A couple of weeks ago another great dataset was made available to the soccer analytics community. In collaboration with Friends-of-Tracking the data provider SkillCorner open-sourced tracking data for nine matches across the Top5 leagues. Here is the link of the 9 matches of broadcast tracking data, we're open sourcing today:https://t.co/4CnxCO1EAC We'll open source soon some tooling to help visualizing the data, computing derivatives or synchronizing the data with event data.

In the first part of this series we constructed a simple expected Goals-model, solely relying on two predictors: the distance and angle from goal for each shot. As a reminder see below the visualization of our xG-estimates from the first part of this series: Our model passed the eye test, i.e. it maps shot locations to xG-values that make intuitive sense to us. In this post we want to evaluate the quality of this model more formally with tidymodels’ yardstick package.

I have recently gone through the Google Machine Learning crash course and was looking for a project to apply these skills to. Coincidentally, it is also not that long ago that tidymodels has gained some traction (at least in my twitter feed) and I am keen to try it out. Of course an Expected Goals-model is a great excuse to combine the two items above. It is relatively easy to set up, readers of this blog will not need a lengthy introduction to the thought process behind it and the feature set used to explain the probability of shots leading to goal is very intuitive.