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Best Sports & Betting Data Scrapers on Apify (2026)

· 4 min read
Yassine El Haddad
Software Developer & Automation Specialist

I build production AI agents, web scrapers, and automation pipelines. Most of what I publish here comes from the actual problems they run into: proxies that get banned, anti-bot stacks that fingerprint your client, RAG that drifts when the underlying data moves. Stack: Python, TypeScript, Go, FastAPI, LangChain, Crawlee, Playwright, deployed on AWS, GCP, and Cloudflare.

Sports actors target live odds, historical match statistics, player performances, and real-time scores across global athletic leagues.

Building predictive sports models or managing a fantasy league requires vast amounts of structured data. The Apify Store hosts highly specific APIs that scrape leading sportsbooks, sports encyclopedias, and live scoreboards to feed your predictive algorithms.

Top Actors in This Category

1. Bet365 Scraper8.5K users · 4.6 ⭐ · $5/1,000 runs

The premier tool for odds tracking. Extract pre-match and in-play betting odds directly from Bet365 across dozens of sports. Crucial for arbitrage betting models and sports trading desks. Try Bet365 Scraper on Apify →

2. Flashscore Scraper4K users · 4.4 ⭐ · $2/1,000 matches

Extract comprehensive live scores, historical match results, league standings, and detailed in-game statistics (possession, shots on target) from Flashscore's global database. Try Flashscore Scraper on Apify →

3. Transfermarkt Scraper3.2K users · 4.7 ⭐ · $3/1,000 players

The ultimate soccer analytics actor. Extract detailed player valuations, contract lengths, agent details, and historical transfer fees for global football leagues. Try Transfermarkt Scraper on Apify →

4. Oddsportal Scraper

Aggregate the betting market. This actor scrapes Oddsportal to return the opening and closing odds from multiple global bookmakers for a specific match, allowing you to calculate the true market consensus. Try Oddsportal Scraper on Apify →

5. FBref Scraper (Football Reference)

Extract advanced soccer metrics. Retrieve xG (expected goals), shot-creating actions, progressive passes, and advanced defensive statistics from FBref to fuel advanced player scouting models. Try FBref Scraper on Apify →

6. ESPN Scraper

Scrape the worldwide leader in sports. Extract box scores, play-by-play data, team rosters, and breaking injury news across major US leagues like the NFL, NBA, and MLB. Try ESPN Scraper on Apify →

7. DraftKings Scraper

Monitor the explicit DFS (Daily Fantasy Sports) and sportsbook markets. Extract player prop odds, over/unders, and moneyline spreads directly from the DraftKings platform. Try DraftKings Scraper on Apify →

8. Racing Post Scraper (Horse Racing)

A specialized tool for equine analytics. Extract daily race cards, horse form, jockey statistics, and trainer records from the UK and Ireland’s premier racing publisher. Try Racing Post Scraper on Apify →

9. SofaScore Scraper

Alternative to Flashscore with rich player ratings. Extract automated match player ratings, heatmaps, and deep statistical breakdowns across soccer, tennis, and basketball. Try SofaScore Scraper on Apify →

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Why These Tools Stand Out

Sportsbook domains and score aggregators employ intense rate-limiting and anti-bot systems to prevent scraping. The sports scraping actors on Apify utilize session management and residential IPs to ensure your data pipeline never breaks during game time.

Free Credits

Apify offers $5 in free monthly credits. You can start building your sports analytics database today.


Start scraping for free →

Frequently Asked Questions

Yes, actors like the Flashscore Scraper and SofaScore Scraper can pull near real-time match data. However, remember that running actors continuously at high frequencies requires a paid Apify plan to handle the compute hours.

Absolutely. Many developers use the Bet365 and Oddsportal scrapers to feed live pricing data into their own value-betting engines or arbitrage alert systems via webhooks.

Yes, you can configure these scrapers to paginate through seasons of historical data (e.g., pulling every box score from an NBA season on ESPN) to build backtesting datasets for predictive models.