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How to Green-Light Better Scripts
From Storyline to Box Office: A New Approach for Green-Lighting Movie Scripts
by
Jehoshua Eliashberg, Sam K. Hui, and Z. John Zhang
The Wharton School University of Pennsylvania
Note : Edited by Jeffrey Taylor for clarity and brevity
Movie studios often have to choose among thousands of scripts to decide which ones to turn into movies. Despite the huge amount of money at stake, this process, known as “green-lighting” in the movie industry, is largely a guesswork based on experts’ experience and intuitions.
We propose a new approach to help studios evaluate scripts which will then lead to more profitable green-lighting decisions. Our approach combines screenwriting domain knowledge, natural language processing techniques, and statistical learning methods to forecast a movie’s return-on-investment (ROI) based only on textual information available in movie scripts.
More than 4,000 movies are produced worldwide each year. In the United States alone, around $9 billion is spent on theatrical tickets. While many of the movies are financed and produced by Hollywood major studios, independent investors, including private investors, equity firms and hedge funds are changing the way films are produced.
Despite the market size and investment interests, new movie production is a risky venture and profitability varies greatly across movies. While producers sometimes make large amount of profit from blockbusters, they also lose millions of dollars in movies that end up in oblivion.
For example, the movie Gigli cost approximately $54 million to produce, but its box office revenue was only around $6 million. Considering that studios generally receive a share of around 55% of the gross box office revenue for their production, Gigli generated a ROI of -96.7% for the studio.
On the other end of the spectrum, although the movie In the Bedroom cost only $1.7 million to produce, it generated more than $35 million in box office revenues and thus a ROI of +667%.
Across a sample of 281 movies produced between 2001 and 2004, the studio’s ROI ranges from -96.7% to over 677%, with a median of -27.2%.
As a result of the huge variance in ROI, the selection of which movies to produce is critical to the profitability of a movie studio.
Deciding which scripts to produce is a dauntingly difficult task. Each year it has been estimated that more than 15,000 screenplays are registered with the Writers Guild of America (WGA), while only around 700 movies are made, which is less 5%. Thus, studios need a more reliable approach to green-lighting films.
For years, major studios have employed an age-old, labor intensive methodology: they hire “readers” to assist them in evaluating screenplays.
Note from Jeffrey : It is my experience that these readers are young, naive and self-centered and have no concept of reality beyond Hollywood. Therefore, the gatekeepers destroy most great scripts from getting to the right people.
Typically, three to four readers are assigned to read each script. After a reader reads a script, he / she writes a synopsis of the storyline and makes an initial recommendation on whether the screenplay should be produced into a movie and the changes, if any, that are needed before actual production.
This approach becomes especially problematic when disagreements among readers and studio executives occur. Even the scripts for highly successful movies, such as Star Wars and Raiders of the Lost Ark, were initially bounced around at several studios before Twentieth Century Fox and Paramount.
For that reason, studios and investors can potentially benefit from a more objective tool to aid their green-lighting processes and to provide a reliable “second opinion” about the potential success or failure of adopting a script.
No such tools are currently available to aid screenplay screening. The main obstacle in developing such a tool has been the lack of reliable predictors for the financial success of a movie at the green-lighting stage: there are simply too few tangible determinants for the success of a movie before it is produced.
Our tool forecasts ROI based only on the storyline and a synopsis of the screenplay.
We extract textual information from them using domain knowledge from screenwriting and the bag-of-word model developed in Natural Language Processing. Once calibrated, these types of textual information are then used to predict the return-on-investment of a movie using Bag-CART (Bootstrap Aggregated Classification and Regression Tree) methodology developed in statistics.
To the extent that the success of a movie depends on the storyline, we can learn what kinds of stories may resonate with audience and what elements in a story will drive ROI performance.
Ideally, we would like to implement our approach with movie scripts in electronic form. However, as most movie shooting scripts are not publicly available in electronic form we restrict our attention to “spoilers”, an extensive summary and amplification of a storyline.
For this study, we reviewed available spoilers for 281 movies. We looked for frequent words such as “guns,” “blood,” “fight,” “car crashes,” and “police”. Since a computer cannot understand storylines, it is impossible for any automated textual analysis to pick up this genre and content information from a script. We therefore needed to add "human judges" to read spoilers and determine if the spoiled satisfied any or all of the 22 Criteria For Success.
Based on our hypothesis and our test samples, we find that the percentage of correct classification using our prediction model was 61.7%.
Note from Jeffrey : 61.7% is highly unacceptable for long-term investment. However, using a computer to analyze potential scripts for success is promising. In order to ensure profitability, the industry needs to be able to eliminate the front-end script review by with cheap interns and replace it with a more sophisticated approach.
In order to do this, the industry has to be able to accept storylines and screenplay summaries in electronic format.
22 Criteria For Success
Clear Premise: The story has a clear premise that is important to audiences.
Familiar Setting: The setting of the story is familiar to you.
Early Exposition: Information about characters comes very early in the story.
Coincidence Avoidance: Story follows a logical, causal relationship.
Inter-Connected: Each scene description advances the plot and is closely connected to the central conflict.
Surprise: The story contains elements of surprise, but is logical within context and within its own rules.
Anticipation: Keep readers trying to anticipate what would happen next.
Flashback Avoidance: The story does not contain flashback sequences.
Linear Timeline: The story unfolds in chronological order.
Clear Motivation: The hero of the story has a clear outer motivation (what he/she wants to achieve by the end of the movie.
Multi-dimensional Hero: Many dimensions of the hero are explored.
Strong Nemesis: There is a strong nemesis in the story.
Sympathetic Hero: Hero attracts your sympathy because he/she exhibits courage and is good/nice, funny, good at what he does or has power.
Logical Characters: Actions of main characters are logical considering their characteristics. They sometimes hold surprises but are believable.
Character Growth: Conflict is important enough to change the hero.
Important Conflict: The story has a very clear conflict, which involves high emotional stakes
Multi-Dimensional Conflict: The central conflict is explained in many different points of view.
Conflict Build-up: The hero faces a series of hurdles. Each successive hurdle is greater and more provocative than the previous ones.
Conflict Lock-in: The hero is locked into the conflict very early in the movie.
Unambiguous Resolution: Conflicts is unambiguously resolved through confrontation between the hero and nemesis at the end.
Logical Ending: The ending is logical and believable.
Surprise Ending: The ending carries surprise and is unexpected.
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