Since the previous blog post on FUMBBL data, I decided to make a separate Github repository fumbbl_datasets that contains the Python code to fetch and construct the FUMBBL datasets. So lets dive in the world of Blood Bowl stats nerdery. I took inspiration from various sources, detailed at the end of this post.

The idea is to make Blood Bowl data analysis (also know as Nufflytics, a term coined by Blood Bowler “Schlice” in reference to Nuffle, the god of Blood Bowl) easier and more accessible to others. The idea of this blog post is to showcase some possible analyses that can be done on the FUMBBL match data I’ve compiled. There exists a lively tournament scene, with thousands of matches played each year. On tournaments, this gives rise to various compensation schemes to make all teams “viable” for competition. Interestingly, the various teams (there are over 20 different ones) require different play styles, and not all team races are equally strong. Blood bowl is a game of Fantasy Football, where fantasy team races (think “Orcs”, or “Elves”) are pitted against each other. This blogpost is about Blood Bowl, a strategic boardgame invented in the late 80’s, that I finally started playing last year.

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