This special episode of FSI Talk, recorded at the Benito Villamarín Stadium during the VII FSI Conference, was moderated by Fabio Nakamura, professor at Universidade da Maia and a leading expert in football performance science. With a strong background in research and applied work with clubs and national teams, Nakamura guided a focused conversation on one of the sport’s biggest challenges: injury prediction.
He was joined by two top-level experts: Chris Carling, Head of Elite Performance at the French Football Federation, and Chris Barnes, Sports Scientist at UEFA and consultant at Brøndby IF. Together, they offered a critical and evidence-based view on data usage, load management, and the real limits of injury prediction models in professional football.
Let’s Stop Talking About Injury Prediction
One of the most impactful moments came early in the episode: we should stop talking about injury prediction altogether.
“It doesn’t work like that. It’s multifactorial.”
In recent years, models such as the acute: chronic workload ratio gained popularity as tools to “predict” injury risk. But as Chris Barnes and Chris Carling emphasize, injuries result from a complex combination of factors — physical, psychological, contextual, and even genetic.
No single metric can foresee when a player will get injured. Instead of prediction, the focus must shift to managing risk and preparing for variability.
Internal Load vs External Load: Both Matter
Another key point discussed was the imbalance between external and internal load monitoring. Many clubs rely heavily on GPS data, distances, speeds, and sprints — the external load — without giving equal attention to internal responses like heart rate, perceived exertion, and neuromuscular fatigue.
“The internal load tells us how the player is truly responding.”
Combining both gives a more complete picture of the player’s status. External load is what we impose; internal load is how the body reacts. Ignoring either dimension can lead to poor decisions and increased injury risk.
Load Management is Highly Individual
Even more critically, load doesn’t affect every player the same way.
“Same load. Different risk.”
Two players with the same age, position, and physical profile can experience very different levels of injury risk, even when exposed to identical workloads. That’s why individualization is essential in load management.
This is not just about collecting data — it’s about interpreting it in context. History, training age, sleep, stress, match demands, and even travel can influence a player’s capacity to handle load.
Coaches Know More Than You Think
A powerful moment in the episode addressed a common bias in sports science: underestimating coaches.
“Some coaches just know. They see a player and feel something’s off — before the data shows anything.”
The discussion highlighted the value of coach intuition, especially in experienced professionals who have spent years with the same group of players. The French Football Federation is even exploring ways to quantify and research coaching intuition, validating it as a legitimate piece of the injury prevention puzzle.
This doesn’t mean data becomes irrelevant — it means intuition and science must coexist, not compete.
Technology Is Not the Problem — Systems Are
Despite all the advancements in tracking technologies, one of the biggest bottlenecks remains: data accessibility and integration.
“It’s not just the tech — it’s how we deal with the data.”
Clubs often face challenges with:
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Accessing raw datasets
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Identifying and removing outliers
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Poor integration between GPS platforms and AMS (Athlete Management Systems)
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Broken APIs or data loss during transfer
The takeaway? Improving workflows, data pipelines, and software communication is just as important as buying the latest hardware.
Is AI the Future of Injury Prevention?
Artificial Intelligence came up as a promising yet misunderstood topic. While AI won’t “predict” injuries anytime soon, it can help in many other ways:
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Automating pattern detection
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Identifying risk clusters
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Improving workload planning
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Reducing time spent on repetitive analysis
“AI won’t replace coaches or sports scientists — but it will save them time.”
The role of AI should be to support human decision-making, not replace it.
What the Modern Sport Scientist Needs
Toward the end of the episode, the focus turned to the evolving skillset of today’s performance staff.
Modern sport scientists are well-versed in data, coding, and physiology, but often lack the soft skills that make them effective in real-world settings:
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Communication with coaches
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Teamwork across departments
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Leadership under pressure
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Flexibility and critical thinking
They know R and Python, but can’t always talk to a coach.”
This highlights the growing importance of well-rounded professionals, capable of integrating technical expertise with emotional intelligence and collaboration.
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