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If my character drops to 0HP but is not killed instantly by massive damage, what is the probability that I will stabilize via death saves unassisted/unhindered?

As a refresher, here are some of the rules on death saves:

Death Saving Throws. Whenever you start your turn with 0 hit points, you must make a special saving throw, called a death saving throw....

Roll a d20. If the roll is 10 or higher, you succeed. Otherwise, you fail.... On your third success, you become stable....

Rolling a 1 or 20. When you make a death saving throw and roll a 1 on the d20, it counts as two failures. If you roll a 20 on the d20, you regain 1 hit point. (PHB p.197, "Dropping to 0 Hit Points")

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up vote 33 down vote accepted

There's a 59.5125% chance of survival.

Naively, we might have thought there'd be a 55% chance of survival as 55% of the roll results are good. But the 20 is a slightly better result than the 1 is a bad one, so that pushes up the probability a bit. Let's see how.

The approach

The simplest way to tackle this is to look at the probabilities of surviving in exclusive ways, then combine those probabilities. Remember the rules of combining probabilities of multiple events:

  • If we want to know the probability of (A or B), when A and B don't overlap, we sum the probability of A with the probability of B. (We'll be constructing all of our scenarios below without overlap.)
  • If we want to know the probability of (A and B), we multiply the probability of A with the probability of B.


We'll use a number or a range of numbers in brackets to indicate the probability of that result on a d20. That is, [4]=0.05, [7]=0.05, and [10-20]=0.55. In this manner we'll generally be concerned with four different results: [1], [2-9], [10-19], and [20].

When we want to indicate a particular sequence of rolls, we'll list them in order: [1][1-9] would be read as "the probability of rolling a 1, then some single-digit number."

When we want to indicate a particular selection of rolls, but don't care about their order* we'll group the unordered results in curly-brackets. Thus {[2-9][10-19][2-9]}[20] would be read as "the probability of rolling two simple failures and a simple success in any order, followed by a 20."

Ways to stabilize after...

The probabilities of stabilizing on the first, second, third, &c. rolls are exclusive: one can stabilize on the first or on the second, but not both. So we'll determine each of these probabilities and sum them up according to our "or-rule" from above.

1 roll: [20]=0.05

2 rolls: [1-19][20]=0.95*0.05=0.0475

3 rolls: [10-19][10-19][10-19]=0.5*0.5*0.5=0.125

or {[1][10-19]}[20]=2*{0.05*0.5}*0.05=0.0025

or [2-19][2-19][20]=0.9*0.9*0.05=0.0405

4 rolls: {[10-19][10-19][1-9]}[10-20]=3*{0.5*0.5*0.45}*0.55=0.185625

or {[2-9][2-9][10-19]}[20]=3*{0.4*0.4*0.5}*0.05=0.012

5 rolls: {[2-9][2-9][10-19][10-19]}[10-20]=6*(0.4*0.4*0.5*0.5}*0.55=0.132

Summing it all up: 0.05+0.0475+(0.125+0.0025+0.0405)+(0.185625+0.012)+0.132=0.595125

Bonus for reading this far: expected time to recovery

With these probabilities in hand it's easy to give an expected time-of-stabilization for those that do stabilize: t_stable=(1*0.05 +2*0.0475 +3*(0.125+0.0025+0.0405) +4*(0.185625+0.012) +5*0.132)/0.595125=3.5278 rounds, or about 21 sec.

* Order is really important. Sometimes. For instance, if we want to know the probability of the first two rolls being a success and a failure, we have to count all the ways that we could get a success then a failure, and add on all the ways we could get a failure then a success.

But some of our rolls are indistinguishable events, and that's where it gets fun. If we want to count the ways of our first three rolls being two failures and a success we only have three orders we can consider (FFS, FSF, SFF), not the six one might expect (ABC, ACB, BAC, BCA, CAB, CBA).

The count of possible arrangements of n objects with multiplicities m_1, m_2, ... is N = n!/(m_1!* m_2!*...). See if you can spot the three times this pops up!

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+1 It would be even better, if you simply gave the general definition of P(A union B), and used examples with and without overlap. :) – Lexible Mar 14 at 21:17
@Lexible if you think that bullet point could be better-stated please feel free to take a whack at it. I don't really want to drift too far towards a general article on simple probability, though, so I'd rather not artificially introduce more ways of calculating into the examples. – nitsua60 Mar 15 at 2:42

Columns are number of failures, rows are number of successes.

                0 failures  1 failure   2 failures
0 successes     0.5951      0.4175      0.2125
1 success       0.7350      0.5650      0.3250
2 successes     0.8855      0.7700      0.5500

So if you've had two failures and no successes, you have a 21.25% chance of survival.

I calculated these by means of a recursive algorithm which simulated the rules as-written.

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This fits nicely with @nitsua60's answer, which describes how (0,0) is actually calculated, by showing the evolving probabilities after a series of roles. – chepner Mar 11 at 19:22

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