Tbf, this prediction is a waste of a supercomputer's resources...
England will crash out in the quarter finals of the World Cup, according to a supercomputer.
The algorithm ranks The Three Lions' chances of bringing home the trophy at just seven per cent.
It predicts an all South American final - with Brazil beating their old foes Argentina. The Netherlands and Germany will be beaten in the semi finals.
Scientists used an AI (artificial intelligence) technique known as 'random forests' to simulate Qatar 2022 100,000 times - match by match.
Statistician Professor Achim Zeileis, of the University of Innsbruck in Austria, said: "This time, the World Cup is overshadowed by many ethical and sportive problems we cannot ignore.
"Nevertheless, for scientific reasons, we have decided to use our machine learning approach, which we have used successfully at previous tournaments, to make probabilistic forecasts."
Favourites in Qatar are Brazil with a probability of winning of 15 per cent, followed by Argentina (11 per cent), the Netherlands (10 per cent), Germany (nine per cent) and France (nine per cent).
Spain (eight per cent) and Belgium (six per cent) will also make it to the last eight. Wales' hopes are ranked at less than one per cent.
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The model, based on adjusted bookmakers odds, has been remarkably successful in the past.
It correctly forecast the 2008 EURO final between Spain and Germany - as well as getting right Spain being crowned World and European champions in 2010 and 2012.
Co author Prof Andreas Groll, of the Technical University of Dortmund in Germany, said: "It is in the very nature of forecasts they can also be incorrect - otherwise football tournaments would be very boring.
"We provide probabilities, not certainties, and a probability of winning of 15 per cent also implies a probability of 85 per cent of not winning."
The controversial tournament could be even more of a lottery after having to be held in winter because of extremely high temperatures in the summer.
Prof Zeileis said: "In addition to the widely discussed ethical problems of this World Cup, this also raises very critical sportive questions.
"In the winter months, all the major football leagues in Europe and South America have to interrupt their usual match schedule to accommodate the tournament.
"This gives the national teams less time to prepare and the players less time to recover before and after the World Cup. Combined with the extreme climatic conditions, this also increases the risk of injuries."
https://twitter.com/FootballJOE/status/1592542783432171522?s=20&t=9LpohYTedWNvFvbQKBJ-0Q
Having a team with many players in the Champions League, Europa League and Europa Conference League could prove to be more of a hindrance than an advantage.
Prof Groll said: "All these factors make it more difficult to predict how the tournament will turn out, as variables that proved to be very meaningful at previous World Cups may not work well or work differently."
Calculations were based on a statistical model for the playing strength of each team across all international matches in the past eight years.
Data also included betting odds from 28 leading bookmakers, players' market value and their countries of origin.
A neural network combined the different sources of information and optimised them step by step.
Prof Groll added: "We fed the model with the current data for the past five World Cups, i between 2002 and 2018, and compared it with the actual outcomes of all matches in the respective tournaments.
"This way, the weighting of the individual sources of information for the current tournament will ideally be very accurate."
The training program can also be used for other forecasts in the future - such as the weather.
As football fans, the researchers wanted to register their dismay by the circumstances under which the World Cup is taking place.
Prof Zeileis emphasised: "The usual joy and anticipation of a World Cup has been crushed by the terrible circumstances this year - from the alleged corruption in the host selection process, to the human rights and working conditions in Qatar, and the lack of sustainability in the construction and operation of the stadiums."
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