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Headshot stack do not fall off outside of combat. Caitlyn is thus constantly somewhere between 0-5 stack of headshot in any given teamfight. Thus, any team fight with only two auto attacks automatically supports +AD.
The math is equivalent. Neither AS nor AD favors Headshot damage output.
This would change if Headshots fell off outside of combat (e.g. Master Yi passive, Kraken Slayer) but they don't.
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
Yeah, you're right, it's a cheap shot.
Slightly off topic, which website do you use for your data Phreak?
lolalytics. Lets me do a lot more item and rune delving, plus has more back patches.
LAst season i went warmogs first after jg item. But that was when jg item increases hp by 20% and right now thornmail is just the best item in the game for tanks with all this excessive healing.
coo thx for the info mate
/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.
That's a good point and one I overlooked.
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.
That's more or less accurate. Though unless I mistyped above, Rumble jungle is actually more early-game skewed. In other words, Rumble jungle falls off faster and harder than Rumble mid. So any server where players FF early is biased toward making Rumble look good.
In general, yes, there are lots of factors that keep solo queue from looking just like pro play. Some of them are server behavior. But we can softly cover many of them by grabbing data globally and comparing the skill levels of players and seeing if that gives us any results. Azir is our poster-child here.
Read moreMy 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...
I appreciate the nuance added. I was using lolalytics.com for most of my info since it has a very convenient 30-day filter to grab lots of information any time you're looking at a more unpopular pick or more constrained brackets.
Sunfire --> Thornmail --> Abyssal Mask is the way to go 99% of the time ( And Plated steelcaps 99% of the time)
oh wild you don't get an early warmogs? I feel like I remember that being a thing KR junglers did awhile ago for Sej
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.
That's exactly my point.
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?
My hypothesis is somewhat contrary to that. I just looked up Lee Jungle and Lee mid as stand-alone picks and jungle has an Azir curve while mid has a very slight Garen curve. (it's pretty flat, all within 1%, but still trending down)
To be direct about the hypothesis, though: I expect Azir curves to be pronounced on any new champion (or new in role). Higher MMR players play more games thus are on average more practiced thus on average are less hampered by being mechanically bad at the champion. Admittedly, this is somewhat of a stretch since we're measuring several different things (hands, brain, coordination, etc.) through the same thing. But the end result is still ultimately the same from what I can see so far.
Why is there a team from my hometown in MSI lol.
This is some bogan sh*t. I love it.
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.
That's a really valid point. There are a ton of champions I looked up and prepped graphics for that didn't fit the script purely for length than anything else. Malphite and Sion both have Garen curves and very low pro win rates. That said, I don't put a lot of faith in +/-5% pro win rates and even if they're true, there are good explanations.
I'll say that Azir and Garen were the first champions I looked up and didn't know their data for certain before collection. I also didn't know what I expected from Lillia when I gathered it.
Read moreI’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...
The tweet wasn't meant to be a well-formed argument. Maybe I should have known better, but it's not like I expected the topic to blow up.
Read moreAbsolutely, 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...
Generally agree, yeah.
Read moreThe 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...
I'd propose it's due to his actual crowd control profile and lack of a real role on the team. He's there providing damage when teams already have champions who do that. FWIW I looked up Rumble jungle's highest winrate mid laners after hearing Pabu's interview about AD mids. The top of the list are all melee AD mids (Nocturne, Renekton, Sett, etc). In some cases, those champions just have very high win rates, so beware the biases, but it also feels accurate: Teams don't need substantial magic damage out of the jungle if they already have it in mid. They don't need a CC-less jungler because how exactly are you going to gank someone who already builds Mercury Treads for the lane matchup? It's the exact same reason people like Taliyah-Renekton and Elise-Renekton.
I'm seeing 51.45% winrate over 344 Challenger games on 11.10 from u.gg but maybe the site is wrong? 344 games probably a big enough sample size to read into the variation, but again I'm not sure. Also not sure how 51.45% that compares to the avg jungler.
I think the intuition has been presented elsewhere, and makes sense to me: people in Bronze-Master are not learning the champ well/fast enough, so the trend is they get punished harder as they get closer to master; then from Master-Chall the players are learning the champ quickly and getting rewarded for it in terms of winrate.
I think this intuition makes a lot of sense because even in pro play junglers have been getting criticized for not playing the champion optimally (heat management during clear, equalizer placement, etc.) so it makes sense that soloq players below Challenger are also having a rough time.
That's a pretty reasonable count of games. I was using lolalytics.
Regardless, I'd want to see more games when we're trying to nail down pretty small deviations. For reference, across 11.9 Rumble mid was 6.7% higher win rate across the patch for challenger (54.7, 48.0) according to u.gg. Right now, Rumble mid is sitting at 45.5 on that site, which is clearly not accurate.
That said, I'd expect some growth, but the changes seem too big. For reference, across all of 11.9 according to u.gg, challenger and GM Rumble jungle were within 0.7%. GM to master was under 0.1%. Master to diamond, 1%. That low-difference trend continues all the way down to Silver. In 11.10, that trend is the same except for the challenger-GM divide is 5%. Nothing makes me believe that Challenger players suddenly uniquely excel at Rumble after May 12. I believe the problem is more with sample size.
Read moreYeah I have no idea how to account for any of that. There’s no proper mathematical way of doing so. That’s why pro play shouldn’t be on the chart at all.
BUT that doesn’t mean that the chart itself is useless. If done correctly, I actually think your analysis has the beginnings of actually statistically correct analysis that can provide meaningful insights for the pro scene as well as soloq in general.
The best way of approaching this IMO would be to keep soloq analysis as soloq, and then draw conclusions from soloq as to new possible strategies and in what contexts they could be good for pro.
From the conclusions you draw from soloq, you can then form hypotheses as to what new strategy (champ/build/rune) might be OP. You then can test these hypotheses in customs in-house and in scrims.
If a strategy passes all these tests and then doesn’t perform well on stage, it doesn’t necessarily mean you were wrong (low srs he sample size). But it could mean...
It makes perfect sense and I agree with virtually everything you wrote.
At the end of the day, it'd be great if teams could test everything. Certainly, it's wise for them to at least test promising candidates. But ultimately they just can't. There just isn't enough time to hypothesis test everything in League of Legends. There are 5.9 * 1021 possible team compositions. Good luck getting enough data on each one.
So by necessity you have to cut corners. You have to go more general. There are billions of possible drafts after the first six bans and a first pick Rumble. No team in the world has accounted for all the possible mid/jungle matchups by the time they made that pick. So I think to some degree a rigorous testing regimen is just not possible anyway, so don't try to hold anything to that standard.
This isn't to say you shouldn't practice anything. Of course your should. But you realistically can't VoD review every game of every champion you pla...
Read moreAh my bad then
It really felt like an answer to their previous stream where they talked a lot about your tweet and your usage of soloQ data since it came right after
Yeah I feel that. It's a popular topic, so it makes sense. It's just a reply to the topic in general as opposed to any one piece of content.
Hey man! Think your content is great and the fact that you're interacting with people on reddit is awesome.
Some other people have mentioned it, but I think your grouping of Masters+ as one tier is misleading, because the skill difference between 0 LP Master and 1k LP Challenger is very significant.
Looking on u.gg, looks like Rumble jungle's Masters+ winrate is 47.71%. When you break it down between Masters, GM, and Chall, his winrate goes from 45.74% in Masters to 46.58% in GM to 51.45% in Chall. So his winrate isn't even dropping in higher skill brackets, the way you group the data just makes it seem like it is.
If you're looking at just 11.10 data, there are currently 49 recorded challenger Rumble jungle games. If you do 14 days, you see a pretty flat ~-3% win rate compared to the average jungler.
I'm interested in the sample being large enough that I'm confident in not having huge swings in win rate because of 1 result (keep in mind 55% win rate in 49 games is just winning 2 more than you lost and that's just unacceptable for this). Second, once I'm confident in low variance, I care about the trend. If a trend doesn't exist when travelling from bronze to diamond, why do you think one suddenly shows up when travelling from masters to challenger? It's possible for one to exist, but shouldn't be a leading theory.
Wouldn't you expect that as Jungle Rumble is played in higher and higher skill brackets that the people playing against Jungle Rumble would better know how to punish an off pick like that?
And pros don't know how to do that? Why would this relationship suddenly flip?
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