Wow. In one sense this is a shocker. Mickey Rourke seemed like a lock. If he didn’t get it, many thought Frank Langella also deserved to win.

Then again, given the subject matter of “Milk,” it’s not surprising that Sean Penn got the nod here. He acknowledged as much in his acceptance speech.

Movie buffs love predicting Oscar winners, but stats guru Nate Silver decided to look at hard data and trends to come up with his own predictions. Political junkies are familiar with Silver, as his blog became one of the top resources for interpreting polls and predicting election results in the last cycle.

After spending most of 2008 predicting the success of political actors—also called politicians—it’s only natural that Nate Silver (FiveThirtyEight.com) would turn his attention to the genuine article: the nominees in the major categories for the 81st Annual Academy Awards (Feb. 22 at 8 p.m. on ABC). Formally speaking, this required the use of statistical software and a process called logistic regression. Informally, it involved building a huge database of the past 30 years of Oscar history. Categories included genre, MPAA classification, the release date, opening-weekend box office (adjusted for inflation), and whether the film won any other awards. We also looked at whether being nominated in one category predicts success in another. For example, is someone more likely to win Best Actress if her film has also been nominated for Best Picture? (Yes!) But the greatest predictor (80 percent of what you need to know) is other awards earned that year, particularly from peers (the Directors Guild Awards, for instance, reliably foretells Best Picture). Genre matters a lot (the Academy has an aversion to comedy); MPAA and release date don’t at all. A film’s average user rating on IMDb (the Internet Movie Database) is sometimes a predictor of success; box grosses rarely are. And, as in Washington, politics matter, in ways foreseeable and not. Below, Silver’s results, including one upset we never would have anticipated.

Check out the article for his predictions. There aren’t many surprises, but it’s interesting to see the probability percentages he allocates to each category.