THE META REPORT NAME IS TOO LONG, TOO DAMN LONG (n°54)

Patch 3.5 - Week 1 - 1..~2..3.. The complete Dark content for LoR

Valentino (Legna) Vazzoler
04-20-2022

Data

Number of Master Players: 4238

Number of HighDiamond 1 Players: 6826

Number of (Ranked) Master matches analysed 104084 or 208168 games.

Number of (Ranked) ~HighDiamond matches analysed 199151 or 398302 games.

Last Update: 2022-04-20 19:18

Games

Patch 3.5 - Week 1 - by the Numbers1
Characteristic3 Master2 ~HighDiamond2
N = 194,2344 N = 104,0844 N = 307,9874 N = 199,1514
Status
Ranked 104,084 (54%) 199,151 (65%)
Other 57,237 (29%) 59,480 (19%)
PathOfChampion 23,198 (12%) 35,919 (12%)
Friendly 9,715 (5.0%) 13,437 (4.4%)
Server
americas 75,143 (39%) 39,763 (38%) 121,181 (39%) 77,137 (39%)
apac 57,871 (30%) 29,316 (28%) 89,643 (29%) 57,151 (29%)
europe 61,220 (32%) 35,005 (34%) 97,163 (32%) 64,863 (33%)
1 Max datetime recovered: 2022-04-20 16:25:41 UTC from 2022-04-13 18:00:00 to 2022-04-20 18:00:00 UTC
2 EU Master 1387/1388 NA Master 1573/1578 APAC Master 1278/1282
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

Characteristic Shard/Server Total
Americas Apac Europe
Player Rank
Master 1,573 (14%) 1,278 (12%) 1,387 (13%) 4,238 (38%)
Diamond 2,580 (23%) 2,012 (18%) 2,234 (20%) 6,826 (62%)
Total 4,153 (38%) 3,290 (30%) 3,621 (33%) 11,064 (100%)

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
Piltover 19.65% 19.66% 18.36% 20.70%
Shurima 17.33% 17.11% 18.92% 16.25%
Noxus 13.42% 12.71% 12.64% 14.88%
BandleCity 8.25% 8.59% 8.84% 7.35%
ShadowIsles 8.24% 7.42% 8.67% 8.82%
MtTargon 7.93% 8.09% 8.98% 6.87%
Bilgewater 7.14% 7.58% 7.09% 6.67%
Freljord 6.80% 6.30% 6.59% 7.55%
Demacia 6.22% 6.62% 5.77% 6.13%
Ionia 5.03% 5.91% 4.13% 4.78%
Patch 3.5 - Week 1 Ranked games from 2022-04-13 18:00:00 UTC to 2022-04-20 18:00:00 UTC Source: Metadata of games collected with RiotGames API Last Update: 2022-04-20 17:15:24.714183

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
Shurima 24.07% 23.76% 26.10% 22.73%
Piltover 19.44% 19.14% 18.62% 20.46%
Noxus 10.95% 10.46% 10.54% 11.85%
MtTargon 7.96% 8.03% 8.66% 7.29%
BandleCity 7.36% 7.81% 7.56% 6.70%
Freljord 6.64% 5.87% 6.64% 7.50%
ShadowIsles 6.61% 6.39% 6.33% 7.10%
Demacia 6.19% 6.71% 5.81% 5.90%
Bilgewater 5.96% 6.21% 5.71% 5.89%
Ionia 4.82% 5.61% 4.04% 4.58%
Patch 3.5 - Week 1 Ranked games from 2022-04-13 18:00:00 UTC to 2022-04-20 18:00:00 UTC Source: Metadata of games collected with RiotGames API Last Update: 2022-04-20 17:15:24.714183

Champions Combinations

Play Rates

Plot

from Master

Data from Master Only. Source: Metadata of games collected with RiotGames API. Last Update: 2022-04-20 17:19:48.115865 FALSE

from ~HighDiamond

Data from ~HighDiamond Only. Source: Metadata of games collected with RiotGames API Last Update: 2022-04-20 17:19:48.317276. 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.

SunDisc Tri-Beam (NX/PZ) Gangplank/Miss Fortune/Twisted Fate (BW/NX) Riven/Viktor Taliyah/Ziggs Fizz/Lulu (BC/PZ) Ekko/Zilean Aphelios/Viktor Miss Fortune/Quinn Trundle/Tryndamere (FR/SI)
SunDisc
NA
65.5%
65.7%
29.9%
31.3%
38.8%
36.0%
46.5%
46.7%
43.4%
46.1%
61.9%
63.0%
54.8%
57.0%
46.7%
47.0%
64.4%
61.1%
Tri-Beam (NX/PZ)
34.5%
34.3%
NA
45.7%
48.8%
53.7%
58.7%
33.5%
37.4%
56.5%
55.9%
58.2%
53.7%
51.3%
59.1%
56.4%
60.4%
18.6%
20.4%
Gangplank/Miss Fortune/Twisted Fate (BW/NX)
70.1%
68.7%
54.3%
51.2%
NA
50.9%
54.5%
69.2%
67.6%
39.2%
44.4%
39.7%
41.4%
41.4%
39.7%
33.6%
36.1%
43.1%
43.1%
Riven/Viktor
61.2%
64.0%
46.3%
41.3%
49.1%
45.5%
NA
56.2%
55.8%
49.4%
43.6%
52.4%
54.2%
39.9%
41.7%
40.0%
41.2%
57.1%
51.7%
Taliyah/Ziggs
53.5%
53.3%
66.5%
62.6%
30.8%
32.4%
43.8%
44.2%
NA
36.7%
39.6%
54.2%
56.8%
55.7%
57.4%
42.2%
43.3%
63.3%
51.7%
Fizz/Lulu (BC/PZ)
56.6%
53.9%
43.5%
44.1%
60.8%
55.6%
50.6%
56.4%
63.3%
60.4%
NA
38.9%
44.5%
49.3%
50.0%
46.5%
39.5%
46.8%
41.2%
Ekko/Zilean
38.1%
37.0%
41.8%
46.3%
60.3%
58.6%
47.6%
45.8%
45.8%
43.2%
61.1%
55.5%
NA
40.1%
32.3%
50.7%
49.4%
41.5%
44.2%
Aphelios/Viktor
45.2%
43.0%
48.7%
40.9%
58.6%
60.3%
60.1%
58.3%
44.3%
42.6%
50.7%
50.0%
59.9%
67.7%
NA
46.2%
41.1%
48.0%
45.0%
Miss Fortune/Quinn
53.3%
53.0%
43.6%
39.6%
66.4%
63.9%
60.0%
58.8%
57.8%
56.7%
53.5%
60.5%
49.3%
50.6%
53.8%
58.9%
NA
65.7%
63.3%
Trundle/Tryndamere (FR/SI)
35.6%
38.9%
81.4%
79.6%
56.9%
56.9%
42.9%
48.3%
36.7%
48.3%
53.2%
58.8%
58.5%
55.8%
52.0%
55.0%
34.3%
36.7%
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.