An editor and an English professor describe the algorithm they came up with to predict a best-selling novel after analyzing 20,000 contemporary novels for themes, plot, character, setting and style.
What if there was an algorithm that could predict which novels become mega-bestsellers? Are books like Dan Browns The Da Vinci Code and Gillian Flynns Gone Girl the Gladwellian outliers of publishing? The Bestseller Code boldly claims that the New York Times bestsellers in fiction are predictable and that its possible to know with 97% certainty if a manuscript is likely to hit number one on the list as opposed to numbers two through fifteen.
The algorithm does exist; the code has been cracked; the results are in; and they are stunning. The system analyzes themes, plot, character, pacing, even the frequency of words and punctuation, to predict which stories will resonate with readers. A 28-year-old heroine is a big plus. So is realism. Giving 30% of your novel to only two specific topics. And if you can include a dog rather than a cat and few sex scenes, you have a better chance of writing a bestselling novel.
The project is an investigation into our intellectual and emotional responses as humans and readers to books of all genres. It is a big idea book that will appeal to fans of The Black Swan by Nassim Taleb, a book for data-mining nerds, as well as a book about writing, reading, and publishing. Anyone who has ever wondered whyGone Girl, Girl on the Train or The Girl With the Dragon Tattoo captured so many readers worldwide will find their interest piqued.
