Fixtures, results, lineups and player statistics collected on a schedule that matches the sport - historic tables monthly, live match state by the minute.
Plans from €169/month · Free project assessment · Reply within 1 business day
Sports Media and Publishers
Fantasy Sports Platforms
Analytics and Scouting Firms
Clubs and Federations
Sponsorship and Rights Agencies
Fitness and Training Apps
Sports Betting Analysts
Research and Academic Groups
Merchandise and Retail Teams
Broadcast Scheduling Teams
Developers
Customers worldwide
Pages extracted
Hours saved for our clients
Sports data splits cleanly into two problems. Historic tables and squad lists are stable and cheap to keep current. Live match state is a different job entirely, and mixing the two into one schedule wastes money on one half and misses the other.
Full calendars across competitions, with kickoff times in local and UTC, venue, and final scores once played.
Score, minute, cards, substitutions and key events, collected at the cadence the sport demands rather than once an hour.
Squad lists, positions, appearances, minutes, goals and disciplinary records across seasons.
Market values, contract expiry dates and transfer history, which is the backbone of scouting and media coverage alike.
Who is out, with what and for how long - the single most requested field for fantasy and preview content.
Standings with the tiebreak columns each competition actually uses, not a generic points table.
Public activity, segment and leaderboard data from training platforms for product and research use.
Coverage depends on the competition and the source; we confirm which fields a given league actually publishes before starting.
Expertly customized web scraping services at a fraction of the cost of building and running collection in-house.
€199 / one-time
setup fee - included
€169 / mo
setup fee €499
€229 / mo
setup fee €499
€349 / mo
setup fee €499
€549 / mo
setup fee €799
Delivery: CSV, JSON or XLSX files, our API or a JSON feed. MCP server on request.
Full plan details →
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Static tables and live match state run on separate cadences, so you are not paying to re-collect last season every minute.
Team and player names differ between sites. We match on aliases, birth dates and squad context so one player is one row.
Standings are built with the tiebreak rules the competition uses, not a generic sort by points.
You pay for the dataset, not for proxies, browser farms and the engineering time to keep them alive.
We watch the sources through the season and fix collectors before matchday.
Learn how to use web scraping to solve data problems for your organization
Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.
Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.
If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.
For live match state we normally work at the minute level, and faster on a narrow set of fixtures. Historic tables and squad data run daily or weekly, since re-collecting them frequently buys nothing.
Yes, and it is usually necessary: no single site publishes everything. The work is entity matching - the same player and team spelled differently across sources - which we handle on aliases, birth dates and squad context.
We can collect publicly published odds where doing so is lawful in the target market, but we check the jurisdiction first. Odds carry rules that fixtures and scores do not.
Football is the most requested by a wide margin, then basketball, American football, baseball, ice hockey and tennis. The approach is the same for any sport with published fixtures and results.
Where the source keeps archives, yes. Historic backfill is a one-off run separate from the ongoing feed, and it is usually the cheaper half of the project.
CSV, Excel, JSON, JSONLines or XML, delivered through our API, a JSON feed, FTP, SFTP, Amazon S3, Google Cloud Storage, Dropbox, Google Drive or email. An MCP server is available on request. We can also write directly into your database.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
KRS: 0000996672
NIP: 5423457175
REGON: 523384582
