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What the 2019 project was trying to predict
The project asked, in effect, “who is likely to die next?” It applied statistical and machine-learning methods to characters in the fictional world of Game of Thrones, using information from the books and television series. ITPro described the work as drawing on methods associated with medical and life-insurance calculations, adapted to fictional character data—not as a general-purpose system for predicting real deaths.
ITPro reported that the team mined information about hundreds of characters. The described inputs combined survival history with biographical, social, and narrative details, including house affiliation, marriage, allies, gender, location, whether a character was major or minor, and how often the character was mentioned in fan-maintained wikis.
How the reported methods differed
Bayesian inference
Bayesian inference estimates probabilities by updating them in light of evidence. In this project’s framing, a character’s recorded traits and story history could contribute to an estimate of that character’s likelihood of dying. ITPro reported that the project used a Bayesian approach, but did not provide enough technical detail to reconstruct the model or its calculations.
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Neural-network methods
ITPro also reported machine-learning methods involving neural networks. These can learn patterns from examples, but the article did not establish how the network was configured, how its inputs were encoded, or how its predictions performed against outcomes. The available reporting therefore does not support a meaningful comparison of accuracy between the Bayesian and neural-network approaches.
What the reported numbers mean—and do not mean
ITPro’s 2019 account named Bronn’s 93.5% as the project’s leading season-eight death estimate. It also attributed these comparisons to the project’s character data:
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- Men had a reported 22% death figure, compared with 11% for women.
- Baratheons were reported as 5% more likely to die than average.
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These are figures as reported by ITPro about a particular fictional-character dataset. They are not real-world population statistics, proof that gender or family affiliation causes an outcome, or independently audited model results. The reporting does not specify enough about the calculations to interpret every percentage more precisely.
Most importantly, a probability estimate is not a spoiler. It describes what a model assigned based on its data and assumptions; it does not establish what the writers would choose, and the cited report does not demonstrate that the estimates were validated against season-eight outcomes.
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Not the first Game of Thrones prediction project
ITPro had covered a similar TUM effort in 2016, when the project analyzed book and television-series data and published predictions ahead of season six. That earlier coverage named Guy Yachdav as the project’s creator. The 2019 Bronn estimate belongs to the later season-eight report; the two rounds of predictions should not be conflated.
The 2016 account also presented the project as a teaching and visualization exercise. Yachdav described it as a fun project, while TUM professor of bioinformatics Burkhardt Rost characterized its classroom use and interactive visual maps as a way to engage students with the subject. That context helps explain why the project could be interesting even without evidence of forecasting accuracy.
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How much confidence should readers place in the forecast?
Only limited confidence is warranted. ITPro’s 2019 article linked to the project blog, but the underlying dataset, feature definitions, model validation, and calculations are not established by the accessible reporting. The strongest supported conclusion is that TUM researchers used character and story data to generate estimates, including a reported 93.5% for Bronn—not that the method could reliably predict fictional deaths.
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