Don’t Blame the Data - ANALYSE FOOTBALL (SOCCER) DATA IN R WITH ZERO R EXPERIENCE (2024)

What data can I get?

The package as at version 0.3.2 provides access to data from the following data sites:

• FBref.com (a whole host of data to analyse, including results, match stats, season long stats, player and team stats, etc);
• Transfermarkt.com (player market values, team transfer history, player transfer history); and,
• Understat.com (shot locations data for matches played in the major leagues)

These three sites are regularly used by analysts the world over, however the package is constantly evolving and may include data from additional sites in the future.

To my knowledge, only FBref provide the ability to export data to a file - the other two don’t, so you’d need to find some other way to get your data (painfully slow copy and paste), and that’s where worldfootballR come in.

Prerequisites

The only prerequisites to this post are that you have a computer, internet connection and the desire to analyse world football data. This post is designed to take any aspiring analyst with absolutely no R coding experience to being able to extract data programmatically using the worldfootballR R library.

What is R / Why would I use R?

From R’s official site (https://www.r-project.org/about.html);

R is a language and environment for statistical computing and graphics.

You can find out a lot more about R as you get more experienced with the language on the home page, but all you need to know for now is that R will be another tool in your toolkit to perform the analyses you want to do. R is an amazing programming framework that allows you to do a number of things, including data cleaning, performing statistical analysis and modeling and building fully customisable visualisations using the R programming language.

Importantly, anything programmed means it’s repeatable. Set up your code upfront, then simply rerun it to get consistent results.

The world is your oyster if you have the drive to learn.

Getting started

Ok so a few things before we dive right in… this is in layman’s terms (because I myself am a layman). R is the engine, the coding language driving everything you want to do, and RStudio is the pretty graphical user interface (GUI pronounced “gooey”) that gives you additional functionality when writing code in it. I am going to propose installing both R and RStudio in this post, but if you don’t want RStudio (I advise you to go with RStudio), you could still run the code to get the data you need (I will assume here everything is done in RStudio).

Install R

You remember how I said I’m a layman? It’ll extend itself here. We’re going to install R and we’re going to use all of the default options (just keep clicking “accept” or “agree”, or “continue”, and nothing else until R has been successfully installed. You can customise the installation when you’re better versed in R, but for now, just show me the data!

So how to find R to install R you ask? Well, you have to type into Google some really sophisticated stuff… “download r windows” or on Mac OS… yep, you guessed it, “download r mac”.

If you can’t be bothered doing that, here are the links:

• For Windows OS: https://cran.r-project.org/bin/windows/base/

Select “Download R X.X.X for Windows”, with “X.X.X” being for whatever is the most recent version of R:

• For Mac OS: https://cran.r-project.org/bin/macosx/

Depending on what version of Mac OS you’re running will determine which version to install, but generally speaking it’ll only be one of the highlighted options below:

Once you have selected either of the Windows or Mac OS versions, install as you would any software, remembering my one special rule at the start of this section… default options all the way.

Now for RStudio

Once R has been installed, we can now go ahead and install RStudio, remember, the GUI that will allow you to code in and provide additional functionality.

Back to our intricate Google search query… “download rstudio”, and select the first result:

Or for the URL: https://www.rstudio.com/products/rstudio/download/

Once we’ve opened the page, we’re presented with a few pricing options, but if you’re like almost all amateur analysts (let’s be honest, even professional analysts), you’re probably only going to need the free version:

We’re (almost) ready to start coding!

Well, we’re almost ready to start coding, but first we’ll do a bit of a tour of RStudio (again, for those that have forgotten, RStudio is the software where we type and execute our R code).

The console pane is where we will be able to see our code running (or run our code in here if we’re not using the scripts), whether there are any “errors” or “warnings” and printed outputs.

The environment is the sections where we will see the objects that we’ve imported, or created, including data frames (think of something similar to an Excel table).

In the viewers pane, there are a few tabs, including being able to navigate through the file system (‘Files’ tab), any visualisations we’ve created (‘Plots’ tab), the libraries/packages we have loaded on our machines and have available to us “locally” (‘Packages’ tab), a help section, where we can get additional details on what we need to provide the functions to work and a ‘Viewer’ tab to see HTML outputs.

Now we’re ready to start coding

Open up an R script (there are a number of different options to code in, but a .R script is the best place to start.

To open up a script, you can either press control+shift+n on a Windows machine, or command+shift+n on a Mac, or you can select a new script with the drop down in the top-left corner:

Changing the layout as such:

We will start writing code in the top-left quadrant.

Writing code

Before I fully get in to the nuts and bolts of the code, I just want to point you to some resources.

A website has been created for the worldfootballR package that will give you a full run down of all the functions available and the arguments you need to pass to those functions to get the data you want. The website can be found here.

Now we can really start writing code.

As we’ve already established, packages contain functions (code to do something). To use these packages they need to be installed (downloaded onto our laptops) first.

Packages will typically (but not limited to) either be housed on CRAN (a central repository for all packages) or on GitHub. worldfootballR is housed on GitHub, so the instructions for installing this package are slightly different to the packages housed on CRAN. Before we can install worldfootballR, we need to install the devtools package to enable the installation of worldfootballR. We will also install another package, xlsx that will enable us to save files in ‘.xlsx’ format.

We type (just copy and paste) these lines of code into our R script the first time we use R OR when we update our version of R OR the package updates its functionality:

# to allow us to install packages from github:install.packages("devtools")# a meta package that contains packages for data cleaning, analysis and visualisationinstall.packages("tidyverse")# to save xlsx filesinstall.packages("xlsx")

You’ll know that they have successfully installed if there are no error messages and you can see the you see the “>” symbol and a blinking cursor in the console section.

Then we will install worldfootballR (it’s what we’re all here for) and importantly load the library - it’s not enough to just install the package, we need to load it to make the functions easily available in our current session.

Note: We only need to install the package the first time we install R OR if we update our version of R, OR if there is a update to a newer version of the package.

devtools::install_github("JaseZiv/worldfootballR", ref = "main")

Note 2: While we only need to install it very infrequently, we need to load the package every time we want to use the package.

library(worldfootballR)

The world is your oyster

There are a number of functions in worldfootballR to enable you to get all the data you need.

To find these functions, you can visit the reference page on the package website here. The reference page will give you a brief summary of what the function does - for more detailed info, click on the function and it’ll take you to the function page.

Additionally, there are instructions and example code on the website for each of the sites data is collected from:

• Click here for FBref data
• Click here for Transfermarkt data
• Click here for Understat data

Let’s get some data

Now the fun part - getting data!

Before we get started, in the code below, when we see a “#” at the beginning of text, that is a comment, not executable code, and it’s how R programmers write notes to themselves, or others who use the code to give an overview of what is being done.

At the start of every script, it is best practise to include all the packages you’re going to be using/needing in your analysis. Installing packages only needs to be done the first time you start using R, when installing new versions or R, or when newer versions of the package become available and you want the updates that have been created.

The code we would run is below. A few things to remember:

1. To ‘run’ code we’ve typed out, there are two main ways;

1. Highlight the code you want to run and press control+enter or command+enter on MacOS
2. Have the cursor on the line you want to run and press control+enter or command+enter on MacOS

2. A “#” in a script signifies comments, and I will use them in the code to tell you what we’re doing

# remember, we need to ensure we've installed the packages we need, but we need to do this very infrequently ( to run the 'install' lines below, simply delete the '#' before the code:# install.packages("devtools")# install.packages("xlsx")# install.packages("tidyverse")# devtools::install_github("JaseZiv/worldfootballR", ref = "main")# once it's been installed, then we need to load the functions (code to let you get the data you want) in the library (this needs to be done every time you want to run your script)library(worldfootballR)library(tidyverse)library(xlsx)

# now let's get our season team shooting data from FBref:prem_2021_shooting <- get_season_team_stats(country = "ENG", gender = "M", season_end_year = "2021", tier = "1st", stat_type = "shooting")

# to save the file as a csv:write.csv(x= prem_2021_shooting, file = "EPL_shooting_2021_season.csv", row.names = FALSE)# or we can save an .xlsx file:write.xlsx(x= prem_2021_shooting, file = "EPL_shooting_2021_season.xlsx", row.names = FALSE)

# get our data ready for plottingprem_2021_shooting %>%  # filter out only the team shooting data, not their opponents also filter(Team_or_Opponent == "team") %>%  # create a new column that removes penalties from the team's goal total mutate(non_P_Gls = Gls_Standard - PK_Standard) %>%  # start plotting: ggplot(aes(x= npxG_Expected, y= non_P_Gls)) + # add a line through the plot with slope = 1 and the yintercept = 0 geom_abline(slope = 1, intercept = 0, colour = "red", linetype=2) + # we want to make it a scatter plot geom_point(size=6, colour="midnightblue", fill="midnightblue", alpha = 0.4, shape=21) + # lets also add team name labels ggrepel::geom_text_repel(aes(label = Squad), colour = "midnightblue", size=5) +  # limit the x and y-axis to start at 10 and finish at 100 scale_x_continuous(limits = c(10,100), name = "Non-Pen xG") + scale_y_continuous(limits = c(10,100), "Non-Pen Goals") + # create a title and subtitle - all plots should have this ggtitle("DID TEAMS SCORE AS EXPECTED?", subtitle = "Teams above the dashed line exceeded their xG for the\nseason, while teams below didn't") + # apply a pre-programmed general theme: theme_minimal() + # but then we can customise our plot even more - first we make the background black: # change the title and subtitle format theme(plot.title = element_text(size=28, face="bold"), plot.subtitle = element_text(size=22, colour="grey30"), # and change where the plot is aligned - in this case it's left-aligned plot.title.position = "plot", plot.caption.position = "plot", # change the size of aixs titles and text axis.title = element_text(size=16), axis.text = element_text(size = 14))

# first we will create a new data set of only the team's performance, and not how their opponents played against themprem_team <- prem_2021_shooting %>%  filter(Team_or_Opponent == "team")# now we calculate the Pearson correlationcor(prem_team$Gls_Standard, prem_team$xG_Expected)

[1] 0.8931299

# let's get the transfer balances for the 2020/21 Bundesliga seasonteam_balances <- tm_team_transfer_balances(country_name = "Germany", start_year = 2020)

# to save the file as a csv:write.csv(x= team_balances, file = "bundesliga_transfer_balances2020_2021.csv", row.names = FALSE)# or we can save an .xlsx file:write.xlsx(x= team_balances, file = "bundesliga_transfer_balances2020_2021.xlsx", row.names = FALSE)

# let's start creating our plot:team_balances %>%  # the new two lines use 'mutate()' from the dplyr package to create or change new columns # here, we create a net_transfer_income column that subtracts the expenditure form income mutate(net_transfer_income = income_euros - expenditure_euros) %>%  # and we can also come up with a flag for if the income is above or below 0 mutate(green = net_transfer_income > 0) %>%  # ggplot is how we visualise our data ggplot(aes(x=net_transfer_income, y= squad, fill = green)) + geom_col() + # manually select colours to use: scale_fill_manual(values = c("darkred", "darkgreen"), name = "Made\nMoney?") + # change the data labels on the x-axis to be formatted to currency: scale_x_continuous(labels = scales::dollar, name = "Net Transfer Income") + # add a caption that sources the data: labs(caption = "Source: transfermarkt.com") + # we also want a title and subtitle: ggtitle("BUNDESLIGA SPENDING IN THE 2020/21 SEASON", subtitle = "Bayern Munich and Borussia Dortmund are big net spenders this season,\nwhile Bayer 04 Leverkusen have made the most money on the transfer market") + # apply a pre-programmed general theme: theme_minimal() + # but then we can customise our plot even more - first we make the background black: theme(plot.background = element_rect(fill = "black"), # play around with the x and y gridlines: panel.grid.major.x = element_line(colour = "grey20", linetype = 2), panel.grid.minor.x = element_blank(), panel.grid.major.y = element_blank(), # change the title and subtitle format plot.title = element_text(size=28, colour = "white", face="bold"), plot.subtitle = element_text(size=22, colour = "white"), # and change where the plot is aligned - in this case it's left-aligned plot.title.position = "plot", plot.caption.position = "plot", # change the colour and size of aixs titles and text, remove the y-axis title axis.title.x = element_text(colour = "white", size=16), axis.title.y = element_blank(), axis.text = element_text(colour = "white", size = 14), # remove the legend legend.position = "none", #format the plot caption: plot.caption = element_text(size = 12, colour = "white"))

Resources

• worldfootballR
• FBref
• Understat
• Transfermarkt
• tidyverse - a collection of very valuable R packages for manipulating, analysing and visualising data