Data
Number of (Ranked) Master matches analysed 61179 or 122358 games.
Number of (Ranked) ~HighDiamond matches analysed 156326 or 312652 games.
Last Update: 2022-02-05 16:20
| Patch 3.00 - Week 2 - by the Numbers1 | ||||
|---|---|---|---|---|
| Characteristic2 | Master | ~HighDiamond | ||
| N = 116,6633 | N = 61,1563 | N = 223,7893 | N = 156,3233 | |
| Status | ||||
| Ranked | 61,156 (52%) | 156,323 (70%) | ||
| Other | 34,184 (29%) | 36,914 (16%) | ||
| PathOfChampion | 11,787 (10%) | 20,684 (9.2%) | ||
| Friendly | 9,536 (8.2%) | 9,868 (4.4%) | ||
| Server | ||||
| americas | 51,622 (44%) | 25,175 (41%) | 95,849 (43%) | 66,090 (42%) |
| apac | 27,524 (24%) | 14,896 (24%) | 48,592 (22%) | 34,231 (22%) |
| europe | 37,517 (32%) | 21,085 (34%) | 79,348 (35%) | 56,002 (36%) |
|
1
Max datetime recovered: 2022-02-05 12:51:31 UTC from 2022-01-12 18:00:00 to 2022-01-19 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 | 16.47% | 16.32% | 16.18% | 16.84% |
| Shurima | 12.70% | 12.55% | 13.32% | 12.45% |
| BandleCity | 11.26% | 11.63% | 9.75% | 11.88% |
| Piltover | 10.90% | 11.21% | 11.46% | 10.14% |
| Ionia | 9.61% | 9.99% | 9.65% | 9.13% |
| Freljord | 9.41% | 8.26% | 9.93% | 10.41% |
| Demacia | 9.08% | 9.29% | 8.61% | 9.15% |
| Bilgewater | 8.73% | 9.10% | 9.15% | 7.99% |
| MtTargon | 6.11% | 5.99% | 6.21% | 6.19% |
| Noxus | 5.74% | 5.66% | 5.76% | 5.82% |
| Patch 3.00 - Week 2 Ranked games from 2022-01-12 18:00:00 UTC to 2022-01-19 18:00:00 UTC Source: Metadata of games collected with RiotGames API Last Update: 2022-02-05 15:16:23.613336 | ||||
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 | 15.07% | 14.42% | 15.12% | 15.81% |
| Ionia | 13.31% | 13.91% | 12.78% | 12.98% |
| Shurima | 12.54% | 12.89% | 13.31% | 11.58% |
| Piltover | 11.08% | 11.38% | 11.57% | 10.37% |
| BandleCity | 10.70% | 10.89% | 9.47% | 11.34% |
| Demacia | 10.23% | 10.48% | 9.47% | 10.48% |
| Freljord | 9.14% | 8.12% | 9.54% | 10.08% |
| Bilgewater | 6.45% | 6.80% | 7.05% | 5.61% |
| MtTargon | 6.41% | 6.24% | 6.47% | 6.57% |
| Noxus | 5.07% | 4.88% | 5.24% | 5.17% |
| Patch 3.00 - Week 2 Ranked games from 2022-01-12 18:00:00 UTC to 2022-01-19 18:00:00 UTC Source: Metadata of games collected with RiotGames API Last Update: 2022-02-05 15:16:23.613336 | ||||
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.
| Senna/Veigar | Miss Fortune/Quinn | Ahri/Kennen (IO/SH) | Pyke/Rek'Sai | Xerath/Zilean (BC/SH) | Trundle/Tryndamere (FR/SI) | Ahri/Kennen (IO/SI) | Championless (FR/PZ) | Fizz/Lulu (BC/PZ) | Kindred/Viego (IO/SI) | |
|---|---|---|---|---|---|---|---|---|---|---|
| Senna/Veigar | NA
|
45.2%
47.3% |
48.4%
51.7% |
40.3%
41.4% |
41.1%
44.5% |
55.7%
50.4% |
54.0%
51.9% |
42.2%
46.7% |
57.4%
61.4% |
57.1%
63.6% |
| Miss Fortune/Quinn | 54.8%
52.7% |
NA
|
55.7%
60.7% |
49.5%
49.0% |
52.0%
52.0% |
59.5%
52.1% |
60.5%
61.9% |
69.4%
63.9% |
45.6%
54.1% |
55.9%
55.2% |
| Ahri/Kennen (IO/SH) | 51.6%
48.3% |
44.3%
39.3% |
NA
|
66.7%
58.8% |
61.8%
62.9% |
49.2%
40.8% |
43.5%
41.9% |
60.7%
65.5% |
49.0%
51.5% |
78.5%
67.8% |
| Pyke/Rek'Sai | 59.7%
58.6% |
50.5%
51.0% |
33.3%
41.2% |
NA
|
49.6%
59.2% |
57.9%
55.1% |
35.5%
39.1% |
50.3%
53.6% |
39.7%
46.9% |
56.6%
54.7% |
| Xerath/Zilean (BC/SH) | 58.9%
55.5% |
48.0%
48.0% |
38.2%
37.1% |
50.4%
40.8% |
NA
|
61.7%
65.0% |
57.7%
53.3% |
26.6%
27.6% |
43.5%
41.9% |
38.3%
41.6% |
| Trundle/Tryndamere (FR/SI) | 44.3%
49.6% |
40.5%
47.9% |
50.8%
59.2% |
42.1%
44.9% |
38.3%
35.0% |
NA
|
54.5%
50.8% |
35.8%
46.2% |
65.4%
68.6% |
27.7%
26.3% |
| Ahri/Kennen (IO/SI) | 46.0%
48.1% |
39.5%
38.1% |
56.5%
58.1% |
64.5%
60.9% |
42.3%
46.7% |
45.5%
49.2% |
NA
|
53.6%
48.4% |
50.9%
47.2% |
54.0%
51.4% |
| Championless (FR/PZ) | 57.8%
53.3% |
30.6%
36.1% |
39.3%
34.5% |
49.7%
46.4% |
73.4%
72.4% |
64.2%
53.8% |
46.4%
51.6% |
NA
|
31.3%
32.2% |
66.3%
62.7% |
| Fizz/Lulu (BC/PZ) | 42.6%
38.6% |
54.4%
45.9% |
51.0%
48.5% |
60.3%
53.1% |
56.5%
58.1% |
34.6%
31.4% |
49.1%
52.8% |
68.7%
67.8% |
NA
|
67.2%
61.4% |
| Kindred/Viego (IO/SI) | 42.9%
36.4% |
44.1%
44.8% |
21.5%
32.2% |
43.4%
45.3% |
61.7%
58.4% |
72.3%
73.7% |
46.0%
48.6% |
33.7%
37.3% |
32.8%
38.6% |
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.