Best Picture?
For a brief time I found myself working in the field of film analytics. One of our mad scientist type projects at the time was trying to predict which films might win an award. As a training exercise, we decided to analyze the Oscars.
With such a great beginning, you might be surprised to find the story does not end well. Collecting the data for such an analysis was a minor endeavor. At the time we had scraped and cobbled together a number of different databases about films, but connecting them to each other was a huge frustration. Around the time we would have been predicting the Oscars, the floor fell out from our funding and we were unemployed three weeks after they announced the Oscar 2015 winners.
Our loss is your gain, as I am now releasing the first cut of the data frame I was using. The data are available in a CSV file here. The columns are as follows:
year
is the year of the Oscars.category
should always beBest Picture
here.film
is the title.etc
is extra information to identify the film.winner
is a Boolean for whether the film won in that category.id
andmovie_id
are internal IDs, and have no use for you.ttid
is the best guess for the IMDb 'tt ID'.title
andproduction_year
are from the IMDb data.votes
are the total number of votes in IMDb for the IMDb film. (This is an old cut of the data.)vote_mean
,vote_sd
are the mean and standard deviation of user votes for the film in IMDb.vote1
andvote10
are the proportion of 1- and 10-star votes for the film in IMDb.- I do not remember what
series
is. total_gross
is one estimate of gross receipts, andbom
is …