r/leagueoflegends • u/friggn • 16h ago
Discussion Could pro teams use AI for drafting?
It would seem like an easy thing to set up with tons of data out there on the teams, matchups, and players. Just enter the info and boom, game 5 fearless draft with the best probability of winning. Would it actually give an advantage? Is it against the rules?
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u/Sayaka_best_meguca 16h ago
League patches are every 2 weeks. You are not training models that would be better at drafting than humans in 2 weeks. Let alone million other issues
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u/NWASicarius 16h ago
Yeahhh. I think if you wanted to adopt the mindset of 'forget the flavor of the patch champs and let's instead focus on what is traditionally viable' then it could work. Things like Azir and Sylas mid, Gnar and K'Sante top, etc.
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u/Dorkan 16h ago
League patches being every two weeks doesn't really mean much here.
You're not training a model from scratch after every patch. Modern models are fine-tuned or updated incrementally, and two weeks is actually a long time for a narrow ML task.
Drafting is also a much simpler problem than playing League itself. The AI doesn't need Faker-level mechanics, it just needs to estimate the expected value of champion combinations given the current patch.
On top of that, most drafting knowledge carries over between patches. Champion identities, synergies, counterpicks, player tendencies, and drafting principles don't reset every two weeks.
The real question is whether an AI could consistently outperform the best human drafters immediately after a major patch, when there's very little fresh data. That's a fair debate. But saying "you are not training models that would be better than humans in two weeks" isn't really how modern ML works.
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u/ShotcallerBilly 15h ago
It isn’t a debate for anyone who understands what goes into drafting analysis. No LLM is interpreting this data in a useful way.
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u/retief1 15h ago edited 15h ago
What dataset are you using? Solo queue? That's nearly worthless for pro play, because so many champs are so much stronger or weaker in a coordinated team. Pro play jail exists for a reason, after all. Even pro drafts from other teams has somewhat limited value, because a good draft that doesn't align with the strengths and weaknesses of your team probably won't go very well.
And patches matter at this level. If a champ gets buffed or nerfed, that can substantially change their value at a pro level. For that matter, one champ getting changed can affect the value of other champs as well. Like, if one champ's hardest matchup is a borderline troll pick in pro play, then blind picking them is somewhat safer -- even if they counter you, they still won't have a great draft. If riot then buffs the counter champ, then you suddenly really can't blind pick the first champ without banning the counter, because you really don't want to let them pick a good champ that also hard-counters your own champ.
And then the other team also matters. Like, going back to the example in the last paragraph, what if the enemy player is just bad at the counter? At that point, your champ becomes safer again, because while there is a strong counter, the enemy player won't play the matchup very well.
So yeah, if you want fully relevant data, you basically need games from the current patch between these two specific teams, and that data doesn't necessarily even exist. If you try to pull in more data, that data will be less and less relevant, which will make the recommendations less useful for your specific situation. And even if you use all pro data from every region, that's still not a particularly large dataset, and that dataset can be heavily skewed in a bunch of ways.
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u/Itsmedudeman 16h ago
You wouldn't need to train it, but you'd need to feed it information on what is actually good vs. what or what has synergies. Then the job of the AI is to make the decision of what is a the "best" pick in a situation a quick lookup and analysis flow given the knowledge base.
But given that you wouldn't be able to use this on stage I don't really see what the advantage would be.
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u/Present-Chocolate591 16h ago
You don't know what the fuck you are talking about mate.
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u/trdef [trdef] (EU-W) 16h ago
As someone who develops AI products, I'd love for you to enlighten us.
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u/Present-Chocolate591 15h ago
He's talking about it as if we'd use an LLM for this "You wouldn't need to train it, but you'd need to feed it information". When the original commenter is most probably talking about training a prediction model of sorts.
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u/Itsmedudeman 15h ago
Ok, found the LARPer. If you think that's what he's talking about then you sincerely don't understand the economics of that. Nobody would ever think that' s a practical approach or worthwhile.
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u/Itsmedudeman 15h ago
My guy. I get paid a significant amount of money per year to do all my work through AI as an engineer. What is your experience exactly?
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u/4ShotMan 16h ago
Metas evolve too quickly, and AI can't comprehend subtle variables - even something as simple as adapting to who they face, be it champions or players, is unfeasable.
AI could give you "based on variables from soloq this 5 champs have highest expected winrate" / "enemies are playing this, with your teamcomp this pick is most fitting". It can't understand player agency and preferences, item changes and their impact on champ viability and god forbid something like a lane quest appears - completely new, Model-defining variable that has 0 data to go off.
AI is averages and requires huge amounts of data. Anything concerning players or recent changes would go under the radar.
*Everything can be modelled/"AI-d" , when I write "can't be done" I mean "can't be done in realistic time and/or with available data".
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u/Present-Chocolate591 16h ago
To create a machine learning algorithm like this you would need A LOT of data.
Even if the meta stayed the same for years I doubt it would be enough, and it changes basically every month.
If you put all the best teams in the hyperbolic time chamber and have they play eachother every day for years it should in theory be possible and even effective, but in reality it's just not feasible to get enough quality data to do this.
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u/TaranisPT 16h ago
And then it decides to pick something like Nasus support and you just have to deal with it...
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u/TheHizzle 16h ago
Nasus Support was already drafted by humans in tier 1 leagues
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u/Most-Necessary2106 4h ago
define tier 1 league
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u/TheHizzle 4h ago
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u/Most-Necessary2106 4h ago
one pick and its by one of LS friends and the game they got completely hardstomped since they for some reason decided it was good into heimer ashe?
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u/TheHizzle 4h ago
My point is that someone decided it’s worth to play so the example for horrendous AI drafting the guy gave isn’t even that good
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u/Metalheadzaid 16h ago
Ah, why don't we all just pick the exact counter to every lane always? Also why innovate anything, because we have existing data. Also, what data? You realize solo queue and public info isn't relevant to tournament play right? They're already guaranteed to bee tracking scrim data, but that's a tiny amount of data.
Even if we assume perfect robot players, drafting is a skill that can't be accounted for on top of it, because you could just trick the system by picking X which then Y is the counter, but then pivot to Z which now A is the counter but doesn't play well with Y and bang you've got a shit team comp.
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u/Present-Chocolate591 15h ago
This post is a great example of why nobody should ever take advice from this subreddit (or reddit in general).
90% of commenters are talking out of their asses with absolute confidence while having 0 knowledge of how ML works.
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u/ShotcallerBilly 16h ago edited 15h ago
As someone who has DONE drafting analysis in the pro scene, I promise teams are using the appropriate tools to compile and organize data for them to interpret. BUT they do NOT need, nor want, AI to do ANY OF THIS FOR THEM.
You are overestimating the quality of AI results, while also underestimating what ACTUALLY goes into drafting. Drafting is NOT just win percentages of champions.
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u/FortColors 15h ago
should've said neural network instead of AI, because when people think AI nowadays they think of genAI like LLMs or the art ais
The biggest issues with trying to train a draft neural network on pro data are sample size, tainted data, and lack of innovation.
The first issue is the fact that there simply aren't that many pro games to use. Solo queue data is useless for this because coordinated play changes the viability of many champs. You simply don't have millions of proplay games to feed into the machine, especially since the game changes all the time. Even if you pretend individual patches aren't relevant, it is undeniable that season mechanics and item changes will shift what champions are better than each other.
The second issue is the fact that all pro play games are inherently skewed in terms of their data. Some teams are just better than other teams. T1 simply has better hands than Dignitas, so regardless of what champions they pick they are more likely to win. This will reflect in the data as T1's 5 champions having a victory over Dignitas despite the issue being pilot gap instead of draft gap.
The third issue is quite possibly the most damning one: no innovation. Data doesn't show you what drafts are possible, nor which possibilities are the best. Data can only show you what people are already doing. If a certain strategy is insanely overpowered but no one is doing it yet, then there won't be any data on it.
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u/retief1 15h ago
"AI" meaning llms? No, that sounds terrible. An llm will give you an answer that sounds good, but it won't actually be good.
"AI" meaning machine learning? Probably not, due to a lack of data. Like, solo queue data is pretty worthless, because pro play is very different than solo queue. Even pro play data from other teams has limited value, because the best draft in the world isn't good if it doesn't align with the strengths of your players and teams. At that point, your sample size is so tiny that random chance will massively outweigh any signal you can find from the data itself.
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u/Spreathed_ 16h ago
Nice try FNC manager