Soccer

Football (soccer) is the most popular sport in the world. It unites people of different ages, genders, social groups, and cultures. To give an idea of ​​the sport’s influence, according to FIFA, years back, the World Cup held in Russia had 3.5 billion viewers, and fans are widely popular across Europe, Africa, Asia, and the Americas, which corresponds to half the world’s population at the time.

In comparison, the main club competition, the UEFA Champions League, has an estimated average of 400 million viewers for its final, distributed across approximately 200 countries. The sport closest in audience for a final, American football, the US National Football League (NFL), has around 115 million viewers watching the game.

This
year, especially in November, the eyes of fans will be on Qatar, the host country of the twenty-second edition of the World Cup. The expectation is that the sporting event will have an even larger audience. The excitement of the fans is so great that speculation has already begun about who will take home the trophy. News
outlets studied by economist Samy Dana. The five favorites to win the title are Brazil, England, France, Argentina, and Spain, respectively.

Artificial Intelligence in
Football

Betting houses, which originated in 18th-century Great Britain, initially offered bets on horse races and later expanded to other sports, such as football. With technological advancements, odds or match quotes began to be calculated using more robust statistical techniques, and today, machine learning algorithms are employed. Machine
Learning is a branch of artificial intelligence in which algorithms are developed that have the ability to learn, recognize patterns and characteristics, to make decisions and predictions. Machine learning algorithms can be used to solve classification problems, where, through observed characteristics, the aim is to predict a class within limited existing possibilities. Classes can be binary or multiple, as in a medical study: sick or not sick patient. Alternatively, when the goal is to predict the results of football matches, we would have 3 classes: win, draw, or loss. So, what information is used to predict match results? Historical information about the teams and other data can be used to predict match results. The construction of variables is a very important step, since it extracts as much relevant information as possible from a dataset, allowing the model to better distinguish each class. The hypothetical table below presents an example of information on the number of goals and shots in a match, which can be easily found online for predicting match results.

From these variables, even more can be constructed to improve the characterization of the selections. Variables can be created in relation to a specific time interval, considering the last 3 or n matches, such as score, number of shots, goals, clean sheets, shot accuracy percentage, average goals scored and conceded, standard deviation, maximum and minimum number of shots, goals, and so on. Binary variables can be used, such as whether the team has scored or conceded goals in all recent matches, the team’s unbeaten record, among others. Furthermore, it is possible to seek other sources of variables not exclusively related to matches, such as the FIFA national team ranking and information about the players of each team.

In this way, after constructing the variables, the machine learning algorithm is trained, where, through the characteristics and the already known result (class), the model learns which information is most relevant for making the prediction.

For classification problems, the following algorithms can be used: decision trees, random forests, logistic regressions, naive Bayes, and support vector machines, among others. The dataset intended for training and learning the model can be the results of the two thousand fourteen and two thousand eighteen World Cup matches, used for predicting the two thousand and twenty-two World Cup.

Through the trained algorithm, it is possible to input information about the two teams at that given moment, and it will return the probabilities of each of the three possible results: victory for team A, draw, or victory for team B. Thus, it is possible to simulate each match of the group stage, define the knockout stage matchups, and perform simulations up to the final.
In the
Years back, at the World Cup, some studies were conducted that previously pointed to France as one of the favorite teams to win the title. In the study by Groll et al., the authors tested Poisson regression models, random forests, and ranking methods, using variables related to economic, sporting, and team factors, such as the number of players who play in the Champions League

Artificial intelligence (AI) predicts decisive results in the 37th round of the Brazilian Championship.

The penultimate round of the national championship begins this Tuesday (2). The 37th round of the Brasileirão starts this Tuesday (2), with two games scheduled. Subsequently, six more games will take place between Wednesday (3) and Thursday (4). Given this scenario, 100percentsurewins! asked Chat GPT to simulate the current stage of the national championship.

Artificial intelligence predicted decisive results for the tournament. At the top of the table, the technology pointed to a victory for Flamengo over Ceará. The teams face each other this Wednesday (3), at 9:30 pm (Brasília time), at Maracanã. If the result is confirmed, the Rio club will win the Brasileirão title.

On the other hand, Ceará would be in trouble at the bottom of the standings. In the Chat GPT simulation, Santos drew with Juventude, Internacional lost to São Paulo, and Fortaleza drew with Corinthians. With this scenario, the relegation zone would continue with Sport, Juventude, Fortaleza, and Internacional, respectively in 20th, 19th, 18th, and 17th place. See the complete results simulated by AI for the 37th round of the Brasileirão.

Vasco 2 x 1 Mirassol;
Grêmio 1 x 2 Fluminense;
Fortaleza 0 x 0 Corinthians;
Juventude 1 x 1 Santos;
São Paulo 3 x 2 Internacional;
Bahia 2 x 0 Sport;
Flamengo 3 x 1 Ceará;
Cruzeiro 1 x 2 Botafogo;
Atlético-MG 1 x 1 Palmeiras;
Bragantino 2 x 0 Vitória

Conclusions

To wrap it up, this article showed, in general terms, how artificial intelligence (AI) can be used to predict the results of football matches in the 2025 World Cup. It is worth noting that there is a wealth of content that addresses in detail the use of machine learning techniques to solve classification problems.