Sports competition has changed a lot in recent years and still has a lot to evolve. The sports analysis market moved more than 920 million euros in 2020, according to Grand View Research, and it is estimated that by 2025 it will rise to 3,380 million, according to "El País". For these calculations, it is taken into account that big data in sport is used above all to obtain intelligence on rivals, put together specific strategies and attract talent.
They are medium-term estimates, but today the collection of data on sports performance and its treatment has become key. We could say that: “Big data and advanced analytics are revolutionizing the world of sports and the profile of the data analyst with big data is becoming essential in many sports entities, always looking for improvements and competitive advantages”.
But… What is this revolution about? The introduction of big data in football helps to enhance the abilities of the players and improve their performance, providing the technical staff with the opportunity to predict and make relevant decisions regarding their line-up. In addition, it is undeniable that its application has had an impact on information management, allowing teams to carry out exhaustive evaluations like never before, as well as helping to prevent injuries.
In this way, we can obtain more complete sports management models thanks to the application of big data and data analysis. as well as superior performance in soccer teams. This is forcing future football research to take a more comprehensive multidisciplinary approach, with the coaching staff primarily including performance analysts, exercise scientists and biomechanists, in order to make sense of complex data sets. Therefore, future collaborations between data analysts and sports will be the key to applying these approaches more efficiently. And, the increasing reliance on more advanced data analysis techniques poses new challenges for future sports scientists.
So what does he refer to? Big data in soccer? The accumulation of a large amount of data or information, as well as the procedures used to find repetitive patterns in the process of analyzing said data. At football This term is included within the field of information and communication technologies (ICTs).
Big Data is the fact that everything we do leaves a digital trail (or not) that can be collected, transformed and analyzed to make decisions.
Contents
Why use Big Data in Soccer?
Know each other and know the rival
One of the aspects that has been key since the beginning of big data in sport is creating a data-driven strategy. “Multiple options are presented related essentially to the analysis of the own team and the rival, commonly known as the competitive environment. In this sense, multiple metrics are used, some more personalized and others less, which serve to objectively describe game models, systems, occupation of spaces and, of course, characteristics of squads and players”, points out David R. Sáez, CEO of Sports Data Campus.
The CEO of Sports Data Campus highlights that video analysis is gaining more and more interest. There are many reports that rely on viewing video images to draw conclusions. New image analysis technologies make it possible to obtain increasing information from these documents.
Daniel Pérez, football analyst, focuses on individual sports, which are usually easier to analyze. “In cycling, for example, [big data] is widely used to measure individual performance live. In this way they can try to predict the efforts that the cyclist needs to make at each moment to achieve a certain performance. Cycling performance is somewhat simpler than soccer. For that reason it is much more effective today.”.
Avoid player injuries
The care of athletes and athletes is another important application of data analytics. "It is used in order, not only to extend your sports career, but also to minimize the risk of injury," says Sáez, referring to soccer in this case.
Here, the information comes from very diverse sources, which influence the health of the athlete. “Sports, biometric, physical, genetic, chemical data are collected. And all of them are used to design training models with personalized load management, with the aim of preventing injuries, especially those caused by muscular overload”.
Talent acquisition
Both in football and in other sports, the role of advanced analytics in transfers stands out. In the king of sports, it is currently the main application: attracting talent.
In football you can have a player with certain characteristics that has worked well in your team, that is, in a context where he has been related to 10 other players on the field, not without pointing out that the rival also plays a role that deserves separate analysis . Big data helps you find that right player for whatever the occasion, using the right variables.
After collecting the information about the game and the performance of the players, it must be processed. This is another crucial part, as it defines what matters and to what degree. At the same time, this phase is where the weight of factors that are not so fundamental in the final result is moderated.
Data cleaning and treatment is usually done through programming languages such as R or Python. PySpark stands out for the design or use of machine learning models and algorithms, focused on the design of analytical, predictive or artificial intelligence models.
Display
Once the treatment has been carried out, depending on the user who is going to work with the results, they are presented in one way or another. As for the design of dashboards or presentation of reports, there are multiple tools. The part known as 'visualization' is key for the process to be efficient.
On many occasions this visualization is aimed at people such as coaches, sports directors, physiotherapists. In short, people who have to make decisions about certain aspects of the competition and the athletes. But they are not specialized profiles in data, so the information has to be transmitted in an easy to see way.
Informed decision making
From here is when the conclusions drawn from the entire process come into play. They become a factor for decision-making, one that is increasingly relevant. Coaches, scouts or sports directors are increasingly using big data tools in their decision processes, but data analysts are not separated from these sports professionals. It is essential that the data scientist who works in the sports environment is clear about the concepts of the game because their analyses, from the genesis phase, will have much more value.
After all, it must be remembered that big data is used to complement the knowledge of sports professionals. Its use is growing but still unequal between different disciplines. A distinction should be made between individual and team sports, since in an individual sport you have fewer variables to analyze and the environment is much more controlled. In a team sport, the possibilities multiply. The key and complexity is interaction. Hence it is more difficult to analyze.
Stay tuned for the second part of this post where we tell you all about how to apply it!
And if you are already clear that Big Data is what you want to dedicate yourself to, then what are you waiting for to carry out our Master in Sports Big Data.
