Shot Classification & Goal-Probability (xG) Estimation Using Graph Neural Networks (HGTs)

▪ State-of-the-art and cutting-edge MSc. Data Science thesis’ research project – modelling football shots as heterogeneous graphs of all players, the ball, as well as the instantaneous spatial & physical context, and then applying Heterogeneous Graph Transformers (HGTs) to classify shot outcomes and estimate their respective goal probability (i.e. xG = Expected Goals).

▪ Successfully demonstrated statistically that HGTs significantly improve the shot outcome classification, and their respective goal-probability estimation & calibration over traditional xG models. This thesis was directly supervised under Dr. Gabriel Anzer (Head of Football Data Analytics at RB Leipzig).

▪ In addition, this project was selected by Founder David Sumpter as 1 of 4 finalists for Twelve Football’s “Pitch to the Pros #3”.

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