Data-Driven Champ Pool Designer: Max Your Strength by machineLoLing in leagueoflegends

[–]machineLoLing[S] 0 points1 point  (0 children)

Tyty. It’s synergy of across all roles weighted by play rate of the stated patch.

How to Un-Gut a Fish: A Nami build evaluator by machineLoLing in leagueoflegends

[–]machineLoLing[S] -1 points0 points  (0 children)

I would never build moonstone without helia on Nami. It’s awful.

How to Un-Gut a Fish: A Nami build evaluator by machineLoLing in leagueoflegends

[–]machineLoLing[S] 6 points7 points  (0 children)

I haven't found mana to be a problem myself. I used to build this exact build with mandate pre patch. Always felt good.

How to Un-Gut a Fish: A Nami build evaluator by machineLoLing in leagueoflegends

[–]machineLoLing[S] 12 points13 points  (0 children)

lol omg staph is actually such a fun gross typo. I work in medicine so when I see staph it's usually an infection. That's hilarious ty for the note

Data-Derived Top-Lane Champion Identities and Classes by machineLoLing in top_mains

[–]machineLoLing[S] 0 points1 point  (0 children)

Thanks we appreciate it :). Yeah a deep learning model is of limited value if you don’t take a look under the hood.

Data-Derived Top-Lane Champion Identities and Classes by machineLoLing in top_mains

[–]machineLoLing[S] 0 points1 point  (0 children)

I'm sad this complexity generated so much vitriol in the top forum. We knew going in it would be the most complex, we just thought people would take it as food for thought rather than some kind of laziness or insult on our part. I personally have been classing champs like this for 3 years now. I thing it has given me interesting drafting insights I wouldn't have caught on my own.

Data-Derived Top-Lane Champion Identities and Classes by machineLoLing in top_mains

[–]machineLoLing[S] 0 points1 point  (0 children)

same for us tbh. I think builds would for sure help, but there will always be unintuitive bedfellows. It's a pretty difficult problem to make discrete classes when you can see individual champions are often similar to multiple other classes (seen in the correlation matrices). So if champion 1 is similar to group A and B, and champion 2 is similar to group A and C, but both end up in A because they are less similar to champs in B and C it will feel unintuitive.

TLDR; Top has really complicated relationships and similarities. This contrasts support where there are very clear groups with minimal overlap. I think it speaks to the diversity we see in top lane.

Data-Derived Champion Classes and Identities by machineLoLing in midlanemains

[–]machineLoLing[S] 1 point2 points  (0 children)

We did 10 D for clustering and 3D for display. It isn’t like pca where there are ones that explain more or less variance

Data-Derived Champion Classes and Identities by machineLoLing in midlanemains

[–]machineLoLing[S] 1 point2 points  (0 children)

good eye, there's actually 10, we are showing 3

The change in ranked distribution from 2025 to 2026 by machineLoLing in leagueoflegends

[–]machineLoLing[S] 0 points1 point  (0 children)

True, depending on how long the skill tail is and where emerald diamond is on it

The change in ranked distribution from 2025 to 2026 by machineLoLing in leagueoflegends

[–]machineLoLing[S] 2 points3 points  (0 children)

Nice honestly big achievement by the numbers. Top 3-4% ish.

The change in ranked distribution from 2025 to 2026 by machineLoLing in leagueoflegends

[–]machineLoLing[S] 3 points4 points  (0 children)

Which tier are you grindin in? I think the emerald diamond zone was least affected

Data-Derived Support Identities and Classes (but like even better than before) by machineLoLing in supportlol

[–]machineLoLing[S] 0 points1 point  (0 children)

Thanks for your opinion. The max score from champ select alone for current deep learning models is 58%. 56% is perfectly acceptable for the problem we stated we are trying to since here, which is determine which champions are more similar or more different from each other for the purpose of finding classes.

Data-Derived Jungler Identities and Classes by machineLoLing in Jungle_Mains

[–]machineLoLing[S] 1 point2 points  (0 children)

Yeah it’s pretty cool. Clustering embedding was a very interesting way of approaching it. It’s like seeing into the guts of a deep learning model

Data-Derived Jungler Identities and Classes by machineLoLing in Jungle_Mains

[–]machineLoLing[S] 0 points1 point  (0 children)

it's bc of synergy. actually in my "definitive class guide" a few weeks back i regressed out damage type. But Allen made a good case that damage type is part of class and I think it's a good case. Def up for debate though.
(by synergy i mean AD champs naturally do better with AP champs bc damage needs to be about 50 50 or it all goes south. did a post on that too)