Hugo Prediction: The Indicators

The main purpose of my blog Chaos Horizon is to use mathematical modeling to predict the winners of the Hugo and Nebula awards. To do this, I use a Linear Opinion Pool constructed by data mining the last 15 years (since 2000) of award-winning data, as provided by excellent websites like SFADB.

The Hugo Formula (see the 2014 prediction here) uses 8 Indicators of Hugo success, each of which is weighted in turn. The percentage afterwards gives the basic reliability of the Indicator, with links to a fuller explanation of each indicator:

Indicator #1: Nominee has previously been nominated for a Hugo award. (78.6%)
Indicator #2: Nominee has previously been nominated for a Nebula award (prior to this year). (78.6%)
Indicator #3: Nominated novel is in the fantasy genre. (50%)
Indicator #4: The nominated novel wins one of the main Locus Awards categories. (57.1%)
Indicator #5: The nominated novel receives the most votes in the Goodreads Awards. (33%)

Indicator #6: Novel was the most reviewed on at the time of the Hugo nomination. (75%)
Indicator #7: Novel won a same year Nebula award. (85.6%)
Indicator #8: Novel received a same year Campbell nomination. (50%)

To generate these, I went through many possible interpretations of the available data. The Indicators are not perfect, nor are they intended to be. For them to be perfect, this would imply that the Hugo award is perfectly predictable—it is not. The pool of voters is too small, and too many outside factors can influence the awards.

Instead, by building a model with multiple indicators like this allows us to not overly-stress one factor, but rather look at a fuller range of issues. Since the point of this model is to generate discussion and have fun, we want the math to be a little elastic to encompass the human element of prediction.


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