NWSL Expected Goals: Six Graphs From Weeks 1 and 2

 

Given the paucity of data and analysis for women’s soccer, I thought it would be a worthwhile summer project to build an Expected Goals (xG) model for the NWSL. If you’re unfamiliar with Expected Goals, I’ve written a few posts about the math behind the model that are probably worth reading: A Very Preliminary NWSL Expected Goals Model: xG 101 and Expected Goals 201: xG For Soccer Analytics Majors. The basic idea is taking characteristics of shots like distance from goal, angle to the center of goal, whether it was kicked or headed, whether it came from a counter attack, etc., and calculating the probability that a given shot turns into a goal. Shots are rated on a scale from 0-1, with the number being the probability of a shot scoring.

I’ve been tweeting some of the things I’ve found, but they’ve been scattered across a number of tweets and days, so I wanted to combine them all into one post and talk about some of the plots in a little more detail than is allowed in 140 characters (116 after the image).

Week 2 Game xG Plot

This plot shows the relative xG scores for each game over the weekend. Most of the games were fairly close in terms of shot quality, except for Houston v. Orlando which was fairly one-sided (both in actual score and shot quality). I don’t have xG maps, but I think this is a clean, clear presentation of what happened in each of the games from the weekend.

Rplot01

The next plot shows cumulative player xG/Shot Quality scores over the first two weeks. Two things stand out here to me. The first is Jessica McDonald’s massive lead on everyone on her team (and the league which we’ll see in a minute), and the balance among the Portland Thorns: few players taking seemingly high quality shots.  Comparing this to FC Kansas City with a larger number of players taking relatively low quality shots.

Week 2 NWSL Individual 20

The third graph shows the top 20 players in the cumulative shot quality rankings. I tried color-coding by team, but with so many teams relying on red or blue it didn’t come out as well as I’d like.  The good news is that Orlando (purple) and Houston (orange)  stand out. USWNT players are doing well here – Alex Morgan is in second place (far) behind, with Jessica McDonald, Lindsey Horan, Carli Lloyd, and Christen Press all in the top few spots. Jessica McDonald is leading the pack by a long way though, with zero goals so far unfortunately for the WNY Flash.

Week 2 NWSL Individual All

This graph is similar to the previous one but instead of the top 20 it includes everyone who has taken a shot this season so you can see where your favorite player ranks.

NWSL Week 2 xg v Actual

The last two plots serve both as a diagnostic plot of my measure: how well does my xG score predict actual goals? In this first one, the dotted line represents a 1:1 relationship between expected goals and actual (non-penalty, non-own) goals scored, which is a “perfect” correspondence between my measure and the “real world.” I’ve got five teams (Chicago, Orlando, Washington, and Kansas City) basically on the line, which I’m really happy with, two other teams (Portland and Seattle) close, and three outliers (WNY, Boston, and Houston). I’m really happy with this so far, and despite the small sample size this season so far I think the model is performing well.

Beyond the diagnostics, if we assume that teams will eventually converge toward the 1:1 ratio, we’d expect WNY (mostly Jessica McDonald) and Boston to start scoring more goals soon, while the overachieving Houston Dash might be in for a dry spell soon.

NWSL Week 2 xGA v Actual

The final plot is the other side of the last one – expected goals allowed for each teach vs. actual goals allowed. There are fewer teams that fit perfectly (Sky Blue and Portland), but there aren’t any extreme outliers this time (with Kansas City being the furthest off the line).

Kansas City is probably due to start conceding more goals soon. Nicole Barnhart has  been strong in goal for them so far, so maybe she’s the reason for Kansas City over-achieving on this measure.  Similarly, despite a strong start by the expansion Orlando Pride, they’ve actually conceded a goal more than my model expects they should have so they may be due for some luck going forward.

This is all I could think of as far as presenting xG/Shot quality data for the NWSL. There’s a lot of data here, and I tried cutting it as many ways as I could to present as much info as I could from a single dataset.






MOTSON’s 2015-2016 Hits and Misses

I may be a  bit premature here, but it seems to me the major parts of the Premier League season are pretty much decided. Leicester City seems uncatchable at the top. Arsenal and Spurs will finish 2nd and 3rd (or 3rd and 2nd) while Man City looks pretty solid for 4th. Two of the three relegation spots are basically sealed, but there’s still the matter of whether Norwich City or Sunderland stay up. Because my interest in the season has waned significantly, I thought I’d do an early “year-in-review” where I assess MOTSON’s biggest hits and biggest misses. I’ll start with the obvious.

Chelsea

Yeah, I don’t know what to say about this. I had Chelsea in second place at the beginning of the season and they look to be stuck in literally the middle of the table. Everyone else was roughly in the same boat MOTSON was, and I honestly don’t know if this could have been foreseen. *Maybe* if you added a “Mourinho third year implosion” variable to the model, but even then would you have guessed 10th place? Nevertheless, it’s a pretty big miss and was the source of the majority of the error in my model.

Leicester City

I’m going to call this a hit and a miss, but more of a miss than a hit to be honest. I’ve been particularly proud of MOTSON predicting Leicester City higher than anyone else – 8th place on 60 points. Not bad, and if they finished 3rd or lower I was willing to call this a huge success for the model. On the other hand, if they win the title this year then it’s hard to say “I had the Champs in 8th place – I win!” I’m proud that my model recognized them as good long before anyone else did, and if you look at a lot of analytics prognostications for next year they’re saying “Leicester’s probably 7th or 8th place” so MOTSON is 9 months ahead of the curve there. But it’s a small victory assuming they win the league with 15-20 points more than I predicted.

That being said, MOTSON was ahead of the curve predicting them as Champions League qualifiers, picking them to qualify as of December 5. I know this because on December 4th I wrote that they should obviously sell Jamie Vardy because they had no expectation of the Champions League and December 6th changed my mind.

Game Theory: Leicester Has To Sell Jamie Vardy in January

Game Theory: Top 4 Contenders Leicester City Should Absolutely Keep Jamie Vardy

Nicolas Otamendi

MOTSON *hated* this signing by Man City back in August, and it turned out to be right. He was a disaster in City’s backline, and is one of the reasons City’s fighting for 4th instead of comfortably coasting into the Champions League.

Transfer Rumors 0817

West Ham

So MOTSON didn’t get West Ham’s success right pre-season, but it did pick up on their top 6 challenge *very* early in the season (October 24). Mike Goodman and I had a conversation about this, and I argued that West Ham banking those 8 points over expectations would be enough to get them a top 6 spot. As of today they’re 10 points over expectation, so they’ve basically broken even since then and look to be in the top 6 at the end of the season.

Leicester City Redux

MOTSON really liked Jamie Vardy to have a big year this year, something I didn’t notice until it had already happened because he wasn’t on my radar.

We Should Have Seen It Coming: Evaluating Jamie Vardy Against the EPL’s Elite Strikers

It also really liked Riyad Mahrez, pegging his replacement as something like a 10 point downgrade.

On the other hand, I never posted anything along these lines, but it didn’t really like the N’golo Kante signing which I’d classify as a pretty big miss. He’s been phenomenal for them and MOTSON would have told them to pass.

Barcelona Will Be Fine Without Messi

Lionel Messi got injured in the early part of the season, out for a month, and “real football men” wrote all sorts of thinkpieces about how Barcelona would be in trouble losing the world’s best player despite having two other world class strikers on the pitch even in Messi’s absence (and decent young backups filling in). MOTSON got it right: Barcelona would be fine without him, and they were. This is one of my favorite analytics pieces I’ve written, so I wanted to bump it.

Will Barcelona Be Fine Without Messi?

Those are the big ones I can remember – plenty of successes for its first year with out of sample data but plenty of room for improvement as well. I may revisit this at some point, but for now I think this is a good recap of the model. Thanks for reading, and this summer I’ll be focusing on bringing statistics to the NWSL so keep an eye out for that.