Nebula 2014: Initial Methodology

So, how do we predict an award?

We might find out who has received the award in the past and look for patterns. We could then translate those patterns into probabilities, and then weigh those various probabilities to come up with a predictive model. By testing that predictive model against past award winners, we could then tweak the model until it becomes somewhat–and only somewhat–reliable. Then, as new data comes out with new awards in future years, we could keep improving the model until we have something accurate.

Don’t worry, if you’re looking to be bored, I’ll get into the math later. For right now, what predictors are reliable for the Nebula?

On first glance, a couple starting points are obvious:
1. Previous award history: Does the Nebula has a tendency to honor people who have already been nominated for the Nebula or Hugo? Does it like repeat winners?
2. Genre: Is the Nebula biased towards fantasy or science fiction? If so, by how much?
3. Popularity: For someone to vote for a novel, they probably (hopefully!) have read it. Do the most popular novels win the Nebula? How would we measure popularity? Sales? Ratings? Votes?
4. Critical acclaim: Does the Nebula go to the most acclaimed novel of the year? How would we measure acclaim? Year end lists? Other award nominations?

If we were able to come up with 10-15 different predictors, then weight those to properly match the past 10-15 Nebula winners, we’d have a good start at an effective model.

I’ve already done a lot of this work, and I’ll be introducing the model and the first prediction over the next couple days.

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