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Chelsea FC uses AI research to improve coaching



The best footballers are not necessarily the ones with the best physical abilities. The difference between success and failure in football often lies in the ability to make the right decisions in seconds, where to run, and when to attack, go through, or shoot.

How can clubs help players to train their brains and their bodies?

My colleagues and I are working with the Chelsea FC Academy to develop a system to measure these artificial intelligence (AI) decision-making capabilities.

We do this by analyzing several seasons of data that track the players and the ball in each game and develop a computer model with different game positions.

The computer model provides a benchmark for comparing the performance of different players. In this way, we can measure the performance of individual players regardless of the actions of other players.

Then we can imagine what could happen if the players had definitely made a different decision. TV commentators always criticize player actions and say they should have done something different without really testing the theory. However, our computer model can show how realistic these suggestions are.

If a critic states that a player has drummed instead of passing, our system can look at the alternative outcome, taking into account factors such as how tired the player was at that point in the game.

We hope coaches and support staff use the system to help players reflect on their actions after a game and, over time, improve their decision-making abilities.

Modeling Decision- Making

Measuring these skills is extremely difficult for several reasons. First, a human being can not track all the events that take place during a game. Second, it is difficult to separate a player's actions from those of another.

For example, if a player passes the ball and a few seconds later the team loses the ball, does the player at the wrong time hand over the wrong player or person Was it someone else's fault?

To address this issue, we use a special branch of the AI ​​known as Imitation Learning. This technology can learn behavioral computer models, such as the behavior of footballers in the field, by analyzing huge amounts of historical data.

In simple terms, the computer model learns to imitate human experts.

Most Decision Systems In AI, such as those used for board games like Go, they are based on reinforcement learning. Here, a computer learns to make decisions by repeatedly performing traits until it receives feedback that it has done the right thing, much as we train a dog to do something by giving it rewards.

But most scenarios in the real world do not attract. There is a certain reward like winning a board game.