With the evolution of the modern game, there seems to be a permanent place for data and numbers in football.
Not in a futile way, but in a way that empowers scouts, analysts, coaches, and decision-makers across the game.
The football world is waking up to the fact that properly used, data is not about replacing the human eye, but about complementing it.
It helps practitioners work more efficiently, challenge cognitive biases, and make smarter decisions.
The benefits of data analysis are there, although it also forces cultural change within football clubs.
It requires coaches, scouts, and executives to be open to new ways of seeing the game.
It also demands much more from data professionals than technical skills; communication, translation, and trust-building matter just as much.
To better understand this world, Total Football Analysis had the great pleasure of speaking with Jan Van Haaren, Head of Football Insights and Innovation at Club Brugge.
He combines a lifelong love of football with a career in technology, showing how passion for the game and data science can really work together.
For football fans and for anyone dreaming of a career in football analytics, Jan’s journey from the beginning to where he is now with Club Brugge is inspiring because it proves you can turn your passion into a profession.
His journey is also a practical one, because Jan shares lessons on skills, communication, and how to succeed inside a football club like Club Brugge.
From The Pitch To The Algorithms
Jan’s story, like so many in football, begins on the pitch.
TFA: When did you first gravitate towards football? Were you a football player in your youth?
Jan: “I actually still am, although I’m 37 now.
I’ve been playing football since the age of six, roughly 30 years.
Football has always been a passion of mine.”
Where Jan differs from many lifelong football lovers is that his other passion was coding.
TFA: How did the machine learning and analytics part enter the picture?
Jan: “It came quite naturally.
My background is in informatics and computer science.
When I was in the final year of my Master’s, I had to do a thesis.
I did it about predicting outcomes of football matches using data and machine learning.
I was in the computer science programme with a specialisation in artificial intelligence and machine learning.
So I’ve always been interested in data, machine learning and AI.
For me, it was the ideal opportunity to combine two of my passions, football and data.
I have also been a data editor for EA Sports FIFA/EA FC.
So, football data has always been a passion of mine; that’s where machine learning came into the picture.
Then I continued to work on it during my PhD.
Initially, I did more fundamental machine learning research, but as time went on, I also had more opportunities to work on practical applications, particularly football.
That’s kind of where it all started 13–14 years ago now, around 2011.”
Lessons From EA, SciSports & Academia
TFA: Before joining Club Brugge, you were at EA Sports and SciSports. What do you think were the biggest lessons you gained from them?
Jan: “I think one important thing I’ve learned is that a lot of data scientists or aspiring analysts focus on acquiring technical skills: they want to learn machine learning or statistical techniques, they want to learn how to program and so on.
But there are other very important skills too.
Communication skills, for instance.
Being able to interact with practitioners like a football coach, scout, or a technical director who is not tech-savvy or data-savvy.
That’s an extremely undervalued skill, and something I learned along the way.
During my PhD, I think students often assume that everyone else knows more about their research than they do: that’s not true.
Your research is specific; you are the expert.
Yet, people explain things in a way that is too complicated for non-experts.
The same happens in football: a data scientist comes into a club and makes things too technical for coaches and scouts.
They don’t speak the same language.
That’s really an undervalued skill and a very important one for success in this industry.
Data scientists tend to convey as much information as possible because they’ve researched a certain topic and learned a great deal.
But they fail to get to the core message. To the practitioner, it can be overwhelming.
The job is to get down to the core idea straightforwardly.
The math and models behind it are important, but coaches don’t need to see them.
What they need is the finding, and maybe a clip of video to support it; that’s what matters.”
Building From Scratch At Club Brugge
TFA: Take me back to 2021, when you arrived at Club Brugge as a data scientist. What was the setup like when you first walked into Club Brugge?
Jan: “When I walked in, there was virtually nothing in terms of data.
I was the first data scientist at the club, at least in the sports department.
So I really had to build things from the ground up.”
TFA: What are the challenges in building something from the ground up?
Jan: “There are two sides: technical and cultural.
Technically, you require access to data providers and infrastructure to ingest, process, and store the data.
But that’s not rocket science; there are off-the-shelf solutions.
Football data isn’t unique; it’s spatiotemporal data, lots of fields work with it.
The cultural side is far harder.
You need to convince people that the information you extract is useful and has value.
In football clubs, processes are in place for a long time.
Introducing data means changing processes, and changing processes is very challenging; therefore, you need to demonstrate value.
For instance, video analysts spent hours manually tagging actions on the ball.
We can just get that from StatsBomb, which is now one of our providers.
Showing them that you can save time and provide accurate information wins buy-in.
That’s what we did: automate, show novel insights, and help them achieve more.
That’s how you build acceptance.”
Becoming Head Of Insights & Innovation
Over time, more people at the club wanted Jan’s help at Club Brugge, from the first-team analysts to the youth teams, to the scouts.
Jan: “Everyone wanted a bit of my time.
I couldn’t handle it alone, so we hired another data scientist.
My role evolved into head of data, then head of data and technology.
At the start of 2025, Club Brugge set up an internal innovation department.
Before, some people in the organisation worked on long-term innovation, but scattered across business and sport.
In busy periods, they got dragged back into day-to-day.
So we centralised them: one department focused on innovation.
Now I oversee the football data science team and the football insights & strategy team.
These teams are separate from daily operations, free to build new metrics, models, and long-term projects.
For example, metrics to measure how well we’re executing our game plan, or models of how football and the transfer market are evolving.
My main role is managing the two teams and building the strategy across the club.”
The Club Brugge Philosophy
TFA: How would you describe Club Brugge’s philosophy regarding data and insights?
Jan: “Our mission is to become a 100% evidence-based organisation.
In the ideal world, we base all our decisions on concrete evidence.
For me, football has become an information game.
The clubs that have the best information, as quickly as possible, should be capable of making the best decisions.
This especially applies to scouting.
There’s almost no time anymore; everyone is chasing the same talents.
The challenge is to get them on your radar as soon as possible.
Data plays an important role, because nowadays we can purchase data for virtually any league in the world, even youth competitions like U19 or U17.
The trick is: even when players haven’t had many minutes, how do we decide they’re worth looking into?
We don’t have the capacity to scout every 16-year-old.
There’s a huge pool of possible players.
Data helps reduce it to a manageable few thousand, allowing for truly thorough scouting.
That’s where innovation is: being cleverer than others.”
Daily Workflow & Hybrid Model
TFA: What does a day in your life look like at Club Brugge?
Jan: “There’s a lot of variety.
Sometimes, though less now, I still get to write code: implement a metric, work on a model, do a simulation.
Most of the time is spent engaging with stakeholders, such as scouts, analysts, and the sporting director.
There are many types of data roles.
On one end, there are engineers building pipelines.
On the other, analysts embedded with a coach.
In between are the data scientists.
Our model is hybrid.
Engineers and scientists are centralised, building infrastructure, metrics and models useful to everyone.
Then analysts are embedded within their departments: the first team, U23s, U18s, scouting.
They are the end users.
My role is to facilitate: to ensure the analysts can leverage the tools and ensure they impact decisions.”
Data Vs Eyes
TFA: How do you balance what can be seen on the pitch with the numbers?
Jan: “That’s the big challenge.
Our philosophy at Club Brugge is to always marry data and video from the start.
What you see elsewhere is siloed: video flow and data flow, only combined at director level.
We don’t believe in that.
We want to combine data and video immediately.
Data tells us which video to look at, video provides context for the numbers.
If you see just a number, it’s hard to interpret.
If you see just video, you miss the measurement.
Together, they accelerate adoption inside the club.
Coaches are not presented numbers; they receive findings with supporting video evidence.
Whether it’s from data or video doesn’t matter, the finding matters.”
Challenging Human Bias
TFA: Is there ever a situation where numbers reveal something unexpected that scouts or coaches may not notice?
Jan: “I don’t have concrete examples now, but the key challenge in scouting and analysis is eliminating cognitive biases.
Humans are susceptible.
A scout might believe a tall player is very strong in aerial duels.
However, the data shows that his aerial win rate is average.
That forces a review.
Maybe the player looks dominant, but in reality, they aren’t.
Perhaps a short player excels at aerial duels due to their timing and jumping ability.
So data challenges beliefs, forces rewatch, and reduces bias.
Eliminating bias is impossible, but we try.
It’s one of the main values of data, especially in scouting.”
Handling Congested Schedules
Club Brugge regularly competes in European competitions.
At the time of this writing, Club Brugge are beginning their UEFA Champions League campaign against AS Monaco.
TFA: How do you structure turnaround time for analysis when Club Brugge balances domestic and European competitions?
Jan: “I’m not involved in day-to-day analysis myself, but obviously more games mean more work for analysts.
We’ve built a workflow.
For opposition analysis, we start on matchday -12, around two weeks before.
That gives us time despite the congested schedule.
We begin with an automatically generated data report.
It’s the same for every opponent.
It flags areas that stand out.
Then, analysts dig deeper.
Sometimes there’s little complexity, sometimes more.
Starting early gives us flexibility.
The same applies to internal analysis; after every game, we review the execution of the game plan and principles.
However, we also conduct periodic reviews, typically during international breaks.
That’s after 6–7 league games, maybe some Champions League.
International breaks are ideal: larger sample and coaches have spare time, with many players away.”
Continuous Learning
TFA: In your line of work, how important is continuous learning? Constantly retraining yourself, updating models?
Jan: “It’s extremely important, especially in this rapidly evolving field.
Data becomes more sophisticated daily.
In the 1990s, we had very basic positional data.
Now we have full-body pose data collected at 50 frames per second in the Champions League by Hawk-Eye, or in the Premier League by Second Spectrum.
That creates possibilities but also challenges: volumes are huge.
So you must always improve in analysis, in storage, in large-scale processing.
You need to stay up to speed with the latest techniques.
Personally, I publish an end-of-year review on my blog where I highlight the research papers and posts I found most interesting.
For me, it’s a way to keep learning and ensure I don’t miss developments in the field.”
Working With Coaches
Since Jan arrived, Club Brugge has had five managers (perhaps six).
TFA: Have most of them been receptive to data?
Jan: “Yes, most were open.
But it doesn’t matter much.
We don’t present raw data to coaches.
We present concrete findings with video support.
Sometimes it’s a number, usually it’s an insight.
Coaches care about usable evidence, not its source.
On a personal level, I see them daily, but professionally, most communication goes via the analysis team.
My team works with analysts and coaches.
It’s a two-step process.”
Advice To Aspiring Data Scientists In Football
TFA: What advice would you give young data scientists, analysts, or engineers who dream of working in football?
Jan: “I touched on it earlier: don’t focus only on technicals.
Everyone applying can code and build models.
What varies hugely is communication.
Can you explain to a coach in their own language?
That’s what matters.
When I hired for a football data engineer and data scientist recently, I paid huge attention to this.
Everyone had the technical basics.
But not everyone could communicate or had domain knowledge of football.
Those who did stood out.”
Ex-Pros Within Data Teams
In recent years, there’s been plenty of debate around what ex‑players can do once they stop playing.
We often hear discussions about players stepping into coaching, and there have even been suggestions to encourage them to become referees or get more involved in officiating.
So, we put a different idea to Jan.
TFA: Would having an ex-pro within your department add value?
Jan: “Interesting idea.
Honestly, I don’t think it’s extremely helpful.
Sometimes, it helps that people haven’t been pros; they look without career-long biases.
My experience is that ex-pros are often more reluctant to embrace data.
If you find a former player who is open and understands the possibilities and limitations of data, it could be useful.
But I don’t think it’s necessary, and it’s not a huge help by default.”
The Future Of Football Analytics
TFA: What excites you about the future of this field?
Jan: “I wonder, where is the limit?
How far can we go?
It feels like we’re just scratching the surface.
Pose data is extremely sophisticated, but no one really knows what to do with it yet.
We’ve been experimenting, like predicting outcomes of dribbles based on approach.
But that’s basic.
Where will it lead?
That’s a blind spot, and it excites me.
Also, beyond data.
Technologies like virtual and augmented reality.
There are huge possibilities there, too.
Exploring what is possible with new technologies and data—that’s what excites me most.”
Personal Memories
After all the talk of models, metrics, and machine learning, we decided to wrap things up on a lighter note, fewer graphs and more goals.
I asked Jan about the first football memory that really stuck with him.
TFA: Finally, what is the first match you vividly remember from your childhood?
Jan: “I think Belgium at the 1998 FIFA World Cup in France.
I was 10.
I also remember my local club, Westerlo, playing Anderlecht, Club Brugge’s rivals.
Westerlo trashed them 6–0 and 6–1.
As a Club Brugge fan from age five, watching Anderlecht being demolished was unforgettable.”
Conclusion
Jan Van Haaren’s journey shows how football and data can work together, from starting with bare-bones setups to shaping long-term strategy at Club Brugge.
Data is here to stay in football, and it will guide the game’s future.
For anyone aspiring to be a football data analyst, it is essential to develop strong expertise and stay current with new advancements.
But it is just as important to speak the language of football and connect with people.
