Advanced Passing Stats – USWNT vs. France – SheBelieves Cup 2018

Similar to the last post I wrote for the USA-GER match, we’re going to look at passing stats for the latest USA-FRA SheBelieves match, with an added look at specific types of passes such as launched and through balls. Like last time, we’ll only look at open play passes – which excludes throw-ins, free kick passes, goal kicks, and corner kick passes.


The United States started out with a 3-4-3 on offense that turned into a 4-3-3 on defense. In the 3-4-3; the back three was Tierna Davidson, Andi Sullivan, and Abby Dahlkemper; the wingbacks were Kelley O’Hara on the left, Abby Smith on the right, and Casey Short on the right after Smith was subbed out; the center midfielders were Morgan Brian, Lindsey Horan, and Savannah McCaskill after Horan was subbed out, and the front three forwards were Mallory Pugh, Alex Morgan, Megan Rapinoe, and Lynn Williams after Rapinoe was subbed out. On defense, the 3-4-3 would turn into a 4-3-3 with the wingbacks dropping back to defend and Sullivan moving up the midfield in front of the backline. In that 4-3-3, Crystal Dunn played as a fullback and Christen Press played as a forward winger

Later in the game, sometime after the 72nd minute after Sullivan was subbed out and an injury to Short, the 3-4-3 stuck to a 4-3-3 for the rest of the game. On attack the fullbacks would continue to move up, but no center midfielder dropped back to form a back three.



The French formation was a 4-4-2 throughout the match that at times turned into a 4-2-3-1 when on the attack. The centerbacks were Aissatou Tounkara and Mbock Bathy, the fullbacks were Amel Majri on the left and Marion Torrent on the right, the center midfielders were Amandine Henry and Onema Geyoro, the wingers were Eugenie Le Sommer on the left and Viviane Asseyi on the right, and the forwards were Gaetane Thiney and Valerie Gauvin. Gauvin was later replaced by Kadidiatou Diani. Thiney was the more withdrawn of the two forwards, often dropping back deeper to receive the ball.

I will go over the passing stats for each group. Scroll to the bottom to see the complete table.

The Centerbacks

In the U.S. backline, Sullivan’s role was largely spent passing sideways – 56.7% of all her open play pass attempts went sideways, the highest of anyone on the field with at least 10 open play pass attempts. Dahlkemper and Davidson were more forward-minded, with 56.7% and 51.9% of their open play pass attempts going forward, respectively. For the French centerbacks, Tounkara and Mbock’s breakdown of open play pass attempts by direction were similarly more forward-minded, with 53.6% and 63.0% of their open play pass attempts going forward, respectively.

There was a great difference in passes attempted, with the three U.S. centerbacks combining for 181 open play pass attempts, compared to 55 for Tounkara and Mbock, showing just how much time the ball spent going through the U.S. backline during the game.

There was also a great difference in the types of passes attempted. The U.S. centerbacks combined for 23 launched balls and 4 through balls out of open play. No other position group, U.S. or French, got even close to attempting as many launched balls. Dahlkemper even drove forward far enough to attempt a cross. The French centerbacks, however, even with less launched balls and only one through ball attempt, were the ones to get goal out of their efforts – Mbock’s through ball to Le Sommer in the 38th minute led to the score that drew the match for France and registered as a key assist.

The Fullbacks

The U.S. fullbacks were a mixed bag, with O’Hara finishing the match but three different players playing on the other side of the field. O’Hara’s was the more involved, attempting 34 open play passes while the other three combined for 25. O’Hara’s 73.5% completion percentage was the highest of any of the fullbacks with at least 10 pass attempts. The entire group of U.S. fullbacks in open play only amounted to 3 launched ball attempts of which one was completed by Short, 0 through ball attempts, and 3 cross attempts that were all incomplete. Short appeared to have been on her way to an offensive-minded day with 5 of her 8 open play passing attempts going forward until she got injured.

The French fullbacks, meanwhile, were much more present on offense. The two combined for 60 open play pass attempts, one short of the U.S. fullbacks’ 59, but appeared to attempt more on the attack – 63.6% of Majri’s open play pass attempts went forward while it was 74.1% for Torrent – even if their success rate wasn’t as high. Majri competed only 54.5% of her open play pass attempts, while Torrent completed 66.7%. Majri was 1/6 on launched balls, 1/2 on through balls, and 1/5 on crosses. Torrent was 2/6 on launched balls, 0/2 on through balls, and 1/2 on crosses.

The Center Midfielders

The U.S. center midfielders were a similarly mixed bag, and possibly a story of what could have been had McCaskill played for the full 95 minutes. Brian attempted 26 open play passes, the most of any U.S. midfielder, and had a completion percentage of 73.1%, higher than any other U.S. player with at least 10 pass attempts who wasn’t a defender. But McCaskill attempted 20 in just 49 minutes which was on pace for 38.7 passes (let’s say we round it up to 39) in 95 minutes. The biggest knock against McCaskill’s passing numbers is her 65% completion percentage, the third lowest in the game for a U.S. player, likely explained by 65% of her passes going forward, second in the entire game only to Torrent if you exclude the goalkeepers. Horan, who played the entire first half, and Lloyd, who played the last 22 minutes, simply didn’t get off enough open play pass attempts. Between the entire group, they were 1/4 on launched balls and 1/1 on through balls thanks to McCaskill.

The French center midfielders were more involved. Henry attempted 36 open play pass attempts with a completion percentage of 80.6%, while Geyoro attempted 24 passes with a completion percentage of 70.8%. They combined for 5/11 on launched balls and 2/8 on through balls thanks to Henry’s two through ball completions.

The Wingers

The U.S. wingers had the lone goal for their team – a goal by Pugh coming off a chaotic set piece. In the open play, they had a tougher time driving the ball forward. Pugh attempted the most passes, 20, but had a 55% completion percentage, the fourth lowest in the entire game of anyone with at least 10 pass attempts. Williams, who played the entire second half, attempted 13 passes but completed 46.2% of her pass attempts, the lowest in the game. Rapinoe, meanwhile, attempted 10 open play passes and completed 7 of them, but only played the first half. Not a single of the U.S. forward wingers completed a through ball and Press, who only played 18 minutes and attempted 5 open play passes, had the only two completed crosses.

Meanwhile, Le Sommer attempted 30 open play passes and completed 80% of them, higher than any other midfielder in the game with at least 10 pass attempts. Asseyi had less pass attempts, 18, and a lower completion percentage, 72.2%. They each completed one through ball, and Asseyi completed one cross.

The Forwards

Morgan had 17 open play pass attempts, a 70.6% completion percentage, and 52.9% of her pass attempts went forward. That was a higher completion percentage and higher percentage of passes going forward than any of the other U.S. forward wingers. Morgan was 0/1 on launched balls and 1/2 on through balls.

Thiney, meanwhile, had more pass attempts, 29, a lower completion percentage, but appears to have been far more aggressive in driving the ball forward from her withdrawn role. She was 1/2 for launched balls, 3/6 on through balls, and 0/2 on cross attempts. Gauvin, meanwhile, often the lone striker at the top of the French formation, attempted 16 open play passes and racked up a higher completion percentage than Morgan or Thiney, 81.3%, but more of her pass attempts, 43.8%, were going backwards, likely to pass on the ball onto an teammate running towards the goal.

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USWNT passing – comparing positions and opponents’ FIFA rankings

Over the past couple of days I’ve been trying to figure out how to create a Tableau workbook that aggregates all our USWNT data in a similar fashion to the NWSL 2016 Tableau workbook. The main challenge has been figuring out how to best show and compare stats from USWNT that, quite frankly, are all over the place due to how varied the quality of opponents has been.

Thankfully, we’re able to use all the USWNT stats tables we’ve got in the GitHub repo and use the database.csv file, with data for all the matches in the WoSo Stats GitHub repo, to create something that can show something like passing stats adjusted for the opponent’s quality.

The visualizations for the USWNT data, for now, are the two worksheets in this Tableau workbook. Below, I’ll explain what each one is, and some more detail on how how the data was calculated and aggregated to make it easier for you to make similar visualizations.

I won’t delve too much into an actual analysis of the data in the two charts. There’s too much there to go into right now – and why have all the fun when you can do that, too? Anyways, on to the charts

Visualizing USWNT Open Play Passing Stats

First, this visualization of USWNT passing stats for the USWNT matches that we have in our database. Each mark on the chart below represents a USWNT player from a match in our database. The x-axis is her total number of open play passes attempted during that match, the y-axis is her open play passing completion percentage. The color is her designated “position” (more on this later) and the shape of the mark is whether or not the opponent, at the time, had a FIFA ranking in the top 15.

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Midfielders and defenders generally pass the ball more, which is to be expected. Forwards, who are often surrounded by defenders, and goalkeepers, who may often launch the ball forward, see less of the ball and have lower passing completion percentages. It’s pretty clear that differences in passes attempted and in passing completion percentage have to do with the nature of a player’s position. We need to better adjust for position.

Adjusting For A Player’s Position

This visualization shows passing stats adjusted for a USWNT player’s position by using her standard deviation from the average for USWNT players in her position.

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Now it’s easier to spot which players, given their “designated” position, attempted to pass the ball more than average and completed their passes at a higher percentage than average. On the other hand, it’s also easier to spot which players passed the ball less than average and completed their passes at a lower percentage than average.

To account for some outliers, in the chart below I used the filters to exclude performances from any USWNT players who played less than 30 minutes and any USWNT players who had less than 10 open play pass attempts.

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A few things stand out. One, it’s easier to rack up more passing attempts with a high passing completion percentage against lesser opponents, as indicated by how many more cross-shaped marks compared to circle-shaped marks are in the upper-right. And playing top opposition can drastically cut down on both, with several circle-shaped marks spread out throughout the bottom-left corner.

Players’ “Designated” Positions and Next Steps

About the positions. Players are only given one for all their matches, instead of one for each match. This means that a player like Allie Long who in this chart is classified as a “midfielder” is being misrepresented for games where she has played as a defender.

And even within positions, some further refinement could be used. Fullbacks like Kelley O’Hara and Ali Krieger, who are correctly classified as “defenders,” have a propensity towards lower passing completion percentages because, as fullbacks, they often play higher up the pitch where a completed pass is less likely. But because they’re defenders, their passing completion percentage’s standard deviation from the average for all defenders looks worse than it really is because they’re counted against centerbacks, who are also correctly called “defenders” but have some of the highest completion percentages in the game.

A next step is going to be to figure out a way to resolve that Allie Long problem and figure out, on a match-by-match basis, a player’s position for a given match. And then further breaking down some positions like defenders into fullbacks and centerbacks.

Another idea is to only show passing stats broken down by thirds of the fields. I suspect the difference in passing stats vs Top 15 opponents and non-Top 15 opponents would be even more stark when we look at the attacking third.

You can help!

This data only happens because of help from fans like you (yes, you)! The WoSo Stats project needs help to log more stats and location data for USWNT stats, and past NWSL seasons. With your help, we can get even more richer data to expand on what we know about the sport.

If you’re interested in logging data for matches (that are all publicly available on YouTube), read more here and email me at or send me a DM at @WoSoStats on Twitter. All the data logged will be publicly available on the WoSo Stats Github repo and will help me and others do more analyses like these!

Exploring passing stats – USWNT vs. GER (SheBelieves Cup 2016)

As part of our project to track stats for women’s soccer matches (please join and help us get more data!), we’ve been working on adding location data to virtually every action we track. Until now, if you’ve been following some of the stuff I’ve posted on Twitter or the WoSo Stats Shiny app, it’s largely been summary data devoid of location data. That is to say, it adds up aggregates of certain stats (such as total passes attempted by a player or team) or in some cases calculates additional stats based on those basic stats (such as a player’s passing completion percentage), none of which take into account where a player was on the field.

This time, I’m going to look at location-based data. In this post, to make things simple, I’m going to focus one match, the USA-Germany SheBelieves 2016 match. To make things even simpler, I’m also just going to look at passing and possession. This is an early dive into the location data we’re getting from this project, and how it can complement what we already know about a match based on its summary stats and, well, actually watching the game.

Passing Stats

One of the most interesting things I found while exploring the stats this project is generating was the impact of pressure on a player’s passing completion percentage.  I expected, based on intuition, to see a player’s passing completion percentage to go down with pressure, but what I saw was that, on average, it barely had an impact.

Impact of Pressure on opPassing


What you’re looking at is the impact that pressure had on a player’s open play passing completion percentage. Open play passes are all passes that aren’t throw ins, free kicks, corner kicks, goal kicks, or goalkeeper throws or dropkicks. I excluded those because those, by definition, can never be “under pressure” by a defender. In the chart above, the further to the right the bar is, the better the player’s open play passing completion percentage got under pressure. To account for differences in open play passing attempts, the darker the green, the more open play passes that player attempted under pressure.

For me, this was a bit of a head-scratcher at first, as I noticed similar numbers across different matches. The median difference is +15%, so it looks like more players’ passing completion percentage actually got better under pressure. I initially chalked this up to, well, these are the two best teams in the world and great players should continue to make good passes under pressure.

However, upon further thought, this does make some sense, which merits further analysis later on. A player under pressure is probably going to be more likely to revert to a “safer” pass, such as a backwards pass, or be forced into a riskier play, such as a take on, due to not having enough space or time to get a pass off. Inversely, a player who isn’t under pressure, with more time and space with the ball, might be more likely to attempt a riskier pass, such as a launched ball, or not even a pass altogether and instead opt for a shot.

It seems pressure might be a better predictor of a player’s passing completion percentage once we are able to break down those decisions a little better, but I’ll save that for another day. What do I want to get at is what happens to these passing stats when we break it down by location.

Adding Location Data

For each pass attempt, we tracked it’s origin (i.e. where the player was passing from) according to which one of the following “zones” on the field she was in.


For this analysis, I grouped together passes in the defensive middle third and attacking middle third as passes that generally happened in the middle third. Now, what happens to a player’s open play passing completion percentage when she’s passing from within that all-important attacking third?


It drops for pretty much everyone in the match who attempted an open play pass in the attacking third. Again, darker colors indicate more attacking third passing attempts, and the further to the right the bar is the better that player’s passing completion percentage got in the attacking third, compared to her passes in the middle and attacking third.

There are some outliers here. Lloyd, Horan, and Pugh had some very stark differences in completion percentage, but also because they barely attempted any passes from within the attacking third. In general, though, it appears that most players in this match had their passing completion percentage negative affected.

Something interesting worth pointing out is that most of the players in the top half of the chart were German. This stands out even more when we take these two different passing completion percentages (in the attacking 3rd vs. everywhere else) and put them on a dot plot, with a color for each team, as shown below.

opPassing by Location - Dot Plot.png

The further to the right, the higher the player’s open play passing completion percentage in the defensive and middle third. The higher up, the higher the player’s open play passing completion percentage in the attacking third. The size of the dot indicates the number of open play pass attempts in the attacking third, so players who attempted more passes in that part of the field stand out more.

Almost every German player was above the median for open play passing completion percentage in the attacking third. Notably, Marozsan was the only player in the 75th percentile (better than 75% of all players in the match) for both categories. Meanwhile, it looks like Brian’s passing in this match was negatively affected the most when attempting a pass from within the attacking third.

Unfortunately for Germany, despite having better passing completion percentages in the attacking third and applying what appears to have been great pressure on the U.S. defense, they still lost due to an incredible take-on by Alex Morgan in the penalty box that led to an equalizer and an equally incredible error from Almuth Schult, the German goalkeeper, that gave Sam Mewis the game-winner.

Better passing in the attacking third, then, wasn’t enough to get Germany the win, which is really all that ultimately matters in soccer. It’ll be interesting to see, though, as we get more data for more matches, if that’s out of the ordinary. All that pressure on U.S. defense did get the Germans a goal and credit as the only team in 2016 to date to score a goal on the United States. It may not be a guarantee of victory, but I suspect it points most team in the right direction.

Either way, the way the U.S. goals came about is a nice segue into an analysis of take-ons (and what a player does afterwards) and changes in possessions (and where they happen), which I hope to do in the coming week with the USA-Colombia matches.

You can view the stats and visualizations used in this blog post on Tableau and the WoSo Stats Shiny app. All the source  data is freely available on the GitHub repository.


Okay, if you’ve scrolled this far down then hopefully you’ll be interested enough to help us contribute to our small but growing database of women’s soccer stats. As almost everyone who’s tried to search for something as simple as passing stats for their favorite player knows, there’s a dearth of even the most basic stats for women’s soccer and really women’s sports in general.

Please help us change that, one match at a time! We need people who are willing to volunteer some time and effort (any and all would be appreciate) into logging data for women’s soccer matches. To see which matches immediately need help, check out this month’s goals. To learn how to help and get started, read here. The hope is, for starters, to track every NWSL 2016 match but we still need more people!