There are precious few pieces of work that have both an immediate and a long-lasting impact like this one. People still talk about Expected Threat, and the blog is still linked to by others, from The Athletic to academic research papers.
The aim of the piece is to work on the age-old question of how to value actions on the pitch, beyond just shots (which have xG).
Assuming you don't lose the ball, there are two things you can do when you have it: move it somewhere, or shoot. So then the calculation is about:
This is the basic logic behind xT. But there are layers to it, so we don't just ask those questions once, we ask them for a chain of five potential actions. To quote the caption that goes alongside an interactive example in the post, "when the team has the ball in the highlighted zone, they will score in the next 5 actions 9.4% of the time".
There had been various ways of applying expected goals to non-shooting actions (i.e. passes, dribbles) before this, but compared to xT they were quite simple. Metrics called xGChain and xGBuildup took the expected goals value of a shot and credited every player who'd been involved in the buildup with that figure. Other work in the early and mid-2010s simply valued a pass based on what the xG value would have been if a shot had been taken from where the pass ended.
The basic attraction of xT is that it, intuitively, says "this pass might not set up a goal directly, but a goal might happen in a couple of seconds time because of it".
It's also, and I say this without any criticism at all, very simple both to calculate and understand. If you need some type of 'possession value' model as part of whatever work you're doing, just whip up xT. It'll get you 70% of the way you need to go, in 10% of the time it'd take to get you 90% (metaphorically speaking, these aren't real numbers).
Finally, the post is fabulously presented. The top of the page has a progress bar (which all blogs and reading-based websites should have as standard in my opinion). It has five pieces of interactivity. It opens with a gif.
It also has mathematical equations, helping to make the method reproducible. It has little note boxes, telling you about scalar fields or Markov chains or just letting you know you can skip the maths sections to get to some more visualisations.
It's rare that the presentation of analytics work is as well-thought through as the analytics work itself. This blog post did that, and I think it's a key part in its instant, and four-year-long-and-counting impact.
'Research in Focus' is like SparkNotes for football analytics: summarising and analysing the best research out there. Get Goalside supporters get access to every post, with a rotating selection free to access for all. Follow this link for the list of all Research in Focus pieces.
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