Oscar, Oscar, Oscar

For you statistical nerds out there, someone has taken a statistical approach to predicting the Oscar winners this year. I don't entirely understand it, but here is the basic idea:

How easy is it to predict the winners? That's the question that decision scientist Iain Pardoe of the Lundquist College of Business at the University of Oregon tackles in the current issue of Chance. He focuses on predicting the winners of the four major awards—picture, director, actor in a leading role, and actress in a leading role—from those nominated each year.

"Although many in the media (as well as movie-loving members of the public) make their own annual predictions," Pardoe notes, "it appears that very few researchers have conducted a formal statistical analysis for this purpose."

A wide variety of factors could serve as predictors, including other Oscar category nominations, previous nominations and wins, and other (earlier) movie awards. To tease out which ones are most significant and create a model for making predictions, Pardoe turned to a technique known as discrete choice modeling.

In a discrete choice model, an outcome is the result of several decisions—a sequence of choices—made among a finite set of alternatives by individuals in the population under consideration. The probabilities are calculated using a so-called multinomial logit model.

The Oscars have been awarded every year since 1928. Pardoe used data from years up to 1938 to make predictions for 1939, then cumulative data for each succeeding year.

If you're interested, check out the link. I'm actually ahead of the game this year: I've actually seen two of the movies that have been nominated for Best Picture (usually I see none): Good Night and Good Luck (highly recommended) and Brokeback Mountain (zzzzzz). Shameless plug: if you're in the Albany/Saratoga area and want to come to a good party, the Saratoga Film Forum (of which I am a Board member) is hosting its annual Oscar Night Gala. More info here.