How AZ Alkmaar and VVV-Venlo Recruit with Player ID
October 15, 2026 · Read Time: 6 mins
Highlights from the Teamworks breakout session at Training Ground Guru Live, 29–30 September 2026
Two Dutch clubs. Two very different histories with data. One shared approach to the transfer market.
At Training Ground Guru Live last month, Teamworks hosted a breakout session bringing together Merijn Zeeman, CEO of AZ Alkmaar, and Jan Peters, Managing Partner at BTPC Sports and part of the VVV-Venlo shareholder group, alongside Jacob Dunlop, Football Data Expert at Teamworks. The brief was simple: walk the room through real decisions from the summer transfer window and show how Teamworks’ pioneering player projections shaped it.
The timing couldn’t have been better: AZ Alkmaar was sitting top of the Eredivisie (currently third), and VVV-Venlo held third place in the Eerste Divisie.
Both clubs are using Teamworks Player ID to underpin their recruitment, but from opposite ends of the adoption curve: AZ have built their player trading ecosystem around Player ID for a decade, while Venlo came on board this year and benefited from relying heavily on the insights immediately as they went straight into the summer window.
Two clubs, one question: will this player become what we need?
Teamworks Player ID, built by a team of the world's leading sports data scientists, projects a player's future performance through advanced data analytics. The model draws on a player's full performance history, adjusts for age, position, league strength and minutes played, and forecasts how he will perform in the future. Both speakers came back to the same point again and again: the value isn't in knowing how good a player is today, it's in understanding how good he is likely to become and how certain you can be about it.
That framing matters differently depending on where you sit in the football pyramid. For AZ, it's about identifying players whose trajectory fits a first team competing at the top of the Eredivisie and in Europe. For Venlo, it's about finding undervalued talent that can grow into a first-team contributor quickly and then, potentially, be sold on to the right buyer.
Merijn Zeeman: how a projection put a Venlo winger on AZ's radar
Zeeman's summer transfer window highlight was a pursuit rather than a signing. AZ identified Björn van Zijl, a 21-year-old right winger at VVV-Venlo, as a target because his Player ID projection lined up with the profile the club was searching for.
Jacob Dunlop, Solutions Engineer at Teamworks, explained the chart on the screen: “Van Zijl's Player ID profile shows a player already performing around the 90th percentile for his position in the Eerste Divisie, with a projection that holds in the high-70s to 90 range through to his late twenties.” In a market where his Transfermarkt valuation sits at roughly €300k, that gap between public valuation and projected output is exactly the kind of signal AZ have spent ten years training themselves to act on. Dunlop added that the chart is only the starting point: the real value comes from drilling into the data underneath it, which lets clubs like AZ understand not just what a player's projection is, but why.
Zeeman was candid that the deal didn't land, and equally clear that this didn't change the process. AZ's decade with Player ID has produced a shared language between the boardroom, the sporting department and the scouts. When a projection matches the brief, the decision is made quickly.
Jan Peters: finding Éanna Clancy in the League of Ireland
Peters told the story of Éanna Clancy, a centre-back Venlo signed from UCD in the League of Ireland this summer. Clancy arrived with a modest €50k Transfermarkt valuation and a Player ID history that most scouting departments would have found hard to read: He started strongly, hitting the 90th percentile in his debut season. After a small dip in year two, he reached the 99th percentile in each of his last two seasons in Ireland.
What Player ID gave Venlo was a way to look past the noise. Dunlop explained: “The projection from age 23 onwards settled around the 90th percentile for a right centre-back in the Eerste Divisie, with a reasonably tight confidence band.” In other words, the model saw a player whose underlying level was consistently high even when his season-to-season numbers weren't.
The result has been immediate. Clancy has gone from an Irish side to a starter for a Venlo team sitting third in the table within a matter of weeks. As Peters put it, the speed of his integration is exactly what the projection suggested was possible, and exactly what a scouting process based on highlight reels and one-season stats would have struggled to justify.
Recruit to sell: Venlo's market-led model
The most strategic part of Peters' talk was about what happens after the signing. For a club at Venlo's level, recruitment is only half the business model. Every player brought in needs a plausible path to being sold on, and to specific markets where Venlo has identified demand.
That changes how the club reads a projection. It's not enough for a player to be good for Venlo; his trajectory has to make him attractive to the leagues and clubs Venlo is targeting as buyers. Player ID lets Venlo test that in advance: does this player's projected level, at this age, at this position, match what the target market is paying for?
Venlo has only been working this way for one window, but the early results suggest the model is already doing what it was brought in to do.
Ten years versus one summer: what the contrast taught the room
The pairing of Zeeman and Peters gave the session an unusual depth. AZ represents what a mature, decade-long relationship with player projections looks like: embedded in the culture, trusted across departments, and fast to act on. Venlo represents the first chapter of that same journey, where a single window's worth of decisions is already visible in the standings.
The lesson that landed with the room wasn't about the technology on its own. It was that data-driven recruitment is a habit, and the sooner a club starts building it, the sooner projections turn into points.
There was a second point underneath it, one both clubs made clear: football clubs are not software companies. Building a data platform in-house is only the start. The models have to be maintained, validated against the market and continually evolved as the game and the data change, which means standing teams of engineers and data scientists whose work never finishes. Player projection is one example, but the same logic applies across the stack.
Every hour and pound spent on that is an hour and pound not spent on what a club actually needs: better recruitment, stronger player development, and the people to act on the insight. For AZ and Venlo, working with a specialist whose sole focus is sports technology means getting that depth without the overhead, with the platform improving continuously in the background while the club's energy goes into the football decisions the data informs. For a club of Venlo's size, the opportunity cost is the difference between competing on insight and being priced out of it.
Teamworks Player ID helps clubs identify, project and develop players with confidence. To learn how AZ Alkmaar, VVV-Venlo and clubs across Europe are using it, get in touch with our team.