A world in which after a match, people do not discuss iffy concepts such as who wanted the victory more, who was stronger and more compact, BUT rather football is viewed through the prism of concrete and objective metrics.
It is time to leave the speculations in pubs and on the stands, and for football experts to embrace a new stage of analyzing the football game, long present in many sports.
What makes football the most popular game in the world is the fact that the better team doesn't always win. Nevertheless, everyone strives to be a better opponent because in the long run, it will certainly bring victories, titles, and prestige.
Analyzing matches using the Data Science approach ensures that maximum information is extracted from each game, which the coaching staff can use to improve their team's game. In the football world, popular metrics such as xG - expected goals and xA - expected assists only tell a part of the story that took place on the field, using only a fraction of the huge amount of data available from one game...
...but recent technological advancements have enabled the analysis of this massive amount of data and brought about the next step in the real Data Science approach. Several teams are leading the way in this field, including Liverpool and Barcelona, which is interesting.
These models serve only as a beginning, and using them, every pass has a certain value, and the compactness of the field becomes a tangible number. The applications of these models are endless, both for player evaluation and team evaluation, and new, stronger, more precise, and better models are constantly being developed. As professional scientists, we are always interested in what is underneath and how we can improve the existing system. That is precisely why we decided to solve the problems that we have been seeing on football fields all our lives.