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LPL Mid 2026

11 members·6 stats

Ranking — Overall

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  • 1
    KnightBILIBILI GAMING
    87
    Mechanics90Laning84Teamfighting87Macro66Consistency96Versatility96
  • 2
    RooKieInvictus Gaming
    80
    Mechanics79Laning76Teamfighting80Macro83Consistency85Versatility79
  • 3
    CremeTOP ESPORTS
    79
    Mechanics79Laning79Teamfighting75Macro69Consistency82Versatility88
  • 4
    CareShenzhen NINJAS IN PYJAMAS
    78
    Mechanics75Laning75Teamfighting68Macro93Consistency89Versatility69
  • 5
    HongQBeijing JDG Esports
    78
    Mechanics72Laning71Teamfighting86Macro84Consistency65Versatility92
  • 6
    TangyuanRoyal Never Give Up
    74
    Mechanics71Laning76Teamfighting76Macro80Consistency80Versatility61
  • 7
    ShanksAnyone's Legend
    72
    Mechanics81Laning76Teamfighting76Macro52Consistency76Versatility73
  • 8
    xiaohuWeiboGaming
    70
    Mechanics65Laning77Teamfighting71Macro71Consistency68Versatility70
  • 9
    BulldogEDWARD GAMING
    67
    Mechanics73Laning64Teamfighting60Macro73Consistency54Versatility75
  • 10
    KarisXi'an Team WE
    65
    Mechanics63Laning77Teamfighting68Macro69Consistency57Versatility57
  • 11
    HeruTHUNDER TALK GAMING
    60
    Mechanics63Laning62Teamfighting66Macro65Consistency54Versatility51
Axis leaders · records

Champion per stat

Overall distribution (11 members)mean 74·median 74
6087

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LPL Mid 2026 ranking at a glance

LPL Mid 2026 compares 11 members across 6 stats including Mechanics, Laning, Teamfighting. The overall top is Knight, with an average of 87. In Mechanics, Knight ranks highest. The overall average here is 74, ranging from 87 down to 60.

Frequently asked questions

Who is ranked #1 in LPL Mid 2026?
The overall #1 in LPL Mid 2026 is Knight, averaging 87.
How many are compared in LPL Mid 2026?
11 entries compared across 6 stats including Mechanics, Laning, Teamfighting.
Who leads in Mechanics?
Knight leads in Mechanics.
How are these scores calculated?
Each axis is a percentile within this ranking, averaged from measured match metrics, with sample shrinkage and — for international events — anchoring to the major-league distribution and a strength-of-schedule adjustment. A rank is not a raw metric; the full method is at everystat.ai/clusters/method.

How the six-axis ratings work →