<p>What if there was an algorithm that could predict which novels become mega-bestsellers? Are books like Dan Brown’s <i>The Da Vinci Code</i> and Gillian Flynn’s <i>Gone Girl </i>the Gladwellian outliers of publishing? <i>The Bestseller Code </i>boldly claims that the <i>New York Times</i> bestsellers in fiction are predictable and that it’s 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. <br>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.<br>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 <i>The Black Swan</i> by Nassim Taleb, a book for data-mining nerds, as well as a book about writing, reading, and publishing. Anyone who has ever wondered why<i>Gone Girl</i>, <i>Girl on the Train</i> or <i>The Girl With the Dragon Tattoo</i> captured so many readers worldwide will find their interest piqued.</p>