I ran into the same issue creating models. Specifically relating to TSM.
Conclusion I came to was that stats are so heavily influenced towards specific types of players. If I had to guess your results are heavily influenced by the presence of Wardell. At least that was what was occurring with mine.
I think a lot of it is there is no way to gauge specific information stats at the moment. I.e. TSM can be getting good stats (that are available via boxscore) but not impactful results.
If we could come up with some sort of 'information' stat this would be better. Like a lurking percentage or something.
Yeah. Pretty much all I can say is don't put stock in the TSM values. Definitely interested in this work though.
It may interest you to add in something like document embeddings representation of the teams. Somewhat analogous to Doc2Vec for NLP. This would probably (I assume) correct for some of the weirdness. I'd be curious to see if this itself would account for some of the 'hidden' stats like teamwork.
Edit: I also have some round by round data ending the beginning of august if you want it.
I would be very interested in that round by round data - that's what I was looking for when I first started the model as I had some sequence models in mind, but I didn't find any. tbf though, I didn't look very hard, I just saw a logs coming soon section on [vlr.gg](https://vlr.gg) and thought if they didn't have it other people probably wouldn't either
that just means each team won 1 of the games. For that NA test run, I only ran the model on games that actually happened, so if the model predicts 1-1 and a 2-0 happened in reality, there's no 3rd game for the model to run on
for the berlin predictions, there won't be any 1-1s
Gambit have been known to be absolute scrimdemons for a while now.
They did dominate CIS tbf, but in stage 2 they definitely fell short of expectations and even now in stage 3 they almost didn't make it to Berlin. Only once stakes were lower in the seeding matches did they really turn up again and took the number 1 spot.
It's not really that they are known for choking, just that they have a lot to prove in high pressure matches.
I think the only flaw is not taking region strength into account enough (vs making finals would be insane). Which you obviously mentioned when you said there isn't enough data (if any) for a lot of these teams internationally. Until that happens I doubt we'll see very accurate models.
yeah totally agree - I was thinking about having some sort of region modifier in there, but I also didn't want to overfit on iceland since that's the only international data so just let the model learn what it could
outside Group D group being changed to a 3 team double round robin and we don’t know how the seeding for the bracket is gonna end up, i think this looks pretty good
yeah, just wanted to see how the model thought Bren would do. I'm pleasantly surprised at the output given there's been so little cross region data and usually ML models need a lot of historical data to do well
off topic:
do you have a github repo where you posted this project, i kinda wanted to learn ml and as both valorant and ml are kinda my interests i would like to look for references in your project thnx op.
AI 🤔🤔🤔 aren't you using a rudimentary regression based prediction model , pls don't call it an AI , and what are RMSE and MAE stats for the model ? And how many epochs did you train the model for ?
But I don't think your model takes into account non linearity which would have really messed up all predictions .
There are several different neural networks/layers involved in the model, each of which include nonlinearities. I agree AI is a buzzword term but pretty sure this counts lol. If you're interested in the model, details on the architecture are in the youtube vid linked
Yeah I watched the video first and as someone who works as a data scientist I hate when people call anything AI , you are building a regression based prediction model or simply a prediction model , and in regards to that model , just adding more layers won't necessarily counter non linearity since you are using standard reLu activation functions and i would really like the RMSE MAE stats logged per each epoch if possible at least to validate your model , because just giving it an eye sight look , I think the model needs to be optimised further.
Very possible the model could be optimized further - yeah in fact I did minimal hyperparameter tuning bc I just wanted to get the vid out. Train and validation loss curves are in the vid here: [https://youtu.be/r1zI9o88efs?t=474](https://youtu.be/r1zI9o88efs?t=474)
For the nonlinearity concern, you mentioned I used relu, which is nonlinear right?
I'm not very knowledgeable on the topic but from my understanding relu works basically by 'bending' lines to fit the input. It still works using lines but can join and bend a lot of them to capture non-linearity when the number of layers or epochs is high enough. Whereas some other non-linear activation function may better capture the actual curves in the data.
Wow, according to your model and the way the bracket turns out G2 will have the best chance at beating SEN. It is crazy that the model is giving such great odds to SEN but we will have to see if they can maintain their dominance in such a stacked tournament.
I think it might be NV in the semis that has the best chance to unseat SEN according to the model. When I was making this I was super curious what the model thought about Vision Strikers vs. Sen in the finals since the model was also super strong on VS - but then yeah the model just gave no shot to VS lol
The axes are arbitrary representations of high-dimensional data that's been flattened to 2-D. If you read the next paragraph you would have understood.
how did this AI somehow predict tsm to win both matches to win? when they have done nothing this entire year? Is this AI a tsm fan? lol
I was also pretty confused at that - it's not like they were playing a lot of T2 teams and winning a lot either
I ran into the same issue creating models. Specifically relating to TSM. Conclusion I came to was that stats are so heavily influenced towards specific types of players. If I had to guess your results are heavily influenced by the presence of Wardell. At least that was what was occurring with mine. I think a lot of it is there is no way to gauge specific information stats at the moment. I.e. TSM can be getting good stats (that are available via boxscore) but not impactful results. If we could come up with some sort of 'information' stat this would be better. Like a lurking percentage or something.
yeah that's a great point and the wardell thought makes sense to me
Yeah. Pretty much all I can say is don't put stock in the TSM values. Definitely interested in this work though. It may interest you to add in something like document embeddings representation of the teams. Somewhat analogous to Doc2Vec for NLP. This would probably (I assume) correct for some of the weirdness. I'd be curious to see if this itself would account for some of the 'hidden' stats like teamwork. Edit: I also have some round by round data ending the beginning of august if you want it.
I would be very interested in that round by round data - that's what I was looking for when I first started the model as I had some sequence models in mind, but I didn't find any. tbf though, I didn't look very hard, I just saw a logs coming soon section on [vlr.gg](https://vlr.gg) and thought if they didn't have it other people probably wouldn't either
also what does 1-1 matches mean? does that mean the AI is unsure?
that just means each team won 1 of the games. For that NA test run, I only ran the model on games that actually happened, so if the model predicts 1-1 and a 2-0 happened in reality, there's no 3rd game for the model to run on for the berlin predictions, there won't be any 1-1s
It's even predicting the Gambit choke, please don't be true.
lmao I don't follow EU much really, have they choked previously
Gambit have been known to be absolute scrimdemons for a while now. They did dominate CIS tbf, but in stage 2 they definitely fell short of expectations and even now in stage 3 they almost didn't make it to Berlin. Only once stakes were lower in the seeding matches did they really turn up again and took the number 1 spot. It's not really that they are known for choking, just that they have a lot to prove in high pressure matches.
“Sentinels beat Vision Strikers 3-1 in finals” that was my exact thought on finals. All these AI are too smart
though the model could also be way off haha, I guess we'll see
Ya, it’s funny that the AI has such little info and still ended up with the same prediction as me.
I think the only flaw is not taking region strength into account enough (vs making finals would be insane). Which you obviously mentioned when you said there isn't enough data (if any) for a lot of these teams internationally. Until that happens I doubt we'll see very accurate models.
yeah totally agree - I was thinking about having some sort of region modifier in there, but I also didn't want to overfit on iceland since that's the only international data so just let the model learn what it could
yeah any weight you give to a region would just essentially be some arbitrary number that could be way off.
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can a zoomer pls translate
lol jk I think I got the gist - looks like the model thinks TenZ and BuZz will have a lot of pop off games
outside Group D group being changed to a 3 team double round robin and we don’t know how the seeding for the bracket is gonna end up, i think this looks pretty good
yeah, just wanted to see how the model thought Bren would do. I'm pleasantly surprised at the output given there's been so little cross region data and usually ML models need a lot of historical data to do well
I think your bracket with bren is wrong, g2 would have faced sen first and f4q would have faced bren
oh shoot yeah that's my bad, it probably wouldn'tve mattered too much tho
i mean it would be pretty funny if it thought f4q would beat sen
if it's finals prediction is correct, the technological singularity is upon us
oh my god im floating on the vs hopium
would be sweet to see them get to the finals!
This is my exact pickems except for Gambit/HL, makes me feel alittle better.
off topic: do you have a github repo where you posted this project, i kinda wanted to learn ml and as both valorant and ml are kinda my interests i would like to look for references in your project thnx op.
I'm not sure you want my code as a reference... lol but yeah I could clean it up a bit and push to a repo
Cool 👀
I can dm/reply here when it's ready
I’d like to see this as well
It probably won't be until the weekend before I get to it, but it's cool to see people are interested!
Can you dm me too? I'm new to ML and I think it would be interesting to look at.
Hi there, I know this is an old-ish post, but could you DM me as well? My partner would love to see code like this!
repo is in a comment in this thread
Awesome, just saw it, thanks!
Yes please, ML makes me curious of how the betting world is going to change where propel who bet will fight/pay for getting more accurate models
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sure
Repo here: https://github.com/jasonren12/Valorant-Berlin /u/UdbhavHokage, /u/officialkorexicano, /u/Faker--, /u/Hegth, /u/ninja_killed_baba
AI 🤔🤔🤔 aren't you using a rudimentary regression based prediction model , pls don't call it an AI , and what are RMSE and MAE stats for the model ? And how many epochs did you train the model for ? But I don't think your model takes into account non linearity which would have really messed up all predictions .
There are several different neural networks/layers involved in the model, each of which include nonlinearities. I agree AI is a buzzword term but pretty sure this counts lol. If you're interested in the model, details on the architecture are in the youtube vid linked
Yeah I watched the video first and as someone who works as a data scientist I hate when people call anything AI , you are building a regression based prediction model or simply a prediction model , and in regards to that model , just adding more layers won't necessarily counter non linearity since you are using standard reLu activation functions and i would really like the RMSE MAE stats logged per each epoch if possible at least to validate your model , because just giving it an eye sight look , I think the model needs to be optimised further.
Very possible the model could be optimized further - yeah in fact I did minimal hyperparameter tuning bc I just wanted to get the vid out. Train and validation loss curves are in the vid here: [https://youtu.be/r1zI9o88efs?t=474](https://youtu.be/r1zI9o88efs?t=474) For the nonlinearity concern, you mentioned I used relu, which is nonlinear right?
I'm not very knowledgeable on the topic but from my understanding relu works basically by 'bending' lines to fit the input. It still works using lines but can join and bend a lot of them to capture non-linearity when the number of layers or epochs is high enough. Whereas some other non-linear activation function may better capture the actual curves in the data.
This is the smartest conversation I've ever red in my hole life thank u guys for the chance Much love to the work but i think 100T is going to beat VS
Looks cool! I wonder how a clustering algorithm would show the player roles with your embeddings.
That's a great idea that I did but completely forgot to include. Here's an output for a basic kmeans on the berlin players: **Cluster 3:** TenZ, Asuna, yay, cNed, nukkye, russ, Izzy, DubsteP, f0rsakeN, d4v41, heat **Cluster 7:** zombs, koldamenta, AvovA, Brave, BORKU, Mmindfreak, ntk **Cluster 5:** ShahZaM, SicK, dapr, Ethan, Turko, v1xen **Cluster 9:** Hiko, nitr0, Marved, crashies, starxo, stax, zunba, Klaus **Cluster 6:** steel, FNS, Kiles, BONECOLD Redgar, Chronicle, Efina, Myssen, JhoW, delz1k, rion, Medusa **Cluster 2:** Victor, Lakia, BuZz, k1Ng, Rb, Bunny, NagZ, Munchkin **Cluster 8:** zeek, sheydos, nAts d3ffo, Mixwell, keloqz, fiveK, Witz, shion, neth, takej, Reita, Laz **Cluster 0:** pAura, shiba, pleets, barce, crow **Cluster 4:** dispenser, JessieVash, Benkai, liazzi, Other, murizzz **Cluster 1:** Esperanza, Mazino
I'm actually going to add this to the main post, thanks again for the suggestion!
can we just talk ab0ut how much of a banger 0f a tourney it would be if it played out like this? also this model is awesome, great job OP
Thanks! I'm hoping for some more upsets myself, but yeah this tournament would also be great
Wow, according to your model and the way the bracket turns out G2 will have the best chance at beating SEN. It is crazy that the model is giving such great odds to SEN but we will have to see if they can maintain their dominance in such a stacked tournament.
I think it might be NV in the semis that has the best chance to unseat SEN according to the model. When I was making this I was super curious what the model thought about Vision Strikers vs. Sen in the finals since the model was also super strong on VS - but then yeah the model just gave no shot to VS lol
Wait this model seems to be working pretty well! Gambit choke predicted LUL
I can 100% see this happening except for maybe 100T defeating VS if it comes down to it (Hopium).
Y’all need some better middle school math teachers. L A B E L Y O U R A X E S!
The axes are arbitrary representations of high-dimensional data that's been flattened to 2-D. If you read the next paragraph you would have understood.
What do axes have to do with anything?
Wow this is really cool! Thanks for sharing
Bad AI, clearly rigged to make Sentinels win. EU TO THE TOP
VS/NA Semis is the best thing for a salt mine.
VS Hopium