FotMob Scraper

Scrapes team data from FotMob and exports it to CSV files, with a Svelte UI to explore squad, fixtures, stats, and transfers.

Demo

What It Scrapes

• Team overview and metadata
• Fixtures and match highlights
• Squad and lineup snapshots
• Player and team stat rankings
• Transfers
• League table and legend
• Venue, trophies, coach history, FAQs, tabs
• Full historical table rows and rules from linked archives
• Match page datasets scraped from public HTML hydration:
  • match details
  • match events
  • match stats
  • shot maps
  • match lineups
  • per-player match stats
• Player page datasets scraped from public HTML hydration:
  • player profiles
  • recent matches
  • career entries
  • market value history
  • trophies
  • trait comparisons

All generated CSVs are written to data/.

Project Structure

• scraper/scraper.py: main scraping/export script
• scraper/config.py: loads .env values
• scraper/supabase_sync.py: optional Supabase sync logic
• supabase/schema.sql: SQL schema for Supabase Query Editor
• data/: generated CSV output
• client/: Svelte + Vite frontend

Requirements

• Python 3.10+
• Node.js 18+

Setup

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Create .env:

cp .env.example .env

Set your team:

TEAM_ID=9773

Optional Supabase sync env vars:

SUPABASE_URL=https://your-project-ref.supabase.co
SUPABASE_SERVICE_ROLE_KEY=your_service_role_key
SUPABASE_SCHEMA=public

Run Scraper

python3 scraper/scraper.py

Optional: sync every exported CSV dataset to Supabase in the same run:

python3 scraper/scraper.py --sync-supabase

Replace all synced rows for this team before inserting the new export:

python3 scraper/scraper.py --sync-supabase --overwrite-supabase

Expected outputs include:

• Core: data/fixtures.csv, data/squad.csv, data/player_stats.csv, data/team_info.csv, data/league_table.csv, data/transfers.csv
• Team depth: data/player_stat_rankings.csv, data/team_stat_rankings.csv, data/match_highlights.csv, data/team_form.csv, data/top_players.csv, data/venue.csv, data/table_legend.csv, data/coach_history.csv, data/trophies.csv, data/historical_seasons.csv, data/historical_table_rows.csv, data/historical_table_rules.csv, data/faq.csv, data/tabs.csv, data/last_lineup.csv, data/last_lineup_players.csv
• Match depth: data/match_details.csv, data/match_events.csv, data/match_stats.csv, data/match_shots.csv, data/match_lineup_players.csv, data/match_player_stats.csv
• Player depth: data/player_profiles.csv, data/player_recent_matches.csv, data/player_career_entries.csv, data/player_market_values.csv, data/player_trophies.csv, data/player_traits.csv

Run Frontend

cd client
npm install
npm run dev

Open http://localhost:5173.

The client visualizes these exports across the existing Overview, Squad, Fixtures, Stats, History, Transfers, and Raw Data tabs, including player compare mode, role-specific summaries, mini trend charts, honor summaries, fixture shot maps, and card/track event timelines with filters.

Supabase Schema

Run supabase/schema.sql in Supabase Query Editor before using --sync-supabase. The schema drops and recreates all exported-data tables in public, with typed columns, primary keys on core entities, and indexes for common lookups. Most relationship-heavy or analytics-heavy tables also get targeted indexes to keep syncs and queries scalable. Default schema used by the scraper is public.

Dependencies

• mobfot
• pandas
• python-dotenv
• supabase
• Svelte + Vite