Combining D3 and R for a Messi Pass Transition Heatmap

In this post I want to show off a nice feature of R that let’s you combine d3 and R workflows flawlessly to generate powerful visualizations. In this crossover of languages, R will take the role of data loading and manipulation while d3 focuses on producing the visualization. Below shows step-by-step how to generate the following pass transition heatmap based on free StatsBomb event data.

Injury Polar Plots

Injury data has been a bit of a guilty pleasure for me recently. When browsing through some of the data from Transfermarkt I looked into different ways of visualizing it. Specifically I was focused on highlighting injury lengths and their distribution over a player’s career. Screenshot from This resulted in the below viz for Marco Reus which is inspired by how I view the (European) football calendar: running counter-clockwise and with the summer break at 6 o’clock.

Lionel Messi - Free Kick Dashboard

I finally had some time to play around with the free Statsbomb data. I previously wanted to look into using the crosstalk package to link interactive charts and this data set which includes all of Messi’s La Liga free kicks gave me a good excuse. Launch Dashboard The initial idea was to link the location of free kicks to their goal impact (or their impact outside of the goal as long as they are not blocked).

Quantifying Injury Rates

Being a Bayern Munich supporter, injuries have been a big part of past campaigns. During most of the past season (2017/2018) Manuel Neuer was injured with a broken foot. For the second match against Real Madrid in the Champions League semi finals the team had to additionally compensate the injuries of Jérôme Boateng, Arturo Vidal, Kingsley Coman and Arjen Robben. While Pep Guardiola was still coach at Bayern his dispute with the long-serving team doctor Mueller-Wohlfahrt culminated into him resigning after 38 years with the team (Mueller-Wohlfahrt was later reinstated in 2017).

Is possession data getting more extreme?

The possession metric is probably one of the football statistics which is cited most often. It has gained even more media attention since we have seen larger divergence in possession across teams. Oversimplified game approaches of teams are often broadly categorized between possession-oriented and counter-attacking: think about Barcelona/Bayern Munich/Manchester City vs Chelsea/Borussia Dortmund/Liverpool. At first glance the possession statistic seems to be fairly trivial: who controls most of the ball during the 90 minutes?