Conducting accurate analyses of soccer matches, and sports events in general, is a highly complex task that requires advanced interdisciplinary skills. First of all, it is necessary to understand the mechanisms underlying a soccer match and identify the parameters that influence the final results. These results can be manifold: for example, a draw or the victory of one of the two teams, the number of goals scored, the actual result, and many others.

At this stage, it is crucial to free oneself from all the typical biases of fans or so-called sports experts. At this point, it is necessary to perform an accurate analysis of the data to be collected and identify the most reliable methods for collecting it. The next step is to carry out a predictive analysis.

The first step of predictive analysis is Deep Learning on historical data to identify the influential factors, the functions that express this influence, and their coefficients of impact on the final result that we want to predict. The second step of predictive analysis is the application of the results obtained from the previous step to current data to calculate the probabilities for each possible outcome. I would like to emphasize that the results of this phase are probabilities expressed numerically and therefore difficult for a human to understand.

The final touch is the analysis of the results and the preparation of the event analysis through a rule-based semantic system to present the results in a way that makes them easily understandable to users. This last phase employs "narrow" Artificial Intelligence techniques to achieve more accurate results thanks to its specificity.

In the end, we believe we have done an excellent job!

This text was written by a human, translated into English by an AI system and, finally, reviewed by another AI system.