data literacy, dataviz, education, presentations

So, you want to make a top rated movie?

A good story follows a pattern

As a former classroom teacher, I have done my share of story retells using story mountains, graphic organizers and more. One story I read every year when I taught 3rd grade was Kevin Henke’s book A Weekend with Windell. It is a story about a young mouse that visits a friend’s house for the weekend. He is not very nice and causes issues. The other girl starts treating him the same way and over time, he changes his attitude. The two mice end up being good friends in the end. I have attached a version of the story mountain that I used with my students.

Telling a story with data

Related to what is going on with data, I was working on a project related to top ranked movies over the past 20 or so years. There was so much data to work with and no real focus. I was just asked to visualize it. Before seeing this activity I had already thought about telling a story with it. Creating a story helped me frame all of the information. I started with the title “So, you want to make a top ranked movie?” I then went through the storytelling process of starting with a question and then focused on four areas (out of all of the data.) I then broke up each of the four areas into mini stories with graphs. In the presentation, the graphs are animated and build on each for suspense. I finished off the “story” by summarizing the findings into the suggestion of criteria for creating a top ranked movie.

For the first area of data, I chose to focus on movie genres. For the data on top 10 movies, they have the genres broken down into main genre, 2nd genre, and for some, 3rd genre. For this study, I focused on the main genre, which consisted of 15 different categories. I didn’t want to just go straight in to the data, I wanted to build some suspense. I started presenting the data with only the genres in alphabetical order down the y-axis. Next, I shared the data in a bar graph. To make it easier, I reordered the data from greatest to least. I then grayed out the other genres so that only the largest was highlighted and finally added in the subtitle sentence related to the findings in the data. Showing only parts of the graph and building it up over time creates a great storytelling situation and keeps the audience engaged.

Top Rated Movie Graph

Next, I took a look at the length of a successful movie. The data showed that the ran anywhere from a low of 81 minutes all the way to the longest movie being 201 minutes. That’s 3 hours and 21 minutes! The shortest ranked movie between 1998-2018 was Ice Age which came in at #8 in 2002. The longest movie was 2003’s The Lord of the Rings: The Return of the King, which ranked #1 and grossed $1,119,929,521 world-wide.

The third category to observe was ratings. Ratings are important because this directly affects the audience that is able to watch the movie, within reason. The four main categories of ranked movies are G, PG, PG-13, and R. I used a similar tactic of storytelling as with genres by only showing parts of the graph as I presented. The final graph shows just how much PG-13 movies dominate the field. This might not have had the same impact if the final graph was shown first.

I finished off the presentation with a graph focused on successful production studios. From 1998-2018, 13 different studios had movies ranked in the Top 10. It was pretty amazing to see that Warner Brothers Studios led the league with 45 movies ranked in the Top 10. Respectively, Walt Disney Studios was a close second with 44 movies.

In the end, based on the four areas researched, if you want to have a successful movie that would be ranked in the Top 10, you should make it a fantasy film that is PG-13 rated and lasts around 124 minutes. If you are lucky and can get Warner Brothers to produce it, you should be good to go!

Chris

Chris Smith is an academic data analyst in the international education field. Throughout his 25 years in education, he noticed schools and educators collecting data but struggling with being overwhelmed or not knowing what to do with the data. So, he began coaching, training, and consulting schools and educators to learn how to discover what data they actually need and how they can turn it into information that leads to insights and informed decision-making.

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