Brazil is the most likely winner of the 2022 football World Cup according to a prediction model from the Alan Turing Institute in London. The publicly accessible model gives Brazil a 1-in-4 chance, with England's chances put at less than 1 in 10. Many people, from bookies to bankers, have run models trying to predict the winner and losers of the men's football 2022 World Cup in Qatar, but most of these models are run behind closed doors. Nick Barlow at the Alan Turing Institute and his colleagues have developed a model that people can run on their laptops at home, with 1000 tournament run-throughs taking 15 minutes on an average laptop. "It's quite important to us for most of the things we do that we make them open source," says Barlow. "We encourage people to get involved, to use our code and to contribute to it."

Champ has been designed so children who are too ill to attend a football game or other sporting event can go out onto the pitch remotely with their favourite team. They can see and here what is going on, and also talk to the players via the robot.

The random-forest technique has emerged in recent years as a powerful way to analyze large data sets while avoiding some of the pitfalls of other data-mining methods. It is based on the idea that some future event can be determined by a decision tree in which an outcome is calculated at each branch by reference to a set of training data. However, decision trees suffer from a well-known problem. In the latter stages of the branching process, decisions can become severely distorted by training data that is sparse and prone to huge variation at this kind of resolution, a problem known as overfitting. The random-forest approach is different.

The random-forest technique has emerged in recent years as a powerful way to analyze large data sets while avoiding some of the pitfalls of other data-mining methods. It is based on the idea that some future event can be determined by a decision tree in which an outcome is calculated at each branch by reference to a set of training data. However, decision trees suffer from a well-known problem. In the latter stages of the branching process, decisions can become severely distorted by training data that is sparse and prone to huge variation at this kind of resolution, a problem known as overfitting. The random-forest approach is different.

The random-forest technique has emerged in recent years as a powerful way to analyze large data sets while avoiding some of the pitfalls of other data-mining methods. It is based on the idea that some future event can be determined by a decision tree in which an outcome is calculated at each branch by reference to a set of training data. However, decision trees suffer from a well-known problem. In the latter stages of the branching process, decisions can become severely distorted by training data that is sparse and prone to huge variation at this kind of resolution, a problem known as overfitting. The random-forest approach is different.

The random-forest technique has emerged in recent years as a powerful way to analyze large data sets while avoiding some of the pitfalls of other data-mining methods. It is based on the idea that some future event can be determined by a decision tree in which an outcome is calculated at each branch by reference to a set of training data. However, decision trees suffer from a well-known problem. In the latter stages of the branching process, decisions can become severely distorted by training data that is sparse and prone to huge variation at this kind of resolution, a problem known as overfitting. The random-forest approach is different.