If you haven't been eagerly awaiting Game of Thrones season six, then you're probably no friend of mine. I'm also going to hazard a guess that you haven't quite hit it off with the cultural touchstone that is George R R Martin's fantasy epic, or its HBO adaptation.
Using the power of Big Data, the research team put together a set of machine-learning algorithms to trawl through data from both the book and the TV show to predict who will get the axe next.
Obviously, with Martin's latest book still being written, and season six of Game of Thrones being totally unknown, nobody really knows who will die next. At least the team at TUM may have given us a better idea.
If you're curious to know who's going to go next, the team have set up A Song of Ice and Data to let you drill down into all the information they've collected.
Interestingly, TUM's data suggests that Tommen Baratheon is the next character to die – although it doesn't actually explain how his death may come about. Perhaps the Sparrows will come for him seeing as they're currently overrunning King's Landing with their religious beliefs, who knows – it's clearly all a bit of fun.
“This project has been a lot of fun for us,” said project leader Dr Guy Yachdav in a statement to The Telegraph. “In its daily work, our research group focuses on answering complex biological questions using data mining and machine-learning algorithms. For this project we used similar techniques.
“Only this time the subject matter was a popular TV show. The epic scale of the worlds created by George R R Martin provides an almost endless resource of raw multidimensional data. It provided the perfect setting for our class.”
Obviously data mining and Big Data can be, and is, used for far more important things that predicting who will die next in a fictional universe. Thankfully, TUM's analyser can't suck all the surprise out of Game of Thrones – after all, both HBO and George R R Martin love to kill off the characters everybody loves. Who knows, perhaps TUM has guided the hand of fate to strike on those you'd least expect.