THE META REPORT NAME IS TOO LONG, TOO DAMN LONG (n°42)

Patch 3.00 - Week 3 - Rise of the AlPACa

Valentino (Legna) Vazzoler
01-26-2022

Data

Shard/Server Americas, N = 1,0811 Apac, N = 9401 Europe, N = 9381
Player Rank
Master 1,081 (100%) 940 (100%) 938 (100%)

1 n (%)

Number of (Ranked) Master matches analysed 81203 or 162406 games.

Number of (Ranked) ~HighDiamond matches analysed 201439 or 402878 games.

Patch 3.00 - Week 3 - by the Numbers1
Characteristic2 Master ~HighDiamond
N = 157,9593 N = 81,1903 N = 288,8963 N = 201,4363
Status
Ranked 81,190 (51%) 201,436 (70%)
Other 51,462 (33%) 51,728 (18%)
PathOfChampion 17,484 (11%) 28,206 (9.8%)
Friendly 7,823 (5.0%) 7,526 (2.6%)
Server
americas 54,927 (35%) 27,241 (34%) 109,498 (38%) 75,927 (38%)
apac 55,516 (35%) 28,317 (35%) 93,429 (32%) 65,080 (32%)
europe 47,516 (30%) 25,632 (32%) 85,969 (30%) 60,429 (30%)

1 Max datetime recovered: 2022-02-04 17:48:34 UTC from 2022-01-19 18:00:00 to 2022-01-26 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

Last Update: 2022-02-04 23:41

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 15.18% 15.85% 15.59% 14.01%
Shurima 13.25% 11.90% 13.82% 14.07%
Piltover 11.16% 12.14% 11.41% 9.83%
BandleCity 10.74% 10.67% 9.96% 11.68%
Demacia 10.70% 11.36% 9.65% 11.17%
Bilgewater 9.48% 9.51% 9.61% 9.30%
Ionia 9.11% 9.06% 9.51% 8.74%
Freljord 8.19% 7.80% 8.83% 7.92%
MtTargon 6.62% 6.43% 6.12% 7.36%
Noxus 5.56% 5.29% 5.51% 5.92%
Patch 3.00 - Week 3 Ranked games from 2022-01-19 18:00:00 UTC to 2022-01-26 18:00:00 UTC Source: Metadata of games collected with RiotGames API Last Update: 2022-02-04 22:37:58.328835

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 13.64% 14.15% 13.54% 13.19%
Ionia 13.32% 13.20% 13.54% 13.20%
Shurima 12.68% 11.64% 13.83% 12.50%
Piltover 11.64% 12.68% 11.76% 10.39%
Demacia 11.15% 11.84% 10.04% 11.64%
BandleCity 10.29% 10.16% 9.60% 11.20%
Freljord 7.97% 7.39% 8.69% 7.79%
Bilgewater 7.16% 7.12% 7.62% 6.67%
MtTargon 7.08% 6.93% 6.36% 8.05%
Noxus 5.08% 4.88% 5.03% 5.35%
Patch 3.00 - Week 3 Ranked games from 2022-01-19 18:00:00 UTC to 2022-01-26 18:00:00 UTC Source: Metadata of games collected with RiotGames API Last Update: 2022-02-04 22:37:58.328835

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.

Miss Fortune/Quinn Senna/Veigar Pyke/Rek'Sai Ahri/Kennen (IO/SH) Ahri/Kennen (IO/SI) Pantheon/Taric (DE/MT) Caitlyn/Teemo (BC/PZ) Fizz/Lulu (BC/PZ) Xerath/Zilean (BC/SH) Trundle/Tryndamere (FR/SI)
Miss Fortune/Quinn
NA
53.6%
53.2%
47.9%
51.5%
54.4%
59.0%
56.1%
64.9%
35.2%
37.2%
68.2%
68.7%
45.6%
45.8%
51.5%
50.2%
59.6%
57.4%
Senna/Veigar
46.4%
46.8%
NA
45.3%
46.2%
49.6%
52.2%
51.5%
46.2%
38.4%
38.5%
45.7%
47.1%
58.5%
56.6%
47.8%
46.8%
51.3%
52.8%
Pyke/Rek'Sai
52.1%
48.5%
54.7%
53.8%
NA
33.6%
38.7%
37.6%
36.2%
45.6%
53.3%
66.9%
62.2%
41.8%
41.3%
51.8%
51.7%
55.9%
55.0%
Ahri/Kennen (IO/SH)
45.6%
41.0%
50.4%
47.8%
66.4%
61.3%
NA
40.2%
43.9%
64.3%
60.5%
60.8%
56.1%
59.4%
54.2%
67.2%
57.5%
45.6%
42.0%
Ahri/Kennen (IO/SI)
43.9%
35.1%
48.5%
53.8%
62.4%
63.8%
59.8%
56.1%
NA
60.6%
58.4%
51.1%
43.6%
53.9%
54.3%
53.1%
48.1%
47.2%
39.2%
Pantheon/Taric (DE/MT)
64.8%
62.8%
61.6%
61.5%
54.4%
46.7%
35.7%
39.5%
39.4%
41.6%
NA
80.4%
71.3%
38.7%
37.8%
66.7%
60.6%
57.4%
59.0%
Caitlyn/Teemo (BC/PZ)
31.8%
31.3%
54.3%
52.9%
33.1%
37.8%
38.9%
43.9%
48.9%
56.4%
19.6%
28.7%
NA
31.1%
32.2%
65.6%
64.2%
57.8%
58.9%
Fizz/Lulu (BC/PZ)
54.4%
54.2%
41.5%
43.4%
58.2%
58.7%
40.6%
45.8%
46.1%
45.7%
61.3%
62.2%
68.9%
67.8%
NA
56.0%
60.3%
32.1%
32.9%
Xerath/Zilean (BC/SH)
48.5%
49.8%
52.2%
53.2%
48.2%
48.3%
32.8%
42.5%
46.9%
51.9%
33.3%
39.4%
34.4%
35.8%
44.0%
39.7%
NA
66.3%
59.3%
Trundle/Tryndamere (FR/SI)
40.4%
42.6%
48.7%
47.2%
44.1%
45.0%
54.4%
58.0%
52.8%
60.8%
42.6%
41.0%
42.2%
41.1%
67.9%
67.1%
33.7%
40.7%
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
Shiloppy 17 Senna/Veigar
AK Jta 15 Pyke/Rek'Sai
Seku 14 Ahri/Kennen (DE/IO)
Apac
Double Lariat 15 Fizz/Lulu (BC/PZ)
춤추는종나니 14 Trundle/Tryndamere (FR/PZ)
VK 영처리 14 Ahri/Kennen (IO/SH)
Europe
chavo03 17 Championless (FR/PZ)
Boky 14 Pantheon/Taric (DE/MT)
Gouda ddy 14 Ziggs/Zilean
Skouroliakou 14 Elise/Trundle
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