Kaneki Ken

Kaneki Ken — CS2 Stats

AR76561199492159743[U:1:1531894015]Steam profile ↗✓ No bans

1,420Tracked matches27%Win rate2023Tracked since
1,793Hours in CS8Hrs last 2 wks
CSDB Rating3.6 LearningSupport
Premier CS Rating17,528Purple band · top ~18.3% of ranked players (population est.)
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 5 Nov 2026 (9 days ago). Ranks are recorded once per day this page is viewed.

No change since then. Play, then come back: the next observation lands here.

Rating over time

Premier CS Rating: 17,528 +242 28 Jun – 5 Nov · 26 days played
16,71321,283peak 21,28328 Jun5 Nov
21,283Peak Premier in tracked matches
54Days played since 2026-06-28

Premier CS Rating

  • 3,755 below peak (21,283)
  • -1,598 over 30 days · declining
  • -3,235 over 90 days
  • Next: Pink band at 20,000 — 2,472 to go
  • Reached: Pink band · Purple band · Blue band · Light Blue band

Premier comes from this profile’s tracked match history, so it reaches back as far as those matches do.

How this compares with the same rank

Median values for Purple band among CSDB-tracked players (n=37,767), from Valve's own lifetime stats. Aim, positioning and utility scores are deliberately not benchmarked here — those are a third-party provider's derived metrics, which CSDB does not store.

MetricThis playerPurple band medianPink band medianvs Pink band
Headshot rate37.6%44.4%46.9%9.3% short
Shot accuracy1.5%11.9%13.0%11.5% short
Kill/death ratio0.851.031.080.23 short
Match win rate46.0%45.2%46.7%meets

This profile matches the typical Pink band player on 1 of 4 comparable metrics.

Widest gap: Shot accuracy. That is the metric furthest from the Pink band median in relative terms — not necessarily the one holding a rank back, which no statistic here can establish.

Share this profile

CSDB.GGKaneki KenPREMIER17,528 · Purple bandcsdb.gg/stats

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Performance scores

Aim36
Positioning48
Utility64

0–100 skill scores via Leetify.

Recent form

COLD43–55–2Last 10043%Win rateLWLLWLLLLL

Last 10 vs previous 10: −20pp win rate · −0.02 avg rating · −0.2pp headshot accuracy · −32ms reaction

Win rate down 20pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp).

Last 5 · 10 · 20 matches

Last 5

  • 2–3 · 40% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 12%
  • Avg reaction 631ms

Last 10

  • 2–8 · 20% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 13%
  • Avg reaction 562ms

Last 20

  • 6–14 · 30% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 13%
  • Avg reaction 578ms

Newest first, from the last 100 tracked matches. Each block is its own sample — one result moves a 5-match win rate by 20 points.

Player DNA

Primary style: Support — Utility contribution stands above the rest of this profile (+3.6 against its own average).

Aim3.6
Utility6.4
Positioning4.8
Opening Duels0.0
Clutch0.0

Style profile from tracked-match aggregates — how this player plays, not how good they are. Classification rules are deterministic and documented in code.

What this cannot see yet: which weapons you use — so CSDB cannot identify an AWPer, and no style here implies a rifle or a sniper. It also cannot see how often you take opening duels, only how often you win them, nor where you hold, so roles that depend on those (entry, lurk, anchor) are deliberately absent rather than guessed. All of it needs round-by-round demo data, which is the next thing being built.

Your pro match

iM

Plays most like iM 56% playstyle similarity

Most alike: utility contribution, positioning profile.

Where you differ: lower opening-duel success; lower aim profile.

Similarity of playstyle shape across shared dimensions — it says how you play, not that you play at their level. Full comparison →

Strengths & areas to improve

Areas to improve

Reaction time. 590ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 62% of shots are taken properly stopped — moving-shot inaccuracy is quietly taxing every duel.

Generated by fixed rules over this profile's own numbers — no model, no guessing; silent when the sample is too small to support a claim.

CSDB Rating breakdown

Aim3.6
Positioning4.8
Utility6.4
Mechanics2.7
Opening Duels2.4
Win Impact0.0

Composite 3.6/10 (Learning), a weighted mean of the bars with a small opposition adjustment (×1.00 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.02+0.01
first ⅓ avg -0.03 → last ⅓ avg -0.02
Reaction time586ms−35ms
first ⅓ avg 620ms → last ⅓ avg 586ms
Headshot accuracy12.9%−0.6%
first ⅓ avg 13.5% → last ⅓ avg 12.9%

Rolling 5-match average across the last 100 tracked matches, oldest to newest. The delta compares the first third of the window with the last.

Personal bests

0.13Best match rating · 13–6 · ancient, 10 Oct →
26%Best headshot accuracy · 11–13 · nuke, 1 Aug →
422msFastest reaction time · 1–13 · ancient, 18 Oct →
13–1Biggest win · nuke, 22 Aug →

Across the last 100 tracked matches.

Highlights

4Longest win streak
5–12In matches decided by ≤2 rounds
8Overtime games

Map breakdown

anubisBest map · 56% over 16mirageWeakest map · 27% over 11
MapGradePlayedRecordWin rateAvg rating
infernoB199–1047%-0.03
anubisA169–756%-0.02
ancientC166–1038%-0.03
nukeB168–850%-0.02
mirageD113–827%-0.02
dust2D93–633%-0.02
cacheC83–538%-0.02
vertigo—32–167%-0.01
train—10–10%-0.00
overpass—10–10%-0.01

Across the last 100 tracked matches.

Mirage is currently your weakest sufficiently-sampled map (27% over 11). Start with the 5 essential Mirage lineups, review the callouts, then spin up a practice server.

Lifetime stats

34,467Lifetime kills
0.85K/D
2,656Matches
46.0%Match win rate · Top 50% of Purple band
37.6%Headshot %
1.5%Shot accuracy
4,106MVPs
984Hours (in match)
3,302Bombs planted
650Bombs defused

Most-used weapons

Lifetime map wins

4,942inferno
4,066nuke
2,301dust2
1,927vertigo
485train
33office
8italy
2assault

Lifetime totals via Steam — visible because this profile's game details are public. Spans CS:GO and CS2.

Faceit stats

Combat

9Matches
56%Win rate
0.79Avg K/D
67.6ADR
42%Headshot %

Clutches & streaks

0%1v1 clutch win
20%1v2 clutch win
2Longest win streak

Recent Faceit resultsWLLLW

MapMatchesWin rateAvg K/DAvg kills
Inferno425%0.5810.0
Anubis3100%1.2113.3
Nuke1100%0.587.0
Vertigo10%0.609.0

Faceit-match stats via the FACEIT Data API — a separate match pool from the sections above.

Skill profile

Aggregate performance across tracked matches — stats via Leetify. Percentile context against other CSDB-tracked players arrives as our own benchmark data accumulates.

12.2%Headshot accuracy
29.4%Accuracy (enemy spotted)
34.9%Spray accuracy
62.2%Counter-strafing
10.4°Preaim
590msReaction time
46.0%T opening success
33.3%CT opening success
0.66Enemies flashed / flash
2.5%Flash assists
12.56HE damage / grenade
10.70Flashes / match

Recommended for you

Chosen by comparing your tracked metrics against the thresholds we flag — the measurement behind each one is shown, so you can disagree with it.

  1. Best CS2 Crosshair

    Your shots are landing on bodies more often than heads — usually a crosshair-height and placement habit rather than raw aim.

    Headshot accuracy 12.2331% — below the 15% mark we flag

    Aim Training →
  2. Advanced Mechanics

    Losing the first CT duel repeatedly usually means holding angles that favour the peeker.

    CT opening duels 33.2568% — below the 40% mark we flag

Spend your practice time on Mirage

Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.

27% win rate across 11 tracked games — your weakest map with enough games to be worth reading into.

Mirage callouts & strategy →Mirage grenade lineups →

Recent matches

MapScoreRatingHS%Date
anubis5–13-0.058%5 Nov →
inferno13–3-0.0421%5 Nov →
dust210–13-0.048%2 Nov →
dust27–13-0.0310%29 Oct →
anubis13–30.0513%29 Oct →
inferno9–13-0.0415%26 Oct →
dust26–130.0415%24 Oct →
cache6–13-0.0312%19 Oct →
ancient1–13-0.0817%18 Oct →
anubis11–13-0.0710%18 Oct →
ancient13–60.1311%10 Oct →
anubis11–13-0.0111%10 Oct →
anubis13–80.0220%8 Oct →
mirage5–13-0.076%2 Oct →
dust211–13-0.0218%30 Sept →
vertigo13–10-0.018%28 Sept →
inferno1–3-0.0322%28 Sept →
anubis16–120.0418%28 Sept →
ancient13–16-0.025%27 Sept →
ancient3–13-0.0711%27 Sept →
mirage3–13-0.0315%27 Sept →
cache2–13-0.0515%26 Sept →
anubis11–13-0.0516%19 Sept →
ancient13–80.0317%19 Sept →
nuke7–13-0.113%19 Sept →
train3–7-0.0020%18 Sept →
anubis9–13-0.038%16 Sept →
mirage5–13-0.0711%16 Sept →
dust213–7-0.049%16 Sept →
vertigo5–13-0.0313%16 Sept →
ancient13–100.0114%14 Sept →
inferno13–80.049%13 Sept →
ancient14–16-0.0318%13 Sept →
inferno13–9-0.050%13 Sept →
inferno10–13-0.0015%12 Sept →
dust24–13-0.0621%10 Sept →
cache11–130.0017%4 Sept →
dust213–100.0113%3 Sept →
mirage13–9-0.039%30 Aug →
inferno11–13-0.0317%30 Aug →
anubis16–130.005%30 Aug →
nuke6–13-0.0610%29 Aug →
anubis8–13-0.0411%29 Aug →
nuke13–9-0.057%28 Aug →
vertigo13–70.0215%28 Aug →
ancient0–5-0.070%28 Aug →
mirage12–12-0.0114%24 Aug →
nuke0–13-0.050%22 Aug →
inferno5–13-0.013%22 Aug →
nuke13–10.076%22 Aug →
dust211–13-0.049%22 Aug →
overpass11–13-0.0113%22 Aug →
nuke13–60.0723%20 Aug →
mirage13–110.0112%19 Aug →
inferno6–13-0.0511%14 Aug →
ancient9–130.0117%14 Aug →
mirage6–13-0.0310%13 Aug →
inferno13–10-0.038%12 Aug →
dust213–5-0.0313%12 Aug →
mirage7–13-0.0212%12 Aug →
anubis13–4-0.026%10 Aug →
inferno13–50.059%7 Aug →
anubis13–10-0.0218%5 Aug →
mirage8–13-0.039%5 Aug →
inferno13–80.0115%1 Aug →
nuke11–13-0.0026%1 Aug →
anubis13–50.0712%31 Jul →
ancient13–110.0018%30 Jul →
ancient1–13-0.0212%30 Jul →
anubis8–13-0.047%26 Jul →
anubis13–11-0.0711%26 Jul →
inferno5–13-0.0818%26 Jul →
inferno13–100.0315%25 Jul →
mirage5–13-0.0124%25 Jul →
ancient1–13-0.0422%24 Jul →
nuke13–10-0.045%23 Jul →
inferno7–13-0.0119%23 Jul →
ancient13–11-0.0516%23 Jul →
inferno11–13-0.0310%22 Jul →
inferno1–13-0.1414%22 Jul →
anubis16–12-0.0416%21 Jul →
cache7–13-0.017%20 Jul →
ancient8–13-0.0616%15 Jul →
mirage13–60.0413%15 Jul →
cache2–130.018%15 Jul →
inferno13–7-0.0517%14 Jul →
cache13–9-0.0919%14 Jul →
nuke13–70.0313%12 Jul →
nuke16–120.0021%12 Jul →
nuke3–13-0.0311%11 Jul →
cache13–70.0116%11 Jul →
nuke4–13-0.1117%9 Jul →
inferno13–10-0.0418%9 Jul →
nuke13–110.0116%4 Jul →
nuke15–15-0.027%1 Jul →
ancient4–13-0.076%1 Jul →
cache13–4-0.0215%1 Jul →
nuke9–13-0.083%1 Jul →
nuke16–130.0114%28 Jun →
ancient13–9-0.097%28 Jun →

Match data via Leetify.

Recent teammates

Milestones

1,000 Matches
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

This profile is built from public Steam data. If it is yours, you can remove it, or delete your CSDB account.