Monday , November 19 2018
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Chelsea FC is using AI research to improve coaching

Chelsea FC is using AI research to improve coaching

The best footballers aren’t necessarily the ones with the best physical skills. The difference between success and failure in football often lies in the ability to make the right split-second decisions on the field about where to run and when to tackle, pass, or shoot.

So how can clubs help players train their brains as well as their bodies?

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

We’re doing this by analyzing several seasons of data that tracks players and the ball throughout each game, and developing a computer model of different playing positions.

The computer model provides a benchmark to compare the performance of different players. This way we can measure the performance of individual players independent of the actions of other players.

We can then visualize what might have happened if the players had made a different decision in any case. TV commentators are always criticizing player actions, saying they should have done something else without any real way of testing the theory. But our computer model can show just how realistic these suggestions might be.

If a critic says a player should have dribbled 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.

Our hope is that coaches and support staff will use the system to help players reflect on their actions after a match and, over time, improve their decision-making skills.

Modeling decision-making

Measuring these skills is extremely difficult for several reasons. First, a human can’t keep track of all the events that take place during a match. Second, it’s difficult to isolate one player’s actions from that of another.

For example, if one player passes the ball and a few seconds later the team loses possession, did the player pass at the wrong time to the wrong person, or was it someone else’s fault?

To tackle this problem, we’re using a specific branch of AI known as imitation learning. This technology can learn computer models of behavior, such as footballers’ actions on the field, by analyzing massive amounts of historical data.

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

Most decision-making systems in AI, such as those used to play board games like Go, are based on reinforcement learning. This is where a computer learns to make decisions by repeatedly trialling moves until it receives feedback that it has done the correct thing, much like we train a dog to do something by giving it rewards.

But most real-world scenarios don’t have a specific reward like victory in a board game.