Expected goals is a statistical metric which ranks how good a shot on goal was.
The Premier League race just started getting hotter, with Tottenham, Chelsea, and Arsenal all moving on up to challenge the leaders at the top end of the table. Liverpool have taken the lead after beating United, pulling a single point away from Manchester City after they lost 2-0 to Chelsea at Stamford Bridge. It still looks like a Liverpool or City title, but at least the competition has got us guessing again.
Of course, there’s only really one way to rank teams in the Premier League, and that is by who is actually winning on points. Yet one of the most recently introduced statistics in football may offer more clues as to who will hold out in the long term. That statistic is known as expected goals (xG), and it allows teams to be ranked based on how they “should” be scoring according to the numbers.
What Are Expected Goals?
Even though it sounds complicated, many football fans already talk in terms of expected goals when they chat casually about games. They say that “the player should have scored” or that “ the team really should have won that game”.
When it comes to pub banter, this sort of talk usually has a lot of bias towards your favourite team, but the expected goal statistic actually quantifies this into (arguably) meaningful data. The xG stat assigns a score to each opportunity that players get on goal to show how good the opportunity really was. This is based on factors such as distance from the goal, position, type of shot and defensive pressure.
A shot from an up-close central position would have a much higher xG than a header from far outside the box.
If you think about it, expected goals offer a potentially powerful way to analyse games and long-term performances of players and teams. Stats like “shots” and “shots on target” do tell us something, but what does it really mean to have 10 meaningless shots that are nearly always going to miss or be saved?
Analysing Teams Based On xG
The expected goals stats also becomes a predictive model when it is used over many games. On a basic level, you can see which teams and players are underperforming. Fans would say “ a team or player is getting lucky”, but actually managers can apply further analysis to get deeper into the reasons why they are scoring more or less than expected.
Liverpool FC provide an easy example of the xG stat in action. They have an expected goals against (xGA) score of 13.58 after 17 games when in reality they have only conceded 7 goals. What is the most obvious reason for this? Keeper Alisson has the best percentage save stat in the division. Meanwhile, Liverpool’s expected goals scored is pretty much in line with their actual goals. Had Salah been showing the same form as last season, you could expect this stat to be skewed too.
The expected goal statistics can then be used to denote expected points (xPTS), based on whether teams would have won their games according to this model. Liverpool have an expected 37.09 points, which would make them second in the league.
This perhaps shows fans that Liverpool are vulnerable at the top despite now being a point ahead, and that City statistically “should” be running in first place. It could also indicate to the Liverpool manager a reliance on certain players, such as Alisson, to keep up their current position.
Of course, anyone who hates the statistical approach to football will by now be ready to scream at the screen. Results are results! Yet statistics, for those who are interested in working with them, can still provide valuable insights into performance.
Top and Bottom Teams On Expected Points
Based on the xG points model, the bottom teams after 17 games would be Burnley, Fulham and Brighton, followed very closely by Newcastle and Huddersfield. Southampton, currently 17th in the league, land themselves 14th on expected points, and would, therefore, be firmly out of the relegation zone. Brighton, meanwhile, find themselves fighting at the bottom on 14.72 expected points, whereas in reality, they are comfortably out of the relegation zone with 21 points.
At the top of the expected points rankings after 17 games are Manchester City, with an expected points hovering just lower than their actual total of 44. Liverpool would be second with 37.09 expected points, and Chelsea would have 34.86. Tottenham trail behind a little bit with 29.96 expected points. Nothing too surprising so far, but next up is Wolves with 28.98 expected points, edging a little higher than Arsenal with 26.30 points.
Again, the statistics reveal that City are a little more likely to pull it back from Liverpool, and perhaps that Wolves are playing excellent football but have not quite managed to capitalise on their form yet. Critics of expected goals, such as Soccer Sunday host Jeff Sterling, would say that this is “the most useless stat in football history”, but it does seem to add depth and room for analysis. What do you think?
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