I am now requesting extra time. I’ve loaded the data using my Haskell program (fixed the parsing late last night), and used it to check
basic stats for number of players, aspects, classes including individual player aspect/class combos, which all seem pretty evenly distributed and none of which seem to effect winrate very much except number of players and that’s not that big either
but still need to
check for interactions and especially look for symmetries in those interactions—relatively constant winrates overall suggests symmetric interactions unless the effects are weak
edited to add:
No wait, the variations in winrate for individual player aspect/class combos don’t look that small at all. Noticed this shortly after making the above comment but didn’t want to actually make the edit until I had got the Haskell program to calculate the p-values though the variations were obviously too big to be random if the variations in total numbers for each combo were assumed to be random. The variations in winrates of classes and aspects, while much smaller, are still strongly statistically significant in some cases (if I got the program to do the right math).
Since I was busy with that I haven’t gotten around to looking at correlations between different players in the same team yet. There definitely do seem to be patterns in which classes go with which aspects for individual player aspect/class combos, though.
And looks like I could use the weekend as well, if that’s OK. Though, if other players object, I do feel like I am abusing this a bit—the time ratio between “data analysis” vs “learning Haskell” has been low.
I am now requesting extra time. I’ve loaded the data using my Haskell program (fixed the parsing late last night), and used it to check
basic stats for number of players, aspects, classes including individual player aspect/class combos, which all seem pretty evenly distributed and none of which seem to effect winrate very much except number of players and that’s not that big either
but still need to
check for interactions and especially look for symmetries in those interactions—relatively constant winrates overall suggests symmetric interactions unless the effects are weak
edited to add:
No wait, the variations in winrate for individual player aspect/class combos don’t look that small at all. Noticed this shortly after making the above comment but didn’t want to actually make the edit until I had got the Haskell program to calculate the p-values though the variations were obviously too big to be random if the variations in total numbers for each combo were assumed to be random. The variations in winrates of classes and aspects, while much smaller, are still strongly statistically significant in some cases (if I got the program to do the right math).
Since I was busy with that I haven’t gotten around to looking at correlations between different players in the same team yet. There definitely do seem to be patterns in which classes go with which aspects for individual player aspect/class combos, though.
Understood, no worries! I’ll aim to post the solution on Friday unless I hear further—if you want another weekend I could instead do next Monday.
And looks like I could use the weekend as well, if that’s OK. Though, if other players object, I do feel like I am abusing this a bit—the time ratio between “data analysis” vs “learning Haskell” has been low.
Fine with me
If it helps, I for one am completely okay with you taking the weekend.
Thanks!