🏈 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.

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.

🎯 Target Audience	💡 Primary Use Cases
NFL analytics teams, DFS players, sports-media producers, fantasy-football tool builders, sports betting modelers, R / Python data scientists, college coaches studying pro data	Play-by-play loaders, fantasy-roster sync, weekly model rebuilds, injury and snap-count dashboards, prop-bet research, podcast research

📋 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.

📊 Data fields

Each record includes: assetCount, assets, author, createdAt, datasetType, description, fileFormats, isDraft, isPrerelease, publishedAt, releaseId, releaseName, releaseTag, releaseUrl, scrapedAt, seasons, totalDownloads, totalSizeBytes. These field names come straight from the actor's dataset schema, so what you see here is what lands in your dataset.

🚀 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.

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

⚠️ 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.

🆘 Need Help?

If you hit a bug, have questions about setup, or need a scraper we haven't built yet, open our contact form or write to parseforge@protonmail.com. We also take on paid custom data projects.

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