Can AI improve soccer skills?
Japanese team uses computer analysis to improve player performance on pitch
It is now commonplace for artificial intelligence to defeat a professional Go or shogi player, so why not take AI to soccer?
Sagan Tosu, a team in the Japanese professional soccer league, has been developing an analysis system that assigns point values to each player’s movements using AI. In collaboration with a developer, the club’s youth teams are testing the system, which can quickly process large amounts of information and make judgments and analyses.
During a soccer match, 22 players are almost constantly moving around the field. As it is difficult to read the developments of the game, unlike in the strategy board games Go or shogi — a Japanese variant of chess also known as the Game of Generals — where patterns form, the AI uses video of a match to determine the good and bad plays.
AI and soccer are no strangers. ASIMO (for Advanced Step in Innovative Mobility, but also named in honour of science fiction author Isaac Asimov), an experimental humanoid robot the Honda Motor Co. introduced in 2000, had kicking a soccer ball among its many abilities. ASIMO is now retired and on display at the Miraikan museum in Tokyo, Japan.
To establish the analysis system for this new project, AI developer LIGHTZ Inc. asked coaches about the movements and roles required of players depending on the situation. For instance, it took the holding of weekly, two-hour sessions over five months just to cover what are good plays during the buildup to turn defence into attack.
The AI system, powered by the expertise of the coaching staff, analyzes data generated from match footage to evaluate players. Even if players do not touch the ball, they are highly evaluated if the AI recognizes them as keeping an opposing defender occupied or effectively using space.
The club aims to apply the system to the top team from November.
“Our accumulated knowledge has been incorporated into this analysis system,” said Yuki Seki of LIGHTZ, who was involved in development. “It has to process a ton of data, but that’s why it’s important to get AI to do the advanced calculations.”
Analytics in baseball has led to the theory behind the so-called flyball revolution, where hitting the ball at certain angles up in the air is more likely to generate hits, rather than the conventional approach of staying on top of the ball.
With the introduction of AI, playing styles and formations that have long been considered unusual in the soccer world might also someday become mainstream.