PhreakRiot

Originally posted by Samwise_Gardner

Maybe not the ideal time or place, but just wanted to say been silently watching your videos for a long time and really appreciate all of the content you put out. Is some of the highest quality content within the scene that actually talks about the game itself. Long form analytical content in which one explains their thoughts/opinions does not seem to perform well compared to other forms of content, but me and my friend group are incredibly thankful someone puts the time into making the sort of content you do so thanks!

Originally posted by maronics

This logic only works in practice mode looking at the damage numbers on the dummy and disregards any "real" world factors of the movement speed or the life steal you mention.

What you're comparing here is two cars on a straight line, ofc the one with more raw power will come out ahead (ignoring downforce/acceleration/grip/whatever for simplicity) while on an actual track with bends n shit the result will be something completely incomparable.

Originally posted by putsandstock

344 is actually not really enough for effects of this size. A 95% confidence interval for the win rate (assuming the “true value” is reasonably close to 50%) is about 2/sqrt(n) wide (using some basic repeated Bernoulli trials as the underlying model), over 10% for this sample size, which in isolation is terrible. People in general tend to underestimate variance and overestimate how good a sample size is, so it’s important to at least come up with some sort of statistical basis behind claims that a sample seems “large enough.”

You can definitely argue that in this case we have other (much more statistically significant!) supporting data, such as extrapolation from win rates and trends in other Elos, which will strongly affect our priors on the topic in question (so perhaps a 95% interval is overkill, and we’d be satisfied with a much weaker level). But taken by itself, a sample size of 344 is not nearly enough to measure effects whose size is <5%.

Originally posted by Insanity_Incarnate

Can confirm, I'm not good at League of Legends.

Originally posted by Fedacking

Quick question thought: other junglers (in particular Rek Sai for this case) provide better winrate in masters+ and have better azir curves. Shouldn't they be played in jungle and lee sin not be picked?

Originally posted by MrCrazyVenom

Is there a reason u focused on mid lee sin instead of top lee sin?

So far at MSI by my calculations there have been 12 lee sin picks with most (7/12) going top lane. Now I'm sold that jungle lee can be better than mid lee. However why aren't you focusing on comparing top lee sin to jungle lee sin?

Originally posted by [deleted]

[deleted]

Originally posted by AfrikanCorpse

So, every single non-subjective form of proof is on my side. And on the other is... The opinion of someone who didn't make playoffs.

Really?

If anyone can be right/wrong (according to your video), regardless of their level of play, why are you discrediting someone's opinion because they didn't make playoffs?

Incredibly hypocritical.

E: props for the edit

Originally posted by HuntedWolf

Slightly off topic, which website do you use for your data Phreak?

Originally posted by bobbybobsen

/u/PhreakRiot Good video, and good to see you explain yourself in a better format than twitter. However you missed the mark on Lillia: Lillia did in fact get a nerf - in patch 11.6 her ult CD was increased by 20 seconds at all levels, and this nerf lines up with her decline in pro play.

Originally posted by RiceOnAStick

That point was just an aside/additional note as to why the data might not be relevant. Rumble as a champion is disproportionately strong early (especially midlane) because of his ability to fight anyone pre-6 and tilt 2v2s in his favor. Because Korean soloq has a tendency to FF, early game champions are more advantaged there. You can frequently see this difference in stats sites; op.gg almost always rates champs very differently than how lolalytics/u.gg tier them with their global statistics.

He's not excluding Korea's winrates from the calculation; his initial argument was based around global winrates which obviously include Korea. He's talking about why using Korean soloq as the statistical model alone could be problematic.

At least from my understanding.

Originally posted by Cahecher

My point wasn't to select the data that suits me, but rather to point out that there are obvious regional differences, that are core to the conversation at hand.

Another thing, the data we are looking at is very different. For example, if we look at the data filtered by korean server from u.gg (my filters: vs all champions, soloq, 11.9, KR):

11.9.

• P+. Jungle w/r: 48,86, games: 47k. Midlane w/r 51.55%, games: 15.5k. W/r diff 2.69

• D+ Jungle w/r: 49.99, games: 11.2k. Midlane w/r 51.8%, games: 3.2k. W/r diff 1.81

• D2+ Jungle w/r: 51.05, games: 5.6k. Midlane w/r 52.58%, games: 1.3k. W/r diff 1.53

• M+ Jungle w/r: 51.07, games: 2.5k. Midlane w/r 52.03%, games: 0.5k. W/r diff 0.96 (though it is an anomaly, the number of games is too low)

I didn't use 11.10 in the reply even though the numbers there strongly support my point, but the amount of games is insignificant since the patch is too...

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Originally posted by Rogue009

Sorry but how is 49 games a large enough sample size? For all we know half the losses on those could have been due to autofilled people.

Originally posted by Jinxzy

I have no doubt that Rumble junglers are relatively inexperienced. But unless I see an Azir curve, I'm not buying it.

Hey Phreak, on somewhat the same topic:

In your Lee Sin example, you show Jungle/Mid winrate differential favoring jungle the higher the Elo. Could one not make the argument that this is because Lee does have an "Azir curve", in both mid & jungle, but it's significantly more prominent in jungle because junglers (especially high level) has years of experience on this champ, where all the solo laners are only just picking it up?

Originally posted by Least_Candle5223

Except what statistics you choose to look at, and how you gather statistics is a system of bias. My sisters PHD is literally about how Big Data skews people’s perspective because they perceive statistics as objective truth. I feel like he’s doing something similar here where he makes stats seem like an objective truth.

Originally posted by tflo91

I’ll play monkey in the middle here

The problem with his initial take is that he selectively removed data from his statistics by removing all of RNG’s rumble games because “they were free wins”. Sorry? But they were still wins and he didn’t remove lesser teams that picked Rumble that also stood no chance of winning. That means any conclusions you draw from the sample will be flawed. The bottom line is that in group stages, the close games that can be used as champion data are few and far between. If he really wanted to get his point across, he could have simply used c9 vs DK as his case study, and suggest a better jungler Canyon could play in Rumbles place.

LS also likes to say how drafts should be assessed on champions being executed perfectly. While that sentiment can be appreciated and even applied in trading card games and even which draft is technically better ON PAPER, humans are inherently not perfect. League is much more open ended than any TCG and at one si...

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Originally posted by topdrogon

Absolutely, you’re 100% correct on this. It’s impossible to play test every situation.

But (and I might be wrong on this) it shouldn’t be hard to play test versus META champs and META styles that are common in pro. You just need to beat what’s common and what you’re opponent are trying to do - you aren’t trying to beat a 99.9999% optimally drafting and playing AI.

For example, you can just play test Rumble jungle versus Lucian or Tristana mid or Leona or Alistar support. Of course different champs have important differences and nuances, both both have similar flavors/playstyles.

Similarly, you can play test the rumble-Morgana and Rumble-Udyr jungle matchup in customs in isolation from laners and then in in-houses with laners. Again, I’m not a league expert, so I’ll leave it to the pro players and coaches who have experience in the various ways champs need to be tested as to the best way for doing this.

Most importantly, I genuinely think your ana...

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Originally posted by Puzzleheaded_Bowl386

The difference grows with player skill, and it's bad for Rumble jungle.

That is true when we are comparing Rumble Mid against Rumble jungle, but imo for the most part they should be considered on their own, because:

Sure, maybe Morgana (liked you suggested in the video) is the best jungler, maybe mid rumble is the absolutely best champion in the game. I think the part that people most take offense to/disagree with is that Rumble jungle is just bad.

And Rumble Mid has the "Azir curve", whereas Rumble Jungle looks to either have the bellcurve (if we want to interpret into the -0.5 between Plat and Master/include Bronze, Silver) or is pretty much completely flat (past gold).

Rumble jungle is underperforming very simlarily among multiple brackets of play. And that flat underperformance can again be attributed to players having less experience on the champion than would be ideal.

One final note, somewhat unrelated: I alw...

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