īoth plots looked identical at the time of writing, so it seems that we have a complete dataset for the given period. To check the dataset, I compared this plot with the plot of weekly game volumes that FUMBBL itself provides at. I labeled the larger leagues as well a recent tournament I took part in myself. The introduction of the new Competitive division with BB2020 rules is marked by a vertical red line. Since we have a proper datetime type variable for each week ( week_date), we can use pandas and plotnine to plot the weekly game volume as a time series. Let’s see what we’ve got! The pandas DataFrame df_matches contains records for all matches played on FUMBBL between august 2020 and march 2022. What data do we have? Weekly game volumes Inducements = pd.read_hdf(path_to_datasets + target) Path_to_datasets = '././././fumbbl_datasets/'ĭf_matches = pd.read_hdf(path_to_datasets + target)ĭf_mbt = pd.read_hdf(path_to_datasets + target) # point this to the location of the HDF5 datasets The code below assumes the datasets are locally stored at the location contained in the path_to_datasets variable: import pandas as pd Here we use Python, with the libraries Pandas and plotnine for data analysis and visualization. CSV would be the format of choice for Excel analysis, whereas the HDF5 format is suitable for scripted languages such as Python or R. The datasets are available both in CSV and HDF5. You can either download the latest datasets manually, or clone the entire repo to your local drive, depending on your expertise and preferences. 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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