The Big(ger) Picture
A dashboard based on a podcast based on a love of movies.
One fateful night, sometime last year, I sat in my neighbor’s upstairs apartment with my girlfriend discussing new movies we wanted to see over a bottle of wine. Our conversation pinballed between reviews we had either read or heard, which eventually led us to the opinions of a podcast that we all commonly listen to, The Ringer’s Big Picture podcast hosted by Sean Fennessey, Amanda Dobbins, and Chris Ryan. One thing led to another and we eventually got to talking about their themed “Movie Draft” episodes in which the three hosts choose a topic, set parameters, and play a game in which they attempt to “draft” the best “roster” of movies as voted on by their Twitter followers.
“We need to make this into our own game,” my girlfriend suggested. So, we immediately set out to turn our borrowed idea into a fun past-time, just like our favorite podcasters had before us. Fast forward 18 months and about 30 iterations of this game, and our new problem was how to decide who had bragging rights to lay claim to victory in this game, as movie tastes are highly subjective. As I am a graduate student enrolled in a data analytics-focused program, I decided to solve this problem by creating a Tableau dashboard that could tell us who won.
So, how did this all work? Before sharing the dashboard design and results, you must first understand the game itself.
The Movie Draft
There are three simple steps to executing a movie draft.
- You must first pick a theme. For this project, I only had time to load our first 10 drafts (150 movies total) into my initial database for consideration with the understanding that I’ll eventually find time to enter all the data that we have hand-written.
- Then you determine the draft order. We usually did this by a simple game of rock-paper-scissors. This game is played using a “snake draft” order, which for those who are unfamiliar means that each person at either end of a draft picks twice, and then the order “snakes” back in the other directions. Ex. — 1, 2, 3, 3, 2, 1, 1, 2, 3.
- Finally, you choose movies. You select movies that fit the common theme until each player has a roster of 5 movies total that fit the common theme.
The Dashboard Design
The first hurdle in deciding the design of this dashboard was how to objectively measure a winner. What would our metrics be? After some deliberation, I decided to break the metrics up into three distinct categories: Profitability, Critical Acclaim, and Popular Opinion.
Profitability was measured in all-time box office gross in US dollars. Critical Acclaim was measured in the number of Oscar wins. Popular Opinion was a bit more difficult to decide and we’ll get to that later.
Originally, I had a hard time deciding whether to display individual draft theme instances, individual player profiles with all of their draft picks, or everything altogether. In the end, the key was the ability to toggle between any or all of player, draft pick, and draft theme. Each metric would have its own coded scatterplot with the same toggles for these 3 variables.
Players, of which there were just the three of us, were coded by color.
Draft themes, of which there were ten different instances, were coded by graphic symbols in the shape of something resembling the theme. Ex. — a boat for “movies featuring oceans.”
Draft picks were plotted on the x-axis of each graph and users are provided with a slider to toggle the range they want to visibly analyze.
The resulting dashboard, when left unfiltered for any of the three variables is viewed as two scatterplot graphs, one for profitability and one for critical acclaim, with coded plot points for every movie that either made more than $96M or won more than 1 academy award. The design principle that dictated these limitations was the readability of the x-axis. Had every movie been included, the eyes of the user would have been bombarded with so many plot points at the base of the axis that you couldn’t have deciphered much of anything.
You might notice the three boxes in the bottom right corner. These are scorecards that tell the user number of movies, the average box office gross, and the average number of Oscars won of the toggled selections. This gives the user access to summary statistics to determine who won by certain metrics. So, who won?!
The Results
The most profitable player was Gary with a $247M average box office gross on all of his movies. His big two were Jurassic Park in the “movies featuring animals” draft and Aquaman in the “movies featuring oceans” draft.
The most critically acclaimed player was Kurt with an average of approximately 1 Oscar win per movie. His big two were Titanic in the “movies featuring oceans” draft and Slumdog Millionaire in the “movies featuring gambling” draft.
The most profitable overall draft was “movies featuring oceans”. Heavy hitters Titanic and Aquaman have already been mentioned as the top two picks by profit in this draft. However, Areesah had the next three most profitable picks in this particular draft with Life of Pi, Moana, and Pirates of the Caribbean: The Curse of the Black Pearl.
The most critically acclaimed overall draft was “movies featuring sweeping landscapes”. 9 of the 15 movies drafted in this instance won multiple Oscars. The top 5 were Lawrence of Arabia, Gravity, and Dances with Wolves with 7 each, and Forrest Gump and Dune with 6 each.
This brings us back to the popular opinion metric. For this, I sourced 4 different audience scores: IMDb score, Rotten Tomato score, Metacritic score, and Rotten Tomato audience score. Ultimately, I decided that these were not objective metrics. In the future, I’d like to combine all four into a single, more objective metric. The other problem this separate dashboard posed was the difficult readability of 4 scatterplot graphs on one dashboard. I still made this publicly available, however, along with a “Movie Draft Player Genre Preference” dashboard. This does not tell you who won but is a fun exercise in descriptive statistics to see what genres of movies were picked by player and draft theme. All three dashboards are available on Tableau Public and will be updated as more data accumulate and more drafts are played.
Movie Draft Profitability/Critical Acclaim Dashboard: https://public.tableau.com/views/MovieDraftProfitabilityCriticalAcclaimDashboard/ProfitOscarDashboard?:language=en-US&:display_count=n&:origin=viz_share_link
Movie Draft Audience Metric Dashboard: https://public.tableau.com/views/MovieDraftPlayerAudienceMetricDashboard/AudienceMetricDashboard?:language=en-US&:display_count=n&:origin=viz_share_link
Movie Draft Player Genre Preference Dashboard: https://public.tableau.com/views/MovieDraftPlayerGenrePreferenceDashboard/GenrePreferenceDashboard?:language=en-US&:display_count=n&:origin=viz_share_link
