Dozens of country editions publish overlapping but not identical coverage. Treating them as one site inflates your counts; ignoring them loses most markets.
ScrapeIt runs the collector as a managed service. You name the editions, competitions, clubs or players; we build the pipeline, run it on your cadence and hand back CSV, JSON, Excel or a push into your warehouse, with editions separated, shared stories grouped and football entities normalised to identifiers that join to your match data.
Cadence is per edition and per period. Transfer windows justify frequent passes; quiet spells in a domestic off season do not. Requests are paced within the crawl rules the site publishes.
Articles are copyrighted and the terms restrict reuse, so the dataset is built for analysis rather than republication: metadata, entity extraction and publicly rendered text for measurement. Bring the use case to your own counsel before the project starts.
The article record covers canonical URL, headline, standfirst, edition, language, section, byline, publication timestamp, last modified timestamp, item type and the article identifier.
Edition and language are separate fields, as on any multi market publisher. Edition tells you the market the story was published for; language tells you the text you are holding. Several editions publish in the same language for different markets and the distinction matters for any regional analysis.
Story grouping across editions is delivered rather than left as an exercise. Where the same story appears in several editions, translated or not, the rows carry a shared story identifier produced by near duplicate detection and entity overlap, so coverage can be counted once globally or once per market.
Football entities are extracted and normalised: clubs, players, competitions and managers mentioned, linked to identifiers that join to squad and transfer data. That is what turns a pile of articles into something answerable, since the questions are almost always about a player or a club rather than about a keyword.
Item type separates reported news from transfer rumour, preview, report and list content, and every row carries the collection timestamp.
Market comparison is the natural output once editions are properly separated. Which markets covered a story, how quickly, at what length, and which ignored it entirely. For a club, a federation or a sponsor operating internationally that is a far better picture than a single global number.
Transfer windows deserve their own handling in any analysis built on this source, because volume spikes hard and the mix shifts towards rumour. Typed content and timestamped rows let a client model the window separately rather than letting it distort a full season trend.
Joining to match and transfer data is the standard extension and the reason this page sits alongside the others in this section. Coverage volume on one side, results and market values on the other, joined on normalised club and player identity, answers whether attention followed performance or preceded it.
Historical work is practical because article URLs are stable and section pagination reaches back, and it runs as a one time job separate from the ongoing feed. As with any media source, front position and prominence exist only if somebody was observing at the time.
Goal.com is a football publisher operating a large set of country and language editions under one brand. Editions share a good deal of coverage, translate some of it, and produce a substantial amount that is entirely local: national team stories, domestic league reporting and market specific transfer coverage.
That structure is the whole design problem. Treating the editions as one site double counts the shared material and produces coverage numbers several times larger than reality. Treating any single edition as the whole publisher loses every market except one. Both errors are common and neither is visible from the output.
The right model is one publisher with many editions, where every row carries its edition and language, and shared stories are grouped so they can be counted once or once per market as the question requires.
Content is arranged into news, transfer coverage, match previews and reports, lists and rankings, with a heavy volume of transfer rumour material that behaves differently from reported news and should be typed as such. Structurally the editions follow a consistent addressing scheme, and the site publishes crawl rules that permit ordinary collection.
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Football media publishes a large volume of transfer speculation, and it is written in the same templates as reported news. A dataset that does not separate them will report that a club was mentioned four hundred times in a window and will be counting rumour, aggregation of other outlets' rumour, and a handful of actual reporting as one thing.
Typing them apart makes both usable. Rumour volume is genuinely interesting as an attention signal, particularly around windows, and reported news is what a communications team actually cares about. Mixed together they are neither.
The second problem is the edition overlap. A story published across twelve editions is one story with twelve placements, not twelve stories. For share of voice work the difference is a factor of twelve, and it is entirely invisible unless the grouping is done during collection.
The third is entity extraction. Nobody asks how many articles contained a string; they ask how much coverage a player or a club received. That requires clubs, players and competitions recognised and normalised to identifiers that join to squad and transfer data, including the many name variants football uses for the same person.
The fourth is that this source is most valuable next to the official and results sources rather than alone. Media volume against actual events is the comparison that answers whether attention tracked performance, and it needs both halves collected on the same identities.
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 as editions and templates change, and repairs it before your coverage numbers develop a gap.
You see a sample first, in your format, across the editions you actually track, with story grouping and entity extraction already applied so you can judge the counts on real coverage.
By grouping it during collection. Where a story appears across several editions, translated or not, the rows share a story identifier produced by near duplicate detection and entity overlap. You can then count coverage once globally or once per market. Without that step a story on twelve editions inflates your share of voice by a factor of twelve.
Yes, and you should insist on it. Speculation is written in the same templates as reporting, and mixed together they make both useless. Typed apart, rumour volume becomes a genuine attention signal around windows and reported news becomes something a communications team can act on.
Any of the country and language editions the publisher operates. Every row carries both the edition and the language, because several editions publish in the same language for different markets and regional analysis needs the distinction. We scope the edition set at the start rather than collecting all of them by default.
Yes, normalised to identifiers rather than left as strings. Nobody asks how many articles contained a name; they ask how much coverage a club or player received, and football uses many name variants for the same person. Those identifiers are what let this data join to squad, results and transfer datasets.
No. The journalism is copyrighted and the terms restrict reuse. What you get is metadata, entity extraction and publicly rendered text for analysis: coverage measurement, share of voice, market comparison. Have your own counsel approve the use case 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.
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