THE META REPORT NAME IS TOO LONG, TOO DAMN LONG (n°48)

Patch 3.3 - Week 1 - Tri (Beam) Again?

Valentino (Legna) Vazzoler
03-09-2022

Data

Number of Master Players: 665

Number of HighDiamond 1 Players: 1857

Number of (Ranked) Master matches analysed 27817 or 55634 games.

Number of (Ranked) ~HighDiamond matches analysed 92158 or 184316 games.

Last Update: 2022-03-09 18:40

Games

Patch 3.3 - Week 1 - by the Numbers1
Characteristic3 Master2 ~HighDiamond2
N = 41,9114 N = 27,8174 N = 122,6414 N = 92,1584
Status
Ranked 27,817 (66%) 92,158 (75%)
Other 9,850 (24%) 17,146 (14%)
PathOfChampion 3,286 (7.8%) 11,027 (9.0%)
Friendly 958 (2.3%) 2,310 (1.9%)
Server
americas 14,045 (34%) 9,645 (35%) 47,384 (39%) 34,465 (37%)
apac 15,309 (37%) 9,275 (33%) 41,013 (33%) 30,808 (33%)
europe 12,557 (30%) 8,897 (32%) 34,244 (28%) 26,885 (29%)
1 Max datetime recovered: 2022-03-09 17:04:00 UTC from 2022-03-02 18:00:00 to 2022-03-09 18:00:00 UTC
2 EU Master 216/216 NA Master 230/230 APAC Master 219/219
3 Metadata from Friendly Matches (that aren't Bo3) is not recoverable, the value may not be perfect since I lack the starting time of the game. The amount of Games to still scrap is also an estimation based on the 'position' of the game
4 n(%) took from the number of matches. When the data is analysed the size is double since we account each different player

Account

Shard/Server Americas, N = 9421 Apac, N = 8121 Europe, N = 7681
Player Rank
Master 230 (24%) 219 (27%) 216 (28%)
Diamond 712 (76%) 593 (73%) 552 (72%)
1 n (%)

Regions

Play Rate

Plot

The Gini Index is a measure of heterogeneity so, in this case and in simpler terms, how much the play rates are similar. The Index goes (when normalized like here) \(\in [0,1]\) and it’s equal to 1 when there’s a single value with 100% play rate or 0 when all play rates are equal. Of course a Gini Index of 1 needs to be avoided but it’s not like the aim should be 0. As said, it’s just to add some additional tools.

Table

Region Play Rate
Relative Frequencies by Inclusion Rate of a Region
Region Freq Shard
America Apac Europe
BandleCity 19.10% 20.02% 18.58% 18.64%
Demacia 15.81% 17.34% 14.49% 15.53%
Noxus 12.56% 12.64% 12.10% 12.94%
Piltover 12.19% 11.69% 12.78% 12.10%
Shurima 8.18% 8.03% 8.35% 8.17%
ShadowIsles 7.62% 7.05% 7.59% 8.29%
MtTargon 7.62% 8.11% 7.41% 7.30%
Freljord 7.49% 7.05% 8.51% 6.90%
Bilgewater 6.56% 5.44% 7.75% 6.52%
Ionia 2.88% 2.64% 2.44% 3.60%
Patch 3.3 - Week 1 Ranked games from 2022-03-02 18:00:00 UTC to 2022-03-09 18:00:00 UTC Source: Metadata of games collected with RiotGames API Last Update: 2022-03-09 17:39:31.882364

Play Rate by number of Cards

Plot

Table

Region Play Rate
Relative Frequencies by number of times a Card within a Region is included in a Deck
Region Freq Shard
America Apac Europe
BandleCity 24.17% 26.06% 23.36% 22.98%
Demacia 14.04% 15.02% 13.30% 13.75%
Piltover 12.84% 12.52% 12.85% 13.18%
Noxus 9.84% 9.11% 10.12% 10.34%
Shurima 9.40% 9.40% 9.45% 9.34%
MtTargon 8.44% 9.28% 7.80% 8.19%
ShadowIsles 6.95% 6.05% 7.12% 7.76%
Freljord 6.59% 6.05% 7.50% 6.24%
Bilgewater 4.94% 3.91% 6.13% 4.83%
Ionia 2.78% 2.61% 2.37% 3.40%
Patch 3.3 - Week 1 Ranked games from 2022-03-02 18:00:00 UTC to 2022-03-09 18:00:00 UTC Source: Metadata of games collected with RiotGames API Last Update: 2022-03-09 17:39:31.882364

Champions Combinations

Play Rates

Plot

from Master

Data from Master Only. Source: Metadata of games collected with RiotGames API. Last Update: 2022-03-09 17:40:30.062496 FALSE

from ~HighDiamond

Data from ~HighDiamond Only. Source: Metadata of games collected with RiotGames API Last Update: 2022-03-09 17:40:30.26933. FALSE

Day by Day

Highlighting the play-rates of most played [^at the moment of the last game] decks over time.

Master

HighDiamond

Win Rates

Meta Decks

Win rates of the most played combination of champions. Play Rate \(\geq 1\%\) in at least one of the servers.

Underdog

Top Win rates of the top10 best performing least played combination of champions. Play rate \(\in [0.1%,1%)\) [^Min number of games 50, during the times a meta/ladder just changed]

Match Ups

Regarding MU, this is not the most accurate estimation you can get from my data. If you want a better picture of the current meta it would be better to look at the dedicated MU-page where I use all “Ranked” games with the current sets of buffs and nerfs. While one may object I don’t account for optimizations and differences in skills acquired during the weeks, the overall number of games / sample size makes them a better source of information. So, in case, please refer to the MU - page for a better “meta-investigation”.

Match-up Grid

The win rates on the grid are among the 10 most played champion combination.

The upper value is from all the Masters players, the bottom one only from HighDiamond.

MU with less than 30 games are not included.

Senna/Veigar Pantheon/Yuumi (DE/MT) Pyke/Rek'Sai BandleTree Akshan/Sivir (DE/SH) Gnar/Teemo/Tristana (BC/DE) Caitlyn/Ezreal (NX/PZ) Gnar/Trundle (FR/PZ) Miss Fortune/Quinn Gnar/Swain
Senna/Veigar
NA
48.1%
45.7%
50.9%
49.8%
30.5%
29.9%
40.4%
42.5%
40.5%
40.5%
53.5%
55.2%
52.0%
53.2%
45.9%
43.4%
57.8%
61.7%
Pantheon/Yuumi (DE/MT)
51.9%
54.3%
NA
51.1%
50.4%
44.0%
45.9%
57.0%
60.1%
52.8%
49.3%
46.4%
49.5%
57.2%
56.7%
69.5%
65.9%
32.8%
39.1%
Pyke/Rek'Sai
49.1%
50.2%
48.9%
49.6%
NA
40.8%
42.8%
39.1%
46.7%
40.9%
46.9%
42.6%
42.5%
41.1%
43.9%
54.9%
52.3%
41.9%
46.3%
BandleTree
69.5%
70.1%
56.0%
54.1%
59.2%
57.2%
NA
37.3%
42.6%
35.0%
38.7%
42.1%
36.8%
37.0%
40.1%
36.4%
39.1%
48.4%
54.0%
Akshan/Sivir (DE/SH)
59.6%
57.5%
43.0%
39.9%
60.9%
53.3%
62.7%
57.4%
NA
48.9%
49.3%
50.3%
50.2%
56.9%
49.9%
57.3%
49.6%
48.6%
54.1%
Gnar/Teemo/Tristana (BC/DE)
59.5%
59.5%
47.2%
50.7%
59.1%
53.1%
65.0%
61.3%
51.1%
50.7%
NA
51.5%
50.5%
53.8%
54.5%
54.2%
51.5%
66.7%
61.8%
Caitlyn/Ezreal (NX/PZ)
46.5%
44.8%
53.6%
50.5%
57.4%
57.5%
57.9%
63.2%
49.7%
49.8%
48.5%
49.5%
NA
41.8%
39.1%
54.0%
56.9%
59.2%
70.1%
Gnar/Trundle (FR/PZ)
48.0%
46.8%
42.8%
43.3%
58.9%
56.1%
63.0%
59.9%
43.1%
50.1%
46.2%
45.5%
58.2%
60.9%
NA
55.6%
43.4%
54.1%
59.9%
Miss Fortune/Quinn
54.1%
56.6%
30.5%
34.1%
45.1%
47.7%
63.6%
60.9%
42.7%
50.4%
45.8%
48.5%
46.0%
43.1%
44.4%
56.6%
NA
58.1%
56.6%
Gnar/Swain
42.2%
38.3%
67.2%
60.9%
58.1%
53.7%
50.5%
46.0%
51.4%
45.9%
33.3%
38.2%
40.8%
29.9%
45.9%
40.1%
41.9%
43.4%
NA
MatchUp values from Ranked games with Master (upper) and ~HighDiamond (bottom) Ranked players. Decks and order of the Archetypes based on the playrate over the last 7 days. Source: Metadata of games collected with RiotGames API

LoR-Meta Index (LMI)

Note: Games from Master Rank only

Tier0 with LMI \(\geq\) 97.5 Tier1 with LMI \(\in [85,97.5)\) Tier2 with LMI \(\in [60,85)\) Tier3- with LMI \(<\) 60

The LMI 2 3 is an Index I developed to measure the performance of decks in the metagame. For those who are familiar with basic statistical concept I wrote a document to explain the theory behind it: , it’s very similar to vicioussyndicate (vS) Meta Score from their data reaper report. The score of each deck is not just their “strength”, it takes in consideration both play rates and win rates that’s why I prefer to say it measure the “performance”. The values range from 0 to 100 and the higher the value, the higher is the performance.

Win Marathons Leaders

Top3 Players (or more in case of ties) from each server that had the highest amount of consecutive wins with the same archetype. The provided deckcode is the one played in the last win found.

Top3 Biggest Win Streak by Server
Cumulative wins with the same Archetype
Player Result Archetype Deck Code
Americas
mati24mayo 17 Akshan/Sivir (DE/SH)
abaan 13 Pantheon/Yuumi (DE/MT)
AK KuroNE 13 Pantheon/Yuumi (DE/MT)
Apac
シンゲツ 15 Fizz/Nami (BC/BW)
sloppy prince 14 Gnar/Teemo/Tristana (BC/DE)
Bluegodsea 13 Gnar/Swain
RGE GilGameSh 13 BandleTree
Europe
WhyNoMomo 14 Akshan/Sivir (DE/SH)
LordVon 13 Trundle/Tryndamere (FR/SI)
PCS Tealcos 13 Senna/Veigar
Games from all Master are collected each hour adding up to the last 20 matches. Unlikely but possible to miss games in case of high frequency games. Metadata of games collected with RiotGames API

Cards Presence

Play Rate

Top 3 Play Rates by Region

Forgotten Cards

Cards that couldn’t find place even in a meme deck.

Not-Standard Archetype Names

Names and rules for the “non standard archetypes” which are not defined by Champion+Regions

Archetype ~Fix
Deck Source
ASZ - Sivir Ionia Akshan/Sivir (IO/SH) or Sivir/Zed or Akshan/Sivir/Zed
RubinBait - <Champ> Burn Deck using <Champ> to bait mulligan
Dragons (DE/MT) (DE/MT) Decks with *at least* Shyvana and ASol
SunDisc Mono Shurima with 1+ Sun Disc
BandleTree 3 copies of BandleTree
Viktor - Shellfolk Viktor + at least one of Curious Shellfolk/Mirror Mage + at least 2 Trinket Trade
Sentinel Control PnZ/SI deck with a combination of Elise/Jayce/Vi

Credits

Special thanks to Jump, bA1ance and for the recent support (ᐛ)ᕗ

Legal bla bla

This content was created under Riot Games ‘Legal Jibber Jabber’ policy using assets owned by Riot Games. Riot Games does not endorse or sponsor this project.


  1. HighDiamond are defined as “players who played against Master player in a Ranked games but are not Master themselves”↩︎

  2. LMI - Early Theory↩︎

  3. LMI - Adding a Ban Index↩︎