A rumour, a confirmed fee and a model estimate are three kinds of number. Put them in one column and the dataset starts lying politely.
ScrapeIt runs the collection as a managed service. You name the leagues, clubs or windows; we build the pipeline with record type on every row, attribution kept on reported claims, valuations labelled as estimates with dates, and hand back CSV, JSON, Excel or a push into your warehouse.
Windows are seasonal, so cadence is set against them - dense during a window, sparse outside it - rather than a flat daily schedule that mostly records nothing.
We collect published data only, honour the crawl rules - non-English paths are disallowed and we do not use them - and pace requests. Terms restrict commercial reuse, so the dataset is for analysis rather than republication, and your counsel should see the use case before the project starts.
Record type is the first field: completed transfer, reported interest, or estimated valuation. Everything else is interpreted through it, and a dataset without it cannot be used safely by anyone who did not build it.
Transfer records carry the player, the clubs on both sides, the window and season, the fee as published with its currency, the deal type such as permanent or loan, and whether the fee was disclosed or reported.
Reported interest records carry the player, the interested club, the date reported and the publication cited where the site names one. Attribution is the field that lets a user weigh the claim; without it a rumour becomes an anonymous assertion in a spreadsheet.
Valuation records carry the estimate, the currency and the date, and are labelled as estimates on the row rather than in documentation nobody downstream will read.
Every row carries the collection timestamp, which matters here because a valuation is only meaningful with a date attached.
Rumour pattern analysis is the output that only works with proper typing: how many outlets linked a player to a club, over what period, and whether the reported interest preceded an actual move. Done across several windows it produces a measurable read on which reporting tends to precede real transfers.
Window timing analysis shows when deals cluster, how that differs between leagues, and how late business concentrates - a practical question for anyone modelling squad planning.
Valuation drift over time is a tractable series once estimates carry their dates, and comparing a provider's estimates against subsequently disclosed fees is the most direct way to judge whether a valuation model is worth anything.
The limits are plain. Non-English paths are disallowed by the crawl rules, so this is an English-language catalogue. Undisclosed fees stay undisclosed and we never impute them. And no provider has complete transfer coverage, so a serious market dataset draws on more than one and reconciles the disagreements rather than picking a favourite.
FootballTransfers covers the transfer market: completed deals, reported interest, and its own estimated player valuations. Those three things sit close together on the site and they are not the same kind of fact.
A completed transfer is a record of something that happened, with a fee that may or may not have been disclosed. Reported interest is journalism about something that might happen, sourced from varying places with varying reliability. An estimated value is a model output - a calculation about what a player might be worth, which is useful and is not a price anyone paid.
A dataset that merges them into a value column produces something that looks like market data and behaves like rumour. The single most important design decision on this source is to type each record for what it is and to keep the source of any reported claim attached.
Transfer and player sections respond directly, and crawl rules are published. They disallow the non-English language paths, so collection runs on the English catalogue and we say so rather than implying wider coverage.
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Transfer data is among the easiest sports data to deliver badly, because the badly delivered version is more convenient and looks more complete.
Flatten everything into player, club and value and you get a tidy table where a completed twelve million transfer, a newspaper claim and an algorithmic estimate are indistinguishable. Anyone analysing it will treat all three as observations of the same thing, and the conclusions will be confidently wrong in whichever direction the rumours lean.
The second reason is that rumours are genuinely interesting once typed correctly. The pattern of reported interest - which clubs are linked with which profiles, how often, how far ahead of a window - is a real signal about intent, and it is a signal about reporting as much as about football. It just is not a record of a transaction.
The third is that fees are frequently undisclosed and the reported figure is an estimate by a journalist. Recording whether a fee was disclosed or reported is the difference between a market dataset and a collection of educated guesses presented as accounting.
The fourth is that valuations are model outputs. They are useful for comparison and ranking and they are not prices. A row that says so protects every analysis built on it.
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, maintains player and club resolution across spellings, and repairs the collector before a transfer window goes half recorded.
You see a sample first, in your format, over the leagues you actually follow, with record types separated so you can see on real deals how much of the table is fact and how much is reporting.
Because merged they are indistinguishable, and anyone analysing the table will treat a newspaper claim as an observation of the same kind as a completed twelve million transfer. The conclusions then lean whichever way the rumours did, confidently and invisibly.
No, they are model estimates, and the row says so rather than a note in documentation nobody downstream reads. They are useful for ranking and comparison. Treating them as prices is the mistake the labelling exists to prevent.
They stay undisclosed, flagged as such. We never impute a figure. A reported fee is marked as reported rather than disclosed, because the difference between those two is the difference between market data and educated guessing.
Where the site names one, yes. Attribution is what lets a user weigh a claim, and without it a rumour becomes an anonymous assertion sitting in a spreadsheet looking like a fact.
No, and we say so rather than selling completeness we cannot deliver. No provider covers the whole market, so a serious dataset draws on several and reconciles the disagreements - which are themselves informative about reporting quality.
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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