excel, JSON, or XML.

🧾 Schema

📦 Sample records

✨ Why choose this Actor

📊 nflverse is the open-data backbone of modern NFL analytics and powers the most widely used R and Python football packages.

📈 How it compares to alternatives

Pick this Actor when you want a clean, language-agnostic index of nflverse releases without standing up an R toolchain.

🚀 How to use

1. 📝 Sign up. Create a free account with $5 credit (takes 2 minutes).
2. 🌐 Open the Actor. Go to the nflverse NFL Data Releases Scraper page on the Apify Store.
3. 🎯 Set input. Pick a dataset family, optionally narrow by season, and decide if you want the full asset manifest.
4. 🚀 Run it. Click Start and let the Actor build your release index.
5. 📥 Download. Grab your results in the Dataset tab as CSV, Excel, JSON, or XML.

⏱️ Total time from signup to a downloaded nflverse manifest: 3-5 minutes. No coding required.

💼 Business use cases

🔌 Automating nflverse Data Scraper

Control the scraper programmatically for scheduled runs and pipeline integrations:

• 🟢 Node.js. Install the apify-client NPM package.
• 🐍 Python. Use the apify-client PyPI package.
• 📚 See the Apify API documentation for full details.

The Apify Schedules feature lets you trigger this Actor on any cron interval. Weekly Tuesday refreshes line up with how most nflverse datasets ship after Monday Night Football.

🌟 Beyond business use cases

nflverse data drives education, research, fan projects, and creative work beyond commercial analytics.

🤖 Ask an AI assistant about this scraper

Open a ready-to-send prompt about this ParseForge actor in the AI of your choice:

• 💬 ChatGPT
• 🧠 Claude
• 🔍 Perplexity
• 🅒 Copilot

❓ Frequently Asked Questions

🧩 How does it work?

Pick a dataset family (pbp, schedules, snap_counts, etc.), optionally narrow by season, and the Actor returns one record per matching nflverse release with the full asset manifest and download links.

📏 How accurate is the data?

nflverse data is community-maintained and widely used by analytics professionals. Accuracy is high for the regular and post-season game data that backs nflfastR. Niche datasets (FTN charting, advanced stats) reflect the underlying provider's coverage.

🔁 How often is the dataset refreshed?

nflverse maintainers re-publish datasets through the season. Most refresh weekly during the regular season; some refresh hourly during live game windows. Each run pulls the current release index.

📅 What seasons are covered?

Play-by-play goes back to 1999. Schedules go further. Player stats, snap counts, and Next Gen Stats have shorter histories (typically 2016-present for snap counts, 2016-present for Next Gen Stats). The Actor returns the seasons array so you can see exactly what is in each release.

📦 Which file formats are available?

CSV, CSV.GZ, Parquet, RDS, and qs depending on the dataset. The fileFormats field on each record lists what is present.

💼 Can I use this data commercially?

nflverse data is published under permissive open-source licenses (typically MIT or CC0 by file). Review the underlying license for each dataset family and the upstream source where applicable.

⏰ Can I schedule regular runs?

Yes. A weekly schedule (Tuesday morning is a popular choice) keeps your downstream NFL pipeline current with new release publishes.

💳 Do I need a paid Apify plan?

No. The free Apify plan covers testing and small runs. Paid plans unlock larger datasets, scheduling, and concurrency.

🔁 What happens if a run fails?

Apify automatically retries transient errors. Partial datasets are preserved so you can adjust the input and re-run.

🆘 What if I need help?

Our support team is here to help. Contact us through the Apify platform or use the Tally form linked below.

🔌 Integrate with any app

nflverse Scraper connects to any cloud service via Apify integrations:

• Make - Automate multi-step workflows
• Zapier - Connect with 5,000+ apps
• Slack - Get run notifications in your channels
• Airbyte - Pipe NFL data into your warehouse
• GitHub - Trigger runs from commits and releases
• Google Drive - Export datasets straight to Sheets

You can also use webhooks to trigger downstream actions when a run finishes. Push fresh NFL data into your model server, or alert your analytics Slack channel when a new weekly file lands.

🔗 Recommended Actors

• 🏎️ AutoTrader Scraper - U.S. used-car listings
• 🚗 Cars for Sale Scraper - Used-car inventory across dealers
• 📈 Indexmundi Scraper - Global demographic and economic indicators
• 🛬 OpenSky Flights Scraper - Real-time flight tracking
• 📊 Figshare Research Scraper - Research dataset metadata

💡 Pro Tip: browse the complete ParseForge collection for more open-data scrapers.

🆘 Need Help? Open our contact form to request a new scraper, propose a custom data project, or report an issue.

⚠️ Disclaimer: this Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by the National Football League, nflverse, nflfastR, or any team or rights holder. All trademarks mentioned are the property of their respective owners. Only publicly available open data is collected.