Took entirely too long, but there is now an R package for the WoSo Stats project!

Go ahead and install it yourself from the WoSo Stats GitHub repo, like so:

devtools::install_github("alfredomartinezjr/wosostats")

This is by no means a finished product. This was admittedly my first, full-fledged, R package, so suggestions and recommendations are welcome. I’ve tried to address the essentials and document the absolutely very essential as much as possible.

Next on the to-do list is going to be to add a bit more documentation for the functions in place, and then a vignette for the package.

Two weeks ago, I touched a bit on open play passing stats for Ali Krieger by breaking down attempts and completion percentage by thirds of the field. Since then, I challenged myself to see how much I could dig into passing stats to try to find some differences between two players who on the face of it look very similar – Morgan Brian and Sarah Killion. They’ve both played primarily as defensive midfielders, they both pass the ball a similar amount of times, and they have almost the same passing completion percentage.

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.

As you read through the post below, please consider that this data is only possible to hard work from fans like you who have been logging matches over the past year. The WoSo Stats project needs your help to log more stats and location data for the NWSL 2016 season, for USWNT matches, and beyond. The more data we get, the better we’ll be able understand the sport. If you’re interested in logging data for matches , 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.

Getting the passing stats

If you’re not interested in the coding aspect of this or how to get this data yourself, feel free to skip ahead to the next section. All the data used is available to download from this Tableau visualization.

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 = “thirds”,location_complete=TRUE,section=”passing”)

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,add_per90 = TRUE,location = “thirds”,section=”passing”)

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

A change from previous posts is the “section” argument. Instead of creating a massive stat table with all sorts of stats you may not be interested in, you can now just create a stats table for a specific type of stats (attacking, passing, possession, defense, goalkeeping). For this analysis, we’ll only need to look at passing stats, so we can just assign “passing” to the section argument.

The “your_stats” data frame is the stats table that is behind the Tableau visualization that has all the charts shown below. The Tableau viz was created with Tableau Public, and you should be able to download it yourself. For now, let’s have a look at the data.

Overall Passing Stats

For starters, let’s look at how Brian and Killion look if we just look at two very basic stats – open play passes attempted per 90 and open play passing completion percentage, sorted by total open play passes attempted per 90.

Both Brian and Killion have nearly the same stats. Brian has 52.1 open play passes attempted per 90 minutes with an 82.3% passing completion percentage. Killion has 53.3 open play passes attempted per 90 minutes with an 84.7% passing completion percentage.

There’s a lot that could be happening deeper underneath those stats, so let’s look at that bar chart, broken down by open play passes attempted per 90 for each third of the field (defensive, middle, and attacking). Here, we begin to see some differences in where Brian and Killion’s passes are happening, and some big similarities as well compared to the players around them.

Killion, per 90 minutes, attempts a couple more open play passes in the middle 3rd. Brian, meanwhile, per 90 minutes, has a few more open play passes in the attacking 3rd. Brian seems slightly more attacking-minded and Killion attempts more of her passes out of the midfield. Killion, quite simply, with the matches we have that have location data logged data, attempts more open play passes out of the middle 3rd of the field, per 90 minutes, than anyone else in the league.

Compared to almost every other played visible here, they pass the ball in open play out of the middle 3rd more times than anyone else except for Barnes, who is only ahead of Brian. They both have a very high percentage of their passes coming out of the midfield.

Now, what about the passing percentages? Below is a chart stacking, for each player, their open play passing completion percentages in each third of the field. Almost everyone’s passing completion percentage drops as they get closer to the opponent’s goal, so here relative differences are what’s interesting to look at.

Recall that Killion had more open play passes attempted out of the middle 3rd. Now we can see that she also has a significantly higher passing completion out of the middle 3rd, 85.7%, than Brian – and almost everyone else in this list of top-16 most open play passes attempted per 90, except for Little, who has an astonishing 90.1%, and Fletcher, with whom she’s tied.

Brian, on the other hand, has a significantly higher passing completion percentage out of the attacking 3rd, 77.5% and nearly 12 points higher than Killion – and also tied with Buczkowski for highest out of everyone visible here. Do the math against Brian’s 8.2 open play passes attempted per 90 out of the attacking 3rd, and she’s good for at least 6 completed passes in that third of the field for any given game.

We’ll break down these middle 3rd and attacking 3rd passes further by breaking them down in two different ways – by the direction of the pass (backwards, sideways, or forwards) and by how many were through balls, launch balls, or crosses. That’ll help us better understand what might be behind the differences in passing percentages and how they might differ in the types of passes they attempt.

Open Play Passes by Direction

Below are bar charts now for only Killion and Brian, showing the percentage of their open play pass attempts that went forward, sideways, and backwards, for each third of the field.

Brian and Killion have virtually the same distribution of open play passes by direction in the middle 3rd, so any differences we can glean from our stats aren’t quite going to be found here. Killion’s open play passing direction in the attacking 3rd, however, is massively different. 71% of her open play passing attempts in the attacking 3rd are going forward, compared to Brian’s 40%. It’s not clear yet, although it might be a smart guess, if these forward pass attempts are what’s bringing down her passing completion percentage. Also recall that this represents about 5.4 and 8.2 open play pass attempts per 90 in the attacking 3rd for Killion and Brian, respectively. Do the math and this means that, even with less attempts in the attacking 3rd, Killion comes out at about 3.8 forward open play pass attempts per 90 compared to Brian’s 3.3. It’s a difference of 1 more forward pass attempt every other game for Killion.

Numbers for attempts by direction are good and give insight into how Brian and Killion are trying to move the ball around but we also have data on passing completion percentages. Below are bar charts breaking down open play pass attempts by direction in the middle 3rd. Each pair of bar charts is for a different direction – backwards, sideways, and forward. The red is incomplete pass attempts, and the orange is complete pass attempts.

Recall that Killion had a couple more pass attempts per 90 in this third of the field, and a significantly higher passing completion percentage, but as far as distribution of direction of passes (the previous chart) they were both very similar. Now Killion and Brian have very similar numbers of pass completed per 90 minutes for backwards and sideways passes, but there’s a significant change for forward passes. Killion is good for almost 3 more completed forward passes in the middle third.

Now let’s look at this same chart, but for the attacking 3rd where there were big differences in the distribution of passes by direction and where Brian had a significantly higher passing completion percentage.

The differences in completed passes are barely above 1, but they do add up, especially considering the total number of pass attempts in this third of the field for both players are in the single digits. So that difference of 0.9 more forward pass incompletions per 90 isn’t massive, but it is chipping away at Killion’s passing completion percentage.

At this point it’s worth noting that the past few charts mean different things depending on how much a “forward pass completion,” a higher “passing completion percentage,” or more “pass attempts per 90” means to you. It intuitively seems to make sense that more of each is good, but with these two players they’ve each had higher numbers in different areas – no one appears to be significantly higher across all stats. Killion in the middle 3rd has a few more forward passes completed, a higher completion percentage, and more open play pass attempts per 90. Brian in the attacking 3rd, however, has slightly more forward passes completed, a higher completion percentage, and more open play pass attempts per 90. If you’re going to get into a discussion about which midfielder is “better” based on these stats, you also need to talk about what you expect out of a defensive midfielder. How good to you expect them to be at passing in the midfield, and – assuming attacking duties aren’t their primary responsibilities – how good do they have to be in the attacking 3rd to make up for a difference compared to someone else in the middle 3rd?

And then there’s the question of how much passing numbers should be adjusted given a team’s players, formation, tactics, and overall performance. If Killion’s passing numbers in the middle 3rd on the face of it are good enough, is there something about the way Brian’s team, the Houston Dash, plays and performs that may forgive lower numbers? The same goes for the attacking 3rd – Brian’s numbers look better, but is there something about Killion’s team, Sky Blue FC, that when taken into consideration makes her a more valuable player than Brian in the attacking 3rd? And, as far as this project is concerned, how much of this extra information is in all the data we’ve already tracked and can thus analyze ourselves?

Some of this additional information is likely sitting in all the match spreadsheets that have been logged for this WoSo Stats project – there’s the potential for further insights if we could get data on passing networks, on situations such as when a team is trailing, on matchups based on the type of players and teams a player is going up against, and likely much more.

For now, let’s look at two more types of passing data. We’ll look at completed passes that go across different thirds of the field, and special types of passes – launch balls, through balls, and crosses in the middle 3rd and attacking 3rd.

Passing Range

The chart below shows the top players by open play passes attempted per 90, with passes completed from the middle 3rd into different thirds of the field (and within the middle third) and with passes completed from the attacking 3rd back into the middle 3rd and within that attacking 3rd. We only have data for completed passes because sometimes it’s not reliably possible to figure out where an incomplete pass was trying to go – such as when it’s blocked right in front of a player trying to pass the ball and it’s not clear just how far down the field the ball was supposed to go.

Killion overall is completing more passes within and out of the midfield, close to 5 more. The great majority of those are passes that stay within the middle 3rd, and the same is true for Brian. Brian has a few more passes completed within the attacking 3rd. Overall, there doesn’t appear to be a whole lot here to differentiate the two. They’re both obviously distinct from a lot of other players visible here, but it looks like all we can tell from this is that Killion completes more passes per 90 minutes within the middle 3rd than Brian.

Through Balls, Launch Balls, and Crosses

Finally, a look at through balls and launch balls out of the middle 3rd, and through balls and crosses out of the attacking 3rd. Numbers for both players here per 90 minutes end up being small. In the red is incomplete open play pass attempts, and in the orange is complete open play pass attempts.

Killion in the middle 3rd appears better at launching the ball forward and completing a through pass, with more completions per 90 and a higher completion percentage for each type of pass.

There’s less to see in the attacking 3rd for either player. Killion and Brian barely complete any through balls from the attacking 3rd, likely because by the time they’re in the attacking 3rd from deep in the midfield most of the opposing team’s defense is already well situated in front of the goal. Killion attempts a negligible amount of crosses, and Brian completes about one cross every other game.

Next steps

These two players were an interesting case study because of how similar they are in playing style and how good they are. I had to explore quite a bit of stats as on the face of it they were quite similar with regards to passing attempts and completion percentage, even when broken down by thirds of the field.

In the future, I’d like to do this with other NWSL players who are also considered defensive midfielders – players like Buczkowski and Winters, and others – to see just how alike everyone who plays this type of midfielder role really is. I touched on this briefly, but something like a passing network visualized, showing just who is getting all these passes, could also shed light on not just where players like Killion and Brian distribute the ball, but who they’re passing it to. Are they passing it off to mostly defenders, wingers, attacking midfielders, or straight to the forwards? There’s also curious cases where each player has lined up not quite in the defensive midfielder role but maybe somewhere further up the midfield or outside the wing – it could be possible to account for those matches. And I haven’t even added any stats related to defending, which is a whole ‘nother aspect of being a defensive midfielder that is arguably just as important as how well they pass the ball.

This is all beyond the scope of this blog post, and I hope to revisit another time. Or feel free to go after it yourself, as the data is all there in the WoSo Stats GitHub repo. For now, I hope you’ve enjoyed a look at how the data we’ve logged can dig into the differences – and similarities – between two very good players who, with very few goals and assists, don’t show up prominently on traditional stats sheets based on goals and assists but, with the stats we’ve got, show up as vital parts of the midfield.

One last thing, and one last time, the WoSo Stats project needs your help! If you’re interested in logging data for matches , 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.

It’s been a year since this WoSo Stats project went live! To be precise, it’s been a year and two days since this tweet, when I first went public with this project.

A year later, we have over 100 matches in this project’s database, and are 75% complete with logging the NWSL 2016 season.

None of this would have been possible without the incredible, hard work of the dedicated volunteers behind this project. There have been dozens that have helped out, some for one or two matches, and some for far more, but each of them have helped us better understand this beautiful game.

It’s been a humbling experience seeing how eager fans have been to help do something that hasn’t been done before in women’s soccer. I truly believe that the growth of women’s soccer could be one of the next generation’s most interesting, fascinating stories in sports. In the meanwhile, we’ve got an NWSL season to finish, and even bigger hopes for this year.

-Alfredo

I added a new sheet in to the “heat-maps-template.xlsx” Excel spreadsheet for “per 90” heat maps, which is going to make it way easier to look at how a player performed across different matches without having to scroll through various heat maps. With a few quick fixes, described below, you can also account for how many minutes the players in the database you’re looking at have played.

If you’re not already familiar with heat maps based on WoSo Stats data, read more about how to create your own heat maps here. Following those instructions, so long as you assign “TRUE” to the “per_90” argument in createMultiLocStatsTabs() function, you should have a .csv file in your working directory named “overall-p90-everything.csv” (if you also assigned “everything” to “match_stat”, which is what the following in this blog post will assume).

In the heat-maps-template.xlsx Excel spreadsheet, like with the stats tables in the “match tables” sheet, in the “per 90 table” sheet you’ll need to copy and paste your “overall-p90-everything.csv” table over the large stats table to the right of the heat map. For now, let’s just look at the per 90 stats table that’s already in the template. It’s a stats table for Sky Blue FC’s 2016 matches from Weeks 1 through 5, and Week 7 (Week 6 is incomplete, for now), and it has some differences with the individual match heat maps beyond the number of players it represents.

Look a the cells I’ve highlighted below in orange. You’ll see that the maximum stat open play pass attempts (“opPass.Att”) for an individual is set at 10.09. But over on the right Caroline Casey has 11.4 open play pass attempts in her own 18-yard box (the D18 zone). This is higher than the individual maximum shown below, but that’s by design.

The formula in that “Ind. Max” column only covers all the rows with players that have played more than 270 minutes. Casey only played in two games, so she missed the cut.

This is a quick and inelegant way to account for some way-too-high per 90 stats for players who played very few minutes. This way, a player who only played for 10 minutes and passed the pall three times out of her defensive third’s left wing (the DL zone) doesn’t jack up the individual maximum with her 27 pass attempts per 90 stat.

This is important because the heat map’s color spectrum is determined by the individual maximum. It starts and zero and ends at the maximum, and if outliers weren’t accounted for then the map would look very light for some very good players. Take Sarah Killion, who at 571 minutes is tied with Rampone for the most minutes played out of any player in this set of Sky Blue FC matches. If the individual maximum was being calculated from all players, regardless of minutes, her open play pass attempts per 90 heat map would look very, very light. She has 10.09 open play pass attempts per 90 out of the defensive middle’s center, but it barely stands out because there’s a player with an individual maximum of 33.75 open play pass attempts per 90 that’s throwing everything off!

Go back to setting the individual maximum based on players who played at least 270 minutes (where now the individual maximum for open play pass attempts is 10.09), and Killion’s volume of passing attempts per 90 from the middle of the field stands out way more.

The above example is for a very specific dataset. What if you created a per 90 stats table for every Portland Thorns match with location data, and what if that table had many more rows? And what if, unlike in the example above where there were 11 players who had played at least 270 minutes, there were 13 players and you had the change the number of rows the “Ind. Max” column is looking up?

The solution, for now, until I find a better solution, is a huge pain in the ass but it works. First find, the row number for the last player above the 270 minute threshold. For the example above, it was row 12 – but let’s say it was actually at row 14. Then, highlight the “Ind. Max” column, search for the number adjacent to the “$AW__” value, in this case it’s 12, and replace that with the row number which in our hypothetical scenario would have been 14.

That’s the short of the per 90 heat map. I haven’t yet touched the “Team Max” column, but I will in a later post. Coming soon, I will just make one per 90 heat map for the entire season and update it as I get more location data. I will also work on making it easier to copy and paste over stats tables so that you won’t have manually change any formulas ever.

Earlier last week, I published a post exploring heat maps for the Portland Thorns’ April 2016 matches, with a focus on Tobin Heath’s performance. Those heat maps are in an Excel spreadsheet, which you can download here.

In this post, I’ll summarize how they work, and how you can create your own for the matches for which we currently have location data. You’ll need a basic understanding of how to use R and how to modify Excel spreadsheets.

How to create your own heat maps

You will only be able to create heat maps for the matches in the WoSo Stats database for which we have location data. You see which ones they are by going to the database.csv file in the WoSo Stats GitHub repository and seeing which matches have a “yes” in the “location.data” column, which will mean they have complete location data for virtually every action that was logged.

If you’d like to help us get more location data logged for more of these matches and you’ve got a couple of hours to spare, you can help!

Getting the data

Anyways, first things first, open up R or R Studio or whatever you use to work in R, and run this code to source the “getting-data.R” and “create-location-stats-table.R” code. The first file will create a data frame in your working directory for the aforementioned database.csv file and a getMatchCsvFiles() function. The second file will create various functions, but the two we’ll be working with will be createMultiLocStatsTabs() and writeFiles().

source("https://raw.githubusercontent.com/amj2012/wosostats/master/code/version-2/getting-data.R")
source("https://raw.githubusercontent.com/amj2012/wosostats/master/code/version-2/create-location-stats-table.R")

Now it’s time to pick the matches you want with the getMatchCsvFiles function. This function has the following arguments:
1. competition.string: The name of the competition you want to analyze as it is written in the database’s “competition.string” column. This MUST match exactly what is written in the column, and this argument MUST be written. For the NWSL 2016, you’d write in competition.string = “nwsl-2016”. If you’d like to pick from every single match in the database, then just write in competition.string = “database”
2. The data range you’d like to pick, written as one of several possible arguments. You can pick a specific “round” (such as a week in NWSL play), a set of various “rounds” (such as multiple weeks in NWSL play), or a specific month. These arguments are the following:

• round: The “round” of the competition, written as round = “nameOfRound” For the NWSL 2016 season, “rounds” are weeks of the season; week 1, for example, would be written as round = “week-1”.
• multi_round: A vector of different “rounds” of a competition (for the NWSL this would be weeks), written as multi_round = c(“X”, “Y”, “Z”). If you wanted weeks 1 through 3, and week 4, you’d write this as multi_round = c(“week-1”, “week-2”, “week-3”, “week-4”).
• month_year: The month and year of the matches you’d like, written as MM_YY. For example, matches from May 2016 would be written as month_year = “05_2016”.
• For now, you can only pick one of these at a time. For example, you can only pick April 2016 matches or Week 1 through Week 3 matches, not all matches from Week 1 through Week 3 that happened in April 2016.
• You can also just leave this argument blank, in which case you’ll pull everything in the database, according to any further filters you set based on the next few arguments.

1. team: This is optional. This is the abbreviation for the team whose matches are the only ones you want, written as team = “TeamAbbreviation”. The abbreviation is based on our list of abbreviations for club teams and based on FIFA’s country codes. Double-check the database to make sure the team you want is actually in our database – beyond the NWSL 2016 teams, we only have a bunch of international teams and one random PSG-Lyon match (as of this writing).
2. location_complete: This is also optional, and is set to default as location_complete = FALSE. What that means is that, by default, you will get all matches, regardless of they have completed location data. For the purposes of this blog post, we will want to set this as location_complete = TRUE

Feel free to play around with this (and let me know if you run into any bugs), but here are some examples of how this function works:

To get all Sky Blue 2016 matches for which we have any data:

getMatchCsvFiles("nwsl-2016", team = "SBFC")

To get all Washington Spirit 2016 matches from the month of June, for which we have complete location data:

getMatchCsvFiles("nwsl-2016", month_year = "06_2016", team = "WAS", location_complete = TRUE)

To get all USWNT matches from 2016 SheBelieves cup, for which we have complete location data:

getMatchCsvFiles("shebelieves-cup-2016", team = "USA", location_complete = TRUE)

For this blog post, we’re going to focus on the code I ran to get all Portland Thorns matches from the first 3 weeks of the season. We already know we have location data for these matches, so specifying location_complete isn’t necessary; however, let’s specify it anyways just in case you weren’t sure.

getMatchCsvFiles("nwsl-2016", multi_round = c("week-1", "week-2", "week-3"), team = "PTFC", location_complete = TRUE)

You should now have a match_list list (a very large one, too) with 3 elements, one for each match spreadsheet, and a match_names vector with 3 elements, one for each matchup name.

Getting the location-based data

The next few steps are pretty simple. Call the createMultiLocStatsTabs() function; set the match_list argument as “match_list” and the match_stat argument as the stat you’re looking for (more on this in the next paragraph); and assign it to variable stats_list. This will create for each match a table with each player in one row and their location-based stats in the columns.

When calling this function, one of the arguments is the match_stat, which is the type of location-based stat you want. As of this writing, you can only get 11 different location-based stats, listed below with the string you need to write in as the argument shown in parentheses. If you wanted to get the largest table possible with columns for each stat (this creates a table with 181 columns), just write match_stat = everything

Or, assign one of these to the match_stat argument:
1. Attempted pases (attempted-passes)
2. Completed passes (completed-passes)
3. Passing completion percentage (pass-comp-pct)
4. Take ons won (take-ons-won)
5. Take ons lost (take-ons-lost)
6. Aerial duels won (aerial_duels-won)
7. Aerial duels lost (aerial-duels-lost)
8. Tackles (tackles)
9. Dispossessions of Opp (opp-dispossess)
10. Opp Poss Disrupted (opp-poss-disrupted)
11. Pressure/Challenges (pressure)
12. Recoveries (recoveries)
13. Interceptions (interceptions)
14. Blocks (blocks)
15. Clearances (clearances)
16. Opp Ball Disrupted (opp-ball-disrupted)

For the set of Portland Thorns matches we are working with, this is the code we would run:

createMultiLocStatsTabs(match_list, match_stat = "everything")

Once you run this, you’ll have a list assigned to the variable stats_list that will have a stats table for each of the three Portland Thorns matches.

Then, write these stats tables as .csv files in your working directory, by running the following. Each stats table’s file name will be determined by the match_stat, which you have to specify again (the data won’t be affected, so you could really name this whatever you want) and by the string values in the match_names vector that was created when we ran the getMatchCsvFiles() function .

writeFiles(stats_list, match_names = match_names, match_stat = "everything")

Run this and, staying with our Portland Thorns April 2016 example, your working directory will now have three .csv files.

To review, here is all of the code that was run since the beginning of this blog post to create the three .csv files that are now in your working directory (the code can also be found here:

source("https://raw.githubusercontent.com/amj2012/wosostats/master/code/version-2/getting-data.R")
source("https://raw.githubusercontent.com/amj2012/wosostats/master/code/version-2/create-location-stats-table.R")
getMatchCsvFiles("nwsl-2016", multi_round = c("week-1", "week-2", "week-3"), team = "PTFC", location_complete = TRUE)
createMultiLocStatsTabs(match_list, match_stat = "everything")
writeFiles(stats_list, match_names = match_names, match_stat = "everything")

Create the heat maps

Now the tricky part: creating the heat maps with the data in the .csv files. First, download the Excel template for the heat maps (click on “View Raw” to download), which is just the Portland Thorns April 2016 heat maps, and open it.

Let’s pretend we had this Excel spreadsheet but without the data that’s shown to the right of the heat map, starting with the PTFC-ORL match. Highlight everything in columns “AC” through “HA” from row 1 down to row 29 (as shown in the images below) and clear the contents (DO NOT delete the columns, though).

The heat map will be blank, regardless of what you write into the “Enter name here:” and “Enter stat here:” cells, and the stat info to the right of the heat map and below the cells where you enter the Player and Stat you want will either be zeroes of NAs. This means that the formulas in all those different cells, including the ones that make up the heat map, are looking for data in those columns that we just cleared, but it’s calculating nothing but blanks and errors as there’s nothing there anymore, for now.

Let’s say we didn’t want to re-create the PTFC-ORL match, but instead wanted to use that space we cleared for a BOS-PTFC heat map. Open the “BOS-PTFC-everything.csv” file that you created in your working directory (the following will only work with the “everything” versions of the stats tables) and highlight only the cells that aren’t blank (for this match it’s 27 player rows plus the header row for a total of 29 rows, times the 181 columns, for a total of 5,068 cells you have to highlight). This will look like this in Excel.

Copy those highlighted cells and paste them into the cell at row 1 and column AC, which will fill in the space that was previously taken up by the PTFC-ORL stats. But wait, you’re not done yet! One thing is left to correct, and that’s the team totals.

See that “PTFC” and “ORL” row of numbers in the lower right below the player stats? Those are the total for those stats for each column, which are referred to when creating heat maps for an overall team view. I like to keep the home team on top, so in this example, change “PTFC” in cell AC31 to “BOS” and change “ORL” in cell AC32 to “PTFC”. Then, highlight cells ADH31 (where the totals start) through cell HA31 and search and replace “PTFC” with “BOS”; this changes the formulas in each cell so that they’re now looking for stats from the right team. Do the same for the rows below, searching and replacing “ORL” with “PTFC”. The totals should now be correct.

Finally, in cell Y17 under “Name entered is a team?” is a formula that reads what’s being written into the “Enter name here:” cell and determines, based on the team abbreviations you’ve given it in an OR() formula, if it’s a team that’s been input. Right now this formula is still looking “ORL” as one of the two teams. Change the cell contents from =OR(B5=”PTFC”,B5=”ORL”) to =OR(B5=”PTFC”,B5=”BOS”).

And you’re done! The heat map should work now.

Warnings

• It’s easiest to create the heat maps with the template I’ve provided if you had created stats tables with match_stat set as “everything.” I added in the option to create smaller stats tables for the future when the “everything” version of a stats table is far, far bigger than 181 columns. For now, though, it makes more sense to work with the “everything” stats tables as 181-column spreadsheets shouldn’t slow down your computer.
• You can ignore the passing percentage heat maps for the overall team views, as those are the sum of the percentages for each player. I haven’t yet figured out a way to get the average for the percentages that can account for whether a 0.0 passing pct is there because there were no attempts at all.

Next steps

• Creating an “NWSL Season” heat map for “per 90 minute” stats. This would be calculating from a table close to 200 rows long, and would also require creating that table somehow.
• Read more about how to create heat maps for “per 90” stats here.

Help us!

Made it this far? Maybe you can help us out a little more. We need help logging this data. This data only happens because of fans like you who have put hours of their free time into logging data onto Excel spreadsheets. But we need more people helping out, as right now we are very low on volunteers and will be lucky to finish the 2016 season by the time the 2017 season even starts! If you’re interested, read more here about how to help and either send a DM on Twitter to @WoSoStats or email me at wosostats.team@gmail.com to get started. All it takes is a couple of hours of your free time, a willingness to learn, and knowing a thing or two about Excel.