Revolutionizing Soccer Strategy: TacticAI’s Geometric Deep Learning Approach
Representing Player Arrangements as Graphs
In a groundbreaking study, researchers analyzed player-tracking data from 7,176 Premier League corners taken during 2020 and 2021. They represented the arrangement of players as a graph, with nodes encoding player position, movement, height, and weight, and lines representing relationships between players. By employing geometric deep learning, which leverages the symmetry of a soccer field, they reduced the neural network’s processing requirements, similar to the approach used in DeepMind’s influential AlphaGo research.
Generating Valuable Insights for Coaches
The resulting model, TacticAI, offers a suite of tools that can be invaluable to soccer coaches. By analyzing player arrangement at the moment of the kick, TacticAI predicts which player is most likely to make the first contact on the ball and whether a shot will be taken. It then generates recommendations for optimizing player position and movement to either maximize the chance of a shot being taken (for the attacking team) or minimize it (for the defending team). These suggestions might include repositioning a defender to cover the near post or placing a player on the edge of the area.
Identifying Key Players and Defensive Weaknesses
Liverpool’s soccer experts appreciated TacticAI’s ability to pinpoint attackers critical to the success of a particular tactic and defenders who were “asleep at the wheel.” Analysts typically spend hours reviewing video footage to identify weak points in opponents’ defensive setups or holes in their own team’s performances. However, tracking 22 players across various situations is challenging. TacticAI helps coaches quickly identify players who are not moving correctly or need to adjust their positioning.
Finding Similar Corner Patterns and Establishing a Common Language
TacticAI can also be used to find other corners featuring similar player patterns and movement, saving analysts countless hours. According to DeepMind, coaches rated the model’s suggestions as useful twice as often as current techniques, which only consider players’ physical coordinates without accounting for movement or physical attributes. Additionally, TacticAI aims to establish a common language for describing corner kick patterns, as soccer lacks the extensive nomenclature found in sports like American football.
Future Developments and Coaching Applications
Looking ahead, the researchers plan to integrate TacticAI with a natural language interface, allowing coaches to query the model using text and receive answers to their on-field problems. While TacticAI could potentially be used during games to refine corner routines in real-time, its primary application is likely to be in the days leading up to a match, freeing up coaches’ time for more creative aspects of their job.
We don’t want to build AI systems that replace experts. We want to build AI systems that amplify the capabilities of experts so that they are then able to do their job a lot more efficiently and have more time for the creative part of coaching.
By leveraging the power of deep learning, TacticAI aims to revolutionize soccer strategy and empower coaches to make data-driven decisions that enhance their team’s performance on the pitch.
6 Comments
Just when you thought soccer was purely human, AI proves it can bend it like Beckham too!
Soccer’s getting an upgrade; guess it’s time for players to take notes from their new AI coach!
And here I was thinking only humans had game-changing moves, guess AI’s playing the field now!
Looks like AI’s about to score a goal in soccer innovation, watch out coaches!
So the AI’s out there teaching pros how to curve the ball, who knew computers could give Beckham a run for his money
Who would’ve guessed AI could crack the code to corner kicks, there’s a new player in town!