Building Open Football Player Transfer Data

Around this time last year I ran a post on European football transfers, taking in the 2017/18 season’s summer window. I got a bit of an itch to refresh this work when the 2018/19 window hit. The aforementioned itch led to me getting in too deep and scraping all major European league transfers going back to the year 2000, naturally.

Here, I tell a short story about how this open data was built and showcases some visualisation pieces that utilised that effort.1 Hopefully this can encourage others to share whatever neat stuff they tap into.

Building the data

I eventually settled on scraping using the Guardian’s Transfer Interactive to power my previous work. This source included transfer timestamps, which allowed for some intra-window time series stuff - it remains handily hosted by Tom Worville in a public, flat-file format. However, it isn’t really set up for investigating historical trends as the Guardian has only run this interactive since 2017, as far as I can tell.

Enter Transfermarkt, a data goldmine of player transfers for a number of major European leagues (e.g. English Premier League, Spanish La Liga, Italian Serie A) and some other oddities (my personal favourite is this list of father/son combos for national teams - glorious and appreciated). Season-level stats like player transfers run back for quite a few seasons, joyously arranged in predictable html tables for bountiful scraping. I wrote a short scraping program2 to collect and clean up player transfers for these (and other) leagues, back to the 1992/1993 season (N.B. this decision was entirely arbitrary).

Et voila - the data is now freely available, hosted on Github as flat .csv files.

Sketching some visuals on top

Now for a couple of visualisation pieces I’ve tried out using this dataset. I’ve included some notes on my process/workflow for each3, if you’re that way inclined. Otherwise, just ~absorb the inspiration~ r emo::ji("idea")

I took a look at the value of player buys vs sales for Premier League clubs in the 2018/19 window, using a Cleveland dot plot (AKA ‘dumbbell’ chart) variant.

This type of visualisation is an elegant and simple way to show ranges of data (i.e. spend vs sales difference) across multiple categories (i.e. football clubs). I did the initial sketch for this using my standard charting workflow in R (mostly ggplot2 and it’s many extensions, including Bob Rudis’ charming ggalt which made this chart type ), but I did export this into Adobe Illustrator (Inkscape is a fine free alternative) to do good text annotations quicker. The final version therefore includes non-reproducible elements that makes refreshing the viz for new transfer windows non-trivial, but that help in telling stories contained in this view of the data. In this one-off case, I think the trade-off is fine.

Next, a look at a single club’s season-by-season transfer spend and sales, following the relationship between these two amounts through time.

This visualisation choice might be a little difficult to follow at first, if it’s your first connected scatter (in this post by Elijah Meeks, the connected scatter example actually includes a link to an explanation of what’s going on). Steve Haroz + collaborators’ research paper was invaluable in guiding my first application of this chart format. In short, they are good at showing changing data for two variables whenever there is a relatively clear pattern of progression. Similarly to the previous example, this was sketched out in R with ggplot2, with some Illustrator annotation fine-tuning.

That’s basically it…let me know if you make something 🔥

  1. You can find the R code used to generate this post here↩︎

  2. For this post I chose to omit lengthy passages on web scraping, as not to deter non-programmers (insights can be gleaned from the cleaned data w/o additional code). However, the code used to scrape, clean and analyse the data is publicly available within the src directory of the transfers GitHub repo, featuring rvest (web scraping for R) in conjunction with purrr (iteration tools for R). ↩︎

  3. For the R code used to sketch the chart examples included in the post (and others that didn’t make the cut), try here↩︎