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