# Forecasting the Oscars Like a Boss

**Posted:**January 17, 2013

**Filed under:**cinemas, optimization |

**Tags:**academy awards, forecasting, logistic regression, movies, oscars, pandas, python, scikit-learn, statistics 4 Comments

For the past eight-ish years, I’ve participated in an Oscar prediction pool with my brothers and some of my friends. We’ve fooled around with a number of different scoring schemes. The version we’ve settled on the past few years has been to sort each prediction by how confident we are, and assign point values to each category accordingly. This seems to keep the contest more interesting longer through the awards broadcast.

These contests have typically resulted in my shame. It turns out that I am terrible at guessing which movies are likely to win Oscars (nevermind the fact that I have NO FREAKING IDEA what the difference between sound editing and sound mixing is, but apparently they each need their own categories. Whatever.). My brother Jon has won handily the past 3 years, and that has to stop.

This year, I resolved to systematize my predictions, by building a model to forecast Oscar results. Why do this, you ask?

1) The lucrative $50 prize, and all of the opportunities that that opens up.

2) I’ve been looking for a non-astrophysics data analysis project, and this seemed fun. Also, astrophysicists throw garbage at you when you try to do inference without a physically-justified model, and I wanted to slum it up a bit with some purely-empirical forecasting.

3) I’ve been wanting to spend more time with the Pandas and Scikit-Learn Python libraries.

## The Data

The Academy Awards is the last ceremony in a long awards season. The most obvious data to use to try to forecast the Oscars are the results from these previous ceremonies. This is especially true since the same people vote for multiple ceremonies, so these awards can be seen as polls for the oscars. There are other potentially interesting variables to consider (Rotten Tomatoes rating, Box office performance, genre or cast information, etc), but I decided to start with the ceremony results.

The IMDB archives the nominees and winners for the major ceremonies. They don’t provide a nice API for grabbing their data, but it’s easy enough to parse from the HTML (enter: BeautifulSoup). As is so often the case, data cleaning is among the most time-consuming tasks of the project. For example, many award categories change names slightly over time (Best Picture -> Best Motion Picture of the Year). After standardizing all of this information, I ended up with a JSON database of all the nominees and winners for 7 awards ceremonies since 1990 (about 8800 nominations in total).

## Exploration

Before doing anything fancy, I wanted to get a grasp on what the data looked like. T0 be concrete, I’ll focus on the Best Picture category for the moment. Here’s a plot of what fraction of movies go on to win the best picture Oscar, as a function of whether they were nominated for or won the award in a different ceremony.

Interestingly (though not all that surprising in retrospect), winning the Independent Spirit Award (which focuses on indie cinema) is anti-correlated with winning the Oscar. The only movie since 1990 to win both the Independent Spirit and Oscar Best Picture awards was The Artist last year. Also interesting is the fact that the Golden Globes correlates so weakly with the Oscars — this ceremony is often touted as being a good predictor for the Academy Awards, but other ceremonies clearly do better.

## Modeling

How do I combine all of this information into an estimate of who is most likely to win the 2013 Best Picture award? Equally as important, how do I assess how confident this prediction is, so that I can wager more points on categories which are most certain?

One of the standard strategies for estimating success probabilities based on a 0/1 outcome (a movie either wins, or it doesn’t) is logistic regression. It’s pretty much the simplest thing to try, so it’s worth checking how well it does.

There are two wrinkles to this model:

- It doesn’t account for the fact that exactly one nominee in each category wins. I address this by re-normalizing the probabilities in the model within each category, and adjusting the likelihood calculation of the data given the model accordingly. This makes finding the optimal model slightly harder, but the dataset is small enough that computing power isn’t much of a concern.
- With only 23 years of historical data, over-fitting is a danger. To address this, I ran a regularized regression. That is, instead of fitting the model by maximizing the likelihood, I maximize a modified likelihood that penalizes Logistic Regression models with large coefficients. The size of coefficients in a Logistic Regression model directly relates to how confident predictions are, so the penalty acts to make the model more conservative, and less likely to draw too-strong a conclusion from a small training dataset. The strength of the penalty is chosen by cross-validation.

The scikit-learn library is really wonderful for this kind of work. First, they provide a lot of functionality out-of-the-box (optimization, cross validation, and implementations of dozens of models). Furthermore, the API is extremely consistent, so that you can build your own custom classifiers, using scikit-learn objects as building blocks. I definitely plan on using it more, even for more vanilla model fitting and optimization tasks (its API is way better than most of SciPy in my opinion).

There are a number of criteria to evaluate whether this model is a good fit to the data.

### How accurate is it?

This model correctly classifies about 75% of the best picture winners since 1990. Furthermore, the years it fails usually correspond to notable upsets; for example, Crash unexpectedly won Best Picture in 2006, despite Brokeback Mountain being a strong favorite. Brokeback Mountain won best picture in every other ceremony in the database, and Crash is the only movie that won the Oscar without even being nominated for the Golden Globe. Other notable upsets include 1999 (when Shakespeare in Love beat Saving Private Ryan) and 1996 (Braveheart won, the model predicted Apollo 13). I see these upsets as indicative of the inherent uncertainty in trying to predict Oscar winners based on other ceremonies.

### How representative is the data?

One nice property about the model is that it is generative — you can use it to simulate hypothetical outcomes for each year, based on the information provided from the other ceremonies. If the model is a fair representation of the data, then the actual outcome should look like these simulated outcomes (if it doesn’t, this suggests an over-fit or mis-specified model).

The left plot shows, for each of 1000 hypothetical results of 23 years of best picture awards, how many years were correctly predicted by the model. The black line is the actual data, and falls near the typical value of this distribution. That’s encouraging.

The right plot is slightly more discerning. It plots, as a function of the confidence at which each prediction is made, how many mistakes were made which had confidences less than this threshold. If these confidences are right, then the model should make most of it’s mistakes at lower confidences — that is, the curve should rise steeply on the left and then level off (I used a similar plot when talking about Nate Silver’s election forecast results). The black lines show 500 simulations, and the red line shows the actual data. Again, it’s encouraging that it runs through the collection of black curves.

This fairly simple model seems to do a pretty good job at characterizing the outcome of the Best Picture category for the past 2 decades. It’s possible that a better model (or additional information about each nominee) could make the predictions more precise (i.e. skew the blue histogram to the right, and make it narrower). I may look into this in the coming weeks. In any event, I’ll be able to make predictions about the 2013 Oscars once the other award ceremonies happen. Look for more blog posts.

I’m coming for you, Jon.

# Critiquing the Visual.ly Divorce Post

**Posted:**March 13, 2012

**Filed under:**visualization |

**Tags:**divorce, education, statistics, visual.ly 3 Comments

*Update: Paul Van Slembrouck, the designer of this graphic, has responded to the critique. Be sure to read his comments below!*

I am a teaching fellow for a class at Harvard called “The Art of Numbers,” which teaches principles of data presentation to undergraduates from all concentrations. For a recent midterm, students were asked to analyze this graphic from Visual.ly:

For valentine’s day Visual.ly posted a series of visualizations of divorce statistics in the U.S.. Several aspects about this graph bothered me, and I thought it would make for a good exam question.