🧮 I computed the most likely opponent for each club in the UEFA Champions League round of 16. Taking into account that clubs from the same group and same association cannot face each other, my constraint solver produced 3,876 possible draws. ⬇️ #UCL #UCLdraw Bayern Munich's (@FCBayern) potential opponents in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw

1. Liverpool - 39.9%
2. Milan - 23.9%
3. Club Brugge & Paris Saint-Germain - 18.1%

🧵 Computing and visualizing advanced soccer analytics metrics is becoming extremely straightforward. Access to event data and basic programming skills suffice nowadays as free open-source software libraries take care of most of the heavy lifting for you! (1/20) The @PySport open-source website (opensource.pysport.org) provides an excellent starting point. The website currently lists no fewer than 2 open-data repositories and 44 software libraries: 24 Python libraries, 19 R libraries and 1 Haskell library. (2/20)

Last month I had the pleasure of giving a keynote talk at @BarcaInnoHub's Barça Sports Analytics Summit. I presented a few event-data based football metrics that we developed for player recruitment. My presentation slides are available on Google Drive.

docs.google.com/presentation/d… 🗣️ "Working with many football organizations across the globe, @SciSportsNL primarily focuses on helping football clubs to identify appropriate transfer targets. Football clubs' recruitment departments are typically faced with a multitude of questions which they need to answer."