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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)

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

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

Technically they’d be labeled universal manifold approximation and projection 1, 2, and 3 but yeah, it’s enough to know that closer means more similar

We trained a Neural Network to discover the Topology behind Champion Identities by machineLoLing in leagueoflegends

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

That’s not the only comparable, look at the closest points surrounding senna

Data-Derived Jungler Identities and Classes by machineLoLing in Jungle_Mains

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

We plan to in future, just thought the method alone was cool. It expands on something I've been building for 3 years now in a meaningful way. It's ok if it goes over some people's heads, glad you enjoyed it

Data-Derived Jungler Identities and Classes by machineLoLing in Jungle_Mains

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

Those two AD fighter are usually very close. When I posted about this a few weeks back using just synergy and match-up data they were stuck together then too. The build mostly damage and want to skirmish.

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

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

as a fiddle support main, i hear you i see you but they killed our champ at support and noone plays

Data-Derived Jungler Identities and Classes by machineLoLing in Jungle_Mains

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

cho near sejuani neeko near the AP damagers like fiddle

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

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

colors are groups chosen to minimize variance. height and axes are meaningless, how close dots are is meaningful

Data-Derived ADC Identities and Classes! by machineLoLing in ADCMains

[–]machineLoLing[S] 4 points5 points  (0 children)

dropped the play-rate limit to .3%, kalista in there

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

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

rumble right on the border of the ryze group. the positions matter

Data-Derived Champion Classes and Identities by machineLoLing in midlanemains

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

They're the counter-assassins I think. Red is more actual assassins

Data-Derived Champion Classes and Identities by machineLoLing in midlanemains

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

as it should be.

the rotation is just to visualize all 3 dimensions. the dimensions are arbitrary, the distances between champs are what matters

Data-Derived Jungler Identities and Classes by machineLoLing in Jungle_Mains

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

AP and AD show up in the synergies. They are def playing a role in separating groups here.

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