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    League of Legends Seraphine FLASH + ULT invisible trick

    October 30, 2020

    League of Legends Seraphine FLASH + ULT invisible trick

    Seraphine FLASH + ULT invisible trick

    Posted: 30 Oct 2020 07:57 AM PDT

    Jankos celebrates 10 years of League of Legends with the perfect team icon

    Posted: 30 Oct 2020 07:39 AM PDT

    Sources: Rogue to sign Odoamne, promote Trymbi

    Posted: 30 Oct 2020 04:16 PM PDT

    WELCOME BACK GOD GILIUS - Gilius renews his contract for the 2021 LEC Season!!

    Posted: 30 Oct 2020 09:11 AM PDT

    Riot has decided to discontinue funding for the LCS Players Association: statements from Riot and LCSPA President Darshan - new elections to be held next week

    Posted: 30 Oct 2020 02:50 PM PDT

    K/DA - MORE Is 0.24 seconds out of Sync and is why the animation looked "off".

    Posted: 30 Oct 2020 07:53 AM PDT

    Firstly, the song bangs. I think it's great. However, on my first watch of the video I couldn't help but feel that something was off and it was extremely apparent on the Motorbike scene, the Evelyn scene and the Seraphine scene. I took the whole video into an editor and worked out that 95% of the video is out by 0.24 seconds. Simply budging the audio forward 0.24 seconds fixed everything bar the Kai'sa scene which seemed to be closer to accurate on the original video.

    Some bits are still really odd but this simple fix did make the whole video better. I think there were some issues with the mouth shaping on Seraphine and Ahri and Seraphine even moves her lips at times when nothing is being sung.

    Here's a link to my corrected video I have uploaded. Due to obvious copyright issues I have unlisted this video and monetization is disabled. Enjoy!

    Title edit: sync*

    submitted by /u/IVIystical
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    TIL, you can edit your settings on the loading screen by hitting escape

    Posted: 30 Oct 2020 10:56 AM PDT

    When was this added? I accidentally hit escape during the loading screen and had access to all the settings and could make changes.

    submitted by /u/zww2000
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    Worlds 2020: Finals Tease | Suning vs DAMWON Gaming

    Posted: 30 Oct 2020 06:00 AM PDT

    1 Button Play

    Posted: 29 Oct 2020 11:44 PM PDT

    Master Akalis Ulti in 30 Seconds! (Super Short Guide)

    Posted: 30 Oct 2020 01:19 AM PDT

    Who's that Top Laner?!?? | Cloud9 LCS Top Laner Announcement

    Posted: 30 Oct 2020 10:02 AM PDT

    Painting of Suning Huanfeng

    Posted: 30 Oct 2020 08:15 AM PDT

    Painting of Suning Huanfeng

    I am super stoked for the finals, and while I really wish both teams could win, I'm gonna have to root for my boys Suning on this one. Made a painting of the absolute mad man Huanfeng last week. Let's go Suning!

    ​

    ​

    https://preview.redd.it/rpdgzbof09w51.jpg?width=2700&format=pjpg&auto=webp&s=7c586af76ae7c1a5caa84b024cf2308b527130fc

    submitted by /u/Alibobaly
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    URF is fast and fun and everything, but there's one mode I really miss more than any other. Doom Bots.

    Posted: 30 Oct 2020 01:45 AM PDT

    I loved that mode, I wish Riot would bring it back. And not the weird Devil Teemo version - the OG one, where Morgana's W spawned Qs, Ziggs blew you up from offscreen, and Veigar's playpen was the size of your lane. I even remember that game the casters had against the doom bots, which was probably the second funniest showmatch in League history (right behind cats vs dogs). Anyone else hankering for some Doom Bots? Or have a good Doom Bots story?

    submitted by /u/PyramidofPolite
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    Seraphine E will always root if you have Rylais

    Posted: 30 Oct 2020 07:57 AM PDT

    Tested this out yesterday and turns on the slow gets applied before E registers. So if you build Rylais your E is now a guaranteed root.

    submitted by /u/Mnkeyqt
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    CLIENT CLEANUP: Champ Select & End Of Game - League of Legends

    Posted: 30 Oct 2020 08:11 AM PDT

    Why is there DRX logo on the world cup in the summoner rift

    Posted: 30 Oct 2020 04:48 PM PDT

    Why is there DRX logo on the world cup in the summoner rift

    It used to be the FPX logo on top of the cup because they won last year worlds, why tf is there the DRX logo now ?

    https://preview.redd.it/xpzqd8bdkbw51.jpg?width=1920&format=pjpg&auto=webp&s=bbd82a059874d8d7e87d926c7ae199e87a512c64

    submitted by /u/Atexyh
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    Behavioral Systems: November 2020 Update - League of Legends

    Posted: 30 Oct 2020 09:10 AM PDT

    Seraphine missions Feel Terrible

    Posted: 30 Oct 2020 11:01 AM PDT

    I understand the concept of having missions to unlock the skins as something unique, but the way it is right now is ridiculous. You need to play a lot, just to get the first transformation. The reason it feels terrible is because of how generic the missions are, it's not even personalized for seraphine or anything. Gain gold, get takedowns, destroy turrets? How does that actually contribute to anything? I sincerely hope we never see missions to unlock skins like this ever again (especially considering the upfront price).

    submitted by /u/YoungXehanort
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    Champion similarities based on neural net embeddings, a mastery point based recommendation system if you want to know which champion to play next

    Posted: 30 Oct 2020 03:49 PM PDT

    Champion similarities based on neural net embeddings, a mastery point based recommendation system if you want to know which champion to play next

    Tl, dr: Graphs with the calculated champion similarities are found in the middle of this post. A player is likely to play champions close to each other in these graphs.

    ​

    Hello everyone

    I calculated champion similarities using neural net embeddings based on champion mastery points and wanted to share the results with you all. It is a way to determine which champions are played by the same person, so the output can be used as a recommendation engine. I did this because I work as a data scientist at a fairly large retailer and wanted to know more about neural net embeddings as that could improve some neural nets I made in the past to predict various things and to improve my knowledge about recommendation engines. We get some time every week for continued education at work, so this was an educational project it did in that time.

    I wrote this post as I hope it will be an interesting read for you.

    ​

    Table of contents

    1. Introduction
    2. Data collection
    3. Neural net embedding and loss function for champion similarity
    4. Graphical representation of the calculated champion embeddings
    5. Additional tables
    6. Thanks for reading

    ​

    Introduction

    The idea behind this project is basically to try to calculate champion similarity based on champion mastery points for many player accounts. The reasoning behind using mastery points is that if a player likes some champions, they are likely to be similar in some way (e.g. I personally like to play enchanters and tanks) so the player will have high mastery points for these champions compared to the others. This is similar to what is behind a recommendation engine, I want to calculate similarity of champions for the players, and could then recommend a player champions close to their main champion to try out.

    To accomplish this I used neural net embeddings following this article. An embedding is a representation for each champion in a (fairly) high-dimensional space with the dot product as similarity measure between individual champions. The dot product is 1 for similar champions and 0 for champions which are not similar (actually, the dot product would go to -1 for antisimilar champions, but using 0 works better with the similarity function introduced later). Sounds rather complicated, so I will give an example:

    Assume we have 5 champions and embed them into a 3-dimensional space. This will give us a matrix like the following one:

    ​

    Champion Dimension 1 Dimension 2 Dimension 3
    Amumu 1 0 0
    Ahri 0.1 0.9 0
    Syndra 0 1 0
    Yasuo 0 0.1 0.9
    Zed 0 0 1

    The dot product is calculated by multiplying two vectors and then suming up the result, e.g.

    dot(Amumu, Ahri) = 1*0.1 + 0*0.9 + 0*0 = 0.1

    For the example matrix above, this would result in a similarity table like this:

    Amumu Ahri Syndra Yasuo Zed
    Amumu 0.1 0 0 0
    Ahri 0.1 0.9 0.09 0
    Syndra 0 0.9 0.1 0
    Yasuo 0 0.09 0.1 0.9
    Zed 0 0 0 0.9

    So for this example embedding, there would be a big similarity between Ahri/Syndra and Yasuo/Zed.

    After learning the embeddings, they are reduced onto two dimensions using a dimension reduction technique. I will use t-SNE, and also provide the result from MDS as you will see that the choice of dimensionality reduction has a visible effect on the output.

    ​

    The code for this can be found here. If you want to use to code for yourself, note that I used a locally installed SQL Server 2019 Express as database to store the data since I still have one on my computer from a work project. You also need a Riot API key in order to access the Riot API to download the necessary data. You can also contact me and I will help you to get it running if you want.

    ​

    Data collection

    A lot of data is usually necessary to reliable train neural networks. To do so I accessed the Riot API to download the mastery points for 120'000 accounts on both EUW as well as NA (I might also do Korea to compare in the future). To get the account names, I started by looking at my account (somewhere in gold I think) and get the account names for my last 100 played games. I repeated this for ~500 randomly selected accounts, which gives me a library of ~300'000 accounts from which I randomly selected the 120'000 accounts.

    For all of these accounts I downloaded and saved the mastery points for all but the newest champions (the newest champion considered is Yone) for a total of 150 champions into the local database.

    ​

    Neural net embedding and loss function for champion similarity

    After downloading the data, I excluded one-trick accounts (remember that I want to recommend you another champion, not to be a one-trick) which I defined as having a champion with more than 50% of all the champion mastery points on the account. I also excluded accounts with less than 100000 champion mastery points over all champions.

    For anyone interested, the neural net including the dot product is defined as

    X = mx.sym.Variable('data') y = mx.sym.Variable('label') symEmb = mx.sym.Embedding(data = X, input_dim = nChamps, output_dim = nDimEmbedding) symEmbChamp1 = mx.sym.slice_axis(symEmb, 1, 0, 1) symEmbChamp2 = mx.sym.slice_axis(symEmb, 1, 1, 2) symEmbReshape1 = mx.sym.reshape(symEmbChamp1, (-1, nDimEmbedding)) symEmbReshape2 = mx.sym.reshape(symEmbChamp2, (-1, nDimEmbedding)) symSkalarProdWinkel = mx.sym.sum(symEmbReshape1 * symEmbReshape2, axis = 1, keepdims = True) symFehler = mx.sym.LinearRegressionOutput(symSkalarProdWinkel, y)

    together with a custom data iterator. The embedding layer has 15 dimensions. The data iterator selects randomly (but skewed towards the more played champions) 15 champions for a random account and calculates the geometric means of the champion mastery point ratios for all the combinations which are the target variables for the neural net:

    geom(Champ_1, Champ_2) = sqrt((Mastery_1 / sum(All mastery points)) * (Mastery_2 / sum(All mastery points)))

    The loss function between the dot products and the target variables is a standard MSE-error.

    Each epoch for the neural net training consists of the combination of the 15 randomly selected champions for 10'000 randomly selected accounts, repeated over 100 epochs.

    ​

    Graphical representation of the calculated champion embeddings (not mobile friendly, but what can you do with 150 champion-icons, sorry)

    The calculated embeddings after neural net training is a 150 by 15 matrix which is nearly impossible to visualize directly. To overcome this, we need to reduce the dimensionality to a displayable amount (namely 2 dimensions), for which I used t-SNE. The results look as follows (if you miss your champion, it can happend that two champions are so close together that one icon is completely covered by the other, e.g. Orrn is behind Urgot for EUW):

    EUW:

    ​

    Graphical representation of the champion similarities for EUW calculated with neural net embeddings followed by t-SNE. Champions close to each other are more likely to be both played by the same player.

    We can nicely see the ADC cluster (with Ziggs) at the bottom and the supports to the bottom left, separated into enchanters, tanks and catchers. And Zyra (my old main) somewhere hanging in there. Lux is also more support than midlane-mage. There is also an assassin/edgelord cluster top left. Interestingly, LeBlanc is located with other mages, not other assassins. In the center and top-right we have tanks and junglers with fighters/juggernaughts being on the right, except Irelia which is in the edgelord-cluster.

    We can also see champions like Lillia, Nidalee, Qiyana, Quinn, Ivern, Aurelion Sol and Yorick far from other champions. This is to be expected as they have unique playstyles or attract one-trick players as they don't have other similarly played champions.

    ​

    NA:

    Graphical representation of the champion similarities for NA calculated with neural net embeddings followed by t-SNE. Champions close to each other are more likely to be both played by the same player.

    While NA looks fairly similar to EUW, there are some differences where the clusters are located relative to each other. We can discuss individual champions or clusters further in the comments.

    ​

    As a comparison of the effect the choice of dimensionality reduction technique has, I also want to show the results from applying MDS on the trained neural net embeddings.

    EUW1:

    Graphical representation of the champion similarities for EUW calculated with neural net embeddings followed by MDS. Champions close to each other are more likely to be both played by the same player.

    Compared to t-SNE, the champions are more evenly spred out. Overall the same clusters as seen in t-SNE still exist, but are way less visible. But you can see the champion icons better here as they overlap less.

    ​

    NA:

    Graphical representation of the champion similarities for NA calculated with neural net embeddings followed by MDS. Champions close to each other are more likely to be both played by the same player.

    ​

    Additional tables

    As I downloaded all the champion mastery points anyway, I also want to show some more information on them which I found interesting.

    Here is a table of the ratio of accounts which had no mastery points for a given champion (out of the 120'000 accounts):

    Ratio of accounts with no mastery points for the specified champion for both EUW and NA for the 120'000 used accounts.

    Not surprisinlgy, a lot of players have not played the newer champions, but also Ivern and Skarner are up there. On the other end, almost everyone has played at least a single game of Ashe or Lux. In addition, it seems that EUW players tend to play/try out a little more different champions than NA players.

    Here is a table for the total sum of all mastery points for the different champions (out of the 120'000) accounts as well as the ratio of these mastery points compared to the total sum of all mastery points:

    Sum of all mastery points per champion for the 120'000 used accounts together with the ratio of the sum to the total amount of mastery points over all champions for both EUW and NA.

    I don't think we have to discuss who will get the next skins are the most popular.

    ​

    Thanks for reading

    Thanks for reading so far. It was a really interesting small project for me and I hope you found something in here that got you thinking. Again, I did this as a personal continued education project and have no affiliation with Riot. If you have questions, I'll try to answer them in the comments. Tldr is at the beginning.

    Have a good day :)

    submitted by /u/giantZorg
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    Riot Forge Teaser

    Posted: 30 Oct 2020 09:21 AM PDT

    https://twitter.com/riotforge/status/1322206634811092994?s=21

    Some people were saying it's for the Ruined King game in the comments, but I didn't want to put that in the title in case it's wrong. Either way it looks fucking SICK, and has me hyped for whatever it is

    submitted by /u/Blue_Lucian_Chroma
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    Quick Gameplay Thoughts: October 30 - League of Legends

    Posted: 30 Oct 2020 10:13 AM PDT

    (Esportmaniacos) Sources: SKGaming will sign Jezu_lol, Blue & TynXlol for their 2021 #LEC roster.

    Posted: 30 Oct 2020 07:26 AM PDT

    MF's Ult doesn't break sleeps, so I put her and Lillia in the botlane together.

    Posted: 29 Oct 2020 05:33 PM PDT

    Vlad's hp from ult indicator is wrong, it counts minions hit as champions, despite them not healing you at all ( ignore that small heal fro ravenous hunter )

    Posted: 30 Oct 2020 08:25 AM PDT

    Facecheck S02E31 - Worlds 2020 Wasn't A Tournament For G2

    Posted: 30 Oct 2020 10:26 AM PDT
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