I analyzed 11,463 D&D Monsters, all Spells and Items. You are playing wrong.

Python Data Analysis Statistics D&D — min read Watch on YouTube
I analyzed 11,463 D&D Monsters

Overview

D&D has been in print for decades. Thousands of monsters, hundreds of spells, more magic items than anyone can keep straight. Most players judge what's powerful from intuition or community consensus. Intuition is not data. This project asks the boring empirical question: across every official source, what does the rulebook actually say?

What I did

  • · Scraped 11,463 monsters across every official D&D 5e sourcebook
  • · Built a normalised dataset of every spell: damage type, scaling, action economy cost
  • · Compared magic item rarity against actual mechanical impact
  • · Modelled which creature types, damage types, and spell schools genuinely dominate
  • · Cross-referenced challenge rating formulas with actual stat distributions to expose CR inflation

Key findings

  • Challenge Rating is a weak predictor of actual encounter difficulty at high levels
  • Roughly 8% of spells account for the majority of combat-effective options
  • Most "rare" magic items perform below the statistical baseline for their tier
  • Fire damage immunity is the single most common resistance in the monster corpus

Stack

Python pandas BeautifulSoup matplotlib Statistics
More Research
Next (→)
I Downloaded All Data from Rule34.XXX (For Science)
Also
To Predict the Apocalypse I Stole NASA's Asteroid Data