A Quick Post Mortem on Oscars Forecasting

I was intrigued to see that statistical forecasts of the Academy Awards from PredictWise and FiveThirtyEight performed pretty well this year. Neither nailed it, but they both used sound processes to generate probabilistic estimates that turned out to be fairly accurate.

In the six categories both sites covered, PredictWise assigned very high probabilities to the eventual winner in four: Picture, Actor, Actress, and Supporting Actress. PredictWise didn’t miss by much in one more—Supporting Actor, where winner Christoph Waltz ran a close second to Tommy Lee Jones (40% to 44%). Its biggest miss came in the Best Director category, where PredictWise’s final forecast favored Steven Spielberg (76%) over winner Ang Lee (22%).

At FiveThirtyEight, Nate Silver and co. also gave the best odds to the same four of six eventual winners, but they were a little less confident than PredictWise about a couple of them. FiveThirtyEight also had a bigger miss in the Best Supporting Actor category, putting winner Christoph Waltz neck and neck with Philip Seymour Hoffman and both of them a ways behind Jones. FiveThirtyEight landed closer to the mark than PredictWise in the Best Director category, however, putting Lee just a hair’s breadth behind Spielberg (0.56 to 0.58 on its index).

If this were a showdown, I’d give the edge to PredictWise for three reasons. One, my eyeballing of the results tells me that PredictWise’s forecasts were slightly better calibrated. Both put four of the six winners in front and didn’t miss by much on one more, but PredictWise was more confident in the four they both got “right.” Second, PredictWise expressed its forecasts as probabilities, while FiveThirtyEight used some kind of unitless index that I found harder to understand. Last but not least, PredictWise also gets bonus points for forecasting all 24 of the categories presented on Sunday night, and against that larger list it went an impressive 19 for 24.

It’s also worth noting the two forecasters used different methods. Silver and co. based their index on lists of awards that were given out before the Oscars, treating those results like the pre-election polls they used to accurately forecast the last couple of U.S. general elections. Meanwhile, PredictWise used an algorithm to combine forecasts from a few different prediction markets, which themselves combine the judgments of thousands of traders. PredictWise’s use of prediction markets gave it the added advantage of making its forecasts dynamic; as the prediction markets moved in the weeks before the awards ceremony, its forecasts updated in real time. We don’t have enough data to say yet, but it may also be that prediction markets are better predictors than the other award results, and that’s why PredictWise did a smidgen better.

If I’m looking to handicap the Oscars next year and both of these guys are still in the game, I would probably convert Silver’s index to a probability scale and then average the forecasts from the two of them. That approach wouldn’t have improved on the four-of-six record they each managed this year, but the results would have been better calibrated than either one alone, and that bodes well for future iterations. Again and again, we’re seeing that model averaging just works, so whenever the opportunity presents itself, do it.

UPDATE: Later on Monday, Harry Enten did a broader version of this scan for the Guardian‘s Film Blog and reached a similar conclusion:

A more important point to take away is that there was at least one statistical predictor got it right in all six major categories. That suggests that a key fact about political forecasting holds for the Oscars: averaging of the averages works. You get a better idea looking at multiple models, even if they themselves include multiple factors, than just looking at one.

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  1. Oscar prognosticators: how did they do at picking the Academy Award winners? ‹ Cyprus Today

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