Arsenal in WSL 22/23: a broad scala of finishing
The new WSL season is set to start soon and one of the questions that is going through my mind is: who is going to become champions in the end? It feels natural at this point, to say that Chelsea are favourites and that Arsenal should be their main rival/contender for the title. But, to do that, Arsenal need to find something solid and build on what they have achieved in the last 2 seasons.
So, Arsenal tended to focus on a few big players who have tremendous attacking talent but couldn’t utilise that plan due to a large number of long-term injuries during the 2022–2023 season. So they needed to diversify the way they scored goals and how the threat was created. And, by whom?
In this article, I will review in what way Arsenal managed to create an attacking threat through expected goals and how it affected different game states. This will mean I will focus solely on shots taken in the 2022–2023 season and only give analysis on this type of data. I’m well aware there are many ways to look at data and analysis, but to keep it compact I will only look at that.
Data
There are different ways of looking at data and different providers, but for the sake of this research, I’ve used Opta positional data — which is X and Y data — of the 2022–2023 season. Based on that, I’ve employed my own expected goals model, which isn’t as precise or accurate as the more advanced ones, but isn’t far off in xG AND gives me more freedom to work on different aspects of the data.
I’m only focusing on the 2022–2023 season and are incorporating all of Arsenal’s shots and corresponding xG, and whether it features in a goal.
Methodology
So, what am I going to do? Every game has a different bunch of scenarios and situations. That influences the xG and I will make a calculation of all the different scenarios and see how well/badly Arsenal did on a team level.
My purpose of this little research is to go beyond that and show what players take on what shots, from which position and how it impacts Arsenal as a whole. Basically answering the question: is the feeling that Arsenal diversify their xG over many players, true or false? This means I will include tables, but also shotmaps of the players in question.
Only players that have taken shots will be included, regardless of position. I will include penalties. But because they make up for a lot of xG in an isolated situation, I will always give the non-penalty xG. Own goals from the opposition are also excluded.
Arsenal: total expected goals
As you can see in the image above, Arsenal have scored 49 goals (in red) out of 394 shots. That’s a goal conversion of 12,44%. There has been an xG of 46,87 with 42,13 xG when we exclude the penalties. It means that Arsenal slightly overperformed their xG: +2,03.
So from which situations did Arsenal get their xG?
Most of the expected goals come from regular play, as you can see with 32,30 xG. Second is xG from corner with 6,16, followed by xG from penalties with 4,74. Set piece — which is as a result of a set piece, not directly from it — is 1,93. On the fast break or counter-attack is 1,34 and lastly, direct freekicks have 0,41 xG.
As you can there’s an overwhelming majority in expected goals from regular play, but shots from corners is higher than penalties, which is quite interesting. So how many xG does Arsenal average per shot?
As you can see the highest xG per shot is from penalties, but that does make a lot of sense due to the nature of the chance. But then, the highest xG comes from the fast break and set pieces, indicating the shots taken from counter-attacks and set pieces represent a higher chance of scoring.
The next question is, in what game state does Arsenal create the most xG and are the chances bigger/smaller in certain game states? With game state I mean: winning, losing or drawing.
As you can see in the table above, Arsenal create the most xG when they are leading with 28,36 xG, then 13,52 xG when drawing and 4,99 xG when they are trailing. This might seem natural, but it does also tell us that it’s hard to create meaningful xG when you are losing. That’s important especially when you are in a must-win game.
How does Arsenal divide over the bodyparts? And, no I don’t mean any kind of murder. I mean, which body part do they create most xG with?
Since the majority of the players have the right as dominant foot in football it is not weird that that is the highest with 27,41 xG. Left-footed shots are second with 11,44 xG and shots from heads are third with 7,20 xG.
Arsenal: Players part in expected goals
So, the main part of my little research is to see whether many players have a small part in the total xG or that a select number of players have a big part in the total xG.
Let us first look at which players have accumulated xG during the season and what their total has been.
It’s a longer list, but what’s most important is that many players contribute to the expected goals of Arsenal during the 2022–2023 season. Blackstenius, Foord and Maanum have the biggest part in creating expected goals for Arsenal, with Little, McCabe, Miedema and Mead with a smaller part.
Now Miedema and Mead are obviously great players, but we will exclude them right now, because they both had long term injuries and didn’t play a big part of the season.
That leaves us with five players: Blackstenius, Foord, Maanum, Little and McCabe. So how do they each generate their xG from shot and where do they do it from?
Blackstenius
Stina Blackstenius has scored 8 goals from 70 shots and generated 9,64 xG. It suggests that she is slightly underperforming with -1,64. She averages 11,26 meters per shot and you can see that in the dots which are largely in the penalty area.
Her xG is the highest from regular play, but we often tend to see that Blackstenius is really good from the break/counter-attacks — and that’s the second on this list. It tells us a little bit more on how coaches might want to use her.
Foord
Caitlin Foord has scored 6 goals from 51 shots and generated 7,19 xG. It suggests that she is slightly underperforming with -1,19. She averages 12,59 meters per shot and you can see that in the dots which are largely in the penalty area, but more shots from outside it in comparison to Blackstenius.
Her xG is the highest from regular play, but in second category we have corners. This is significantly lower, which tells us she is most comfortable and a threat from regular play.
Leonhardsen Maanum
Frida Leonhardsen Maanum has scored 9 goals from 62 shots and generated 6,08 xG. It suggests that she is overperforming with +2,92. She averages 16,36 meters per shot and you can see that in the dots. She shoots a lot from outside the penalty area and with success: 2 goals come from way outside the penalty area
Her xG is the highest from regular play, but she does possess the ability to have a good shot from distance from free kicks, which can definitely help her team going forward.
Little
Kim Little has scored 4 goals from 12 shots and generated 3,82 xG. 3 of the goals came from penalties, so from open play she had 0,66 xG and 1 goal. It suggests that she is overperforming with +0,34. She averages 13,72 meters per shot and you can see that in the dots, but that mostly comes from the penalties she has taken.
Her xG is the highest from penalties, but that’s quite logical.
McCabe
Katie McCabe has scored 3 goals from 24 shots and generated 3,42 xG. She had a non-penalty xG of 1,84 and 3 goal. It suggests that she is overperforming with +1,16. She averages 15,73 meters per shot and you can see that in the dots, but that mostly comes from the penalties she has taken and some shots from a distance outside the penalty area.
Her xG is the highest from penalties, but that’s quite logical given she has taken penalties. Her xG from regular play comes second with 1,57 xG and that’s very close too.
Final thoughts
Coming into my data research I thought that Arsenal would divide the expected goals over a bigger number of players. And while that is partly true, the gross of the xG was generated by a handful of players. This means that still some player have a huge part in xG and goals, but it also means that Arsenal can score loads of goals without Miedema and Mead, which is a huge plus going into the 2023–2024 season.
