Brazil will win the World Cup in Qatar, according to artificial intelligence

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Spain is the sixth team with the best chance of winning the trophy with 7.5%

Times move forward and technologies even more so. We have moved from the octopus Paul, who predicted the results of the football World Cup by selectively devouring the mussels that his caretakers had strategically placed next to the flags of the various participating countries, to artificial intelligence. But the latter has yet to prove its effectiveness, applied to a sport in which the contingency and the magic of the ball make up a large part of the result. Paul the octopus correctly predicted the outcome of Euro 2008 and, shortly before he died, the 2010 World Cup, where he guessed the eight team combinations presented to him. The brand is not easy to beat. Those who took up the challenge are a team of specialists from the universities of Innsbruck (Austria), Ghent (Belgium), Luxembourg and the technical universities of Dortmund and Munich (Germany). Their computers simulated the World Cup in Qatar a hundred thousand times, match by match, according to the draw for the tournament and all FIFA rules, until they came to the verdict: the Brazilian national team will win the competition.

Brazil, with a 15% chance of winning, would be the favored team. The Argentina team also has “many chances to win the title” (11.2%). They are followed by the Netherlands (9.7%); Germany (9.2%) and France (9.1%). Only later does the Spanish team appear with a chance of 7.5%. The forecast combines several statistical models about the “playing strength” of the teams and is fed with information about the structure of the teams and their players, including data such as the market value or the number of players in the Champions League. , as well as socio-economic factors of the country of origin that are considered relevant, from population and age structure to gross domestic product.

In each simulation, the system has learned all the probabilities with which the teams will or will not progress to the different rounds of the tournament and the probabilities that they will eventually win the cup or not. no certainties”. “A 15% chance of winning also means an 85% chance of not winning,” emphasizes Achim Zeileis from the Department of Statistics at the University of Insbruc, who doesn’t get too wet. Zeileis points out that similar studies had already been carried out for previous competitions, but that the World Cup in Qatar is especially interesting from a scientific point of view because of the unusual date on which it is held: the fact that it is held in winter, but with the high summer temperatures.

In the winter months, all major football leagues in Europe and South America have had to interrupt their regular fixture schedules to accommodate the tournament, giving national teams less time to prepare and players less time to recover before and after the event. Extreme weather conditions increase the risk of injuries, a variant that was included in the study. “Having a team with many players in international competitions, such as the Champions League, the Europa League or the Europa Conference League, can therefore be a disadvantage rather than an advantage this year,” explains Zeileis, who has started football from the ground up. viewing. point of view scientist and gets excited about the math challenge. “All these factors make it more difficult to predict how the tournament will turn out this time, as the variables that were very important in previous World Cups may not work well or differently,” adds Andreas Groll, another of the scientists who participated . in research published by Reports and Proceedings.

The scientific paper documenting the study explains that the calculation was based on four sources of information: a statistical model of each team’s “playing strength”, based on all international matches over the past eight years, and another of the “playing strength” ” of the game. « of the teams, based on bets from 28 bookmakers. The third base is additional information about the equipment and the fourth is a machine learning model that combines the different sources and optimizes them step by step.

Source: La Verdad

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