Two collection jobs live on this site and mixing them wastes money on one half while missing the other: an archive that barely changes and a live state that changes by the minute.
ScrapeIt runs the collection as a managed service. You name the competitions and the phases you need; we build the pipeline, run the archive backfill once and the live collection on a per match schedule, and hand back CSV, JSON, Excel or a push into your warehouse, with events as a stream and time zones explicit.
Cadence is set per competition and per phase rather than uniformly: fixtures light, lineups in the pre match window, live state at minute level for the matches you care about, nothing at all once a match is finished. That is what keeps a live feed affordable.
We work within the crawl rules the site publishes, which means we do not collect the paths its robots file disallows, standings among them; where you need tables we take them from the official competition source instead. Content and compiled data belong to the operator and the competitions, so the dataset is built for analysis and product use rather than for republication of their pages. Bring the use case to your own counsel before the project starts, particularly if the output touches betting.
The fixture record covers sport, competition and stage, season, match identifier, home and away team, scheduled kickoff in both local time and UTC, venue where published, and match status.
Live state is delivered as its own timestamped rows rather than overwriting the fixture: current score, half time score, elapsed minute, and the event stream of goals with scorer and assist where published, cards, substitutions and penalties. Keeping the stream means a client can reconstruct the state of a match at any point rather than only its ending.
Lineups are collected where the source publishes them: starting eleven with positions and shirt numbers, substitutes, and formation. Lineup publication timing is itself informative, since teams are usually confirmed about an hour before kickoff and that is when a great deal of downstream activity happens.
Match statistics are captured as published per competition, which varies: possession, shots, shots on target, corners, fouls and the more advanced measures where a competition supplies them. We record what exists rather than filling gaps with estimates, and the absence of a metric is recorded as absence.
Every row carries the collection timestamp and the source competition, and time zones are stored explicitly, because a kickoff time without a zone is the single most common cause of a fixture dataset being an hour wrong twice a year.
Entity matching is the hidden cost in any multi source sports dataset and it starts the moment you add a second source. Team names differ between services, clubs share names across countries, and player names appear with and without accents, with initials, and in different transliterations. We match teams on competition context and aliases, and players on birth date, squad and position, then keep every surface form encountered so a join can always be audited.
Breadth is worth scoping deliberately rather than taking wholesale. Hundreds of competitions is a lot of crawling, and most clients need forty of them properly rather than four hundred badly. We define the competition set at the start and price the live half against it.
Crawl rules are honoured rather than worked around, and on this source that has a concrete consequence: standings are disallowed and we do not take them. When a client needs tables, we collect them from the competition's own site, which is both permitted and more authoritative, and join them to the fixture data on the normalised competition identifier. Saying no to one path and yes to a better one is usually the cheaper answer anyway.
Historical backfill is a one time job and normally the cheaper half of the project. Once a season is in, it never needs collecting again.
Flashscore is a live results service covering football in depth and a long list of other sports alongside it, across hundreds of competitions from major leagues down to regional divisions. Breadth is its distinguishing feature: for many smaller competitions it is one of very few places the results exist in structured form at all.
The site is addressed by sport and competition, with a match page per fixture carrying the score, the minute, the events and, where available, lineups and match statistics. A sitemap is published, which makes enumerating competitions and fixtures practical rather than guesswork.
Its crawl rules are specific and we work inside them: the robots file disallows the standings, draw and newsfeed paths while leaving match and competition pages open. Standings, therefore, are not collected here. Where a client needs tables we take them from the competition's own official source, which is more authoritative anyway.
The most important structural point is temporal. A finished match from last season is a static record that will never change again. A match kicking off in ten minutes is a stream of state that changes every few seconds. Collecting both on one schedule means either paying to re-read a permanent archive or missing the live half entirely.
Get a Quote
Developers
Customers worldwide
Pages extracted
Hours saved for our clients
€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
Sports data splits cleanly into a permanent archive and a live state, and almost every project that goes wrong here does so by treating them as one thing.
The archive is cheap. Last season's results will not change; collect them once, keep them, never look again. Paying to re-read a completed competition every hour is pure waste, and at the scale of hundreds of competitions it is a great deal of waste.
The live half is the opposite. During a match, score, minute and events change continuously, and a feed that arrives every ten minutes is not a live feed, it is a slow archive. Anything built on it that claims to be current will be wrong in the way that matters most, at the moment people are watching.
So the design is per competition and per phase. Fixtures are collected ahead of time on a light schedule. Lineups are collected in the window before kickoff, when they appear. Live state is collected at minute level for the matches that matter to the client, and only for those. After the final whistle a match moves into the archive and stops being polled at all.
The second reason this source is worth the trouble is breadth. Major leagues are covered everywhere; second tier and regional competitions frequently are not, and for anyone whose product needs coverage rather than headline fixtures, that long tail is the reason to be here at all.
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.
ScrapeIt is a managed extraction company, not a tool you have to learn. Our team builds the pipeline, watches it through the season as pages and competitions change, and repairs it before matchday rather than after.
You see a sample first, in your format, over the competitions you actually cover, including a live window on a real fixture so you can judge the latency rather than take it on trust.
Minute level on the fixtures you nominate, and faster on a narrow set. What we will not do is promise second level latency across hundreds of competitions at once, because that is not a schedule anyone can hold politely. The design that works is a defined match list collected fast and everything else collected slowly.
Not from this source. Its robots file disallows the standings path and we honour that. Where you need tables we collect them from the competition's own official site, which is permitted and more authoritative, and join them to the fixture data on a normalised competition identifier. The result is better data than the one we declined to take.
That is the main reason to use this source. Major leagues are available everywhere; second tier and regional competitions often are not, and this is one of very few places they exist in structured form. We scope the competition set at the start, because forty competitions collected properly beat four hundred collected badly.
Yes, and the work is entity matching rather than collection. Team names differ between services, clubs share names across countries, and player names vary with accents and transliteration. We match teams on competition context and aliases and players on birth date, squad and position, keeping every surface form so any join can be audited.
The data is sports results and fixtures, and what you may do with it depends on your jurisdiction and your licence rather than on us. Compiled sports data also carries rights held by operators and competitions. We collect for analysis and product use rather than republication, and we ask every client whose use case touches betting to clear it with their own counsel before the project starts.
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
NIP: 5423457175
REGON: 523384582
