Sports Odds Scraper & Matcher
Budget: €30 – €250 EUR
My goal is to collect real-time odds and event data for soccer, basketball and tennis directly from a set of betting platforms, then automatically match identical fixtures across those sources so I can analyse line movement and pricing differences in one place.
The deliverable I need is a fully-functional scraper—Python is ideal, and I’m comfortable with libraries such as Requests, BeautifulSoup, Selenium or Playwright—paired with a matching layer that:
• Harvests every scheduled event the platforms publish (kick-off time, teams/players, league, market type and current odds).
• Normalises naming conventions so the same fixture coming from two different bookmakers is recognised as a single event.
• Outputs clean, structured data in JSON or CSV and writes to a small Postgres or SQLite database.
A simple CLI command or lightweight REST endpoint that triggers the scrape and returns the merged dataset will be perfect for my workflow. Clear setup instructions and commented code are essential; I want to be able to extend the scraper later for additional sports or markets.
I’ll verify completion by running the tool against at least two live platforms and confirming that:
1. All soccer, basketball and tennis events scheduled within the next 24 hours are present.
2. Odds pulled from multiple sources are correctly matched to a single event record.
3. No duplicate or orphaned entries appear in the final dataset.
If this fits your expertise, let’s move forward—I’m ready to get started right away.
The deliverable I need is a fully-functional scraper—Python is ideal, and I’m comfortable with libraries such as Requests, BeautifulSoup, Selenium or Playwright—paired with a matching layer that:
• Harvests every scheduled event the platforms publish (kick-off time, teams/players, league, market type and current odds).
• Normalises naming conventions so the same fixture coming from two different bookmakers is recognised as a single event.
• Outputs clean, structured data in JSON or CSV and writes to a small Postgres or SQLite database.
A simple CLI command or lightweight REST endpoint that triggers the scrape and returns the merged dataset will be perfect for my workflow. Clear setup instructions and commented code are essential; I want to be able to extend the scraper later for additional sports or markets.
I’ll verify completion by running the tool against at least two live platforms and confirming that:
1. All soccer, basketball and tennis events scheduled within the next 24 hours are present.
2. Odds pulled from multiple sources are correctly matched to a single event record.
3. No duplicate or orphaned entries appear in the final dataset.
If this fits your expertise, let’s move forward—I’m ready to get started right away.