Data

Sample population: Ranked games / Patch 2.11 / Master players 1

Number of games: 25689 2 (check the footnote for additional information)


1 Regions


1.1 Play Rate

1.1.1 Plot

1.1.2 Table

Region Play Rate America Asia Europe
Bilgewater 17.04% 14.5% 19.8% 18.3%
Demacia 3.89% 4.9% 2.9% 3.4%
Freljord 7.43% 7.2% 7.7% 7.6%
Ionia 13.1% 15.3% 10.7% 12.0%
MtTargon 4.99% 6.1% 3.7% 4.5%
Noxus 12.75% 12.3% 10.3% 14.5%
PnZ 11.1% 12.8% 11.5% 9.1%
ShadowIsles 5.91% 5.9% 6.2% 5.7%
Shurima 23.77% 21.0% 27.3% 24.8%

1.2 Play Rate by number of Cards

1.2.1 Plot

1.2.2 Table

Region Play Rate America Asia Europe
Bilgewater 16.05% 13.16% 18.37% 17.88%
Demacia 4.9% 6.34% 3.15% 4.30%
Freljord 7.71% 7.35% 8.09% 7.88%
Ionia 13.33% 16.34% 10.07% 11.88%
MtTargon 4.85% 6.12% 3.63% 4.16%
Noxus 11.56% 10.99% 9.44% 13.33%
PnZ 11.95% 13.33% 12.46% 10.19%
ShadowIsles 5.8% 5.86% 6.18% 5.53%
Shurima 23.85% 20.51% 28.61% 24.85%

2 Champions Combinations

2.1 Play Rates

In this section I provide the play rate of which combinations of champions (plus the regions) are used in a deck. The champions showed right before a game starts for example. Right now it’s a simple approximation of the archetypes played in the ladder as such information is not restrictive enough.

2.1.1 Plot

2.1.2 Table


2.2 Day by Day

The plot is no more interactive, it was fine but probably useless. It still highlist (propably better) the top5 most played decks (at the moment of the last game played) and aside for reporting the values for each day the curves are relative for each game that was included.


2.3 Win Rates

Tie games are excluded

Win rates of the most played combination of champions, against all decks, this week. This time I left all cases with more than 1000 games


2.4 Underdog(?) Win Rates

Top Win rates of the least played combination of champions. Min 100 games (from 300 usually) and a play rate of less than 2% play rate


3 Match Ups

3.1 Match-up

The win rates on the grid are among the 10 most played champion combination. Match-ups with less than 300 games are not included

While this is one of the most interesting data for many the results are still heavily affected by the small sample size. They may be the 10 most played combination of champions but it’s still a 100 cell grid. Also, while my approximation for archetypes usually works, this weeks more than usual, two decks are not well reported: Dragons and Azir/Noxus. In these two cases the inclusion of J4 / Garen / Zoe and Draven / Darius create different values for in the end it’s the same deck with just a couple of different card. I already have an idea and part of the code how to solve this but will use it for next week. I could use Dr.LoR approach with bayesian statistics but I want to be consistent with my metholody in order to have a better comparison for my results. Of course I can still make changes in the future if the quality vastly improve with them.


3.2 Match-up Table

Filtered cases with less than 30 games

3.3 Match-up Table v2

Just a different way to print the table of the MU grid, more an experimentation


4 Deck Structure of the week

Sivir with Zed is already quite popular so for this week I have chosen an “Underdog(?)” deck like “Draven / Riven” (soon to be Riven Akshan?) and “Jinx / Lulu”

Note: all deckcodes are limited to this week (sometimes I expand it to a bigger timeframe because of the low numbers)

4.1 Draven / Riven (NX/PZ)


4.2 Jinx / Lulu

How to read the table:
- Play rate: How often a card is included in this class of decks / the table is order by this column.
- 3/2/1 is the relative and absolute frequency of the number of copies in the decks that plays them
- Frequencies from 50% to 100% are colored from shades of green to white to identify more easily the highest values


5 LoR-Meta Index (LMI)

The LMI 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.


6 Cards Presence


6.1 Play Rates


6.2 Top 3 Play Rates by Region


6.3 Forgotten Cards

Cards that couldn’t find place even in a meme deck.

Region Bilgewater Demacia Freljord Ionia MtTargon Noxus PnZ Shurima ShadowIsles
1 Brash Gambler Detain Scarthane Steffen Nimble Poro Sneaky Zeebles Shiraza the Blade Mushroom Cloud Unworthy Possession
2 Scrapshot Plucky Poro Unscarred Reaver Sown Seeds Grandfather Rumul Crimson Aristocrat Golden Crushbot Arise! Splinter Soul
3 Sunk Cost Vanguard Lookout Stalking Wolf Scaled Snapper Shards of the Mountain Savage Reckoner Amateur Aeronaut Destined Poro Sap Magic
4 Wise Fry En Garde Feral Mystic Emerald Awakener Fledgling Stellacorn Aurok Glinthorn Patrol Wardens Waking Sands Encroaching Shadows
5 For Demacia! Bloodsworn Pledge Horns of the Dragon Stargazer Wrathful Rider Sandstone Chimera The Etherfiend
6 Vanguard Cavalry Ancestral Boon Porofly
7 Silverwing Diver
8 Kadregrin the Infernal
9 Battlefield Prowess
10 Towering Stonehorn

    • EU Master players in the ladder: 204 while the number of Master players I recovered from matches is: 202 so missing: 2
    • NA Master players in the ladder: 245 while the number of Master players I recovered from matches is: 240 so missing: 5
    • ASIA Master players in the ladder: 111 while the number of Master players I recovered from matches is: 107 so missing: 4
    ↩︎
    • Games from 2021-07-07 19:00:00 CEST up to 2021-07-14 19:00:00 CEST timezone
    • Max datetime of a match: 2021-07-14 14:56
    • Match Id recovered in patch 2.11: 79544
    • Match metadata recovered in patch 2.11: 75183
    • Match not collectable since friednly matches: 4.9%
    • Match metadata still to collect from 2.11: 428
    • Last update: 2021-07-14 17:08 (UTC)
    • possible games missing because of data collection not running for a few hours a few days ago.
    ↩︎
  1. LMI - Early Theory↩︎