The first thing I did was plot each trait’s score against the success rate for all students with that score in that trait.
All the graphs looked fairly linear, if noisy, but that seems reasonable for this size of dataset. I added a best-fit line in excel and got these values:
Trait Slope R^2
CHA 2.20 0.861
CON 1.72 0.760
DEX −1.52 0.868
INT 1.18 0.641
STR 1.69 0.734
WIS 2.33 0.908
DEX appears to have a negative correlation with success rate while WIS and CHA are most important.
Since I’m only able to increase my scores, it might be interesting to only look at students in the range I could reach by adding at most 10 points to my current trait scores. However, there are only 7 students in this group. (This seemed surprisingly small at first, but looking at this same group for other students gives an average size of 4.15 and a median size of 2.5, and 22.4% of students have a reachable group size of at least 7, so I’m actually above average here though not a major outlier.)
It seems like the best plan is to put all ten points into some combination of WIS and CHA. If allowed, I would also remove most of my DEX even if that didn’t give me more points to spend elsewhere. WIS appears to have a slightly larger effect than CHA, but there are fewer students with very high scores, so it’s hard to tell if the linear relationships hold at the extremes. I’m thinking somewhere between (WIS +8, CHA +2) and (WIS +6, CHA +4) will be my best bet.
In case the trait contributions are not independent, I tried filtering for both low-mid CHA and mid-high WIS, but this still showed fairly clean linear relationships for both traits with WIS still slightly stronger than CHA. There is a high outlier at exactly WIS=15, so I looked at the range WIS(12-20) and CHA(6-14) and got this:
WIS CHA N Win%
12 14 56 79%
13 13 54 72%
14 12 65 72%
15 11 75 79%
16 10 43 81%
17 9 34 76%
18 8 15 80%
19 7 10 80%
20 6 1 100%
There’s still a bump at WIS(15-16), but it looks like this is probably an artifact of small sample size.
I did one last run filtering for low-mid on CHA and STR and mid-high on the other traits (N=172):
Trait Slope
CHA 5.23
CON 2.86
DEX 0.81
INT 2.40
STR 3.19
WIS 4.74
Here DEX is slightly positive and CHA is slightly better than WIS, although with more than an order of magnitude smaller sample size.
My final decision is (CHA +4, WIS +6) for a resulting stat line of:
CHA 8⁄20
CON 14⁄20
DEX 13⁄20
INT 13⁄20
STR 6⁄20
WIS 18⁄20
The first thing I did was plot each trait’s score against the success rate for all students with that score in that trait. All the graphs looked fairly linear, if noisy, but that seems reasonable for this size of dataset. I added a best-fit line in excel and got these values:
Trait Slope R^2
CHA 2.20 0.861
CON 1.72 0.760
DEX −1.52 0.868
INT 1.18 0.641
STR 1.69 0.734
WIS 2.33 0.908
DEX appears to have a negative correlation with success rate while WIS and CHA are most important.
Since I’m only able to increase my scores, it might be interesting to only look at students in the range I could reach by adding at most 10 points to my current trait scores. However, there are only 7 students in this group. (This seemed surprisingly small at first, but looking at this same group for other students gives an average size of 4.15 and a median size of 2.5, and 22.4% of students have a reachable group size of at least 7, so I’m actually above average here though not a major outlier.)
It seems like the best plan is to put all ten points into some combination of WIS and CHA. If allowed, I would also remove most of my DEX even if that didn’t give me more points to spend elsewhere. WIS appears to have a slightly larger effect than CHA, but there are fewer students with very high scores, so it’s hard to tell if the linear relationships hold at the extremes. I’m thinking somewhere between (WIS +8, CHA +2) and (WIS +6, CHA +4) will be my best bet.
In case the trait contributions are not independent, I tried filtering for both low-mid CHA and mid-high WIS, but this still showed fairly clean linear relationships for both traits with WIS still slightly stronger than CHA. There is a high outlier at exactly WIS=15, so I looked at the range WIS(12-20) and CHA(6-14) and got this:
WIS CHA N Win%
12 14 56 79%
13 13 54 72%
14 12 65 72%
15 11 75 79%
16 10 43 81%
17 9 34 76%
18 8 15 80%
19 7 10 80%
20 6 1 100%
There’s still a bump at WIS(15-16), but it looks like this is probably an artifact of small sample size.
I did one last run filtering for low-mid on CHA and STR and mid-high on the other traits (N=172):
Trait Slope
CHA 5.23
CON 2.86
DEX 0.81
INT 2.40
STR 3.19
WIS 4.74
Here DEX is slightly positive and CHA is slightly better than WIS, although with more than an order of magnitude smaller sample size.
My final decision is (CHA +4, WIS +6) for a resulting stat line of:
CHA 8⁄20
CON 14⁄20
DEX 13⁄20
INT 13⁄20
STR 6⁄20
WIS 18⁄20