# Analyzing the 2016 Hugo Noms, Part 1

No use putting this off any longer. I was hoping we’d see some more leaked information/numbers, but we’re stick with pretty minimal information this year. Here we go . . .

**Where We’re At: **Yesterday, the 2016 Hugo Nominations came out. Once again, the Rabid Puppies dominated the awards, grabbing over 75% of the available Hugo nomination slots.

If you’re here for the quick drive-by (Chaos Horizon is not a good website for the casual Hugo fan), I’m estimating the Rabid Puppies at 300 this year, with a broader range of 250-370. Lower than that, you can’t sweep categories. Higher, more would have been swept. Given this year’s turnout, 300 seems about the number that gets you these results. Calculations below. Be warned!

**EDIT 4/28/2016:** Sources are telling me that there were indeed withdrawals in several categories. This greatly muddies the upper limit of the Rabid Puppy vote. As such, I think the 250-370 should be read as the lower limit of the Rabid Puppy vote, with the upper limit being somewhere in the range of 100 higher of that. I did some quick calculations for the upper RP limit using the Best Novel category, assuming Jemisin got 10%, 15%, or 20% of the vote. We know she beat John C. Wright’s *Somewhither*. That gives upper limits of 335, 481, and 615. I think 481 is a good middle-of-the-road estimate. Remember that Best Novel numbers are always inflated because more people vote in this category than any other, so a big RP number in Best Novel doesn’t necessarily carry over to all categories.

So revised RP estimate: 250-480. If there were many withdrawals, push to the high end (or beyond) of that range. Fewer withdrawals, low end. Perhaps the people who withdrew will come forward in the next few days and this will allow us to be more precise. If those withdrawals are made public, please post them in the comments for me.

EDIT 4/28/2016: Over at Rocket Stack Rank, Greg has done his own Hugo analysis, using a different set of assumptions. While I assume a linear increase of “organic” voters (non-Puppy voters), he uses a “power law” distribution. Most simply put, it’s the difference between fitting a line or a curve to the available data. I go with the line because of the low amount of data we have, but Greg is certainly right that the curve is the way to go if you trust the amount of data you have.

Using his method, Greg comes up with a lower Rabid Puppy number (around 200), but that’s also accompanied by a lower number of “organic” voters than my method estimates. Go over and take a look at his estimate. It’s a great example of how different statistical assumptions can yield substantially different results. I’ll leave it up to you to decide which estimate you think is better. I personally love that we now have multiple estimates using different approaches. It really broadens our understanding of this whole process. Now we need someone to come along and do a Bayesian analysis!

**T****he Estimate: **This year, MidAmeriCon II released minimal data information at this stage. They’re not obligated to release any, so I guess we should be happy with what we got. Last year, we got the range of votes, which allowed us to estimate how strong the slate effect was. This year, we only have the list of nominees and the votes per category. Is that enough to make any estimates?

Here on Chaos Horizon, I work with what I have. I think we can piece together an estimate using the following information:

- The Rabid Puppies swept some but not all of the categories. That’s a very valuable piece of information: it means the Rabid Puppies are strong, but not strong enough to dominate everything. With careful attention, we should be able to find the line (or at least the vicinity of the line).
- Zooming more closely in, the Rabid Puppies swept the following categories: Short Story, Related Work, Graphic Story, Professional Artist, Fanzine. Because of this, we know that the Rabid Puppies had to beat whatever the #1 non-Rabid Puppy pick was in those categories.
- The Rabid Puppies took 4/5 slots in Novella, Novelette, Semiprozine, Fan Writer, Fan Artist, Fan Cast, and Campbell. This means that, in the categories, the #1 non-Rabid Puppy pick had to be larger than Rabid Puppy slate number.

With that information, if I could just find out what the number of votes the #1 non-Rabid Puppy pick likely received, I could estimate the Rabid vote. Now, couldn’t I use the historical data—the average percentage that the #1 pick has received in past years—to come up with this estimate?

One potential wrench: what if people withdrew from nominations? There’s no way to know this, and that would screw the numbers up substantially. However, with more than 10 categories to work with, we can only hope this didn’t happen in all 10. If you believe at least one person withdrew in Novelette, Semiprozine, Fan Writer, Fan Artist, Fan Cast, and Campbell, add 100 to my Rabid Puppy estimate for 400. There’s also the question of Sad Puppy influence, which I’ll tackle in a later post.

Or, to write it out: In the swept categories, Rabid Puppy Number (x) is likely greater than the Non-Rabid voters (Total – x) * the average percentage of the #1 work from previous years.

In the 4/5 categories, the Rabid Puppy number (x) is likely less than the Non-Rabid voters (Total – x) * the average percentage of the #1 work from previous years.

While that won’t be 100% accurate, as the #1 work gets a range of numbers, it’s going to give us something to start with. Here’s the actual formula for calculating the Rabid Puppy lower limit in swept categories using this logic:

x > (Total – x) * #1%

x > #1% * Total – #1% * x

x + #1% * x > #1% * Total

(1 + #1%)x > #1% * Total

x > (#1% * Total) / (1 + #1%)

So, quick chart: we need the #1%, the average percent of vote the #1 work gets, i.e. the highest placing non-RP work, in all categories that were either swept or had 4/5. I’ll use the 4/5 Rabid categories in a second to establish an upper limit.

Off to the Hugo stats to create the chart. I used data from 2010-2013, giving me 4 years. I didn’t use 2014 and 2015 because the Sad Puppies and Rabid Puppies changed the data sets by their campaigns. I didn’t use 2009 data because the WorldCon didn’t format it conveniently that year, so it is much harder to pull the percentages off. I don’t have infinite time to work on this stuff. :). I also had to toss out Fan Cast because it’s such a new category.

**Chart #1: Percentage the #1 Hugo Nominee Received 2010-2013**

2013 | 2012 | 2011 | 2010 | Average | High | Low | Range | |

Short Story | 16.2% | 12.3% | 14.0% | 13.7% | 14.0% | 16.2% | 12.3% | 3.9% |

Related Work | 15.4% | 11.1% | 18.4% | 21.6% | 16.6% | 21.6% | 11.1% | 10.5% |

Graphic Story | 29.7% | 17.4% | 22.3% | 19.0% | 22.1% | 29.7% | 17.4% | 12.3% |

Professional Artist | 23.9% | 40.1% | 26.9% | 33.6% | 31.1% | 40.1% | 23.9% | 16.2% |

Fanzine | 26.9% | 25.2% | 20.3% | 16.1% | 22.1% | 26.9% | 16.1% | 10.8% |

Novella | 17.6% | 24.8% | 35.1% | 21.1% | 24.7% | 35.1% | 17.6% | 17.6% |

Novelette | 14.5% | 12.1% | 11.3% | 12.9% | 12.7% | 14.5% | 11.3% | 3.2% |

Semiprozine | 42.6% | 29.3% | 37.8% | 32.4% | 35.5% | 42.6% | 32.4% | 13.3% |

Fan Writer | 23.9% | 21.7% | 21.7% | 13.8% | 20.3% | 23.9% | 13.8% | 10.1% |

Fan Artist | 16.7% | 22.6% | 26.1% | 20.6% | 21.5% | 26.1% | 16.7% | 9.4% |

Campbell | 18.7% | 13.1% | 20.3% | 16.0% | 17.0% | 20.3% | 13.1% | 7.2% |

Notice that far right column of “range”: that’s the difference between the high and low in that 4 year period. This big range is going to introduce a lot of statistical noise into the calculations: if I estimate Best Related work to get 16.6%, I’d be off as much as 5% in some years. I could try to offset this by fancier stat tools, but 4 data points will produce a garbage standard deviation, though, so I won’t use that. On 300 votes, this 5% error would throw a +/- halo of 15 votes. Significant but not overwhelming.

Okay, now that I have this data, let’s use it to calculate the lower limit of Rabid Puppies:

**Chart 2:** **Calculating Min Rabid Puppy Number from 2016 Swept Categories**

Swept Category | Total Votes | #1 % | Min RP |

Short Story | 2451 | 0.140275 | 301.52 |

Related Work | 2080 | 0.166225 | 296.47 |

Graphic Story | 1838 | 0.2211 | 332.8 |

Professional Artist | 1481 | 0.310975 | 351.31 |

Fanzine | 1455 | 0.22125 | 263.6 |

Average | 309.14 |

Okay, what the hell does this chart say? The Short Story category had 2451 voters this year. In past years, the #1 Sad Puppy pick grabbed 14% of the vote. To beat that 14%, there needed to be at least 302 Rabid Puppy voters. With that number, you get 302 Rabid Votes, (2451-302) = 2149 Non-Rabid votes, voting at 14% = 301 votes. Thus, the Rabid Puppies would beat all the Non-Rabid votes by 1 point.

Now, surely that number isn’t 100% accurate. Maybe the top short story this year got 18% of the vote. Maybe it got 12%. But 300 seems about the line here–if Rabid Puppies are lower than that, you wouldn’t expect it to sweep.

Keep in mind, this chart just gives us a minimum. Now, let’s do the other limit, using the categories were the Puppies took 4/5. This is uglier, I’m warning you:

**Chart 3:** **Calculating Max Rabid Puppy Number from 2016 4/5 Categories**

4/5 Category | Total Votes | #1 % | Max |

Novella | 2416 | 0.246575 | 477.89 |

Novelette | 1975 | 0.12665 | 222.02 |

Semiprozine | 1457 | 0.35505 | 381.76 |

Fan Writer | 1568 | 0.20265 | 264.21 |

Fan Artist | 1073 | 0.2151 | 189.95 |

Campbell | 1922 | 0.170125 | 279.44 |

302.54 |

Ugh. Disaster befalls Chaos Horizon. This number should be higher than the last one, creating a nice range. Oh, the failed dreams. This chart is full of outliers, ranging from that huge 477 in Novella to that paltry 190 in Fan Artist. Did someone withdraw from the Fan Artist category, skewing the numbers? If I take that out, it bumps the average up to 325, which fixes my problem. Of course, if I dump the low outlier, I should dump the high outlier, which puts us back in the same fix.

A couple conclusions: the fact that both calculations turned up the 300 number is actually pretty remarkable. We could conclude that this is just about the line: if the Rabid Puppies are much stronger than 300 (say 350), they should have swept more categories. If they’re much weaker (250), they shouldn’t have swept any. 300 is the sweet spot to be competitive in most of these categories, with the statistical noise of any given year pushing some works over, some works not.

It also really, really looks like Novelette and Fan Artist should have been swept. Withdrawals?

To wrap up my estimate, I took the further step of using the 4 year high % and the 4 year low % (i.e. I deliberately min/mixed to model more centralized and less centralized results). You can find that calculation on this 2016 Hugo Nom Calcs. This gives us the range of 250-370 I mentioned earlier in the post. I’d keep in mind that the raw number of Rabid Puppies might be higher than that—this is just the slate effect they generated. It may be that some Rabid Puppies didn’t vote in all categories, didn’t vote for all the recommended works, etc.

There’s lots of factors that could skew my calculation: perhaps more voters spread the vote out more rather than consolidating it. Perhaps the opposite happened, with voters taking extra care to try to centralize their vote. Both might throw the estimate off by 50 or even 100.

Does around 300 make sense? That’s a good middle ground number that could dominate much of the voting in downballot categories but would be incapable of sweeping popular categories like Novel or Dramatic Work. I took my best shot, wrong as it may be. I don’t think we’ll do much better with our limited data—got any better ideas on how to calculate this?

### 33 responses to “Analyzing the 2016 Hugo Noms, Part 1”

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I’m not sure how or if this impacts your numbers, but if you look at that 477 in Novella, we should keep in mind that compared to some of the other categories, Rabid Puppies selected a number of nominees that would generally appeal to the more traditional Hugo voter. Bujold and Sanderson are previous winners, Reynolds is a previous nominee, and the Daniel Polansky was a very well regarded novella from Tor.com Publishing that I think had a very real shot at the ballot in a world without RP (and even if it wasn’t on the list).

Sanderson also allowed potential Hugo voters to request an e-copy of his novella direct from his website if they wanted to read and consider it – which given his general popularity, could not have hurt.

Polansky and Reynolds were on my ballot and I am not part of the RP contingent.

Good thing to keep in mind, and the Novella category is always more centralized. However, it doesn’t affect this calculation because my calcs are looking at the “least popular Rabbid Puppy pick” when we run these kind of numbers–i.e. the pick that was in the #5 or #6 spot that likely only got Rabid votes. For Novella, that’s Nick Cole. I know nothing about him or this work, but this wasn’t a text I heard mentioned in many other circles. While Cole may have picked up some other voters from outside the RP process, I estimate/assume that it had to be small.

300 looks awfully low to me.

I’m hearing that there were withdrawals in several categories, which means the number should be increased by around 100. I’ll add an edit.

I’ll post my own conclusions on RSR, but I think 300 is too high. I originally got 270, but after several revisions, I’m getting between 200 and 220, assuming that people in the Novelette, Best Editor, Long Form, Fanwriter, and Fan Artist categories declined the nomination.

I think the reason you’re getting higher numbers is that you’re assuming that the #1% scales linearly with the total number of votes, but it actually goes down. I’m using a least-squares fit to a power-law distribution to estimate the number of “organic” votes in each rank, but otherwise using about the same method you are.

For example, for Best Novel, I’m estimating between 203 and 234 slate votes and between 480 and 462 organic votes for the #1 position. (Changing the estimated number of slate votes changes the estimated number of organics.)

Have you tried using this method on the 2015 numbers to see what you predict? I end up predicting between 160 and 216 rabids, which accords fairly well with the 163 I think is correct. (The number who nominated Vox Day.)

I look forward to seeing alternative numbers on Rocket Stack Rank. I’m not sure I buy the Least-Squares assumption, given that the number of available works to vote for is limited, and sharply so in some categories (Best Novel, etc.). I do think it’s definitely worth pursuing, though, and it’s a good alternative to my linear approach.

For other readers, Greg and I are making one different core assumption: I’m making the assumption that increasing Hugo voters brings more “similar” voters, thus keeping patterns roughly the same; Greg is assuming that it’s bringing in more “different” voters who will vote along different patterns, thus spreading things out. I’m not sure we have enough data to tell which assumption is best. I think you’ve got some headroom in Best Novel (if you’re doing #1 at 480-462). Rabid Puppies could be around a hundred high and produce the same results, which gets you pretty close to the range I predicted. I see how you got the minimum limit for RP—how are you doing the top?

What’s good about these predictions it’s that they’re resolved by seeing the actual stats 4 months from now!

As soon as you get your Rocket Stack Rank, let me know and I’ll post a link here, so people can weigh both methodologies for themselves.

Will do. It’s still a min/max problem–just like you’ve got.

I can compute a minimum in any category with at least one slate nominee (which is all of them) by computing the organic score it needed to beat.

I can compute a maximum in any category with at least one organic result (which is ten of them) by computing the organic score that had to NOT be beaten.

The minimum for Best Dramatic Presentation (Long Form) is too high (280), but that’s because it breaks the assumption that at least one slate nominee is so bad that no one else voted for it. So I discard that number. It’s the only minimum I need to discard. The rest range from 67 to 203.

The maxima for six of the 10 look very nice (208 to 280), but four of them are from 148 to 88. If you assume that all of those were really swept but that several nominees declined, they all still have comfortably small minima (under 200).

I end up with a biggest minimum of 203 and a smallest maximum of 208. So call it 205. The test will be how many nominations for Best Editor Vox Day got. And, of course, the list of people who actually declined their nominations.

Good stuff. I can wait for the post, but what formula are you using to calculate the organic #1 number?

I work with this pair of inequalities:

a*( (N – S*w)/(N0*(i+1)) )^r < S < a*( (N – S*w)/(N0*i) )^r

a and r come from the regression.

N is the total number of votes this year.

S is the unknown number of slate voters.

w is the number of candidates the slate proposed for this category (usually 5). N0 is the total number of votes in this category for the year I used to compute the regression. I tried each of 4 years and picked the one with the best r^2.

i is the rank. For #1, use i = 1, but it works for other positions.

You do the regression on the logs of ranks and votes. (Ranks really are just 1, 2, 3). If the result is slope m and intercept b, then a = exp(b) and r = -m.

Use Newton's method to solve each side of those inequalities for S. Left side is the min and right side is the max.

By the way, I estimate that using a linear method gives you an error of about 50%, which entirely explains the difference in our results. 🙂

What is your guess for non-slate novel #4?

Even if I assume 50% of the new voters will choose totally different books rather than voting in the old patterns I get Non-Slate #4 (Aurora would be my guess) at ~250 votes.

I get it at between 195 and 202. So (as I figure things) the slate just barely edged it out. They got two best-novel finalists by the skin of their teeth.

For a dash of historical data on Robinson, he got 135 votes for his novel

2312back in 2013, ringing in at 12.13% percent of the 1113 votes.This year, we had 3695 voters. Knock out 300 (or 200, if you want, it doesn’t make much difference), and that leaves 3395 “organic” voters in this category. That makes almost 2,000 new voters. My linear analysis has those voters producing at 12.13% for Robinson, adding some 240 more votes, popping his number up to 370, i.e. the top range. If we model only 50% of that vote coming through for Robinson, we get close to the 250 number you mention. If we think the new voters aren’t voting for Robinson at all, but are in fact a different kind of fan, we get Greg’s number. Three different approaches, three very different estimates!

Oh! He wanted the name of an actual book. That’s not something I attempt to figure out. I only care whether it was a slate or an organic nominee.

Greg:

I disagree with a least-squares approach when you have very few discontinuous data points. If you have a “turning point” event in the data, you have to capture this. In a least squares approach, your turning points can be either with the slope term, the intercept term or both. Slope is even more complicated since the actual “slope” could be a change in the function of the curve itself.

I think you are imposing a degree of modeling precision not warranted by the data itself. A guess would be a min/match approach to cross a threshold (mostly what Brandon did) would fit an environment if you thought you had a state of nature change. That could also be adapted in a Bayesian format using a threshold condition to pass into the #5 rank spot over #6.

If actual vote totals are given, then with a couple of assumptions you can start to accurately estimate votes. Probably easier to do in the sub-categories since the Novel category and the long-form can get non-rabid votes who disagree with longer run Hugo voter pools.

I’ve done a number of consulting jobs where I had to estimate market prices in small markets with shocks, and imposing a rigid structure on the model often gives you answers that don’t make sense in the current state of nature.

The best stats guys I’ve ever known (and done work with) who published in publications like the Journal of the American Statistical Association and similar outlets always cautioned their students about complex models when you are uncertain about your basic data (means, standard deviations, population percentages and the like).

But of course, all of this is just an educated guess without hard data to go on.

Yes, it would be better if the Hugo folks released the top 50 rather than just the top 15.

Have you had a chance to read the blog post? (Brandon added it above in an edit.) http://www.rocketstackrank.com/2016/04/analysis-of-slate-voting-for-2016-hugos.html

Or, better yet, the PDF with mathematical details: https://onedrive.live.com/redir?resid=9E2E0DEA251A3F4A!29350&authkey=!AKm7bQRJkUSFG8U&ithint=file%2cpdf

When you get an r^2 greater than 0.95 with 15 to 17 data points, there is no turning-point event in the data. If you compute this for all categories over 5 years, you get very, very few with r^2 < 0.9. It really does seem like an underlying property of the distribution. None them has the sort of turning point I think you're talking about.

Also, this is exactly the sort of data that's typically power-law distributed, so it shouldn't be a big surprise.

Did you stumble across the 2009 nomination data? That WorldCon went rogue and released the whole nomination data set.

I didn’t see the 2009 data before. Nice! Just looking at best novel, the power-law distribution has an R^2 fit of 0.9639 across 87 points.

Weirdly, though, Novella in that year is a relatively poor fit to a power law– R^2 = 0.88. If you break it into two ranges: 1-16 and 17-44, then each range best fits a simple line, with R^2 of 0.987 and 0.971 respectively. (The low end actually fits a power law to 0.94, but the upper end only fits to 0.74.)

Power-law distributions can be weak at the high end (I saw that a lot at Amazon and Microsoft over the years) but I don’t think I’ve ever seen anything quite like this. I wonder what caused it.

Best Novelette and Short Story are both power-law distributions with R^2 of 0.95 and 0.97 respectively. It would probably be helpful to understand what’s going on with novella. I note that it looks a whole lot like the 2015 graph for Best Short Story.

For 2015, if I subtract 162 from the top 5 items (to remove the RP slate) and resort, I again get a power-law fit to 0.95. But it’s hard to believe anyone had a slate just for Best Novella in 2009. Hmmm.

I’ve gone back and read Greg’s blog post. I’ve also rexamined Brandon’s numbers.

Both of you have plausible estimates of a straight slate vote. If you have more knowledgeable voters in a category, you have higher vote totals arriving a specific nominee. Smaller vote totals make the n arriving at a P*x n = x votes calculation harder to beat a straight slate of just garbage. The higher total N of votes in Novels and Long-Form make it easier to overcome a straight garbage slate.

I think the two of you have arrived at reasonable “boundaries” of the lower bound of the last vote total of the worst nominee given the data of a something close to a straight slate vote.

If you ever get more data to do some analysis I suggest that you use John Wright’s book and Butcher’s book vote totals to estimate the impact of a proportion of the Sads interested in good action-oriented SF.

Butcher’s 2015 novel was the best, popular, action-oriented SF book released. It was politically neutral. Although Wright is religious and politically conservative, his writing is not action oriented SF. I did not vote for Wright but voted Butcher #1. I think there are voting differences within the Sads. Some for Action and others for politically more conservative/religious works. The vote totals for Wright and Butcher (if they are released) should give you a starting point on this. Ringo’s 2015 release is another data point. Not as good as Butchers, but action oriented. Not on rabid, but to estimate a faction of Sads.

Greg, the differences in the data this year have hopefully persuaded you that the Sads are not a lock-step group. Readers with different opinions than traditional Hugo voters – yes. Lock-step, nope.

Greg: the June Baen monthly bundle contains two different short SF anthologies not tied to a single world universe:

Galactic Games

Best Military SF of 2015

Since you are interested in this type of short fiction, this is an inexpensive way to pick up the bundle.

I’ve already purchased the bundle (can’t get it until early June) because of the David Drake book, John Ringo anthology, and the military SF book.

Last, I’m going to Gencon this year. If either of you are going and interested in having a beer, respond here and I’ll email you directly. As a math & SF person, I’ll buy either of you a round.

I’m not going to a Worldcon, ever. Kansas City is one of my favorite places to visit, but Worldcon would not be fun for me.

No Gencon for me this year . . . too far from New Mexico for me to make it easily. I hate to pass up an offer for a free beer, though!

Thanks for the tip, but it looks like those stories are all reprints. There’s nothing wrong with reprints, of course, but I have my hands full reading and reviewing stories published in 2016.

I’d be happy to chat with you over a beer, but at the moment NorwesCon and WorldCon are the only ones Eric and I attend.

As for the numbers and the Sad Puppies, they suggest to me that the group has disappeared. My best guess is that ~40 of them joined the Rabid Puppies and the rest either dropped out or just did their own thing this year.

That would make sense, if I take them at their word, since the Sad Puppies had believed that they really did represent what most fans wanted. The vote at Sasquan had to have beeen tremendously disappointing. It would make sense for most of them to just decide to put their efforts into something else, and for a small core group to join the “burn it down” team.

What will be really interesting to watch will be to see how many puppies pay to vote at WorldCon this year, which will be reflected in the votes for Vox Day for Best Editor. I’d expect a big drop from the ~580 last year, since everyone knows it’s an exercise in futility now, and EPH will cut their nominating ability in half next year.If not, it’ll show truly remarkable determination. But I think this round of nominations was their last hurrah–for puppies of any stripe.

We know she beat John C. Wright’s Somewhither.Do we? The Sad Puppy influence was certainly rather less this year than last, but 40 finalists were on their official recommendation list, including 12 which were not on the Rabid slate. The effect of even a #1 Sad Puppy recommendation clearly fell short of knocking out any Rabid nominations by itself, but it still looks as if even a relatively low place on the recommendation list gave a useful boost to any potential finalist with significant enough support elsewhere.

And Somewhither was the top Sad Puppy recommendation for Novel as well as being on the Rabid slate.

I am going to be totally unsurprised if it turns out that John C. Wright was one of the withdrawals – he does not seem to have enjoyed the experience of being one of the main Rabid poster boys last year, and I find it easy to believe that he did not want to repeat the experience.

Well, going by my numbers, the slate fell 100 votes short of what they’d have needed to get another candidate into best novel–even assuming zero declines. So I’ll be very surprised if JCW was a withdrawal.

Oops. make that 50 votes. I looked in the wrong column.

There is nothing on his blog about withdrawing. Even in his post about nominations.

The data that Greg has for the 2012-2016 nominations are very convincing that a power law is the right way to go. Its hard to argue with a correlation coefficient of 0.95b with just 15-17 data points used.

I’m a believer in the ~200 RP estimate.

Greg – I don’t know any of the Sads personally so I am unwilling to predict their behavior. I’m guessing that the total voter pool increased with all of the publicity from SP3. I found about the whole situation through the Wall St. Journal or http://www.realclearpolitics.com The Hugos last year got a lot of publicity.

Personally, the Hugos started being an “anti-buying” signal for Novels about 20 years ago. They became way too literary for my tastes. I really liked latest Butcher book and I’m glad I got to vote for it. I’m hoping that the Dragon Awards have enough of a voter pool and specific enough categories to “crowd source” recent books in genres that I like. I nominated for the first time this year. Butcher, Uprooted and then an obscure YA book and the latest Ringo Zombie book. The only ones I thought would reach the final 5 were Butcher and Uprooted – but that is ok.

As long as the voter pack is viable, I’ll probably continue buying associate memberships. I read a bunch of shorter fiction last year that I enjoyed. I also greatly enjoyed the Goblin Emperor – something I never would have read except for the voter packet. The amount of money for voting rights is not that significant to me.

In the 60s, 70s and early 80s the Hugos gave me reading signals that gave me a lot of pleasure. I’d like to see a broader voter pool which recognizes more action-oriented work – at least to the finalist stage. It seems that the Hugo voter pool does not match my interests for longer works. it just is what it is. If you don’t participate – nothing changes.

Can’t see ever going to a Worldcon – even in Kansas City which is wonderful for BBQ, Professional Baseball, the WW1 Museum, the Truman Library and Cabelas.

From a math perspective, I don’t think that EPH will work as well as 4 of 6. I’d have to do the modeling to see how they would work in combination.

The issue this year was one toxic individual who either bought a bunch of memberships or inspired a lock step vote. If you are correct about 200 votes, that is only $8,000 to generate a ton of publicity and probably enough sales to make this a profitable venture for the individual’s publishing house. $8,000 is nothing for a marketing budget. An $8k strategy to build sales could well be profitable. The individual could make enough money in sales of SJWs Always Lie alone to recoup a profit on an $8k investment. In everything I’ve read over the last year plus about the Hugos, I can’t recall anyone running a profit calculation. The more I think about it, the more I think this is a viable marketing strategy.

SJWs Always Lie retails for $4.99 on Amazon (I just looked it up). For illustration, lets assume the individual makes $4 on an ebook sale. This title is only available via an ebook. $8,000 is spent to buy 200 Hugo votes (200 x $40 = $8,000).

To break-even on the $8,000, you have to generate an incremental sales of:

$8,000 / ($4.99 – $1 cut to Amazon) = x sales.

At 2,006 incremental sales the $8,000 investment is repaid. This is an absurdly low level of sales for a guerilla marketing strategy to reach break-even.

The Hugo issue in 2016 is can the individual hit 2006 incremental sales on one book alone to recoup a sunk cost (i.e. the time to write the original book). If you do a chain-ratio analysis of incremental sales once you get a first time purchase to your publishing house, this becomes a really obnoxious, but easily profitable strategy in a micro-market.

My issue with the Hugos is a personal preference in literature coupled with an strong interest in business and statistics. But I’m not seeing any obvious glitch in the profitability of this strategy. My professional credentials in business and marketing are very strong. It seems to me the way for the Hugos to counter an obvious profitable strategy to railroad non-serious nominees is to hit the profit angle.

I’m not a Hugo insider. I don’t want to go to WorldCon. But this is such an obvious gaming in a nonserious way of a literary award for profit that the only way to combat it is to hit the profit angle. I don’t know or care enough about the Hugo administration to talk about what can be done within the rules system – but the profit angle here is obvious.

My math and business geek side took control and I spent half an hour on looking at this as a business/marketing problem – but the issue when you step away from it is pretty obvious. Easy solutions are also pretty obvious. Make it unprofitable as a business strategy it becomes a lot more “costly” from a business sense to drop $8k+ as a gesture.

Brandon/Greg – ponder this profit analysis and marketing problem. Chain ratios for new customers and discounted life-time-profit value of a new customer can get into some middling algebra, but even those are not that complicated. I don’t think that a more detailed analysis is necessary. 2006+ sales and you get a profit on a sunk cost. Why get more detailed than that?

But the basic break-even math can be done by a freshman in High School. If you concur with this basic analysis, I could write this up on my professional letter head with my academic credentials and send it to whomever individuals on the Hugo awards hierarchy. For this issue, coming from me on my letterhead would at least get people to do the accounting/marketing problem. The two of you probably know whom I should write – if I choose to do so. Brandon – you and I have personally corresponded so I’m guessing that my business credentials for something like this would be accepted for what they are and people would focus on the accounting and not me personally.

I’m hellishly busy over the next 7 days. But then my schedule opens up and I’ll have a chunk of time with my laptop.

Brandon – I’ve visited New Mexico several times. My wife and I love Hatch Chilis and NM is beautiful. My wife is very arty and always enjoys Santa Fe and Taos. If I ever make it back there I’ll email in advance. It is on the other side of the country from me so I don’t get out to that part of the world but once a decade or so. The USA is a very big country.