I checked out what happens if you remove games that include any “trash picks” (A,B,D,T,W), in addition to requiring teams to include one character from each group. This further reduces the dataset significantly, but I noticed that in this set of games, the opposing team FSR has the highest winrate, which suggests it is a very strong team against other conventionally strong teams, even if it doesn’t exploit weaker teams that well.
In this further reduced set, the second highest winrate is JLM, then CLP, then JLP.
Given the low amount of data points, however, these winrate variations between the top teams in the further restricted set could easily be random, so I don’t think there’s all that strong a case to change my picks, and my choices above are unchanged for now. However, this does suggest JLM as an alternate candidate against FSR, and the opposing team FSR itself as a possible PVP pick (if people don’t just submit their PVE picks, or you think people will fail to counter it).
edit:
oh wait. For the top teams, the wins are higher if you include trash picks, but the losses often aren’t. This means that these teams are basically always winning against trash picks, and the apparent higher number of data points is effectively an illusion, and the trash-pick-including win rates are distorted by how often teams were matched against bad teams.
examples (strong = has one character from each group, no trash picks, weak = has one character from each group, but at least one trash pick)
team | wins against strong | losses against strong | wins against weak | losses against weak
CLP | 24 | 14 | 118 | 0
JLP | 20 | 12 | 92 | 0
CSP | 23 | 17 | 102 | 0
but on the other hand:
HLP | 21 | 19 | 96 | 3
JLM | 28 | 15 | 100 | 7
FSR | 26 |12 |99 |10
I don’t know to what extent failing to defeat all the weak teams should be taken as evidence that a team isn’t good in general (so that the good numbers against strong teams are more likely to be a fluke).
Takeaways: my data is really thin even in the larger restricted set and I should pay little attention to these winrate variations between full teams; I should try to find more general patterns. I should also maybe look at what particular “trash” picks can beat FSR, in case it is losing reliably to some narrow counter as opposed to just not reliably beating weaker teams in general.
Update in view of the answer likely being soon to be posted:
I got sidetracked among other (non-D&DSci) things by trying to semi-automatically categorize the team compositions in the games with only the restricted team compositions (one character from each group, no trash picks) into similarity clusters. This was tricky because there is a lot of noise in this much smaller dataset, and I didn’t take into account games outside this restricted set at all.
Ultimately, I did get three clusters which seemed to have a rock-paper-scissors interaction. One cluster is Felon-heavy (indeed seems to maybe have all Felon teams) and FLR seems to be a fairly archetypal example. Another cluster is Samurai-heavy and Golem-light; HSM seems to be a fairly archetypal example. The third cluster is Pyro-heavy and JGP seems to be a fairly archetypal example.
Anyway, the FLR cluster tends to beat the HSM cluster which tends to beat the JGP cluster which tends to beat the FLR cluster.
The PVE opposing team, FSR, mostly seems to be in the FLR cluster but is not very central, leaning a bit to the HSM cluster. It hasn’t faced the JGP cluster a lot (maybe 5-6 games depending on cluster definition) and has won maybe 3 or 4 of those, atypical for an FLR cluster member, but that could easily be random due to the low number of games.
Notably, my current PVP pick, CLP, seems to be in the JGP cluster and, as is typical for members of this cluster, tends to lose to members of the HSM cluster. In the absence of reasons to believe that other players have picked teams from the HSM cluster (hmm, but yonge picked HMP (which isn’t in this restricted dataset since it has two characters from the same group) - would that behave like HSM??) I don’t see a compelling reason to switch, though I might change my mind if I post this comment and then the answer isn’t posted for a long time.
Anyway, I’m not sure whether the rock-paper-scissors effect seen in the clustering derives from some collective interaction or is just a result of character pair interactions. Some apparent counters in this restricted dataset:
F>S;P>F;G>F;F>R;J>P;S>J;R>J;S>C;P>L;S>P;C>F
Also:
I’ve now gone and looked at what FSR wins against and adjusted my PVE pick accordingly. I’ll likely adjust my PVP pick as well if I end up having time to check what sort of things candidate PVP picks (and other players’ PVP picks where posted) do well against.
edit: looks like this comment was after aphyer posted the answer, but I checked for any new posts after my PVE edit above and didn’t see aphyer’s post of the answer.
Sorry, wasn’t expecting anything today! I’ll update the wrapup doc to reflect your PVE answer: sadly, even if you had an updated PVP answer, I won’t let you change that now :P
Also:
I checked out what happens if you remove games that include any “trash picks” (A,B,D,T,W), in addition to requiring teams to include one character from each group. This further reduces the dataset significantly, but I noticed that in this set of games, the opposing team FSR has the highest winrate, which suggests it is a very strong team against other conventionally strong teams, even if it doesn’t exploit weaker teams that well.
In this further reduced set, the second highest winrate is JLM, then CLP, then JLP.
Given the low amount of data points, however, these winrate variations between the top teams in the further restricted set could easily be random, so I don’t think there’s all that strong a case to change my picks, and my choices above are unchanged for now. However, this does suggest JLM as an alternate candidate against FSR, and the opposing team FSR itself as a possible PVP pick (if people don’t just submit their PVE picks, or you think people will fail to counter it).
edit:
oh wait. For the top teams, the wins are higher if you include trash picks, but the losses often aren’t. This means that these teams are basically always winning against trash picks, and the apparent higher number of data points is effectively an illusion, and the trash-pick-including win rates are distorted by how often teams were matched against bad teams.
examples (strong = has one character from each group, no trash picks, weak = has one character from each group, but at least one trash pick)
team | wins against strong | losses against strong | wins against weak | losses against weak
CLP | 24 | 14 | 118 | 0
JLP | 20 | 12 | 92 | 0
CSP | 23 | 17 | 102 | 0
but on the other hand:
HLP | 21 | 19 | 96 | 3
JLM | 28 | 15 | 100 | 7
FSR | 26 |12 |99 |10
I don’t know to what extent failing to defeat all the weak teams should be taken as evidence that a team isn’t good in general (so that the good numbers against strong teams are more likely to be a fluke).
Takeaways: my data is really thin even in the larger restricted set and I should pay little attention to these winrate variations between full teams; I should try to find more general patterns. I should also maybe look at what particular “trash” picks can beat FSR, in case it is losing reliably to some narrow counter as opposed to just not reliably beating weaker teams in general.
Update in view of the answer likely being soon to be posted:
I got sidetracked among other (non-D&DSci) things by trying to semi-automatically categorize the team compositions in the games with only the restricted team compositions (one character from each group, no trash picks) into similarity clusters. This was tricky because there is a lot of noise in this much smaller dataset, and I didn’t take into account games outside this restricted set at all.
Ultimately, I did get three clusters which seemed to have a rock-paper-scissors interaction. One cluster is Felon-heavy (indeed seems to maybe have all Felon teams) and FLR seems to be a fairly archetypal example. Another cluster is Samurai-heavy and Golem-light; HSM seems to be a fairly archetypal example. The third cluster is Pyro-heavy and JGP seems to be a fairly archetypal example.
Anyway, the FLR cluster tends to beat the HSM cluster which tends to beat the JGP cluster which tends to beat the FLR cluster.
The PVE opposing team, FSR, mostly seems to be in the FLR cluster but is not very central, leaning a bit to the HSM cluster. It hasn’t faced the JGP cluster a lot (maybe 5-6 games depending on cluster definition) and has won maybe 3 or 4 of those, atypical for an FLR cluster member, but that could easily be random due to the low number of games.
Notably, my current PVP pick, CLP, seems to be in the JGP cluster and, as is typical for members of this cluster, tends to lose to members of the HSM cluster. In the absence of reasons to believe that other players have picked teams from the HSM cluster (hmm, but yonge picked HMP (which isn’t in this restricted dataset since it has two characters from the same group) - would that behave like HSM??) I don’t see a compelling reason to switch, though I might change my mind if I post this comment and then the answer isn’t posted for a long time.
Anyway, I’m not sure whether the rock-paper-scissors effect seen in the clustering derives from some collective interaction or is just a result of character pair interactions. Some apparent counters in this restricted dataset:
F>S;P>F;G>F;F>R;J>P;S>J;R>J;S>C;P>L;S>P;C>F
Also:
I’ve now gone and looked at what FSR wins against and adjusted my PVE pick accordingly. I’ll likely adjust my PVP pick as well if I end up having time to check what sort of things candidate PVP picks (and other players’ PVP picks where posted) do well against.
edit: looks like this comment was after aphyer posted the answer, but I checked for any new posts after my PVE edit above and didn’t see aphyer’s post of the answer.
Sorry, wasn’t expecting anything today! I’ll update the wrapup doc to reflect your PVE answer: sadly, even if you had an updated PVP answer, I won’t let you change that now :P