*by Jocelyn Barker, Data Scientist at Microsoft*

I have a confession to make. I am not just a statistics nerd; I am also a role-playing games geek. I have been playing Dungeons and Dragons (DnD) and its variants since high school. While playing with my friends the other day it occurred to me, DnD may have some lessons to share in my job as a data scientist. Hidden in its dice rolling mechanics is a perfect little experiment for demonstrating at least one reason why practitioners may resist using statistical methods even when we can demonstrate a better average performance than previous methods. It is all about distributions. While our averages may be higher, the distribution of individual data points can be disastrous.

### Why Use Role-Playing Games as an Example?

Partially because it means I get to think about one of my hobbies at work. More practically, because consequences of probability distributions can be hard to examine in the real world. How do you quantify the impact of having your driverless car misclassify objects on the road? Games like DnD on the other hand were built around quantifying the impact of decisions. You decide to do something, add up some numbers that represent the difficulty of what you want to do, and then roll dice to add in some randomness. It also means it is a great environment to study how the distribution of the randomness impacts the outcomes.

### A Little Background on DnD

One of the core mechanics of playing DnD and related role-playing games involve rolling a 20 sided die (often referred to as a d20). If you want your character to do something like climb a tree, there is some assigned difficulty for it (eg. 10) and if you roll higher than that number, you achieve your goal. If your character is good at that thing, they get to add a skill modifier (eg. 5) to the number they roll making it more likely that they can do what they wanted to do. If the thing you want to do involves another character, things change a little. Instead of having a set difficulty like for climbing a tree, the difficulty is an opposed roll from the other player. So if Character A wants to sneak past Character B, both players roll d20s and Character A adds their “stealth” modifier against Character B’s “perception” modifier. Whoever between them gets a higher number wins with a tie going to the “perceiver”. Ok, I promise, that is all the DnD rules you need to know for this blog post.

### Alternative Rolling Mechanics: What’s in a Distribution?

So here is where the stats nerd in me got excited. Some people change the rules of rolling to make different distributions. The default distribution is pretty boring, 20 numbers with equal probability:

One common way people modify this is with the idea of “critical”. The idea is that sometimes people do way better or worse than average. To reflect this, if you roll a 20, instead of adding 20 to your modifier, you add 30. If you roll a 1, you subtract 10 from your modifier.

Another stats nerd must have made up the last distribution. The idea for constructing it is weird, but the behavior is much more Gaussian. It is called 3z8 because you roll 3 eight-sided dice that are numbered 0-7 and sum them up giving a value between 0 and 21. 1-20 act as in the standard rules, but 0 and 21 are now treated like criticals (but at a much lower frequency than before).

The cool thing is these distributions have almost identical expected values (10.5 for d20, 10.45 with criticals, and 10.498 for 3z8), but very different distributions. How do these distributions affect the game? What can we learn from this as statisticians?

### Our Case Study: Sneaking Past the Guards