13 Jun How to Train with Science: 4 Real-World Cases Applied in LALIGA
Science is Meant to Make Coaches Better, Not Replace Them
At LALIGA’s Football Intelligence Department, we believe data is a means—not the end. A means to help coaches make better decisions. Real decisions, on the pitch, with names, roles, and context.
The challenge isn’t having data. It’s having clarity. Because data without narrative, application, or tactical translation becomes noise—just another unopened file.
Coaches don’t want data. They want facts. And this leads us to ask:
What does a coach really want to know before a match?
How can data help prepare the match plan?
What should we train tomorrow to increase our chances of winning?
Let’s answer those questions as an elite coaching staff would.
Scenario: Next Match – El Clásico. We Are Real Madrid’s Coaching Staff.
We’re analyzing FC Barcelona to prepare our match plan. We’re not after universal truths—we want keys to win this specific game. We apply four models to detect weaknesses and patterns. Here’s what we find:
1. Space Occupation: Can We Exploit the Space Behind Their Fullbacks?
In Barcelona’s medium-high defensive block, wingers like Lamine Yamal and Ferran Torres stay wide, while fullbacks such as Alejandro Balde push high. In transitions after losing possession:
Is there space between centre-back and fullback?
Is there time to activate coverage?
Who recovers if De Jong is bypassed?
We observed recurring blind spots—interior channels exposed because Balde is advanced, pivot delayed, centre backs hesitant, and wingers not tracking back.
The question: What if we fix them wide to attack through the middle?
2. Defensive Structure and Pressure: Can We Hurt Them Between the Lines?
We examined how Barcelona’s midfield shifts when defending in a medium block. Players like Pedri and Frenkie de Jong tend to drift wide.
What happens around the top of the box?
Who steps up if someone receives between the lines?
And what if nobody steps?
We found a 15–20 m “pressure‑free zone” where, if a player like Jude Bellingham receives, he can turn, break lines, and deliver key passes.
The question: Can we lure them wide to open the centre?
3. Offensive Width: Is More Width Always Better?
In possession, Barcelona maintains width even when their depth decreases in poor performances.
Could opposing wingers become too detached from danger areas?
Could width without depth lead to sterile attacks?
Comparing successful vs failed possessions showed opponents regain easily when there’s no clear threat behind the defense.
The question: What if we deliberately invite them wide, but block their routes inside?
4. Defensive Transitions: Where Do They Break?
We used a Convex Hull model of Barcelona’s backline plus midfielders during defensive setup.
Do they stay compact during opponent transitions?
What happens if they lose the ball high up the pitch?
In several sequences, players like Koundé and Balde were separated from the defensive core. Their shift was slow, causing a broken defensive line.
The question: Can we exploit those moments with interior runs immediately after gaining possession?
Conclusion: From Data to Training
These models highlight concrete patterns. But the real value lies in asking the right questions, applying them in the right context, and translating them into actionable tactics.
Have we discovered something we can train tomorrow?
Have we created a competitive advantage?
Can we present this data in the coach’s language?
When the answer is «yes,» we’ve trained with both science—and awareness.