Just like in the movie Moneyball with Brad Pitt, the signing of players through Big Data is gaining more and more ground, thanks to the incredible results it has achieved across the globe. Stars such as Kevin De Bruyne and Memphis Depay have defined their future thanks to the information, and clubs are incorporating more and more technology into their decision-making.
What is Big Data and what is it for?
Big data could be defined as an amount of information of such volume and complexity that it is pretty difficult or impossible to process with traditional methods, and for that reason, technologies and tools are used to extract insights from such data.
It is the conclusions drawn from big data that are of transcendental importance for many industries, including the football industry. Tens of millions of pieces of data are generated in a football match, and the question that immediately arises is: how can we process and benefit from them?
From transfer policy to game analysis, the uses of Big Data are as many as creativity and tools we have. The data has always been there, the difference is in how we treat it. Using the information to choose signings is possible, just as it is possible to prevent injuries, analyze opponents and improve aspects of the game.
Every time a team innovated in terms of Big Data, the results were positive and even in some cases truly historic.
How do clubs use Big Data to recruit players? The case of Sevilla
The word scouting is well known in the world of football, where a team of people is in charge of looking for players, watching hours of video and visiting stadiums in order to find those talents that best reinforce the team. This has changed a lot in recent years, and Big Data has become increasingly important.
The most emblematic case in Spain is Monchi at Sevilla, which under the premise of "buy cheap and sell expensive" was in charge of making the most of Big Data tools to see those players that "nobody saw". The first step was to create a department of AI, Machine Learning and Big Data. Then, to implement its database that has tens of thousands of players who are possible signings for Sevilla. The results? Sevilla, until 2005, had won four titles in their history and in the last 15 years, they have won ten.
"Data has always existed, the difference is that now there is much more data and that is why we call it Big Data. Big Data is not a panacea or a philosopher's stone. It is a magnificent help. The data exists, but the skill lies in separating the smoke from the signal" says Monchi, General Sporting Director of Sevilla F.C.
Over time, many teams have incorporated similar signing policies and today no team signs players without complete clarity on player metrics and performance.oy ningún equipo ficha sin tener claridad sobre las métricas de los jugadores y su rendimiento.
How do players take advantage of Big Data when it comes to signings?
Kevin De Bruyne. One example that came to light was when Kevin De Bruyne negotiated his contract without an agent, only thanks to the help of Big Data. His agent, Patrick De Koster, was arrested in 2020 for fraud and money laundering, so the Belgian player decided to leave the negotiation tasks in the hands of technology and let the data do the talking.
Thanks to this, De Bruyne managed to increase his salary to a figure close to 20 million pounds, being the highest paid of the squad led by Pep Guardiola and in the top 3 of the Premier League, only behind Cristiano Ronaldo.
Memphis Depay. In 2017, after two seasons at Van Gaal's Manchester United, Memphis decided to change teams as he had not performed what the coach and fans expected.
To decide his future, Depay contacted Giels Brouwer, founder of SciSports, a company specialized in BigData. In a meeting in Manchester, where the player used to live, he told Giels that he wanted to play in a team where he could play "freely", being important for the team, and also that the team should be part of one of the 5 "big" leagues in Europe.
Considering Memphis' requests, the next step was to analyse Depay's performance at PSV and Manchester United. The conclusions were that in England the player was responsible for much more defensive actions and that Van Gaal's style of play did not help him to show his best side.
The next step was to enter all the information, plus Depay's performance analysis, into the computer. Aspects such as the absence of a star in his position, the speed of transitions and passes, team consistency, style of play and future projections, among other key metrics, were taken into account. Depay was clear that he wanted to join a team where he would enjoy playing and could show his best version, money was secondary in this choice.
"In the end, we drew up a report with five possible clubs that would suit his style of play and Lyon was one of them. Of course, it's up to the agent and the club to come to an agreement, as our work ends there" Giels Brouwer, founder of SciSports.
The results? In Lyon, he not only fought the championship against PSG for the Ligue 1 but also achieved a historic qualification to the Champions League semi-finals in 2020, beating Guardiola's Manchester City in the quarterfinals. As if that was not enough, his great level took him to FC Barcelona and he also returned to be a key player in the Netherlands National Team, where he will surely be a starter in the next World Cup in Qatar.
Other uses of Big Data in football
In addition to transfers, Big Data is used in many other aspects. One of the most common uses by clubs is the analysis of rivals, but most importantly, the analysis of their own performance in the game. An example of this is when Germany won the World Cup in 2014, and according to the protagonists themselves, thanks to SAP, a tool that allowed them to lower the average ball possession from 3.4 seconds to almost 1 second, something that was reflected in the historic 7-1 win over Brazil in the semi-finals.
Big Data also arrived with GPS technology to revolutionize football. The analysis of data to obtain metrics of each player, in order to improve performance and also to plan training sessions is something that no team wants to be left out of. GPS technology is used to monitor each player individually, allowing for analysis of many aspects that at first sight are almost impossible. In addition, it is increasingly used for injury prevention, as it allows to compare loads of players and to know when a player is at risk of injury or is ready to return to competition after one.
Unlike the already mentioned technologies, GPS technology has a low-cost option that has made it possible for many football teams, to access relevant data, the GPS OLIVER. A tracker that, through a web portal and a mobile app, provides information on every detail of the player's performance. The price is so affordable that many teams that usually could not access it are now taking advantage of the benefits of Big Data, both in professional and amateur football, as well as in women's, futsal and youth football.
"OLIVER is a platform that allows not only to monitor player performance but also generates knowledge in the entire club structure" commented José Edmilson, president of FC SKA Brazil and former world champion with Brazil in the 2002 World Cup.
Big Data has many more edges that have been left out of this article, but it definitely came to change football and certainly not to change the magic. Professionalization is intrinsically linked to technology, and surely over the years those who do not adapt will end up being at a serious disadvantage, and those who innovate are going to be the ones that will maximize their benefits, as Sevilla, Kevin de Bruyne, Depay or the German national team did.
"Every team is on the lookout for innovative ways to gain competitive advantages over their rivals," said Bierhoff, Brand Ambassador for SAP, the company that supported Germany's Big Data in 2014.
