When Liverpool FC became Premier League champions in 2020 (and now in 2025), fans worldwide celebrated a long-awaited victory. But while the spotlight was on players like Mohamed Salah and Virgil van Dijk, the real story behind the scenes was about something far less glamorous — data.
That’s right. Data played a massive role in helping Liverpool break records, win matches, and dominate the football world. Behind every goal by Mohamed Salah or every defensive block by Virgil van Dijk was a layer of analytics — from opponent analysis and player recruitment to training loads and match strategies.
Liverpool used advanced algorithms, tracking technology, and predictive modelling, led by William Spearman, Liverpool’s director of research, to make smarter decisions than their rivals.
So when rival manager Mikel Arteta claimed, “Winning trophies is about being in the right moment in the right place,” the statement oversimplifies reality. Success at the highest level isn’t just about chance or moments of brilliance. It’s about preparation meeting opportunity, and in Liverpool’s case, that preparation was powered by data.
By investing in sports science, machine learning models, and performance analytics, Liverpool created those “right moments” repeatedly and intentionally. In modern football, data is a game-changer.
And here’s why this matters to you, especially if you’re a student thinking about your future: data is changing everything. Whether you dream of working in sports, fashion, business, or engineering, data science is becoming one of the most powerful skills you can learn today.
Let’s look at how Liverpool used data to rise to the top — and how students everywhere can apply the same principles to build smarter, more successful careers.
Liverpool’s winning formula: data + football
To understand how Liverpool’s winning era came to be, we must return to Jürgen Klopp’s early years at the club.
Most football fans think success only comes down to the coach, the players, and maybe a bit of luck. But Liverpool took a very different approach under Jürgen Klopp. Behind the scenes, the club brought in a team of data scientists led by Dr. Ian Graham (now replaced by Spearman), a physicist from Cambridge.
Instead of relying on gut feelings or scouting alone, Liverpool made decisions based on data models. These models crunched numbers on everything from player movements to match outcomes.
Here are a few ways Liverpool used data to their advantage:
- Player recruitment: They used expected goals (xG), sprint patterns, and pressing stats to sign players who fit Klopp’s high-energy system. That’s how they found Salah, Sadio Mané, and even goalkeeper Alisson Becker.
- Injury prevention: By using AI and military technology to track training data and player workloads, Liverpool could predict when someone might get injured and rest them before it happens.
- Match strategy: They used match data to plan how to break down opponents, which zones to press, and when to make substitutions.
The result? In just a few years, Liverpool has won it all: the Premier League, Champions League, FA Cup, Carabao Cup, Super Cup, and the Club World Cup. And much of it came down to one thing: making smart decisions using the power of data.
Liverpool’s English defender Trent Alexander-Arnold wears analytical data headgear during a training session at their training ground in Liverpool. Source: AFP
What is data science and why should you care?
Now, you might be thinking, “I’m not trying to run a football club, why should this matter to me?”
Good question.
Data science is the ability to collect, understand, and use information to make better decisions. It’s not just for coders or statisticians. It’s a skill that’s becoming useful in every industry. The US Bureau of Labour Statistics show that employment in this field is expected to grow 36% from 2023 to 2033, much faster on average compared to other occupations.
Here’s what data science involves:
- Finding patterns in large amounts of data
- Making predictions based on those patterns
- Using tools like Python, Excel, SQL, or AI models to analyse and present results
And it’s everywhere.
In business, companies use it to understand what customers want and where to advertise. In healthcare, doctors use data to predict illness and improve treatments. In finance, banks use it to detect fraud or manage risks. In education, schools use data to track student progress and personalise learning.
And of course, in sports, it’s being used to transform everything from tactics to ticket pricing. No matter what career you aim for, understanding how to work with data will give you a competitive edge.
Data science is changing the way we solve problems by giving us clear answers from lots of complex numbers. Source: AFP
Where to study and build data skills
1. Choose a university with strong data-focused programmes
Look for universities that offer specialised degrees in Data Science, Business Analytics, Artificial Intelligence, or Computer Science. But even if your major is something else, check if your university offers data-related electives or modules.
Examples:
- Carnegie Mellon University (US): Offers interdisciplinary data science degrees, including a Bachelor of Science in Statistics and Machine Learning, that mix tech, business, and social impact.
- National University of Singapore (NUS): Ranked among the top in Asia for data science and analytics programmes, such as Bachelor of Science (Honours) with Major in Data Science and Analytics.
- ETH Zurich (Switzerland): Offers a BSc in Computer Science with strong data science and AI components.
- Jheronimus Academy of Data Science (Netherlands): The BSc in Data Science takes a multidisciplinary approach, combining technical expertise with insights into the legal, social, ethical, and business dimensions of data.
These universities also offer master’s programmes in data science.
2. Take online courses to build practical skills
Even if you’re not studying data science full-time, you can learn the basics online. Platforms like:
These courses help you gain certificates to add to your CV or LinkedIn profile, and they are especially useful if you’re looking for internships or remote roles.
Picking up data skills now can open doors to many future careers and give students a strong advantage. Source: AFP
3. Join university clubs, hackathons, or student projects
You can apply your skills in:
- Case competitions (business, sustainability, sports, etc.)
- Hackathons (coding and analytics sprints — great for networking too)
- Student research projects (offer to help a professor with data collection or analysis)
- Clubs focused on data, tech, or entrepreneurship.
4. Learn the tools employers want
No matter what you study, get comfortable with these common tools:
- Excel – Still used in almost every job.
- Python / R – Great for data cleaning, analysis, and machine learning.
- SQL – Helps you pull and manage data from large databases.
- Power BI / Tableau – Tools for building interactive dashboards.
- Google Sheets & Forms – Handy for small-scale data collection and analysis.
Most of these have free student versions, so you can start practising anytime.
Data science can teach students how to think critically, ask the right questions, and find answers backed by facts. Source: AFP
Hear from students who’ve explored and applied data science
Haysam Shakeel, First Team Head Coach at the University of Liverpool Football Club
“Today, data is used everywhere: recruitment agencies, scouts, analysts — all rely on data. We also use a lot of data in performance analysis, which involves data visualisations and extensive data collection. For example, if we feel we’re weak at defending crosses, one week we’ll analyse from which areas of the pitch the crosses are delivered and how many come from each area.
We review the numbers, review visual data, and decide what to focus on in the next training sessions. That’s an example of how we use data. If you like numbers and data, I think that’s definitely a career path one should consider.”
Faw Ali, Founding Full Stack Engineer at Cleve.ai
“Everyone talks about AI and machine learning, but companies are struggling just to get their basic data infrastructure working. I see businesses with terabytes of information they can’t even access properly or make use of. The real opportunities are in building the pipelines that make data actually usable —data engineering.
Most of what companies collect is completely worthless without someone who knows how to dig and pull out what actually matters. The valuable data lives deep — transaction logs, user interaction patterns, sensor feeds — while everyone else makes decisions and fights over the surface-level datasets that are already cleaned and processed. Data engineering is becoming the hottest field in tech, and its unique skillset is exactly what companies are desperately seeking right now.”
Gülnaz Çavuşoğlu, Junior Data Analyst at 433
“I believe data analysts are becoming more involved in shaping user experiences, especially with the growing focus on personalisation, which is a hot topic right now. It’s no longer just about providing reports and dashboards; it’s more about collaborating with product and marketing teams to improve content and make smarter business decisions in real-time. I think that’s the direction data analysts are moving toward.”
Learning data skills is a great way to prepare for many different jobs and stay ahead in today’s world. Source: AFP
How data skills apply to every job
No matter what career path you’re pursuing, that data degree or elective you took can give you a serious advantage. You don’t need to become a full-time data analyst, but being able to read, interpret, and use data can help you make smarter decisions, impress employers, and stand out in a competitive job market.
Let’s break it down by field:
- Marketing: Data helps track which ads are working and which products customers love. You can use tools like Google Analytics or social media insights.
- Hospitality: Hotels and airlines use booking trends, guest reviews, and seasonal data to adjust prices and improve services.
- Engineering: Predict when machines will break down before they do. That’s called predictive maintenance, and it saves companies millions.
- Fashion: Brands use search trends and purchase data to plan collections and avoid overproduction.
- Entrepreneurship: You can test business ideas using customer feedback and sales data. It’s like having a compass instead of just guessing.
These are the same principles Liverpool used. Facts win, as should you in your future career.
