16 September 


   5 Minutes


Summer is long gone, and we in Shapelets are back with a new “Behind the Data Scientist” interview. 

Today, we inmerse ourselves in a passionate sector: Football! And we asked Daniel Ruiz Antequera to join us and explain a bit what are the main challenges for Data Science in the football and sports sector.  

Daniel is a Data Scientist & researcher in the Complex Systems Group (Football & Networks) at University Rey Juan Carlos. They work on football players evaluation and sport performance analysis with SD Eibar and CF Fuenlabrada football teams in Spanish Liga SmartBank.  

Watch the interview here below, don’t miss it!  


Could you please share your professional background with us?

Daniel Ruiz – Yes, of course. I am Physicist. When I finished the Degree, I studied a Master about Sports Analytics and Big Data. After that, I joined the research group “Football & Networks” in Rey Juan Carlos University.   

This season we are very happy because we have been able to collaborate with two Second Division professional football clubs such as Fuenlabrada and SD Eibar.   

With Fuenlabrada the collaboration has been especially close, with constant communication with the entire coaching staff, attending the matches… It has been a great experience.  


Daniel Ruiz

Why did you choose Data Science?  

Daniel Ruiz – Well, when I was finishing Degree in Physics, I was convinced that Data Science was my next step, a profession with a great future.   

In the last months of the course, there was a Big Data event in my university where I listened for first time that this methodology could be applied to Sports.   

I saw that there was the possibility of mixing data analysis with sport and it was clear to me.  

What do you like the most about your job? 

D.R.Feeling that you are helping to analyze and to solve problems that may have an impact on a football scoreboard. That feeling.  

Who is your role model?

D.R. Many names come to mind: Raúl Peláez (Ailite), Javier Fernández (Zelus Analytics), Mario Prieto (Atlético de Madrid), Jesús Lagos (Scout Analyst), Sergio Llana (FC Barcelona), Manu Heredia (Be Soccer), Laurie Shaw (Manchester City), Ian Graham (Liverpool), David Sumpter… But I would have to choose only one, I would say the role model that I have closer, Javier M. Buldú, who is a scientist working on football analysis in Football and network analysis group with me. 

What are the main challenges for Data Scientists?

D.R.In Sports Analytics world I think there are a lot of challenges for Data Scientists, but the main challenge I think it’s getting to speak the same language as the coaches when carrying out the analysis and that data help them to make decisions. 

What do you value the most in a data analysis technology or software?    

D. R. – Speed. Speed is what I value the most. In football time is gold.  

What are the main issues when communicating insights to the business area and stakeholders?

D.R.In my opinion, the key issue is choosing the information correctly, differentiating what is useful from what is interesting so as not to saturate them with too much information.. 

What approach do you follow when working on a data analysis-based project?    

D.R. – I work to solve coaches problems or concerns, so my first step is to formulate a specific question and from there, with data and programing, I try to arrive to a solution or to give them relevant information about the question.  

What skills does a data scientist need for 2022?  

D.R.For 2022 and for the next years…I would recommend being patient and keeping on learning new methodologies. Data analysis is going to spread along Spanish football but the transition will be step by step. In a few years, data scientists will be part of the technical staff of any club. 


Thank you Daniel for taking the time to chat with us and for sharing your experience!  

This interview really helped us to deep into the big challenges data scientists and data professionals face in their daily work. Moreover, it was very helpful to analyse the issues that rise then communicating insights to stakeholders.  

As Daniel points out, it is key to understand your audience and the main motivations of a specific sector. Speed ​​in this case and therefore efficiency is key in this industry (as in many others). The use of tools that help us to be more effective and efficient is the key to developing solutions for companies.

Do you want to participate and share your experience in “Behind the Data Scientist”? Contact Shapelets at and tell us your story!   

Our goal is to help data scientists and data professionals build a strong personal brand and advance their careers. Join and start now!

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