(A warning before you start this post: It’s a little wonky. I dive pretty deep into my algorithm. If you dislike detailed discussions of numbers, percentages, algorithmicalizations (I made that up), and the like, you might skip it. You’ve been forewarned!)
If you’re a regular reader of the Submitit blog (thanks!), you may know that Submitit’s algorithm is basically a database of “scores,” in various categories, for literally hundreds of literary journals (486 at last count). These categories—things like lyricism, difficulty, experimentation, topicality, humor, etc.—are the same ones I use to score my clients’ stories. So, when I email a client a list of journals for submissions, the “match percent” next to each journal’s name is simply a measure of the similarity of a story’s and a journal’s scores: 100% is a perfect match; 0% means don’t submit to this journal, ever.
But, when I make submission decisions, there are three factors that the algorithm does not reflect: inception date, acceptance percent, and submission volume. Until now!
Inception Date
First, inception date: I like to target new journals (those less than a year old), especially in the second round of submissions. In the past, I simply skimmed a list of journals, looking for the newer ones, but these newbies didn’t automatically rise to the top, and if the match percent wasn’t high enough, I’d miss them completely. Now, I add a 5% bonus score to new journals. This should help ensure that plenty of them show up when I run the algorithm in the second round.
Acceptance Percent and Submission Volume
Incorporating the second two factors—in a numerical (algorithmic) sense—was trickier than I was expecting. For example, we can agree that journals with high acceptance rates (greater than 5%) should be targeted first (all else being equal). This is a no-brainer. But how, if at all, should I consider submission volume (the relative number of submissions a journal receives)?
My first thought was that lower submission volume (meaning a journal is not bombarded by tons of submissions) is always a good thing, simply because there is less competition. But, upon deeper reflection, I don’t think this is always correct. For example, I should clearly favor a high acceptance–high volume journal over a high acceptance–low volume journal, mainly because I have more trust in the acceptance percent of the former: more data, more confidence.
For low-acceptance journals (less than 3%), however, I usually consider low volume a good thing. I’d rather submit to a low-acceptance journal that only receives a few dozen submissions a year (like Subtropics) than one that receives many hundreds (like The New Yorker).
In other words: submission volume should be considered in combination with acceptance percent.
The “Bonus Score”
So, without getting into more detail than necessary (unless it’s too late for that!), I now add to my old match percentages a score that considers inception date, acceptance percent, and submission volume—I’ll call this a “bonus score.” This score ranges from 0% (for a low-acceptance–high-volume journal) to 11% (high-acceptance–high-volume new journal). For example, if a story had a match percent of, let’s say, 80% with the old version of the algorithm, that number could now be as high as 91% (for a high-acceptance–high-volume new journal).
If you’ve made it this for, you might be wondering: (1) Do I really need to understand this stuff? And (2) Has Erik lost his mind? My answers are: (1) No. You may take comfort in knowing that my new algorithm will better help me identify journals that match your stories and essays. And (2) Maybe.
An Example
For those willing to forge onward—ye brave souls!—I’ll give you an example. I’m going to use one of my client’s stories (it happens to currently be “live” in my new algorithm).
This story has a score of 90% for the following three journals: Nonconformist, Fractured Lit, and Blue Lake Review. Nonconformist has a relatively high acceptance percent (5–10%) and very low volume (fewer than 10 reports on Duotrope), which calculates to a bonus score of 5%. Fractured Lit has relatively low acceptance percent (1–3 %) and a very high volume (greater than 600 reports on Duotrope), which calculates to a small bonus of 1%. Finally, Blue Lake Review has a high acceptance rate (greater than 10%) and a medium volume (around 70 reports), which calculates to a high bonus of 8%. (None of these journals is less than a year old, so I did not add a new-journal bonus.)
For this story, the old match percent (pre–bonus score) would have been highest with Fractured Lit and lowest with Blue Lake Review, with Nonconformist in the middle. But my new algorithm, thanks to the bonus scores, rated these journals equally. I think this is cool.
Conclusion
I believe these new bonus scores will strengthen the algorithm—and, thus, your chances of getting published in a literary journal. If you’re ready to give it a try, sign up here. Thanks for reading!
Erik Harper Klass is the founder of Submitit, the WORLD’S FIRST full-service submissions and editing company. He has published stories and essays in a variety of journals, including New England Review, Yemassee, Slippery Elm, Summerset Review, and Open: Journal of Arts & Letters and he has been nominated for multiple Pushcart Prizes.