I was just looking at LowBudgetFun’s blog and thought you guys might enjoy this piece that he has graciously agreed to share with us. Check it out!
I’m looking for feedback on this idea I have for a data science experiment. I think it could be one of the first experiments run in unscripted television production. If you are a data scientist who would be interested in partnering up, feel free to reach out to me on twitter: @lowbudgetfun
Unscripted television production generates a lot of waste in the form of unused scenes. Scenes that are recorded and edited, but ultimately end up on the cutting room floor. These unused scenes represent wasted resources (time & money) and should be minimized. However, the creative process is mysterious and often requires playful exploration (trial & error), which is fundamentally at odds with the goal of minimizing waste. While eliminating waste completely is impossible, perhaps it is possible to reduce production waste by bringing certain trends to the attention of the story team during the planning process of subsequent seasons.
I hypothesize that by categorizing and analyzing all of the scenes that make it into a show, and comparing them to the scenes that are left out, trends could be identified that would enable Production Companies to avoid shooting similarly wasteful scenes in the future. In addition, I think that analyzing scene story summaries with a tool like Google’s Cloud Natural Language Sentiment Analysis could provide additional insight into why some scenes work, while others do it.
What is a Beat Board?
If you’ve visited a writing room you might have noticed a corkboard with lots of index cards on it. Informally known as a Beat Board; it is a tool writers utilize to help them visualize the structure and flow of the story they are trying to tell. Each index card represents one scene or story beat. By rearranging the cards on the board writers are able to quickly experiment with alternative story opportunities.
I first came up with the idea for this experiment after reading Lean Startup and learning about this methodology’s relentless pursuit of reducing waste:
The critical first question for any lean transformation is: which activities create value and which are a form of waste?
It was around that time when I walked into the EP’s office and observed that the number of cards representing scenes that didn’t make it into the show almost out numbered the cards of scenes that made it in. After chatting with colleagues on other shows, I anecdotally confirmed that this is not out of the ordinary.
What are Story Summaries?
This idea really clicked into place for when I learned about the influence data science is having on the humanities and I realized that many unscripted productions generate their own large body of textual material.
After a Reality Television crew records a scene for the day, a Field Producer will summarize, in writing, what happened. Story summaries can range from being a relatively objective description of an event, to impassioned prose about the cast’s feelings.
Since the gap between when a scene was shot and when it begins editing can be several months, Story Producers will review a the story summary before they start the post production process of creating a Cutdown to hand off to the editor. Therefore, the value of good story summaries is also an underappreciated practice on many unscripted productions.
In order to maximize the utility of this experiment, I think it will necessary to develop a system of scene categorization. Broadly, scenes can be separated into internal (Int.’s) and external (Ext.’s) locations. Scenes can also be organized by the number of cast members in them (1 – 7) and any additional people who appear on screen (family members or show ‘friends’). Scenes can also be categorized by their technical or production aspects, such as: cameraperson, field producer(s), or camera type (ENG or Car Camera, etc). A full taxonomy will be expanded upon in a future post.
Developing a Framework
The goal of this experiment is to develop a tool for unscripted story teams to use during pre-production while planning a season’s scenes. My hypothesis is that certain elements cause scenes to become unusable, but these elements are currently unknown. By identifying similarities between used and unused scenes, story teams will be able to reduce waste by avoiding things that won’t work.
A few years ago I wrote about my experience working on a competition show and manually digging through the sequences and budgets to uncover the cost of Loading, Grouping, and Storing the show’s car camera footage for the entire season was approximately thirty thousand dollars. And yet only one car scene made it into the entire twelve episode season. I’m not saying that eliminating the car cameras was the right decision. But I believe that it should have been discussed. Perhaps eliminating the car cameras would have freed up money for an additional challenge. Or an additional camera operator to gain additional coverage of the events. Or perhaps the savings could have been spent on an additional editorial team. These are all options that have vast creative implications.
We are on the cusp of having these trends brought to our attention with minimum friction. As we become more familiar with these types of analysis, our resistance lessens and we become empowered to make smarter decisions. I’d like to build the tools that enable these conversations.