In the board game Blood Bowl, coaches develop their teams over a series of matches. In this post, we use the fumbbl_replays Python package to plot and analyze Gnome team development choices during league play.
The fumbbl_replays package is a Python utility package for the board game Blood Bowl. It allows users to plot board positions, either from scratch, or from existing (FUMBBL) game logs. In addition, it has some functionality to analyze FUMBBL game logs.
Automating deployment of an inexpensive Linux R/Stan development environment in the Azure public cloud.
Yet another Blood Bowl post! This one is to introduce the Super League, the ultimate tournament-style Blood Bowl available online.
Yet another Blood Bowl post! This one is to warm up to the upcoming World Cup, analyzing variation in Tournament Roster choices.
Restate my assumptions: If you graph the numbers of any system, patterns emerge. In this post we’ll use clustered heatmaps to graph the numbers from the Blood Bowl Fantasy football game, and see what patterns emerge!
This blog post demonstrates that the presence of irrelevant variables can reduce the performance of the Random Forest algorithm (as implemented in R by ranger()). The solution is either to tune one of the algorithm’s parameters, OR to remove irrelevant features using a procedure called Recursive Feature Elimination (RFE).
This blogpost is about Blood Bowl, a boardgame I started playing last year. The idea of this blog post is to showcase some possible analyses that can be done on the FUMBBL match data I’ve compiled.
To play Blood Bowl online on FUMBBL.com, a Java client is used that works with Java Web Start. On my Ubuntu linux system, open source versions of java and java web start (openJDK and IcedTea) take care of this. This post describes my suffering caused by the client not working anymore after a Ubuntu software update, and might be helpful for others encountering the same issues.
This blogpost is about Blood Bowl, a boardgame I started playing last year. The goal of this blog post is to use Python API and HTML scraping to fetch Blood Bowl match outcome data from FUMBBL.com, and to create a structured dataset ready for analysis and visualization.