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.

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.

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.

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 wosostats.team@gmail.com or send me a DM at @WoSoStats on Twitter. All the data logged with be publicly available on the WoSo Stats Github repo.