2015 Nebula Prediction: Indicators #1-#4

Let’s leave the Hugo Award behind for now—the controversy swirling around that award has distracted Chaos Horizon, so it’s time to get back on track doing what this site was designed to do: generating numerical predictions for the Nebula and Hugo Award based on data mining principles.

Over the next three to four days, I’ll be putting put the various “Indicators” of the Nebula Award, and then we weight and combine those to get our final prediction. For a look at the methodology, check out this post and this post. If you’re really interested, there’s an even more-in-depth take in my “Building the Nebula Model” posts. Bring caffeine!

With the basics of the model built, though, all that’s left is updating the stats and plugging in this year’s data. Here’s Indicators #1-#4 (out of 11). These deal with past awards history:

Indicator #1: Author has previously been nominated for a Nebula (78.57%)
Indicator #2: Author has previously been nominated for a Hugo (71.43%)
Indicator #3: Author has previously won a Nebula for Best Novel (42.86%)
Indicator #4: Author is the most honored nominee (50.00%)

The best way to understand each of those is as an opinion/prediction of the Nebula based on data from 2001-2014. So, 78.6% of the time, someone who has previously been nominated for the Nebula wins the Nebula Best Novel award, and so on. The only tricky one here is the “Author is the most honored nominee”: I add up the total number of Hugo Noms + Wins + Nebula Noms + Wins to get a rough indicator of “total fame in the field.” 50% of the time, the Nebula voters just give the Nebula Best Novel award to the most famous nominee.

All of these indicators flow from the logical idea that the Nebula is a “repetitive” award: they tend to give the Best Novel award to the same people over and over again. Take a look at my Repeat Nominees study for justification behind that. This repetition is also a kind of a “common sense” conclusion: to win a Nebula you have to be known by Nebula voters. What’s the best way to be known to them? To have already been part of the Nebulas.

Don’t think this excludes rookie authors though—Leckie did great last year even in my formula, and that’s why these are only Indicators #1-#4. The other indicators tackle things like critical reception and same-year award nominations. Still, they give us a good start. Let’s check this year’s data:

Tables 1 and 2: Past Awards History for 2015 Nebula Nominees

Table 1 Past Hugo Nebula Data Info
Table 2 Past Hugo Nebula Data

The chart is for award nominations prior to this year’s award season, so no 2015 awards are added in
Nebula Wins = Prior Nebula Wins (any category)
Nebula Noms = Proir Nebula Nominations (any category)
Hugo Wins = Prior Hugo Nominations (any category)
Hugo Noms = Prior Hugo Wins (any category)
Total = sum of N. Wins, N. Noms, H. Wins, and H. Noms
Total rank = Ranking of authors based on their Total number of Wins + Nominations
Best Novel = Has author previously won the Nebula award for Best Novel?
Gray shading of boxes added solely for readability
All data mined from http://www.sfadb.com

Jack McDevitt breaks out of the pack here: his prior 17 Nebula nominations (!) make him the most familiar to the Nebula voting audience. He only has 1 win for those 17 nominations, though, so I don’t think he’s in line for a second. McDevitt is going to suffer in indicators #6-10, as his books tend to not get much critical acclaim. McDevitt currently has a 10% win rate for the Nebula Best Novel award. If he keeps getting noms, I’m going to have to add a “McDevitt” exception to keep the formula working.

Jeff VanderMeer’s Hugo nominations are all in Best Related Work, not for fiction, although his other Nebula nomination is for Finch. He’s well-known in the field, although Annihilation hasn’t picked up many award nominations for 2015.

Leckie, who was a rookie last year, now does very well across the board: her prior Nebula noms, Best Novel Nebula win, and Hugo nom will all give her a boost in the formula. The real wild-card in Indicators #1-#4 is The Three-Body Problem. Cixin Liu’s novel was translated by Ken Liu, who is very well known to the Nebula and Hugo audience: he has 3 Hugo nominations (2 wins), and 6 Nebula nominations (1 win), to make him one of the most nominated figures in recent years. If SFWA voters think of The Three-Body Problem as being co-authored by Ken Liu, they’re more likely to pick it up, and that will really boost the novel’s chances. I haven’t decided the best way to treat The Three-Body Problem for my formula. What do you think? Should I include Ken Liu’s nominations as part of the profile for The Three-Body Problem?

Tomorrow, we’ll start looking at Indicators tracking genre and critical reception.


2 responses to “2015 Nebula Prediction: Indicators #1-#4”

  1. DJ (@MyLifeMyBooksMyEscape) says :

    Would it be too much trouble to do both versions of The Three Body Problem? That is interesting… I hadn’t put too much thought into him being a co-author or not. Personally, I’d be interested into see how much a difference there would be, or what his average chance of winning would be.

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