Counting articles about a team is easy. Knowing how many were reporting and how many were takes is the part that makes the number mean anything.
ScrapeIt runs the collection as a managed service. You name the sports, teams or the full feed; we build the pipeline with article types classified and confidence attached, timestamps for publication and modification, and hand back CSV, JSON, Excel or a push into your warehouse.
Where coverage timing matters we collect frequently and keep revisions rather than overwriting, because an article that changed is a different article.
We collect published editorial metadata, honour the crawl rules and pace requests. The content is copyrighted: the dataset is for measurement and analysis, not republication, and anything surfacing article text to end users is a licensing conversation with the publisher. Your counsel should see the use case before the project starts.
Article records carry the headline, publication timestamp, author as bylined, sport, team section, article type, tags, word count and the address.
Article type is the field that makes the dataset useful rather than merely large. Classification uses structural signals - section, tags, headline pattern, presence of sourcing - and it ships with a confidence value, because it is an inference and the rows where it is uncertain are exactly the ones an analyst should look at.
Team and sport are separate fields. An article can concern a team, a league or a player, and collapsing those into one subject column makes coverage analysis ambiguous at the point where it matters.
Body text is collected where a client needs it for analysis, with the clear understanding that it is copyrighted and the dataset is for measurement rather than republication.
Every row carries the collection timestamp and, where available, the last modified time, since sports articles are frequently updated after publication.
Sentiment analysis comes up on every media brief and deserves a caution rather than a sales pitch. Sports writing is full of hyperbole, irony and idiom that general purpose sentiment models read badly, and a negative score on a headline about a demolition or a thrashing usually means the model has misread a scoreline. We deliver the text and the structure and are candid that sentiment here needs domain-tuned work rather than a library call.
Multi-outlet comparison on a shared schema is the more defensible product: which publishers cover which teams, at what volume, in what mix of article types, and how that differs by market.
Timeline analysis around events - a transfer, an injury, a coaching change - shows how coverage builds and decays, and needs the publication and modification timestamps to be right.
On rights we are direct: the content is copyrighted. Metadata and measurement are the deliverable; republishing the text is not, and a product that would surface article bodies to end users is a licensing conversation with the publisher rather than a scraping one.
Bleacher Report is a large American sports media site covering the major North American leagues plus football and combat sports, publishing at high volume across news, analysis and opinion.
Its structure suits data work well: content is organised by sport and by team, so a team section is a ready-made corpus of coverage about one subject. Those sections respond directly and carry a great deal of content.
What the data needs, and what a naive collection skips, is a distinction between kinds of article. This publisher mixes straight reporting, analysis, opinion and aggregation of other outlets' reporting, and a coverage volume metric that treats all four as equivalent measures something close to nothing.
Crawl rules are published and disallow advertising placeholder addresses and account paths, leaving editorial content open.
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Media monitoring in sport almost always starts as a counting exercise and almost always fails for the same reason: the count includes things that are not comparable.
A club that received forty mentions in a week has had a very different week depending on whether those were match reports, injury news or opinion pieces about its manager. Without article types the number moves and nobody can say why, which makes it useless for the thing it was bought for.
The second reason is the update problem. Sports articles change after publication - scores fill in, transfers are confirmed, headlines are rewritten. A dataset with only a publication timestamp silently mixes the article as published with the article as it now reads. Recording the modification time, and re-collecting where it matters, is what keeps a corpus honest.
The third is that high volume is a feature here rather than a nuisance. A publisher producing at this rate gives enough data for week-on-week movement to be meaningful rather than noise, which is rarely true of a smaller outlet.
The fourth is scope. One publisher is one voice. Share of coverage is a multi-source question, and we scope it as several publishers with a shared schema rather than presenting one outlet as the media landscape.
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, retunes the article classifier as section structures change, and repairs the collector before a coverage series develops a gap around the event you cared about.
You see a sample first, in your format, over the teams you actually track, with article types and confidence visible so you can judge the classification on real headlines rather than trust it.
Because a team with forty mentions had a very different week depending on whether those were match reports, injury news or opinion. Without types the count moves and nobody can say why, which makes it useless for the purpose it was bought for.
It is an inference and it ships with a confidence value on every row. The uncertain rows are exactly the ones worth a human look, which is why we surface the confidence rather than hiding it behind a clean-looking label.
We can, with a caution we would rather give up front. Sports writing is full of hyperbole and idiom that general models misread - a headline about a thrashing scores negative when it describes a win. It needs domain-tuned work, not a library call, and we will say so rather than shipping a number that looks fine.
We record the modification time and re-collect where it matters. Sports articles change constantly - scores fill in, headlines get rewritten - and a corpus with only a publication timestamp silently mixes the article as published with the article as it now reads.
No. The content is copyrighted and the dataset is for measurement and analysis. Anything that would surface article text to your end users is a licensing conversation with the publisher, not a scraping project, and we will say so rather than let it become your problem later.
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
