How Data Availability Affects the Ability to Learn Good xG Models

Motivated by the fact that some shots are better than others, the expected goals (xG) metric attempts to quantify the quality of goal-scoring opportunities in soccer. The metric is becoming increasingly popular, making its way to TV analysts’ desks. Yet, a vastly underexplored topic in the context of xG is how these models are affected by the data on which they are trained. In this paper, we explore several data-related questions that may affect the performance of an xG model. We showed that the amount of data needed to train an accurate xG model depends on the complexity of the learner and the number of features, with up to 5 seasons of data needed to train a complex gradient boosted trees model. Despite the style of play changing over time and varying between leagues, we did not find that using only recent data or league-specific models improves the accuracy significantly. Hence, if limited data is available, training models on less recent data or different leagues is a viable solution. Mixing data from multiple data sources should be avoided.