Gamestate xG Score: Expected goals adjusted by game state

Expected goals. You might think oh no here we go again, but I think it might be the one metric that has become part of normal conversations, without actually knowing the power or versatility of it. That also means we often talk about expected goals or xG and make wrong assumptions/conclusions. This can lead to a completely distorted point of view and discredit the work data people do in sports.

So, where am I going with this? In my opinion, xG can be used in multiple ways — if the context is right. I was looking for a way to see whether I could see that the quality of the chances or likelihood of a shot being converted into a goal would be different according to game state. In this article, I will break down how I use event data to calculate xG and how to use game state adjusted xG scores.

What is expected goals?

There are various explanations, but I thought the one The Analyst gave is pretty accurate:

“Expected goals (or xG) measures the quality of a chance by calculating the likelihood that it will be scored by using information on similar shots in the past. We use nearly one million shots from Opta’s historical database to measure xG on a scale between zero and one, where zero represents a chance that is impossible to score, and one represents a chance…