Artificial intelligence models predict the outcome of corner kicks in soccer matches and help coaches design tactics that increase or decrease the probability of a player taking a shot on goal.
petar veličković Google's DeepMind and colleagues have developed a tool called TacticAI as part of a three-year research collaboration with Liverpool Football Club.
A corner kick is awarded when the ball crosses the goal line and goes out of play, creating a good scoring opportunity for the attacking team. For this reason, football coaches make detailed plans for different scenarios, which players study before the game.
TacticAI was trained on data from 7176 corner kicks from England's 2020-2021 Premier League season. This includes each player's position over time as well as their height and weight. You learned to predict which player will touch the ball first after a corner kick has been taken. In testing, Ball's receiver ranked him among TacticAI's top three candidates 78% of the time.
Coaches can use AI to generate tactics for attacking or defending corners that maximize or minimize the chances of a particular player receiving the ball or a team getting a shot on goal. This is done by mining real-life examples of corner kicks with similar patterns and providing suggestions on how to change tactics to achieve the desired result.
Liverpool FC's soccer experts were unable to distinguish between AI-generated tactics and human-designed tactics in a blind test, favoring AI-generated tactics 90% of the time.
But despite its capabilities, Veličković says TacticAI was never intended to put human coaches out of work. “We are strong supporters of AI systems, not systems that replace AI, but augment human capabilities and allow people to spend more time on the creative parts of their jobs,” he says.
Velicković said the research has a wide range of applications beyond sports. “If you can model a football game, you can better model some aspects of human psychology,” he says. “As AI becomes more capable, it needs to understand the world better, especially under uncertainty. Our systems can make decisions and make recommendations even under uncertainty. It’s a good testing ground because it’s a skill that we believe can be applied to future AI systems.”
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Source: www.newscientist.com