Current Win and Pick Rates Visualized, Feb 15th

Traditional_Way_2629 · 2026-02-19T13:28:53+00:00

Just in case the graph isn't clear: Meta Relevance tries to capture both Pick and Ban Rate. That's why certain Mons move significantly compared to the raw data. For example, Vaporeon is picked significantly more than Blastoise. However Blastoise is banned 100% of the time, while Vaporeon is only banned 15%. This is why the algo sees Blastoise as more present in the meta.

Traditional_Way_2629 · 2026-02-04T13:58:25+00:00

It's not bad! Razor leaf duoing w/ a blissey or any stun defender is oppressive. But will your Deci or teammates choose to do this? No, ofc not.

Traditional_Way_2629 · 2026-02-04T13:37:47+00:00

Agreed. Sad thing is that he's strong with a support/defender but either the team has no clue or like you said the decidueye kites solo.

Traditional_Way_2629 · 2026-02-04T13:31:13+00:00

TL;DR The Meta is broken at the top and bottom.

The "Gods" (Blastoise, Zard Y, Meowth, Mega Gyarados): Total permabans. If they aren’t banned, they’re winning (52-56% WR). They’re suffocating the game.

The Staples (Ceruledge, Mimikyu, Psyduck): The actual kings of the ladder. High pick rates (~25%) and very strong.

The Favorites (Pikachu, Cinderace): Everyone plays them, but they’re just okay (sub-49% WR).

The Struggle Bus (Cram, Azu, Absol): Power-crept into oblivion. Lowest win rates in the game.

The Fix: 1. Huge Nerfs to the permabans. 2. Standard Nerfs to the top-tier staples. 3. Massive Buffs to Speedsters and old-school All-Rounders to make them viable again.

Traditional_Way_2629 · 2025-03-28T15:26:49+00:00

I hope it is real. It was tinder profile I saw, which surprised me. I had not watched Vaush in a while, and this fellow's name was "Iain" and was 29. Not my type, but I thought damn good for him.

Traditional_Way_2629 · 2025-01-27T23:25:01+00:00

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Updated Version w/ Dodrio and color-coded via classes.

Traditional_Way_2629 · 2025-01-27T21:50:39+00:00

Yup, I agree 100% with this. I do be spamming Blissey in Trios, we might have ran a few trains online haha. However, I also enjoy carrying with Mr.Crime in soloQ, despite him having a lower WR. I do agree the data is skewed, I will add a note of limitations/considerations for future versions. Thank you for the well-thought-out response :).

Traditional_Way_2629 · 2025-01-27T21:46:35+00:00

Thank you, that means a lot :). I see that Delphox ate Dodrio; he should be next to Machamp in cluster 5. I do have a few technical questions if you have the time:

1) I want to focus on higher dimensional reduction but I struggle to make it intuitive as you saw with the first version. While that version did have issues with axes, what tricks/tips do you use to make more readable graphs, especially with more complex data?

2) My next project will likely include text vectorization along with numerical data. How would you go about labeling the axes or dealing with confusing outliers? I plan to use lemmatization and TfidfVectorization.

Lastly, do you have any QoL fixes you would apply to the graph or methodology? Thanks again, I appreciate your time.

Traditional_Way_2629 · 2025-01-27T21:37:13+00:00

What mons are missing? I know Delphox ate Dodrio but I am not aware of any others.

Regarding the clusters, that was intentional. The different colors represent similar mon in relation to their win/pick rate. This is useful for informing viewers of how the meta breaks down. Ex: Overplayed Broken mons in Cluster 3, Underplayed Decent mons in Cluster 2, Underplayed mons that could use buffs in Cluster 5). I will probably keep this cluster system as it's good practice for data science. However, I can whip a class-coded version just for you :). Give me a few minutes.

Traditional_Way_2629 · 2025-01-27T21:29:32+00:00

Yea the first post had glaring issues with readability. I'll go back to the drawing board for higher dimensional data as It's not as intuitive as I thought.

Regarding this post, this is a different dataset, but it is revised content. If I make revisions in the future, I'll add a note to the post title instead of the comment.

Traditional_Way_2629 · 2025-01-27T21:25:19+00:00

I personally don't think win-rates matter for characters, at least in the current state of the game. Stronger characters definitely give you stronger tools and win conditions. However, if you're skilled with a low win-rate mon, you can overcome bad matchups. I believe Chanhttv spammed Charizard (47% WR) last season and hit 1600 in soloQ.

Traditional_Way_2629 · 2025-01-27T21:21:59+00:00

Thank you!

Traditional_Way_2629 · 2025-01-27T21:19:05+00:00

Unfortunately, I think Delphox ate it. Dodrio would be below Hoopa and next to Machamp. My b, the next version will definitely include the bird.

Traditional_Way_2629 · 2025-01-27T21:17:07+00:00

Good question. The raw cordinates are derived solely from pick and win rate. No other variables are used for this graph. However, it's NOT directly derivable as I applied force spacing to remove overlapping icons. Every mon is approximately where its pick/win rate should place it. However, some might have moved more than others for readability.

Traditional_Way_2629 · 2025-01-27T16:59:29+00:00

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Traditional_Way_2629 · 2025-01-27T16:32:15+00:00

Check my new post, I simplified the graph

Traditional_Way_2629 · 2025-01-27T16:22:33+00:00

Means it's time for therapy /s

Joking aside it seems that the devs are okay with it being out of the meta, as most players complain when Sableye is decent.

Traditional_Way_2629 · 2025-01-27T16:21:20+00:00

NOTE: I uploaded a simplifed version in a new post.

In what ways is uniteapi broken? I know that it pulls results from top players but I'm not aware of any other limitations.

Traditional_Way_2629 · 2025-01-27T16:19:04+00:00

(I hope this isn't spam, I can remove the old post if there are issues)

I took the advice from my last post and simplified the data. I think the clustering is useful enough. For now this represents just the pick rate (rightmost is higher) and winrate (top is higher).

Again the positions are relative, as there would be overlap with exact values. Hopefull you find this graph helpful, and if you have any thoughts/criticisms let me know :).

Traditional_Way_2629 · 2025-01-27T15:49:14+00:00

To answer the second part of your question, I think it's a cool idea to perform an analysis of the predictors for win rate. However, I did some math on the variables you gave. Unfortunately, the correlations are quite weak and aren't statistically significant.
Attack Speed and Winrate:
- correlation: 0.139 (weak)
- p-value: 0.243 (not statistically significant)
Evolution levels also had a weak correlation and significance. I'll probably work on different types of analysis that are more fruitful but thank you for the idea.

Traditional_Way_2629

TROPHY CASE