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Message: <blockquote data-quote="Steven Winter" data-source="post: 6711747" data-attributes="member: 6686829">I agree on all counts. The DMG's CR guidelines do seem to produce tougher monsters (or lower final CRs), but I haven't applied that system to enough monsters from the MM to generate a statistically significant amount of data. (If someone has and posted it online, I'd love to see it; it would save me tons of time.) If you create a basic, no-frills monster that's nothing but AC, HPs, and damage, the "Monster Statistics by Challenge Rating" table (DMG pg. 274) definitely spits out monsters with far higher HPs and DPR than what's seen in the MM. My initial suspicion -- and again, I don't yet have hard data to back this up, it's just a suspicion that could turn out to be wrong -- is that the DMG's hit point multipliers for resistances and immunities are too generous. Even when a creature is immune to two or three types of damage, PCs have an easy time working around it, so an advantage that supposedly increases a monster's effective HP by 150% or 200% turns out to be no real advantage at all. What's more, I don't understand why there's no corresponding adjustment when a monster has a vulnerability. I've harped on this before; vulnerability to fire is why the mummy lord weighs in nowhere near CR 15, but that crippling vulnerability seems to have carried no weight at all in the CR computation. It's crucial to understand that the R&D staff didn't create a monster-balancing system and then design monsters to fit it. That's the 4E approach, and we all remember the rivers of abuse that were poured on 4E's by-the-numbers monsters. Instead, they designed interesting, dynamic monsters and established their CRs through exhaustive playtesting. Only then did they try to create a backward-looking mathematical system that could duplicate those results. So it's no surprise to find flaws in the system. Statistically, some members of the population inevitably are going to fall outside the standard deviation of whatever formula your regression analysis spits out, and they'll look like errors even though they're not. All of which is sort of a different problem from where this thread started out, but it's closely related.Steve</blockquote>

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