Passing networks for the Seattle Reign’s 2016 season

Following up on the previous post that delved into the passing network for the Portland Thorns’ 2016 season, now it’s time for the Seattle Reign. Same approach as last time, and the Seattle Reign’s passing networks as an Excel workbook can be downloaded here from the WoSo Stats GitHub repo.

I’ll also look at how the numbers compare to the Portland Thorns’ passing network, although as I write more of these for every team it’s going to be harder to keep these comparisons within the scope of one blog post.

First things first, the first sheet in the Excel workbook, and explanations again for what we’re looking at.


The rows are players passing the ball and the columns are players receiving a completed pass. The cell in the bottom-left area where the “Yanez” coumn meets the “Barnes” column, then, is the total number of passes that Yanez completed to Barnes throughout the 2016 NWSL season.

Each cell only represents completed passes. This is extremely important, because we’re missing out on data about how many times a player was actually targeted by another teammate. This data is missing because, well, it can get extremely hard, if not outright impossible, to determine both from looking at the match spreadsheet and even during a match where a missed/blocked/cleared/intercepted pass was supposed to go. Maybe in the future we, or someone else, can go back through all these matches or future matches and figure out how to do that, but for now we’re going to have to go without that. But at the very least understand that these passing numbers only represent completed passes

The darker the green, the higher the value of the cell. The whiter the cell, the closer it is to zero.

These are raw numbers for the entire season, and they don’t take into account how many minutes each player combo was actually on the field. The table below does, with each cell now representing passes completed per 90 minutes on the field that player combo was on the field.


As was done with the previous post, I hid the columns for players who never were on the field with any teammates for 270 or more minutes to exclude any extremely high passing per 90 numbers that may show up merely because a few passes were exchanged during very limited minutes.

Despite that, there are two player relationships with extremely higher completed passes per 90 than anyone else – Reed-to-Kawasumi (17.1) and Solo-to-Corsie (13.0) Outside of those two, there’s a concentration of passing relationship with relatively high numbers in the upper left portion of the spreadsheet, with a few more darkly-shaded cells further down the defender columns and defender rows.

Compared to the Portland Thorn’s per 90 passing network, where that upper-left region of the spreadsheet is lighter, a greater proportion of Seattle’s completed passes were coming from defenders or going to defenders. That doesn’t necessarily mean that Seattle’s midfielders or forwards were doing less. If you look at the raw numbers in the area for midfielders-to-midfielders and midfielders-to-forwards, it actually looks like Seattle had more completed passes per 90 going on – there was just even more passing going on in the back.

Below is the same spreadsheet, but with each row (each passer’s recipient) highlighted individually.


This table makes more sense if you look at the columns and look for players with a high number of very dark cells, indicating that they’re a top completed passing target for several players.

With that in mind, Barnes and Fishlock stand out. Barnes was the #1 or #2 target for the most completed passes per 90 for six different players – Kopmeyer, Fletcher, Pickett, Fishlock, Utsugi, and Winters. Fishlock was the #1 and #2 for four different players – Barnes, Corsie, Little, and Solaun.

As for the forwards, the two biggest targets appear to have been Kawasumi and Yanez, with a relatively high number of passes per 90 going her way from the midfield, other forwards, and the defense.

Below, the highlighting is flipped around and each column’s highest values are highlighted.


Now, look at which rows have a higher number of darker cells, indicating that they’re a top origin for completed passes per 90 for several players.

Defenders stand out as a top origin for completed passes, as opposed to the Thorns’ passing network where those columns were a lighter shade. The Reign in general appear to have some pretty extreme differences throughout this spreadsheet, with players like Utsugi, Fishlock, and Kawasumi passing to certain teammates way more than anyone else.

A look at Ali Krieger’s passing and defending stats in the NWSL

Yesterday, I did a bit of how-to and analysis in one post for open play passing stats in the NWSL 2016 season – at least for the matches for which we have location data (40 out of 103, as of this writing, to be exact).

Now, I’m going to work with the same dataset in combination with the NWSL 2016 rosters database we’ve got to look at a very statistically interesting type of player – the fullback. We’ll focus one highly productive example – the Washington Spirit’s Ali Krieger, one of the fullbacks in our dataset with the most open play passes attempted per 90 minutes. We’ll look at how their passing and defensive responsibilities change in each third.

The following visualizations and data are all available to interact with and download from this Tableau visualization. All the data can be calculated from our WoSo Stats GitHub repo by following the instructions in yesterday’s post for how to use the R code I’ve created.

The following data is also only for 40 out of 103 NWSL 2016 matches that we’ve logged with complete location data. To see the list of matches this data represents, see the database in the WoSo Stats Github and look for all the matches with “yes” in the “location.complete” column.

Open Play Pass Attempts by Thirds of the Field

Let’s look again at this chart from yesterday- it’s all players with a minimum of 270 minutes logged with location data by the WoSo Stats project, sorted by open play pass attempts per 90, and broken down by what third of the field a player’s passes originated.


Takes just a little bit of mental math to notice that out of all the defenders up here, the two with a noticeably higher percentage of pass attempts in the attacking 3rd are O’Hara and Krieger. Those of you who follow the sport will know why – they’re both fullbacks, asked to join the attack much more often than their counterparts at centerback.

Now, using the rosters database, let’s filter out anyone who isn’t a defender.

Sticking with these passing stats one more time before we delve into other stats, I duplicated the chart above, but I filtered out anyone who isn’t a defender. And now the stacked bar charts represent the percentage of all passing attempts that were within each third of the field. In other words, we’re now looking at defenders passing attempts, now broken down by what percentage was in each third. I sorted the following by percentage of passes in the attacking 3rd, so the defenders with the highest percentage of passes in the attacking 3rd are at the top.



The top, for those of you familiar with the NWSL 2016 season, is full of fullbacks. Pickett, Reed, Catley, Gilliland, Hinkle, they’re all there. Reed and Pickett, the two Seattle players visible here, are notably the only defenders with more than 25% of their pass attempts in the attacking 3rd. Further down the chart, we run into more defenders who were shuffled between the fullback and centerback role throughout the matches we’ve logged, and then more and more centerbacks.

Back to Krieger. 18.8% of Krieger’s open play pass attempts were in the attacking 3rd, compared to the other fullback on her team visible here, Kleiner, with 15.3%. Krieger’s middle 3rd open play pass attempts were 60.1% of all her attempts, and her defensive 3rd open play pass attempts were the remaining 21%.

There’s a lot of different things we could look at with regards to passing stats. How is Krieger passing the ball out of her defensive 3rd, relative to other defenders? What’s she doing in the midfield with over 60% of her pass attempts? And what’s going on with those 18.8% of passes in the attacking 3rd compared to everyone who’s above her – and below her?

Open Play Passing in the Attacking 3rd

Let’s just look at those attacking 3rd open play passing attempts. Below is the same group of players from above, now charting the percentage of open play pass attempts in the attacking 3rd vs. their open play passing completion percentage in that attacking 3rd.

Out of all the defenders whose percentage of open play pass attempts in the attacking 3rd is over the 75th percentile, Krieger is among two others – Reed and Klingenberg – whose completion percentage hovers around the the 75th percentile for open play passing completion percentage in the attacking third.

Screen Shot 2017-02-26 at 1.42.34 PM.png

Recall from yesterday’s post that the median open play passing completion percentage in this 3rd of the field for all players was 60.6%. Krieger’s is 65.4%.

Defending in the Middle and Defensive 3rd

Now let’s turn to defending stats. I started off with what in the stats table are called “possession disruptions,” – successful tackles and dispossessions of the opponent. That is, instances where a defender was attempting to go 1 on 1 with an opponent and strip the ball away. Below is a chart for all defenders with at least 270 minutes logged with location data, sorted by opponent possessions disrupted per 90 minutes, and broken down by what third of the field they were in.

Screen Shot 2017-02-26 at 2.25.50 PM.png

Krieger doesn’t even show up in this list. She’s down below in the middle of the pack, not even getting more than two opposing possessions disrupted per 90.


But there’s more than one way to defend and her contribution to defense is much more apparent when we look at a different type of defending stats – “ball disruptions.” That is, interceptions, blocks, and clearances of the opponent’s ball – usually a pass attempt. Below is a chart for all defenders with at least 270 minutes logged with location data, sorted the players by ball disruptions per 90 minutes, and broke it down by what third of the field they were in.

Screen Shot 2017-02-26 at 2.32.00 PM.png


Krieger is not only up there at #3, but she’s also surrounded by mostly centerbacks.

Now let’s look at those disruptions in the attacking 3rd and middle 3rd, broken down by whether they were interceptions, blocks, or clearances.



Krieger is also out of the top 15 when we look at ball disruptions in the defensive 3rd, but in the middle 3rd she’s ridiculously ahead of every other defender. Even when I included all players, not just defenders, in this visualization, she was still far and away the top of the list.

Screen Shot 2017-02-26 at 2.37.36 PM.png

The number of interceptions per 90 minutes, 4.1, that Krieger gets by themselves are higher than all ball disruptions for some players. I personally think extremely highly of interceptions, as they’re instances when a defending player not only stops the ball but wins clear possession of it – essentially getting credit for a turnover in possession. To get them that high up the pitch compared to every other player might not just make her a good defender, it can also make her a dangerous attacker.

We need your help!

As was noted above, this is only 40 matches out of a 103-game NWSL 2016 season. The WoSo Stats project desperately needs your help to log more basic stats and location data for the 2016 season. The more data we get, the better we’ll understand 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 with be publicly available on the WoSo Stats Github repo.



How to break down NWSL passing stats by thirds of the field

In this post, we’re going to look at passing stats by location.

We’ll create two spreadsheets, one with stats for all NWSL 2016 matches that have been logged with location data by the WoSo Stats project, and another with those same stats but broken down by thirds of the field.

I’ll show you the R code used to generate them, and we’ll go over some Tableau visualizations I’ve created to dig into the passing data a little further.

The instructions for how to use the creating-stats.R file are here in the WoSo Stats Github repo. If you’re familiar with R, first things first, source this R file and then run the getStatsInBulk function with the arguments shown below:

your_stats_list <- getStatsInBulk(competition.slug=”nwsl-2016″, location_complete = TRUE)

This will take about a minute. Then run the mergeMatchList function with the following arguments to get the stats table as a data frame named “your_stats”:

your_stats <- mergeStatsList(stats_list = your_stats_list, location = “none”, add_per90 = TRUE)

In there are columns for open play passes, which in the columns are called “opPass.” Open play passes are defined as all passes that aren’t one of the following – namely, dead ball plays:

  • Throw-ins
  • Corner kicks
  • Goal kicks
  • Free kicks
  • Drop kicks or throws by the goalkeeper

The columns we’re going to be primarily concerned with are those named “opPass Att per 90” “opPass Comp Pct,” and it might be useful to also look at “opPass Comp per 90.” When we break these down by thirds of the field further below, they’ll be prefaced with their respective location – so, there will be “A3 Pass Att per 90,” “M3 Pass Att per 90,” and “D3 Pass Att per 90.”

If you don’t know anything about R, don’t worry, you can just follow along with the charts below and ignore all these details about the code and spreadsheets.

The data represented in this post will be available to download from this Tableau visualization. There, you can also interact with the charts shown below.

Another fair warning: the following data only represents 40 matches out of the 103 NWSL 2016 season. They’re all the NWSL 2016 matches in the database with “yes” marked off in the location.complete column. We need more help logging data, and that help could be you!

On to the data, though. What do open play passes look like, without regard for where they came from?

Open Play Passes (without location)

This chart shows open play passing completion percentages, sorted by open play passes attempted per 90. That is, the players at the top attempted the most passes in open play per 90 minutes (take their open play pass attempts, divide it by the number of minutes they played, and multiply that quotient by 90).


Here is a table showing the data behind this chart, with an added column for open play passes that were actually completed. “GP” is games played (really, the games that we’ve logged) and “MP” is minutes played.

Screen Shot 2017-02-25 at 10.53.50 AM.png

The top 15 is full of players with generally very high passing completion percentages – all are above the median of 74.9%, except for Fishlock and Krieger.

This chart is stacked with Seattle Reign players, but it’s also stacked with largely defensive-minded players. Corsie, Fletcher, Barnes, Averbuch, O’Hara, Hickmann Alves, and Krieger – nearly half the players are defenders. Defenders usually have higher passing percentages (or at least they should), and they probably see more of the ball than the rest of their teammates, so it shouldn’t be surprising that, since we sorted by open play pass attempts per 90, we got a lot of defenders, and that most of them have pretty good passing completion percentages.

How to look at open play passing stats, then, in a way that accounts for a lot of passing going on in the defensive third. What’s going in with Little? Does her passing completion percentage fall off the top 15 if we could look at her passes in the attacking third? And what about O’Hara, a player who is known to run up and down the field? What does her passing look like in the defensive, middle, and attacking thirds of the field?

To get this data, we have to run some R code again.

Open Play Passes (broken down by thirds of the field)

To get a stats table with all stats broken down by thirds of the field (attacking, middle, and defensive thirds), run this code.

your_stats_list <- getStatsInBulk(competition.slug=”nwsl-2016″, location_complete = TRUE, location = “thirds”)

your_stats <- mergeStatsList(stats_list = your_stats_list, location = “thirds”,add_per90 = TRUE)

You might be sitting there for a few minutes, but the “your_stats” data frame, a 900-column table, will have what we’re looking for.

Now, when we sort by open play passes attempted per 90 and break down passes by thirds of the field for that top 15, it becomes clearer where everything was going on.


Fishlock – who, in this dataset, it should be pointed out only has 4 matches logged with location data – is far ahead of the pack when it comes to open play pass attempts, but very few are from her own defensive third. The brunt of her open play pass attempts, as it is for almost everyone seen here, are in the middle of the field, but there is a significant portion of attempts going on in the attacking third.

Another player who had a relatively low open play passing completion percentage was Krieger, and the distribution of her passes is more even. Roughly 60% of her passes were in the middle, and roughly 20% in the defensive and attacking thirds. Her passing completion percentage is probably pretty good in the defensive third, but we’ll soon have look at what it’s like in the middle and attacking third.

And then there’s Little, who had a better open play passing completion percentage by over 20 percentage points than Fishlock, and that’s with a higher percentage of passes in the attacking third (27%, compared to Fishlock’s 24%).

What this chart lacks is passing completion percentages for each third of the field. For that, we can look at a chart, similar to the first one, but for each third of the field.

Open Play Passes in the Defensive 3rd

When looking at open play passing completion percentages in the defensive 3rd, and sorting by how many open play passes were attempted (per 90) out of the defensive 3rd, the chart is exclusively defenders and goalkeepers.

Screen Shot 2017-02-25 at 11.45.50 AM.png

Unsurprisingly, the media open play passing completion percentage, at 81%, in the defensive 3rd is higher than the median for all open play passes. There’s quite a range of passing completion percentages, from over 90% for the likes of Kallman and Fletcher and at or below 70% for Pressley and D’Angelo (a goalkeeper). That’s probably more of a reflection of how they’re trying to get the ball out of their own 3rd – D’Angelo and Pressley are probably launching more speculative long balls into the midfield and attacking 3rd, while Fletcher and Kallman might be passing the ball around in the defensive 3rd much more.

That requires a deeper look at the type of passes out of the defensive 3rd, but we’ll save that for another day. Now, let’s look at this chart, but for passes in the middle 3rd.

Open Play Passes in the Middle 3rd

In the middle 3rd, when looking at open play passing completion percentages in the middle 3rd and sorting by open play passes attempted in the middle 3rd, it’s a different story.

Screen Shot 2017-02-25 at 11.53.48 AM.png

Defenders are all out of the picture now, except for Barnes, and the top 15 is now stacked with midfielders. For those of you who follow the NWSL pretty closely, you’ll also notice these are mostly defensive-minded midfielders. Killion, Brian, Winters, Zerboni, Kyle, and Colaprico are all midfielders generally known to lie deep in the field and support the defense. And it makes sense they’d appear at the top of this list, and generally with such high passing completion percentages, as they’re likely to get the ball a lot, either from the defense, other midfielders passing back, or by winning it from the opposing team.

Little is no longer the #2 player, but she is #1 when looking at passing completion percentage for this top 15. She has an impressive 90.1% passing completion percentage in the middle 3rd with 33.2 open play passes attempted per 90 minutes in that third of the field. Killion is up there, too, with an 85.7% completion percentage in the middle 3rd with 36.5 open play passes attempted per 90 minutes.

Meanwhile, the rest of this top 15 is generally at or above the median of 76.3% for passing completion percentage. Fishlock sticks out for the wrong reason – with the most open play passes attempted per 90 in the middle 3rd (42.7) but with a passing completion percentage of only 65.5%, well below the 25th percentile.

What else could be look at here? There are a lot of passes here. How good are these numbers when we look at passes going forward? How many are being launched forward, or how many are going back to the defense? That’s another analysis for another day, but it’s worth considering if simply looking at pass attempts vs. pass completion percentage is going to hide players who maybe don’t pass the ball a lot out of the midfield and don’t have highest completion percentages – but, maybe they’re more likely to complete a through ball at the expense of a higher passing completion percentage from safer passes, or maybe they’re launching the ball forward and into aerial duels that their teammates are losing but are still creating dangerous loose balls their teams can capitalize on.

The median for passing completion percentage has been dropping the further up we go up the field. It was at 81.3% in the defensive 3rd, 76.3% in the middle 3rd, and now we’re going to see how far it drops in the attacking 3rd.

Open Play Passes in the Attacking 3rd

When we look at open play passing percentages in the attacking 3rd, and sort by open play passes attempted in that third per 90, the percentages are all over the place. There’s also a lot of new names – namely forwards and more attacking-minded midfielders.


The median open play passing completion percentage in this 3rd is low, at 60.6%. That makes sense, as you’re likely not going to have an easy time moving the ball around that close to an opponent’s goal. There’s several players who still stand out, though.

Back to Kim Little, her passing completion percentage out of this third, at 76.5%, is nearly 10 percentage points lower than in the middle 3rd. But compared to the rest of the field, she’s a star, over six percentage points over the 75th percentile.

Perhaps even more impressive is Washington’s Banini, who we haven’t even seen in the top 15 by open play pass attempts per 90 until now. With 14.4 open play pass attempts in this 3rd per 90, she’s getting off a completion percentage of 83.0%. That would be above the 75th percentile even in the middle 3rd!

Fishlock is here, too, although her passing completion percentage is comparable to the rest of the field, unlike in the middle 3rd where she was relatively very low. Relatively low compared to everyone else, though, is Mathis and Leon, who attempt to pass the ball a lot in this 3rd but struggle to get half of them completed.

If we were to break this down further, we’d want to look at how many of these completed passes are staying in the attacking 3rd of if a significant amount of passes out of this 3rd are going back to the midfield. Also, what about crosses, and are those types of high-risk-high-reward passes behind Melis’ and Leon’s low completion percentage? And what about forwards like Alex Morgan and Lynn Williams, who aren’t even in this top 15? Should we even expect them to have high passing attempt numbers, or should a table like this only include fullbacks, midfielders, inside forwards, and exclude players who’s job is to shoot first?

We need your help!

As was noted above, this is only 40 matches out of a 103-game NWSL 2016 season. The WoSo Stats project desperately needs your help to log more basic stats and location data for the 2016 season. The more data we get, the better we’ll understand 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 with be publicly available on the WoSo Stats Github repo.

Aerial duels in the 2016 NWSL season (through 54 matches)

In the WoSo Stats Shiny app is a section titled “Aerial Duels” that has data for how many times a player goes up for an aerial duel, and how often she wins them.  In the 2016 NWSL Season Tableau workbook, I originally didn’t include a visualization for aerial duels, but I recently created one to get a better look at how the distribution of players looks when you compare the amount of times they go up for an aerial duel per 90 minutes to the percentage of times they win an aerial duel.

You can view the “Aerial Duels” section of the Tableau visualization for yourself. As of this writing, with 54 matches logged for the season, two players, Dagny Brynjarsdottir and Natasha Kai, stand apart pretty clearly from the rest of the league for how often they are involved in an aerial duel per 90 minutes.

It of course makes sense that they’d have a lot of aerial duels; they’re both tall and are typically thrown into attacking positions high up the field. After Kai (15.6 aerial duels per 90) and Brynjarsdottir (13.6 aerial duels per 90), the rest of the field appears starting with another Portland Thorn, Lindsey Horan (10.1 aerial duels per 90).


The players with the highest aerial duel win percentage with a significant number of aerial duels per 90 (beyond the 25th percentile, the left edge of that light grey rectangle you see running parallel to the y-axis) are further back, with far less aerial duels per 90 but with generally greater defensive duties. The top four – again, with 54 matches logged so far – are Whitney Engen (82% of aerial duels won), Becky Sauerbrunn (78%), Julie King (78%), and Alanna Kennedy (72%).

Sauerbrunn and Kennedy noticeably have a very high win percentage while still being above the 75th percentile of aerial duels per 90. As is evident by looking at the chart, more aerial duels appears to correlate with a winning percentage approaching around 45%.

Finally, I looked at how each team compares. The Western New York Flash stands out for having four players -Erceg, Kennedy, McDonald, and Mewis – clustered in the top-right corner of the chart. No other team has a cluster like that.

Screen Shot 2016-11-03 at 6.54.58 PM.png

Meanwhile, have a look at the Seattle Reign. Their players are generally clustered behind the 75th percentile.


An interesting follow-up to this chart would be to break down the per 90 and win percentage by location. Each match with complete location data has the location of each aerial duel logged, so this is something that should be possible to visualize and analyze once a way of coding through the matches and sorting out the aerial duels by location is resolved.

Another more complex follow-up question is what happens after each of these aerial duels. If it went out of bounds, it was recovered by a teammate of the player who won the aerial duel, if it was cleared away, if the aerial duel resulted in a foul, and so on. This data is deep in the spreadsheet that is logged for each match and I haven’t yet figured out an easy way to do that type of analysis, but it is going to be worth digging into.

In the meanwhile, feel free to dig through the chart and have a look at this for yourself!