A Quick Look at the FAWSL So Far

Expected Threat

After expected goals, the next big thing in football analytics looks like it could be possession value models. There’s already plenty to read and watch about the different models. Some things that I’ve seen and would recommend are below:

Thanks to the socceraction package by @TomDecroos it’s easy to run through the notebooks and apply both VAEP and xT models on the free StatsBomb data.  A post was published about the two models in the package here. I’ve used both but I was getting some funny values with the VAEP model, so I’ve opted to just use the xT model for the time being.

You’re better off reading the post by Karun (or watching the presentation from the StatsBomb conference) to better understand xT (I don’t want to butcher the definition of it here), but the basis of it seems to be looking at how actions increase a team’s chance of scoring. In the ‘Vizualisng xT’ section of the original xT post, you can see the xT value in different areas of the pitch, which is explained as the percentage of times a team will score in the next five actions when the ball is in that zone. So, applying it on a player level, we should be able to find players who move the ball into dangerous areas and increase their side’s chance of scoring.

Selfishly, my first port of call when getting player values was to see how they compared to the progression figures I’ve been doing since the summer. My progression method is simple and just looks at the distance players moved the possession towards goal, but I thought it’d be interesting to see how they differ.

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(The dark plots happened because how I was originally going to post these included a dark background and it all blended in but I’m too lazy to change it for here)

For the most part, it seems my progression method overrates defensive players and underrates attacking players. This isn’t too surprising as my lists of most progressive players are almost always dominated by defensive players. It looks at distance gained and they have the most distance to play into. xT is better in that it looks at increasing a team’s chance of scoring rather than just moving the ball forward.

The standout on the graph is Manchester City’s Janine Beckie. Beckie started the season in an attacking position but, since the injury to Aoife Mannion, has been playing as a very attacking right-back in recent weeks. She’s supplied the width down the right for City and has been a strong attacking asset for them. She’s also been the most unlucky player in front of goal in the WSL this season too. She has a non-penalty xG of 3.7 – the 4th highest in the league – but is yet to score.

The plot below, showing her most valuable actions, shows how she’s offered width on the right and how most of her high-value actions are crosses.

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This may also account for some of the differences with attackers – I took crosses out of my progression numbers a while back. It was probably silly, but when I did the method I was looking more for players who advance the ball with passes as opposed to crosses. The plot below shows a comparison of the values with crosses removed from the xT values.

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The defensive bias of my progression numbers is still on display with the trio of Vivianne Miedema, Leah Galton and Remi Allen being the standouts for showing up much better with xT. Removing crosses sees Ji So-yun move into top spot for xT per 90 too, though it’s a lot closer with Kim Little and a few others not too far behind.

I’m going to leave this here for now, while there’s plenty more to talk about I just wanted to give a quick intro to xT in the WSL and I’ll use it in pieces from now on, rather than giving a big overview here. To sign off though, here are the top ten players for xT per 90 this season (more than 300 minutes played).

NameTeamxT per 90
Janine BeckieMan City0.54
Lisa EvansArsenal0.44
Leah GaltonMan United0.42
Ji So-yunChelsea0.40
Kim LittleArsenal0.39
Tessa WullaertMan City0.39
Beth MeadArsenal0.39
Guro ReitenChelsea0.38
Rinsola BabajideLiverpool0.36
Ramona BachmannChelsea0.36

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