THE META REPORT NAME IS TOO LONG, TOO DAMN LONG (n°40)

Patch 3.00 - Week 1 - The meta that fits in the hand (like a Poro)

Valentino (Legna) Vazzoler
01-12-2022

Data

Number of (Ranked) matches analysed 52916 or 105832 games. / Master Players

Number of (Ranked) matches analysed 136488 or 272976 games. / ~HighDiamond Players

Last Update: 2022-02-05 16:47

Patch 3.00 - Week 1 - by the Numbers1
Characteristic2 Master ~HighDiamond
N = 92,6853 N = 52,8473 N = 191,4413 N = 136,4283
Status
Ranked 52,847 (57%) 136,428 (71%)
Other 22,371 (24%) 29,932 (16%)
Friendly 11,271 (12%) 11,935 (6.2%)
PathOfChampion 6,196 (6.7%) 13,146 (6.9%)
Server
americas 43,883 (47%) 24,390 (46%) 87,116 (46%) 60,864 (45%)
apac 16,573 (18%) 8,717 (16%) 31,544 (16%) 22,395 (16%)
europe 32,229 (35%) 19,740 (37%) 72,781 (38%) 53,169 (39%)

1 Max datetime recovered: 2022-02-05 12:51:31 UTC from 2022-01-05 18:00:00 to 2022-01-12 18:00:00 UTC

2 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

3 n(%) took from the number of matches. When the data is analysed the size is double since we account each different player

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
ShadowIsles 19.11% 19.46% 19.08% 18.71%
Piltover 13.96% 14.14% 16.85% 12.48%
Freljord 13.64% 13.13% 15.90% 13.27%
Shurima 11.21% 10.59% 11.57% 11.81%
Ionia 9.69% 10.21% 7.65% 9.93%
BandleCity 7.70% 6.97% 6.61% 9.06%
Noxus 7.49% 7.73% 6.94% 7.43%
MtTargon 6.34% 6.99% 5.21% 6.03%
Demacia 5.78% 5.95% 5.06% 5.88%
Bilgewater 5.09% 4.83% 5.13% 5.39%
Patch 3.00 - Week 1 Ranked games from 2022-01-05 18:00:00 UTC to 2022-01-12 18:00:00 UTC Source: Metadata of games collected with RiotGames API Last Update: 2022-02-05 15:44:55.144032

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
ShadowIsles 19.27% 19.08% 20.00% 19.18%
Piltover 14.34% 14.25% 18.29% 12.71%
Ionia 13.29% 13.71% 10.73% 13.89%
Freljord 11.90% 11.54% 13.20% 11.78%
Shurima 10.21% 10.09% 10.71% 10.14%
BandleCity 7.55% 7.00% 6.03% 8.90%
Noxus 6.68% 6.89% 6.54% 6.48%
MtTargon 6.51% 7.02% 5.24% 6.44%
Demacia 6.01% 6.15% 5.02% 6.28%
Bilgewater 4.25% 4.28% 4.24% 4.21%
Patch 3.00 - Week 1 Ranked games from 2022-01-05 18:00:00 UTC to 2022-01-12 18:00:00 UTC Source: Metadata of games collected with RiotGames API Last Update: 2022-02-05 15:44:55.144032

Champions Combinations

Play Rates

Plot

from Master

from ~HighDiamond

Day by Day

Highlighting the play-rates of most played1 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%)2

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.

Championless (FR/PZ) Ahri/Kennen (IO/SH) Senna/Veigar Draven/Rumble (NX/PZ) Pyke/Rek'Sai Elise (NX/SI) SunDisc Elise/Trundle Kindred/Viego (IO/SI) Trundle/Tryndamere (FR/SI)
Championless (FR/PZ)
NA
31.7%
32.7%
57.6%
55.6%
37.3%
36.8%
47.4%
45.0%
40.1%
36.0%
76.9%
78.6%
62.5%
59.6%
61.6%
59.1%
62.3%
61.8%
Ahri/Kennen (IO/SH)
68.3%
67.3%
NA
51.4%
48.7%
50.4%
46.3%
71.1%
59.4%
28.1%
29.0%
85.5%
79.3%
54.2%
49.3%
78.3%
67.5%
39.9%
37.5%
Senna/Veigar
42.4%
44.4%
48.6%
51.3%
NA
51.1%
55.5%
43.6%
43.3%
61.7%
66.1%
56.3%
54.1%
50.0%
53.1%
61.0%
63.6%
49.1%
45.2%
Draven/Rumble (NX/PZ)
62.7%
63.2%
49.6%
53.7%
48.9%
44.5%
NA
51.4%
48.5%
70.7%
61.4%
74.0%
76.4%
45.4%
41.2%
54.5%
55.2%
37.7%
44.3%
Pyke/Rek'Sai
52.6%
55.0%
28.9%
40.6%
56.4%
56.7%
48.6%
51.5%
NA
51.3%
39.9%
77.9%
67.2%
40.5%
42.9%
54.6%
58.0%
53.6%
54.1%
Elise (NX/SI)
59.9%
64.0%
71.9%
71.0%
38.3%
33.9%
29.3%
38.6%
48.7%
60.1%
NA
71.8%
75.3%
44.6%
46.0%
59.8%
63.2%
22.6%
24.9%
SunDisc
23.1%
21.4%
14.5%
20.7%
43.7%
45.9%
26.0%
23.6%
22.1%
32.8%
28.2%
24.7%
NA
41.3%
46.9%
27.8%
39.4%
38.7%
43.9%
Elise/Trundle
37.5%
40.4%
45.8%
50.7%
50.0%
46.9%
54.6%
58.8%
59.5%
57.1%
55.4%
54.0%
58.7%
53.1%
NA
35.6%
35.3%
50.0%
51.2%
Kindred/Viego (IO/SI)
38.4%
40.9%
21.7%
32.5%
39.0%
36.4%
45.5%
44.8%
45.4%
42.0%
40.2%
36.8%
72.2%
60.6%
64.4%
64.7%
NA
76.7%
72.4%
Trundle/Tryndamere (FR/SI)
37.7%
38.2%
60.1%
62.5%
50.9%
54.8%
62.3%
55.7%
46.4%
45.9%
77.4%
75.1%
61.3%
56.1%
50.0%
48.8%
21.7%
27.6%
NA
The upper value is from Last-Seasononal Players while the bottom value is from ~HighDiamond. MU with less than 30 games are not included. Order of the Archetypes based on the playrate over the last 7 days from the last-update from the upper value population. 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 3 4 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
Busdude 15 Championless (FR/PZ)
UnhlpfulYoda 15 Ahri/Kennen (IO/NX)
0xymor3 14 Elise/Kindred (BC/SI)
For Syria 14 Ahri/Kennen (IO/SH)
gui bertini 14 Elise (NX/SI)
inept 14 Diana/Zoe (MT/NX)
Logïc 14 Gangplank/Sejuani
Apac
카레김치볶음밥 18 Gangplank/Sejuani
UzAng 17 Pantheon (DE/MT)
花にひさぎ 16 Ahri/Kennen (IO/SH)
Europe
wolfoxcs 15 Elise/Kindred (PZ/SI)
Bowisse 13 Ahri/Kennen (IO/SH)
Kai44 13 Kindred/Viego (IO/SI)
Puresword 13 Kindred/Viego (IO/SI)
RossPierrDol 13 Senna/Veigar
ShiangHigh 13 Pyke/Rek'Sai
Szychu 13 Anivia (FR/SI)
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
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 bA1ance for the recent support (ᐛ)> (January 2022)

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. at the moment of the last game↩︎

  2. Min number of games 50, during the times a meta/ladder just changed.↩︎

  3. LMI - Early Theory↩︎

  4. LMI - Adding a Ban Index↩︎

Citation

For attribution, please cite this work as

Vazzoler (2022, Jan. 12). LLoRR Stats: THE META REPORT NAME IS TOO LONG, TOO DAMN LONG (n°40)
. Retrieved from https://www.llorr-stats.com/report/meta-report-040/

BibTeX citation

@misc{vazzoler2022the,
  author = {Vazzoler, Valentino (Legna)},
  title = {LLoRR Stats: THE META REPORT NAME IS TOO LONG, TOO DAMN LONG (n°40)
},
  url = {https://www.llorr-stats.com/report/meta-report-040/},
  year = {2022}
}