Rank | Mean | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Highest | |||||||||||||||||
2nd highest | |||||||||||||||||
3rd highest | |||||||||||||||||
4th highest | |||||||||||||||||
5th highest | |||||||||||||||||
6th highest | |||||||||||||||||
Mean |
(The preceding chart and table are for a single array.)
Pricing method | Mean | SD |
---|---|---|
Raw total | ||
D&D 5e point buy (ref) | ||
Pathfinder point buy (ref) |
I built this using Pyodide, Chart.js, and of course, my own Icepool Python library (formerly hdroller), itself powered by numpy. A polynomial-time algorithm for keep-highest allows this calculator to deliver precise results at an interactive rate. It runs in your own browser, not requiring a server once loaded. Note that this page uses an older version of hdroller, which is less flexible but gives a faster response time for this particular application.
Compare previous AnyDice and Monte Carlo approaches.
If you want to play with hdroller more directly, try this example Starboard notebook, which computes the distributions of the total ability scores generated by the four Advanced Dungeons & Dragons 1st Edition methods.
Questions, comments, or suggestions? Find me on Reddit or Twitter.