gilgamath


Chess Tactics.

Thu 30 March 2017 by Steven E. Pav

I have become more interested in chess in the last year, though I'm still pretty much crap at it. Rather than play games, I am practicing tactics at chesstempo. Basically you are presented with a chess puzzle, which is selected based on your estimated tactical 'Elo' rating, and your rating (and the puzzle's) is adjusted based on whether you solve it correctly. (Without time limit for standard problems, though I believe one can also train in 'blitz' mode.) I decided to look at the data.

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Another Thalesians Talk

Tue 14 March 2017 by Steven

Matt Dixon 40th Birthday Talk

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Lego Pricing.

Mon 06 March 2017 by Steven E. Pav

It is time to get kiddo a new Lego set, as he's been on a bender this week, building everything he can get his hands on. I wanted to optimize play time per dollar spent, so I set out to look for Lego pricing data.

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Odds of Winning Your Oscar Pool.

Mon 30 January 2017 by Steven E. Pav

In a previous blog post, I used a Bradley-Terry model to analyze Oscar Best Picture winners, using the best picture dataset. In that post I presented the results of likelihood tests which showed 'significant' relationships between winning the Best Picture category and conomination for other awards, MovieLens ratings, and (spuriously) number of IMDb votes. It can be hard to interpret the effect sizes and \(t\) statistics from a Bradley-Terry model. So here I will try to estimate the probability of correctly guessing the Best Picture winner using this model.

There is no apparent direct translation from the coefficients of the model fit to the probability of correctly forecasting a winner. Nor can you transform the maximized likelihood, or an R-squared. Moreover, it will depend on the number of nominees (traditionally there were only 5 Best Picture nominations--these days it's upwards of 9), and how they differ in the independent variables. Here I will keep it simple and use cross validation.

I modified the oslm code to include a predict method. So here, I load the data and the code, and remove duplicates and restrict the data to the period after 1945. I construct the model formula, based on co-nomination, then test in three ways:

  • A purely 'in sample' validation where all the data are used to build the model, then tested. (The film with the highest forecast probability of winning is chosen as the predicted winner, of course.) This should give the most optimistic view of performance, even though the likelihood maximization problem does not directly select for this metric.
  • A walk-forward cross validation where the data up through year \(y-1\) are used to build the model, then it is used to forecast the winners in year \(y\). This is perhaps the most honest kind of cross validation for ...
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