Soccer's Big Data Revolution

The world of sports is undergoing a massive revolution-one which you are probably completely oblivious to. Athletics, swimming, baseball have all embraced the change. And now soccer is joining the fray.

Data scientists monitor, analyse and interpret every vitals they possibly can. Heart rate, breathing rate, power, speed, stamina and recovery time, ease of movement, concentration level can all be quantified. Those along with motion tracking data allow data scientists to formulate specific profiles for each player. And with the introduction of the Adidas Micoach Elite System in next year's Major League Soccer (MLS) season, data scientists will have access to this deluge of data in realtime during the course of a game.

Starting next year, every MLS soccer kit will be fitted with a small chip from Adidas. Located between a player's shoulder blades, the chip will transmit 200 data records a second from the player to a local information system. From there onward, the data can be transmitted to the coaches' laptops or tablets to provide clear overviews of the physical and physiological situations of the players on the pitch. It is not far-fetched to imagine coaches viewing a Football Manager game-like interface which showcases individual players' level of fatigue, concentration, etcetera, varying in real time.

Access to such phenomenal amounts of data, termed big data, can fundamentally change soccer. Decisions will increasingly be taken based on quantified player attributes, tactics will evolve and injuries may be averted.

Usage of big data in sports is not an entirely new phenomenon. As the book (and its subsequent film adaptation) Moneyball portrayed, Billy Bean, the General Manager of Oakland Athletics, heralded data analysis and statistical principles in baseball a decade ago. By modelling their team using similar principles, the Boston Red Sox, a few years later, famously won their first World Series in 86 years. But since then, big data in sports has evolved and gone even bigger. Lolo Jones, an American 100-metres hurdle runner who is aiming for gold in the upcoming Olympics for instance, is attended to in training by 22 scientists and technicians. With high-tech motion capture and high-speed cameras, the data scientists monitor every aspect of Jones' running from the amount of sway she's susceptible to to her precise speed peaks.

Soccer is actually now catching up and the MLS may pave the way for more "smart leagues" in the future. With the ever-increasing processing powers of computers, the capture, storage and transmission of large data sets is now possible and may become commonplace in a few years' time.

There is one big barrier to the wider adoption of big data in the game, however: FIFA, soccer's governing body. FIFA has maintained a staunch stance against the advent of technology in soccer. It only agreed to goal line technology (which will use hypersensitive cameras fitted on goal posts to clearly show whether the ball has crossed the goal line or not) this year after numerous high-profile blunders from referees. Proposals for the use of other camera systems to aid referees spot fouls, simulations or tricky offsides though have all been crucified.

Regardless, such is the penetration of big data in other sports and sectors that its spread from the MLS to other leagues is likely only a matter of time. The technology is there and so is the will to adopt it by teams. If FIFA maintains its technophobia in the face of big data, it may well face a losing battle.

Image credit: marcp_dmoz (from flickr)