In the next few weeks, a hot topic will dominate our conversations: #Euro2020, which takes place, between June 11th and July 11th, 2021, in 12 European cities.
But what do football and data have to do?
In the early 2000s, the book and movie "Moneyball" put the use of data in sports under the spotlight, and show us how important analytics can be in the success of any individual or a team.
Many had said it was impossible in football because the game is too fluid and too chaotic. Player movements are too difficult to track reliably. But today, the use of data and technical analysis has become indispensable for football clubs and is no longer limited to the Premier League teams. As the software is cheaper and more accessible, smaller clubs are also starting to participate. As investments in data analytics begin to provide them with a competitive advantage, early adopters of major football leagues are booming: Liverpool, Alkmaar and Brentford are just a few of the rapidly growing list of successful case studies. The clubs that do not intend to join the analysis trend risk to being left behind.
Numbers are not new in this sport, for decades, commentators have compiled statistics ranging from winning streaks to the most passes in a game. But in the past ten years, a more scientific way of operation has emerged, which not only changes the results of the team but also changes the way that funds are allocated for recruiting new talent. They not only bring opportunities on the field but also translate into rankings, customer and fan satisfaction. Ultimately, all of this means increasing sources of income.
Training analysis (the metrics used to measure attributes in a player);
Match analysis (it takes many forms, from summarising the advanced statistics of the game to analysing the player's position on the field, and even the position and direction of the body when passing the ball);
Player recruitment (by using advanced clustering methods, clubs can now quickly find new players similar to other players).
Many football clubs use data in their decision-making process, but the way they do it and the extent to which they do it varies from club to club, and most of them prefer to keep their data confidential.
In the past few years, the exponential speed of improvement in the technologies supporting the collection, storage and analysis of data has gone hand-in-hand with an exponential increase in the human capital invested in sports analytics.
However, football data have many different challenges. Such as, collect and organise the data that it can be analysed effectively, and sometimes it takes more time in the preparation of the data than the actual analysis. Another challenge is to translate the questions from managers, coaches, sporting directors and scouts into formal problems that can be addressed with data and translate the solutions to those problems back into answers in the football language. The key challenge for a club is to put the increasing amount of data that they have at their disposal in the right context and to draw the right conclusions from the data.
Of course, that football teams need data to make good decisions, but also analytics to make sense of it. For example, if football clubs are flooded with huge streams of numbers, but lack the internal know-how to interpret them and extract insights, data becomes almost pointless.
Technological advancements in football have seen a variety of data-related fields enter into the game. The introduction of data into professional football has been an innovative process and one that has included the emergence of different personas within the game. Nowadays. data analytics have come to play an important role in the football industry.
Big data is allowing clubs to gain a competitive edge on and off the pitch. However, analyse and extracting valuable insights is not always an easy task, it’s important to remember that data alone isn’t enough to gain a competitive edge, what’s even more important is the ability to interpret it.
And now let's support the Portuguese National Football team!