withqwerty

withqwerty / reep

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The football entity register. Maps player, team, and coach identities across Transfermarkt, FBref, UEFA, Sofascore, and 25+ data providers.

15
0
100% credibility
Found Mar 30, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Reep provides downloadable lists and search tools to match football players, teams, and coaches across more than 30 sports data websites using stable Wikidata identifiers.

How It Works

1
⚽ Discover Reep

You stumble upon Reep while searching for ways to match football players across different websites like Transfermarkt and FBref.

2
📖 Explore the football lists

You check out the ready-made lists of players, teams, and coaches with all their matching codes from over 30 sites.

3
Pick your way to use it
📥
Download lists

Grab the simple files with everyone’s details to open in your spreadsheet anytime.

🔍
Quick search

Type a name into the handy search tool to find matches instantly online.

4
⭐ Find your player

Search for someone like Cole Palmer and instantly see all their codes from every site.

5
📋 Grab the matches

Copy the linked codes to connect player stats from all your favorite sources.

🎉 Analyze like a pro

Now you easily combine data from everywhere to dive deep into football insights.

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Star Growth

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AI-Generated Review

What is reep?

Reep is a football entity register that maps players, teams, and coaches across Transfermarkt, FBref, UEFA, Sofascore, and 25+ data providers using stable Wikidata IDs. Developers get downloadable CSVs with 430K people and 45K teams, a REST API for lookups/resolves/searches, and a Python CLI for quick ID translations like Transfermarkt to FBref. It solves ID mismatches in github football data pipelines, letting you focus on analysis over entity resolution.

Why is it gaining traction?

Zero-cost API on Cloudflare Workers handles searches, resolves, and batch queries without auth walls, plus offline CSVs and a scriptable CLI (e.g., `reep resolve transfermarkt 568177`). Weekly Wikidata rebuilds ensure fresh coverage without custom scraping, beating fragmented github football databases. Hooks football prediction github projects and betting scripts needing cross-provider links.

Who should use this?

Data engineers building github football betting models juggling Sofascore and Understat IDs. Analysts scraping for football manager sims or random match generators. Teams needing a github football database for player/team bios and 25+ provider mappings in prediction pipelines.

Verdict

Grab it for clean football entity resolution in your github football data stack—CSVs and API work out of the box. At 15 stars and 1.0% credibility, it's niche and maturing; strong docs and tests make it reliable now, but Wikidata contributions boost coverage fast.

(198 words)

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