SELECT player_name, COUNT(*) AS total_goals FROM goals JOIN players ON goals.player_id = players.player_id GROUP BY player_name ORDER BY total_goals DESC LIMIT 5; (Expected: Miroslav Klose, Ronaldo, Gerd Müller, etc.)
| Table | Sample Columns | |-------|----------------| | matches | year, home_team, away_team, home_goals, away_goals, stage, attendance | | teams | team_id, team_name, confederation (UEFA, CONMEBOL, etc.) | | players | player_name, team_id, position, caps, goals | | goals | match_id, player_id, minute, own_goal, penalty | | tournaments | year, host_country, winner, top_scorer, total_goals | Data covers 1930–2022 (all 22 tournaments, 900+ matches, 2,700+ goals). I’ve prepared a clean, version-controlled copy for you. No registration, no paywalls. worldcup database sqlite download
👉 ** Download worldcup.db (SQLite 3, ~4.2 MB)** (Replace with actual link) SELECT player_name, COUNT(*) AS total_goals FROM goals JOIN
Tagline: Stop scraping Wikipedia. Here’s a ready-to-use SQLite database of every World Cup match (1930–2022). 👉 ** Download worldcup
📦 github.com/yourusername/worldcup-sqlite (Update with real link) 📧 Contact: data@yourblog.com Final Word Stop wrestling with messy web scrapers. Grab the SQLite file, open your terminal, and start asking real questions of World Cup history.
If you’ve ever tried to analyze World Cup history—from Uruguay 1930 to Qatar 2022—you know the struggle. Data is scattered across JSON files, messy CSV exports, or behind rate-limited APIs.