The market value here is a community estimate, not a price anyone paid. Say so and it is the most useful number in football; hide it and the analysis is built on sand.
ScrapeIt runs the collector as a managed service. You name the leagues, clubs or player set; we build the pipeline, run the historical backfill once and the updates on your cadence, and hand back CSV, JSON, Excel or a push into your warehouse, with valuation history as rows, transfers typed and undisclosed fees marked as undisclosed.
Cadence is modest by design. Valuations update on a cycle rather than continuously and squads change in windows, so most clients run weekly or monthly with change records, plus tighter collection during a transfer window when it actually matters.
We collect what the site renders publicly and pace requests within the crawl rules it publishes. Player data here is professional information about public figures, and we collect no personal contact details. The database is a compilation with rights attached, so the dataset is built for analysis rather than for republication of their pages. Bring the use case to your own counsel before the project starts.
The player record covers player identifier, name, date of birth, nationality and second nationality, position and secondary positions, height, preferred foot, current club, shirt number, joined date, contract expiry date and agent where published.
Market value is delivered as a history rather than a single current figure. Each valuation carries its amount, currency, the date it took effect and the club the player was at, so a valuation curve can be drawn across a career. The current value alone answers almost nothing; the trajectory answers most of what people ask.
Transfer records come as their own rows: date, season, club from, club to, transfer type covering permanent moves, loans, returns and free transfers, and the fee where it is disclosed. Undisclosed fees are recorded as undisclosed rather than as zero, which is a distinction that ruins aggregate analysis when it is lost.
Squad and performance data covers season, competition, appearances, minutes, goals, assists where published, yellow and red cards, and the squad the player belonged to.
Club records carry club identifier, league, squad size, average age, foreigner share, total squad market value and stadium where published, with the collection timestamp on every row.
Squad composition analysis follows naturally once players, values and contracts are collected together. Age profile against value, foreigner share, positional depth, and how these change season over season describe a club's strategy more honestly than any statement about it does.
Loan networks are a specific and often overlooked structure. Clubs that systematically loan players out, and the clubs that receive them, form a network that is visible only if loans are typed separately from permanent transfers rather than flattened into one movement column. For anyone analysing club groups or feeder relationships, that typing is the whole analysis.
Historical depth is one of this source's real strengths. Squad lists, transfers and valuation histories reach back many seasons, so a longitudinal panel is buildable as a one time job. Since past seasons never change, that backfill is collected once and never revisited, and the ongoing feed is comparatively small.
Cross referencing with match data is the standard extension. Valuations and contracts on one side, appearances and minutes on the other, joined on a normalised player identity, answers whether a valuation is tracking performance or reputation. That join needs careful player matching, which is the same entity work described on our live scores page.
Transfermarkt is the reference database for football squads, transfers, contracts and player valuations. It covers leagues down to a surprising depth, and its transfer records and contract expiry dates are used across the industry, in media and in scouting.
The market value is the number everyone comes for and the number most often misunderstood. It is not a transfer fee and not an audited valuation; it is an estimate produced through a community and editorial process. That does not make it useless, quite the opposite, because it is consistent, comparable across the whole database and updated on a regular cycle. But it has to be labelled for what it is, and any model that treats it as an observed price will produce confident nonsense.
Alongside valuations the site carries actual transfer records with fees where disclosed, loan arrangements, contract expiry dates, squad lists per season, appearances, minutes, goals and disciplinary records, plus agent and club affiliation data.
Structurally it is addressed by competition, club, season and player, with stable identifiers throughout, which makes systematic collection practical. Its robots file allows general crawling while disallowing specific clients, so ordinary polite collection is within the published rules; we pace requests accordingly.
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The single most common request on this source is the current market value of a squad, and it is the least useful thing the data can produce. A number without history has no direction, no context and no way to judge whether it is a rise, a fall or a plateau.
The curve is where the analysis lives. A player whose value has doubled over eighteen months and one whose value has halved may sit at the same figure today and represent opposite decisions. A squad whose aggregate value is drifting down while its average age rises is describing a problem that a single snapshot conceals entirely.
The contract expiry date is the second field that carries disproportionate weight. A high value player with a year left is a very different asset from the same player with four years left, and the entire economics of a transfer window turn on that gap. Collected across a league, expiry dates predict where the market will be active months in advance.
The third is the fee versus valuation comparison. Because both exist here, the difference between what a player was valued at and what a club actually paid, where the fee was disclosed, is a measurable premium. Tracked by selling league, by position and by age, it is a genuinely commercial insight and it needs both fields on the same row.
The fourth is the caveat that must travel with all of it: valuations are estimates from a community process. We label them as such on every row rather than in a footnote, because a model that treats them as observed prices will be confidently and invisibly wrong.
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 transfer windows and season changes, and repairs it before your valuation series develops a gap.
You see a sample first, in your format, over the leagues or clubs you actually track, with the valuation history already populated so you can judge the curve rather than a single number.
No, and this matters more than anything else on the page. It is an estimate produced through a community and editorial process, not an audited valuation and not a price anyone paid. It is consistent and comparable across the database, which makes it genuinely useful, but a model that treats it as an observed price will be confidently wrong. We label it as an estimate on every row rather than in a footnote.
Yes, as rows: amount, currency, effective date and the club at the time. The curve is the analysis; the current figure on its own answers almost nothing. Two players at the same value today, one doubling over eighteen months and one halving, represent opposite decisions.
As undisclosed, never as zero. Recording an undisclosed fee as zero drags every average downwards and the error is invisible in the output. Transfer type is also kept separate, so permanent moves, loans, loan returns and free transfers do not get flattened into one movement column.
Because they predict market activity. A valuable player with a year remaining is a different asset from the same player with four, and the economics of a transfer window turn on that gap. Collected across a league, expiry dates show where the market will be active months before it happens.
Many seasons, for squads, transfers and valuation histories. Because past seasons never change, the backfill is a one time job collected once and never revisited, which makes it the cheaper half of most projects here. The ongoing feed is comparatively small.
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
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