Data Science in Football: The Story Behind the Numbers

Data science has fundamentally changed football analysis. xG (expected goals) is the most popular metric, providing the probability of a goal for every shot position. For example, a shot from 11 meters has an xG=0.76, meaning there is a 76% expectation of it being a goal. With the help of xG, a team's true strength can be evaluated — sometimes a team that wins frequently may actually just be lucky.

Other important metrics: PPDA (passes per defensive action — pressing intensity), progressive passes (forward-oriented passes), and expected threat (xT — the increase in goal danger with every movement). These metrics allow for a deeper understanding of football beyond simple "scored or didn't score."

Data science in Uzbekistan football is still in its early stages. However, opportunities to collect and analyze Super League statistics are increasing. With tools like Python, R, and Tableau, anyone can analyze football data. This field represents a great career opportunity for young Uzbek specialists.