Real-time football data API built on continuously updated scrapers, sold to indie app developers and betting-adjacent analytics startups priced below Opta and Sportradar
Billions flow through football analytics and fan apps yet affordable clean data APIs barely exist for small developers, a clear structural pricing gap
Built for fantasy sports platforms and sports analytics firms.
10x cheaper than licensed sports data incumbents by owning the scraping infra and targeting the long tail of developers priced out of Opta or Sportradar
“For scraping I use my own tools made in Java or Pyhton. I will need just few hours to update the scraper for Soccerway website and start pulling the data. Then …”
The receipts — real demand
“For scraping I use my own tools made in Java or Pyhton. I will need just few hours to update the scraper for Soccerway website and start pulling the data. Then ... Read more”
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demand score 6.5 — the receipts are below
Why this is a gap
Surfaced from a high-intensity complaint with clear willingness to pay and a specific, reachable audience.
The market
Fantasy sports platforms and analytics firms need fresh football player and match stats. Zero search volume for the buyer keyword suggests this demand is met by existing open-source and enterprise APIs.
Competition & the opening
Crowded 9/10 market with mature free options (soccerdata, ScraperFC, StatsBomb Open Data all offer Python libraries covering major leagues) and enterprise players (Sportradar with full API coverage, API-Football / football-data.org with freemium REST access). The gap is thin: custom scraping for niche leagues or proprietary stat calculations.
What's hard to build
Legal risk is high: sports data and official league stats are often owned or licensed (UEFA, Premier League). Building reliable scrapers for multiple sites (Soccerway, FBref, Understat) requires reverse-engineering site structure and handling rate limits and blocks. Data freshness matters in sports; maintaining realtime or near-realtime pipelines across sources is operationally expensive.
Why now
Free open-source libraries (soccerdata, ScraperFC) require Python skills; StatsBomb and Sportradar are expensive; a managed scraper service filling the mid-market gap.
How you'd monetize
usage-based ($0.01–0.05 per match/player record queried) or $99–299/mo subscript