XG is it something that’s vitally important in the game or somewhat of a flawed KPI?

XG has been in play for a long time but recently it’s become more frequent, being used by coaches in football especially recently here in Ireland.

So what is XG?

XG was introduced in 2012 by Opta's Sam Green. In football, "xG" stands for expected goals, a statistic that measures the likelihood of a goal being scored from a specific position or situation on the pitch. It's a way to assess the quality of a chance, taking into account factors like distance to the goal, angle, and the nature of the shot.

A lot of leading software companies use it in their data analysis reports ie Wyscout, Hudl, Understat, etc. this is one of many different KPI’s used.

So why has XG became more popular?

It’s always been a popular KPI in football but maybe not so much in the public domain but seeing it being used on popular tv channels like sky sports giving it a lot of airtime has made people more aware of its existence, so now when coaches use it in interviews people understand it more now in the modern day.

Is XG an accurate KPI stat?

While xG (expected goals) is a valuable tool for football analysis, it's important to understand that it's a probabilistic estimate, not a certainty, and its accuracy can vary depending on the sample size and context.

XG does have its flaws, here is an example during the Arsenal VS bayern game, over a year ago where Sané is in a clear position to score a goal. Had he shot, it would've been counted in the xG and would've been acknowledged. but since he didn't have the best of second touches, and carried the ball into a closed angle, he tried to turn back and eventually lost the ball, hence not being accounted in the total xG of the game.

This means that at the end of the game and according to xG, this occasion never existed, resulting in a painted idea that Bayern was less dangerous than it actually was. In fact, at the beginning of this chance, most of us would've thought it would've resulted in a goal, most of us would've thought a goal was to be Expected, hence my point of it being counted in xG.

The fundamental insight of our analysis is that confounding effects in the training data mean that xG models do not represent an “average player” and that biases in the data can have a surprisingly large effect.

Conclusion:

We always tell our clients while we love using data and stats, never hang your hat on them, they are a basic guide call it starting point to give you an insight to research further. The issues I have with XG specifically here in Ireland is that most stats are gathered from software companies that are based in other countries whilst that you may say shouldn’t be an issues I have found several flaws in their tagging, for example tagging a shot when you go to the clip it’s a throw in or a pass so the stats in the reports aren’t accurate this happens a lot, the other issue I have is perceptions, the person taggings perception could be completely different to a clubs this also makes the reports not accurate, at our system theClubHub the club tags it’s KPI so everyone’s on the same page as to what a KPI should be making a more accurate system and report and relevant to your club.  I’ll go back to my point of not hanging ur hat on data or reports use it as a guide. I seen recently an interview from a manager stating that the XG was better than previous year, again this is where I see flaws in XG as the previous year that team scored more goals and was in higher league position but XG was lower. All this comes down to you could say interpretation of the data, which then leads to having experienced people deciphering the data know how to use it and when to use it. For me and my experience instead of speaking about the XG being higher look at why you don’t have more goals because XG doesn’t win you football matches.