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Data For Progress’s RuPaul-Predict-a-Looza

Data for Progress launched the RuPaul-Predict-a-Looza (and winner), the first ever RuPaul’s Drag Race prediction competition. Statistical models versus NYC Council Speaker Corey Johnson. The prize: bragging rights and the ability to add one policy question on the next Data for Progress survey.

First predictions are due this Thursday (February 28). I made a notebook with the rules and a simple model in Stan.

I talked with Ben (Goodrich) and Michael (Betancourt) about possible improvements for folks to try:

  1. I get divergent transitions with the default adapt_delta = 0.8. Michael’s intuition is that constraining eta for losing to be the -1*eta for winning is “manifesting in a little bit of misfit which induces some nastiness in posterior geometry.” Ben suggested “scaling eta by some negative number with an expectation of -1.”
  2. Michael also notes that we lose information by only looking at winners and losers and not the full ranking. He suggests doing an ordinal regression, though the number of categories would be changing.
  3. The easiest improvement is adding more covariates.

NOTE: If you aren’t ready by this Thursday (February 28) you can join next week! If a team does not submit for an episode, it counts as an incorrect prediction. Since early episodes have many contestants and little information for prediction, most teams are likely to get them wrong anyhow. So folks can catch up in later weeks.


  1. Jim says:

    Shira —

    The probability of loss isn’t quite right–the softmax gives the correct probabilities assuming a Gumbel distributed error term, which isn’t symmetric. It might be a reasonable approximation sometimes, but can go pretty wrong.

  2. Chris Marcum says:

    Hey Andrew,

    Sociologist Thomas Elliott has written a pretty good routine to predict outcomes in this show. His website is here:


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