The Big Data Revolution in Soccer
How big data is transforming the way soccer is viewed and played.
The German national soccer team and others, Germany
In the summer of 2018, soccer fans from all over the world had their eyes on Russia for the World Cup, a global competition in which 32 international teams competed for the most prestigious prize in the sport.
The viewing experience of many was enhanced by access to continuous streams of data about team performance, team rankings, individual players and much more. Instead of passively watching the action, access to data generated by 32 teams playing dozens of matches across 12 stadiums enhanced the viewing experience as sports fans could actively analyze what was happening on the field of play.
Transforming Team Performances
But big data hasn't only changed soccer for the fans, it's also brought tremendous changes for coaches and their support staff. No longer do they have to rely on gut instinct and observations because they can turn to the data and create strategies and develop tactics based on their own players' performances and those of other teams.
For example, England’s head coach Gareth Southgate has a group of people studying data relating to goalkeepers during penalty shoot-outs. The team has lost six of their seven penalty shoot-outs at major tournaments and so analyzing data about how opposing goalkeepers behave in these crucial situations is now a key part of the preparation.
World Cup Success
The 2014 World Cup was won by Germany and their success was due in part to a specially built database that measured and analyzed individual and team performance as well as strategies. A vast amount of data on its own and opposing players was collected and this was distributed in a visual and easily understandable manner via a custom-built app to players, trainers and coaches.
Among the most important pieces of data came from the on-field cameras that surrounded the soccer fields, which the database viewed as grids. Each player was tracked digitally and the resulting data was used to measure key performance indicators such as average possession time of the ball, number of touches/kicks and movement speeds. One example of how the German side used the data was to improve the speed of passing the football. In 2010 the average time a player had a ball in his possession was 3.4 seconds but by 2014 this was cut to 1.1 seconds. The importance of this improvement was that it allowed the side to play their more aggressive, fast-paced style.
Today, big data is also being used in a predictive capacity to determine how individuals are going to play. Analysis of past playing strategies combined with current performance can help a coach predict how a player will play in an upcoming match. Changes can then be made to who will play in the side and on-field strategies to maximize the chances of success.
Soccer has always been a numbers game but as the years march on big data is going to be making an even greater impact into how one of the most popular sports in the world is played and viewed.
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