So who will die next in Game of Thrones? Computer scientists think they have the answer
Published 20/04/2016 | 08:56
Game of Thrones starts in a few days, and of course everyone is very excited.
The show is known for the amount of deaths rack up each series - it's commonly said you can't have a favourite character, because choosing one dooms them to die.
Who is going to die next? Scientists think they have the answer.
A computer science class at the Technical University of Munich (TUM) in Germany scoured the Game of Thrones data to find who will die next.
They then put together a website that reports which characters are most likely to die in the upcoming sixth season of the TV series.
The students used an array of machine learning algorithms to answer these questions.
Read more: See Games of Thrones titles from any angle
This algorithm accurately predicted 74 percent of character deaths in the TV-series and the books, has many surprises in store.
According to the algorithm, a number of characters thought to be relatively safe in grave danger.
The program has predicted there is a good chance that the villainous Ramsey Snow (64pc likelihood of death) will outlive his runaway captive and mortal enemy Theon Greyjoy (74 percent likelihood of death).
Sansa Stark is only 3pc likely to die soon, but Tommen Baratheon is the most likely to die next, at 97pc.
Stannis Baratheon is the second most likely to die, at 96pc, and Daenerys Targeron is shockingly likely to die soon, at 95pc.
“This project has been a lot of fun for us,” says Dr. Guy Yachdav, who lead the class and was in charge of the project.
“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 multi-dimensional data. It provided the perfect setting for our class.
“Data mining and machine learning are tools that enable digital medicine to benefit from modern biology for diagnosis, treatment and prevention of disease."
"Turning to such a ‘real life’ challenge created a didactical jewel, winning students for these subjects,” summarizes Burkhardt Rost, Professor of Bioinformatics at the Technical University of Munich. “And the interactive visual maps created in the project might open a new approach to data visualization that we will follow up scientifically.”