Why Did Artificial Intelligence Fail in the FIFA World Cup 2018?

Kaveh Bakhtiyari, Why Did Artificial Intelligence Fail in the FIFA World Cup 2018?, Medium, July 16, 2018.

There are different approaches to predict the results of FIFA World Cup. One approach is to simulate every single match in a paired comparison in terms of team’s capabilities and the winning odds. Zeileis, Leitner, and Hornik (2018) used the same technique, and they predicted that Brazil would win the FIFA World Cup 2018 with a probability of 16.6%, and it is followed by Germany (15.8%) and Spain (12.5%) [1]. 

Swiss Bank UBS also predicted the same three teams as the top 3 teams but in a different order. They predicted Germany (24.0%) as the champion, followed by Brazil (19.80%) and Spain (16.1%). Their generated model was based on four factors: 1) the Elo rating; 2) the teams’ performances in the qualifications preceding the World Cup; 3) the teams’ success in previous World Cup tournaments, and 4) a home advantage. The model was calibrated by 10,000 Monte Carlo simulations to determine winning probabilities and the results of the last five tournaments [2]. 

On June 8, 2018, four researchers (A. Groll et al.) from Technical University of Dortmund (Germany), Ghent University (Belgium), and Technical University of Munich (Germany) published a research paper on arXiv predicting the results of the FIFA World Cup 2018 using a well-known algorithm of Artificial Intelligence, Random Forest, and Poisson ranking algorithm [3]. This paper was published online days before the opening game of the world cup between Russia and Saudi Arabia, on June 14. They used a dataset covering all matches of the last four FIFA World Cups (2002–2014). They predicted Spain as the champion, followed by Germany and Brazil as runner-ups....

A. Groll et al. considered various features related to the team itself, such as 1) Economic factors (GDP per Capita, Population); 2) Sportive factors (ODDSET probability, FIFA Ranking); 3) Home advantage (Host, Continent, Confederation); 4) Team’s structure factors (Maximum number of teammates for each squad, Average Age, Number of Champions League players, Number of Legionnaires); 5) Team’s coach factors (Age, Duration of tenure, Nationality). In total, they had 16 features for each team and each world cup.... 

Besides the internal factors, the results of a football match may also be significantly influenced by some external factors as well, such as an unfair referee, weather, political situation, even personal problems of players, etc. These important features are usually very difficult to be measured and collected. In addition, there is always some chance of exploration, and uncertainty, for instance having a critical mistake or scoring an own goal, which are not easily predictable.  

In a nutshell, stochastic and dynamic environments such as FIFA World Cup or human activities are those areas that the today’s technology of AI cannot perform very well. This is a very good example to note that we have to be very careful about the applicability of AI in the similar dynamic fields. Also, by having a very complex data structure, it might be very difficult to audit the trained models for any potential bias. The existence of bias in AI can simply lead to discriminative decisions against a particular group. The implementation of such systems responsible as the sole decision maker may cause huge problems for both individuals and companies. Governments and companies are recommended to use AI for stochastic and dynamic environments only as a supplementary decision-making platform.