Fewer, but seen more often: an inferential re-analysis of sex, alignment and appearances among Marvel and DC characters
Year: 2026
Type: preprint
Abstract: In 2014 the data journalism site FiveThirtyEight scraped the fan-maintained Marvel and DC wikis and reported that women made up about one in four characters in each publisher's mainstream universe. That analysis was descriptive: it counted characters, but did not model how often characters appear. This article re-analyses the same public data with standard inferential methods. It focuses on 18,013 characters with a recorded appearance count, an alignment (good, bad or neutral) and a male or female sex. Because appearance counts are extremely skewed (median 4, maximum 4,043), all models use the base-10 logarithm, and results are reported as geometric means. A typical DC character appears about 1.7 times as often as a typical Marvel character (6.9 against 4.2 appearances). Good characters appear more often than neutral and bad ones (7.4, 4.6 and 3.6), but alignment explains only about 6% of the variation. The most notable result concerns sex. Female characters are a minority (24.5% at Marvel, 28.5% at DC), yet among the characters that exist, women appear more often than men at both publishers, and the gap is larger at Marvel (5.4 against 3.8) than at DC (7.6 against 6.7). The interaction is small but clear. It agrees with FiveThirtyEight's observation that women's share rises among frequently appearing characters, and it suggests that counting characters and measuring their prominence answer different questions. The article sets out why these associations are not causal. Bad-aligned characters are mostly male and appear least, appearance counts accumulate over time, and fan wikis record popular characters more fully. It closes with a randomised design that could test causal questions. The R script and the data accompany this preprint. Status: preprint, not peer reviewed. Files: the article (PDF; CC BY-NC-ND 4.0); comics_analysis.R, the full analysis in one R script (MIT licence); comic_characters.csv, FiveThirtyEight's 2014 comic-characters data (the DC and Marvel wiki files merged, with a publisher column added; CC BY 4.0, source: https://github.com/fivethirtyeight/data/tree/master/comic-characters); a README and the licence file. Use of generative AI: the article's text was drafted with Claude (Anthropic; Claude Opus 5.5) from the author's analysis outputs. The author ran every analysis, reviewed and revised the text, and takes full responsibility for the content (see the declaration in the article).
Author: Syed, Abdul Nafey
Institution: Fenerbahçe University
Language: English
Subfield: Gender Studies
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7223564294