Big Data in football

If you ask any soccer fan what has been the biggest change in the sport in recent years, many would surely say the use of VAR technology – Video Assistant Referee to review different situations. that occur in a match such as goals, penalties and red cards. Others would speak of the increase in player salaries or the amounts paid by clubs in the transfer market. But few would mention the use of Big Data in soccer, a technology that is changing every aspect of the beautiful game – from the analysis of matches, the physical condition of the players, to the scouting of new signings.

In this post, we analyze the main uses of Big Data applied to football and we see some of the tools and applications most used by the most successful clubs today.


Today, thanks to tools such as Opta Stats, one of the tools that you will learn in the Master in Sports Big Data at the Real Madrid University School – European University, coaches can make decisions about tactics based on data instead of their own. opinions.

For example, in the case of a midfielder he has to create opportunities for the forwards. A big data specialist can analyze how many key passes to make, what part of the field to cover, and whether to play long or short passes. Similarly, Big Data analysis can be used to study opposing teams and discover weaknesses to exploit. Taking into account as an example the sides of Real Madrid – Marcelo and Dani Carvajal. Both are very good in possession and complete a very high percentage of passes. Big Data could show you that Marcelo takes longer to drop his passes. As a result, you could set up your team in a way that puts pressure on them more quickly and leads to more mistakes.


Previously, a player’s physical condition was primarily based on what a player communicated. The analyzes were started to be used by medical personnel and specific personnel to facilitate recovery, but if a player said they were fine, they were almost always chosen. Technology has changed this . The devices provide us with a quantity of data about each player on the team, whether it be a training session or a game. Wearable technology provides very detailed information about the player’s physical condition. How fast and intense are they running? Is your heart rate adequate? Are they sweating excessively? This information provided to the medical teams allows them to have very advanced knowledge to verify if the player is in his best physical moment.


The transfer market of the summer of 2020 ended with almost four billion Euros spent by the five main leagues in Europe. Most fans who see a player they like think, “My team should sign him.” This is how this market no longer works.

The clubs are building a dream squad. This means that they would have a team with specific qualities that can play in a certain way. The data currently available in the game allows you to search for players who meet a specific profile before sending scouts to observe them.

Not only does it provide a very precise approach to clubs, but it also saves time and money on personality tests and unnecessary headhunting activities. They will already have a pretty good idea of ??a player’s mindset and skill which should lead to a more successful integration into the team, with fewer problems.

Let’s take the example of Real Madrid. Sergio Ramos is coming to the end of a successful career and, sooner or later, he will have to be replaced. But how do you replace such a man? The answer is: you can’t. He is one of the, if not the best, defensemen in the world. What the Real Madrid hierarchy could do is review the key parts of Ramos’ game and focus its search on players who have similar abilities and who have the potential to fill this gap.


As we have mentioned before, over the last few years there has been an explosion in the use of Big Data in football. Clubs, organizations, scouts, fitness, and game analysis have several tools available to obtain data and make key decisions in every aspect of the game. Some of the data analytical tools, statistical languages ??and / or programming most commonly used today, among others, on the sports data that is generated (Mediacoach, GPS, …), would include:

  • Microsfot Power BI
  • Tableau
  • Qlik
  • Pentaho
  • Language R
  • MySQL

If you want to better understand how to use and manage these tools and applications, you can do so by studying the Master in Sports Big Data or with our offer of online innovations where you will find related trainings such as the Course in Artificial Intelligence and Sports Big Data or our Course in Sports Physiotherapy and Artificial Intelligence of the Real Madrid University School – European University.