In the 2010 World Cup in which the Spanish national team was the champion, the octopus Paul was one of the most prominent figures who caused a sensation all over the world. Cephalopods at the Sea Life Center aquarium in Germany and expect
Correctly and better than any bettor, the results of many matches came true and Spain’s choice to win the cup.
Now, with the Qatar 2022 World Cup underway, teams of big data scientists armed with powerful supercomputers are hoping to emulate the successes, follow the legacy of an octopus, and perform predictive analytics to predict the team. One of the most popular sports in the world.
One such prediction, made by experts in data analysis, data modeling and interpretation from the Alan Turing Institute in London, UK, headed by Nick Barlow, They used a mathematical algorithm that they had already used for the English Football League.
They made adjustments to this form to avoid bias, for example, they added all the results of international matches from 1872 to 2022, including matches between teams from different FIFA regions, as Brazil, for example, has not played against any European team since 2019 .
They point to addressing this Researchersintroduced parameters to measure the relative strength of the different continental federations. “We also modified our model to take into account the fact that the home-court advantage does not apply in international tournaments, unless the host nation is playing.”And the They point out in a statement from the institute. In the analyzed data, they gave more importance to World Cup matches and their weight decreased depending on whether it was continental tournaments, qualifying rounds and friendly matches. It also includes the official FIFA Rankings to provide an up-to-date estimate of each team’s performance.
They also modified it to give more weight to the results of some matches, such as the semifinals and finals, and more recent games, as well as running the model in previous tournaments to see how well their predictions matched up with real-world results, and adjusting it. based on their performance.
One of the factors that the model did not take into account is the performance of a particular player, which limits its analysis because in many World Cups, players have proven themselves with great performances, as was the case with Pele in 1970, Beckenbauer in 1974, and Maradona. in 1986, Zidane in 1998 and Ronaldo in 2002.
Scientists say: “This year’s tournament will certainly be lit by a football star.” However, we don’t see any of that. Predicting line-ups for national teams, who play together a few times a year, is much more difficult than Premier League teams playing week in and week out. ”
However, the data set included 44,150 international soccer match scores from the first official match in 1872 to the last week of 2022. Brazil, Mathematical Models Barlow and their colleagues ran a model algorithm to analyze the results. Possible outcomes for the World Cup in Qatar are around 100,000 times and the data showed that Brazil won 25% of the time, followed by Belgium at 19% and Argentina at 13%.
That is, according to the model of the Alan Turing Institute, the champion will be Brazil with a probability of 1 to 4, which can win the sixth World Cup in Qatar.
This model is open source and can be implemented by anyone with basic knowledge, on their computers at the following address. Each analysis with 1000 replay cycles takes 15 minutes on a laptop computer. “For most of the things we do, it’s very important that we open source them,” says Barlow. We encourage people to participate, use our code and contribute.
Although these models are currently used in many sectors, one of the first models applied to football dates back to 1996, developed by Mark Dixon and Stuart Coles, from Lancaster University in the United Kingdom, who created elements called “prediction circuits for the game”. “Soccer” which takes into account factors such as a team’s offensive strength, defense strength and home advantage, and uses Bayesian statistics to calculate the most likely outcome of a match.
Published in the Journal of Statistics Advance of the Royal Statistical Society, This model was developed to predict the outcome of games, bookmakers still use some variants of this model which focus on goals scored and conceded and distributed around an average value. Right now, bookmakers have teams of data scientists working full time on models based on the one developed by Dixon and Coles, but they’re more complex because they take into account many other factors.
In another mathematical calculation developed by scientists from the University of Oxford, which meditates on a million pieces of data with an algorithm that repeats 100,000 times, the Brazilian national team is also the winner, which similarly beats Belgium in the final. According to this model developed by the Institute of Mathematics of the British University, Brazil competes in one semi-final match with Argentina and in the other semi-final Belgium defeats France. This account also analyzes every international match since 2018 and according to the results Mexico will reach the Round of 16 where they will defeat France.
Another study that does not match the tournament’s predictions for Brazil is the study of the Lloyd’s insurance company, which used the collective insurance value of the team’s players to predict that England would win the final by defeating Brazil. This same model correctly predicted that Germany would win the 2014 World Cup and France would win the 2018 World Cup.
Compared to other sports, football is a game of high variability and complexity, so teams often lose when they don’t lose and win when they don’t. This is due to several reasons, for example, there are factors affecting the game that are less controllable compared to other sports due to the size of the outdoor field, dynamic play, etc. But the human factor of 22 players in each game also intervenes.
It is a long time ago for football to begin systematically collecting large data sets for scientific analysis with the aim of improving team play, so researchers may not yet have access to the achievements of Octopus Paul to successfully predict the outcome of the FIFA World Cup in Qatar.
Comments and suggestions: abanav, [email protected], and sapiensideas.com
