We everybody know last half century English became as international language. You can speek and write it wiz every foreighner, who ever learn it, and you will understand each ozer. No matter where you go, it can everywhere help you. Everybody learn it, so it should make easy every international communication.
Technologikal progress of XXI century give to our life many gadgets and ozer thinks what make our life easier. If previous generations always aspired to conquer new heights, nowadays peoples became lazy and afraid do a lot of moving. So funny, but most of them afraid to learn languages too:
Afraid to learn Spanish, bekoz can’t prononce Spanish R.
Afraid to learn French, bekoz 90 = 4 * 20 + 10.
Afraid to learn Deutsch, bekoz it have heavy prononciation and complicated grammar.
Afraid to learn Russian, bekoz it have different alfabet and complicated grammar.
Afraid to learn Chinese, bekoz instead of alfabet it use many thousands of hieroglyphs.
Afraid to learn Japonese, bekoz it have 3 different writing systems, including Chinese hieroglyphs, using together and complicated grammar.
But why they not afraid to learn English? It have confused orthography, what make writing similar to hieroglyphical. Troublesome “th” sound and smooth unclear prononciation of everything. Many unlogical frases wiz strange meaning…
I don’t know why, but all of that lazy peoples can’t see that “international language” is not easier than ozers, and sometimes much harder. But they anyway spend a lot of time and a lot of money to learn it. And no matter how good they can speak English, they still afraid to learn anozer language.
Except of natural langwidges, to make international communikation easier, since XIX century was invented many handmade languages: Esperanto, Interlingua, Glossa, etc. All that languages are really neutral and really logical and comfortable to use. But who cares? All that languages now using only by small groups of entusiasts. But there not bekoz languages bad, mostly political reasons not give them widespread.
Okay, lets see how English can help our world wiz communication.
English is only langwidge in ze world where wrighting and spicking not same. What you see and what you hear always different. Sometimes too much different. See: one, hear: wan. See: apply, hear: eplai. See: luxury, hear: laksheri. See: ghoti, hear: fish. See: business, hear: biznes. See: giant, hear: dzhayent. See: science, hear: sayens. See: action, hear: ekshen. See: hour, hear: aua. See: psycho, hear: saikou. See: throurguohruotrgourgh, hear: fu. All different! How to understand what exaktly you hear? Every word have it’s own orthographia.
https://preview.redd.it/yklyxk4wx9a81.png?width=1024&format=png&auto=webp&s=635f16c5c242b3316d2b0beec7c93faf5ba2e96e
- What is it?
- It’s a parrot.
- No, it’s a tree!
- Why? I see parrot there!
- Yes, but it prononce like “tree”.
- Why? It absolutly not looks like tree!
- Yes, but it’s our grammar. Okay, what you see on another picture?
- It’s a dog!
- No, it’s a plane!
- WTF? Again? I see only dog there!
- Yeah, need write “dog”, but prononce “plane”.
Last year we all have pandemia of coronavirus. We all know what it means and many times was see in news. But, word “virus” only in English prononce “vaires”. WTF? It’s Latin word, not English! In Latin it prononce exactly like it writing. All other langwidges it prononce exactly like it writing. But in “international” it unexpekted prononce same as “wires”. Coronawires, right. If you want to nativ English speeker finally prononce it right, you should write “veeroos”, but it became unanderstandable for ozer nations.
Coronawires is so scary!
Coronawires is so scary!
Same about all ozer international words. Why need say strange “plas” and “maines” instead correct “ploos” and “meenoos”? Even geographical toponyms not better. If you want NES (Nativ English Speeker) finally prononce word “Asia” right, you need write it in very pervert form, like “Ahseeyah”. Otherwise you will always hear that ugly “Eyzha”.
Fonetical map of world
Fonetical map of world
To make communication easier, need add to English something like Pinyin in Chinese or Romaji in Japonese:
If English have \"Pinyin\", it will be very helpfull
If English have "Pinyin", it will be very helpfull
Zen evribadi will si hau tu pronons words end evoid konfyuzing.
Despite in English using Latin alfabet, like in all European countries, island citizens want to show off and call Latin letters not same as all ozer world. When speaker of continental European language hear English name of A, he automatically think is 2 letters (ei, ey, ej, …), but both of them anyway not A. Same about C (si) – 2 letter, and both not C. And why C is “si”? If T is “ti”, P is “pi”, S should be “si”. When you chatting wiz foreighners, some of zem write “I.C.” instead “I see”, and think you will understand it. But only in 1 language this 2 letters prononce as “ai si”. Same about “C.U.” (si yu).
Pizza for \"Ee Tze Winner\"
Letter K only in English called “key” instead “ka” in all other languages. But when you hear “key”, you think first about small metall thing to unlock the door, not about letter.
But the one of most confusing letters is E. When you somewhere aboard in aeroport hear announcement in English, you hear you need go to gate “I”. You trying to find this gate, but it is impossible – last gate is F. You look on your ticket, there writed “Gate E”. You hear announcement on another language, and they say “Pintu E” or “Puerta E”. Exaktly E, not I! What the edeot confused E and I in “enternateonal languagi”? E es I, whin you spiak Inglesh, you niid always rimimbir et.
In song “Old McDonalds had a pharm” all kids always hear “I-A-I-A-Yo”. But when they find lyrics, they can’t believe, there writed “E-I-E-I-O”.
International language have so international alfabet!
International language have so international alfabet!
So funny, but if in “international language” change Latin alfabet to something else, like Katakana or Hangul, it became very helpful for everybody, bekoz writing and speaking will finally same. In some countries it already happend: ストライク=Strike, 킥보드=Kickboard, Мениджър=Manager, Πάρκινγκ=Parking.
Draw the line from country name what you hear to it's flag
English is one of the world’s hard-to-hear languages. Harder to hear than English is only Asian tonal languages: Chinish, Vietnamian, Thailandese. Even if you know right prononsieyshen, you anyway not will sure what exaktly you hear. Alright, I understand if confusing T and D. Alright, I understand if in some languages confusing L and R. But how possible to confuse T and R? This both is absolutly unsimilar sounds! But you always hear R instead T. Especially in songs. “Little bit” sounds like “lirabiri”, “everybody” sounds like “evribari”, instead “butter” they always saying “bara”, “sharap” instead “shut up”. And song “Bring me her letter” sounds like “Bring me cholera”. Luciano Pavarotti sing in English much better than all of NES.
A lirabiri Monika in my life
Another unanderstandable thing – many foreighners very like to say A instead O. So, words like: song, box, stop, hot, top, dock… you always hear like: sAng, bAx, stAp, hAt, tAp, dAck… Why? When I learn Englisch in school, I remember O need prononce like A only in words “love” and “monkey”. But in other words O need reed exaktly like O. Olrait, maybe it’s difference between Britisch and Amerikan dialects. Anyway, if O need prononce like A, what need to prononce like O? Ah, right, A need prononce like O: all, always, ball, fall. And how to understand what you hear then? Fox, Fax, or F%cks? Socks, Sax, or S*cks? Box, Bucks, or Bugs? Hot, Hut, or Hatt? Dock, Duck, or Dark? Walk, Work, or Wok? Warm or Worm? Warship or Worship? All same! Even “God of War” sounds like “gAd of wOr”.
- Worm climat of Arrakis is good environment for arrakian sandwarms.
- Nice game “World of Worships”!
- I need go to church to make some warship there.
Only in English word “genes” prononce absolutly same as “jeans”… but, very few foreighners know it. Sometimes it provides funny situations.
Only English nativ speakers can understand this dialogue
One Amerikan blogger always prononce Nikon as “Naikon”. Olrait, if “Nikon” need read like this, how to read “Nippon Kogaku”? Naipon Koudzheykyu? And no matter how it sounds in original language.
And somebody know how to read Tommy’s surname? Hilfiger? Hilfaiger? Hilfaidzher? Hilfidzher? Ilfizher? Ilfihher? In every country it sounds different, but everywhere peoples think they prononce it right.
Macro and Micro sounds very similar, but they’s meaning is opposite. How to understand, big or small. If read second word as “meekro”, it will make more difference.
Dinning room means “room for dinner”, right? Why need read this 2 words different, if they have same root? “Sign” need read as [sain], but “signatura” is not [sain]atura. Mommy is [mami]… Mummy unexpected is [mami] too – very funny for kids. Number “30” sounds like [töti], [söti], [föti] or [foti]. Number “40” sounds like [föti] or [foti] too – when you ask price, you can pay 1/3 more if you understand wrong. “Eyes” sounds same as “Ice”. And what exaktly letter sounds [a] in “eye”?
- So hot today, pliz add some eyes to my cocktail.
- I see expression in your ice.
I guessed before that English grammar consisted by somebody drunk. The more I know this language, the more I sure about it. Now I absolutly sure, grammatical scientists at that day go to work directly from pub, after allnight celebrating winning of Manchester United. No wonder if they can’t see text clearly in their condition: letters always jumping, bending, stretching, dancing, running, moving back and forward, appearing and disappearing, talking and singing… So no wonder if we have what we have. Somebody drunk forget about second G in word “gigant”. But 1 letter is not biggest problem. Prononciation changed to unrecognizable “dzhayent”. Same drunk man confused letters in word “capitan”, so now we have strange “captain”.
https://preview.redd.it/n1d0k20ky9a81.png?width=1280&format=png&auto=webp&s=1d68f42dea17ddd7e719d399622f7961f54f20c6
Maybe French have complicated reading rules too, but in French all rules always same: “au” and “eau” always need read like O, “ch” always read like “sh” in English or “sch” in Deutsch. ALWAYS!!! U always prononce like Ü, but “ou” like normal U, etc. So, you only need to learn that rules once, and you can easy read after it. In “interneyshenal” this way absolutly impossible. E.g.: word “tomorrow” prononced as [tumarou]. First O is U, sekond O is A, and the last O is finally O. And this all inside only 1 word!
But same as Frenches very like to add in end of words unprononsable consonants, Englishes very like to add unprononsable “gh”. Reallygh, froghm everygh Eghnglish worghd caghn deleghte 2-3 letteghrs, aghnd proghnonciation anyghway willgh noght changedgh. Word “daughter” possible to write just like “doter”, 5 letters instead 9. But NES will always correct you, or became stupid and can’t understand what you mean.
And this is the biggest problem – “international language” not help you to understand ozer peoples and to be understandable. It always confuse you and confuse ozers. It always provide strange or funny situations… and sometimes not funny. Bat pipls eniwey traying tu lörn it end tu yuz evriwer, even if zey ken’t anderstend eni foreyners. Hope in nearest future we all will have more logical and understandable language.
Gud ivning tu evribadi. Senkyu for ridding.

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https://preview.redd.it/z6yf2p2per081.png?width=832&format=png&auto=webp&s=536200d65117d50a9b0f83d6964d5b0111441149
Rengar, Evelynn, Nocturne, Qiyana, Pyke, Rek Sai, Katarina case :
These champions areassassins. Even riot buffed their mythics and added new item seems like assassins actually struggle now. I think objective bounties might have a huge impact on this, because games are a lot longer by now and these champions are usually stompers so when they are 3/0 they usually had free game in s11 now, but now if you are 3/0 it actually means you are losing. (You will get soon outscaled and enemy gets obj bounties.) Also the new defensive mage item seems actually really effective against assassins.
Ivern case :
Seems interesting maybe his shields are just too low? I am not sure what happend to ivern.
Illaoi case :
I think she was even bad in s11 she easily gets kited down, she lacks some good mobility. Riot still buffed her MS but that wasnt enough. Also this meta isnt for her seems like
Lillia case :
Almost every mage item got somehow changed or buffed, this isnt Lillia case shes going Liandry and Zhonyas seems like she might lack Queen Of the Shatted aswell like other mages, but if she is not going for Liandry then she does negative damage. Also in my oponion its really bad meta for her.
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https://preview.redd.it/hswv1swqgr081.png?width=821&format=png&auto=webp&s=65ac0317d425f2bcde13aec1411cf3c7131482cd
Jayce, Viktor case :
First strike seems really good for both. Also jayce fits in this meta a lot.
Dr. Mundo case :
Frostfire Gauntlet change seems really effective on Mundo.
Master Yi, Vayne, Veigar case :
These champs are truly scailers, so that the fact games are longer now these champs are good now.
Jax, Tryndamere case :
Lethal tempo being really good on these champs.
Lulu case :
Her winrate might be inflated because her Pix bug, its fixed now.
Lissandra case :
She is really good teamfighter so the objective bounties are really good atleast for her...and meta fits her i think.
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STATS ARE FROM GOLD+ ALL SERVERS, ALSO SORRY FOR MISTAKES ENGLISH ISNT MY FIRST LANGUAGE. HOPE ITS ATLEAST A BIT UNDERSTABLE

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.
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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.
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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

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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:
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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.
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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.
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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.
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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.
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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:
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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.
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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.
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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.
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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.
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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.
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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 :)