kim jung un

kim jung un — CS2 Stats

US76561198187224194[U:1:226958466]Steam profile ↗✓ No bans

177Tracked matches43%Win rate2022Tracked since
CSDB Rating5.5 SolidSupport
Premier CS Rating21,472Top 50% of 204,150 CSDB-tracked playersPink band · top ~6.9% of ranked players (population est.)
CSDB Leaderboard#61315 of 251271 tracked
FaceitLevel 5Top 67.6% of ranked FACEIT players
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 24 Sep 2026 (18 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: 21,472 +8,336 21 Jan – 24 Sept · 15 days played
13,13623,463peak 23,46321 Jan24 Sept
23,725Peak Premier in tracked matches
42Days played since 2024-10-23

Premier CS Rating

  • 1,991 below peak (23,463)
  • Next: Red band at 25,000 — 3,528 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.

Share this profile

CSDB.GGkim jung unFACEITLevel 5STANDINGTop 67.6% of rankedPREMIER21,472 · Pink bandcsdb.gg/stats

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Performance scores

Aim58
Positioning54
Utility60

0–100 skill scores via Leetify.

Recent form

STEADY51–46–3Last 10051%Win rateLLWLWWLLWL

Last 10 vs previous 10: +20pp win rate · +0.04 avg rating · −4.1pp headshot accuracy · −8ms reaction

Win rate up 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 638ms

Last 10

  • 4–6 · 40% win rate
  • Avg rating 0.02
  • Avg headshot accuracy 12%
  • Avg reaction 620ms

Last 20

  • 6–14 · 30% win rate
  • Avg rating -0.00
  • Avg headshot accuracy 14%
  • Avg reaction 624ms

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 (+1.3 against its own average).

Aim5.8
Utility6.0
Positioning5.4
Opening Duels2.8

Effective flashes

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

ZywOo

Plays most like ZywOo 81% 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. 635ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

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

Aim5.8
Positioning5.4
Utility6.0
Mechanics6.6
Opening Duels3.9
Win Impact2.8

Composite 5.5/10 (Solid), a weighted mean of the bars with a small opposition adjustment (×1.02 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating0.01−0.02
first ⅓ avg 0.02 → last ⅓ avg 0.01
Reaction time636ms−34ms
first ⅓ avg 670ms → last ⅓ avg 636ms
Headshot accuracy14.5%−6.7%
first ⅓ avg 21.2% → last ⅓ avg 14.5%

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

0.11Best match rating · 13–5 · mirage, 21 Jan →
41%Best headshot accuracy · 13–4 · nuke, 7 Feb →
477msFastest reaction time · 13–9 · anubis, 19 Feb →
13–0Biggest win · dust2, 7 Feb →

Across the last 100 tracked matches.

Highlights

10Longest win streak
L2Current streak
8–13In matches decided by ≤2 rounds
10Overtime games

Map breakdown

nukeBest map · 75% over 8ancientWeakest map · 40% over 10
MapGradePlayedRecordWin rateAvg rating
dust2A3520–1557%0.00
anubisC188–1044%0.00
ancientC104–640%0.01
mirageA95–456%0.01
nukeS86–275%0.02
infernoS64–267%0.02
trainA53–260%0.02
overpass—40–40%0.01
cache—21–150%0.01
office—10–10%0.03
basalt—10–10%-0.07
edin—10–10%0.05

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (40% over 10). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Skill profile

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

14.7%Headshot accuracy
35.5%Accuracy (enemy spotted)
32.0%Spray accuracy
79.6%Counter-strafing
9.9°Preaim
635msReaction time
48.0%T opening success
43.4%CT opening success
0.73Enemies flashed / flash
7.2%Flash assists
12.15HE damage / grenade
6.68Flashes / 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 14.7402% — below the 15% mark we flag

    Aim Training →
Spend your practice time on Ancient

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

40% win rate across 10 tracked games — your weakest map with enough games to be worth reading into.

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

MapScoreRatingHS%Date
anubis8–130.039%24 Sept →
dust210–13-0.0515%24 Sept →
dust213–40.0814%22 Sept →
dust214–160.0115%22 Sept →
cache13–110.019%22 Sept →
dust213–80.078%22 Sept →
ancient8–13-0.018%22 Sept →
ancient6–13-0.0311%21 Sept →
dust213–90.0817%21 Sept →
cache10–130.0112%20 Sept →
anubis10–130.0421%20 Sept →
dust213–10-0.0718%20 Sept →
nuke5–13-0.065%20 Sept →
mirage4–13-0.0512%19 Sept →
dust27–13-0.078%19 Sept →
mirage7–130.0028%19 Sept →
ancient13–90.0914%19 Sept →
dust211–13-0.0514%13 Sept →
mirage11–130.0024%13 Sept →
anubis12–16-0.0415%13 Sept →
anubis13–3-0.0218%13 Sept →
mirage13–50.0718%13 Sept →
anubis13–80.0310%13 Sept →
ancient16–120.0216%2 Aug →
anubis13–90.0215%23 Aug →
mirage5–13-0.038%23 Aug →
dust216–140.0211%18 Aug →
dust213–8-0.0219%18 Aug →
overpass11–130.0424%30 Jul →
overpass8–13-0.0123%27 Jul →
dust211–13-0.019%26 Jul →
overpass11–130.0018%26 Jul →
inferno13–40.0713%26 Jul →
dust213–90.0221%26 Jul →
dust213–10-0.0133%25 Jul →
inferno13–50.0030%23 Jul →
nuke5–13-0.0124%23 Jul →
mirage13–11-0.0313%21 Jul →
ancient13–70.0720%21 Jul →
mirage13–6-0.007%19 Jul →
dust26–13-0.0616%19 Jul →
overpass11–13-0.0036%19 Jul →
inferno11–13-0.0518%29 Jun →
dust25–13-0.0616%29 Jun →
nuke13–80.0916%29 Jun →
dust213–90.0425%27 Jun →
inferno13–90.0511%25 Mar →
anubis9–13-0.0220%11 Mar →
dust213–8-0.0421%11 Mar →
ancient4–130.019%8 Mar →
inferno4–13-0.0124%8 Mar →
nuke13–8-0.0519%8 Mar →
dust211–130.0121%8 Mar →
dust213–50.0615%8 Mar →
dust22–13-0.0621%8 Mar →
anubis10–13-0.0423%4 Mar →
nuke13–50.1124%4 Mar →
dust23–13-0.038%2 Mar →
anubis6–130.0222%25 Feb →
ancient11–130.0012%23 Feb →
ancient13–16-0.0721%20 Feb →
dust24–13-0.068%20 Feb →
dust216–130.0719%20 Feb →
anubis6–13-0.078%20 Feb →
anubis13–9-0.0220%19 Feb →
anubis13–80.0131%19 Feb →
mirage13–11-0.0128%19 Feb →
anubis15–15-0.0433%19 Feb →
anubis9–130.0327%19 Feb →
dust213–9-0.0214%19 Feb →
train11–130.0322%19 Feb →
anubis10–130.0129%18 Feb →
dust213–100.0020%18 Feb →
dust213–8-0.0118%17 Feb →
dust29–13-0.0234%17 Feb →
anubis16–12-0.0218%16 Feb →
dust213–110.0513%16 Feb →
nuke13–70.0614%16 Feb →
ancient11–130.0220%16 Feb →
dust216–140.0222%16 Feb →
dust29–130.0216%15 Feb →
dust23–13-0.0313%15 Feb →
dust213–11-0.0114%14 Feb →
dust213–100.0725%12 Feb →
nuke13–100.0227%9 Feb →
train13–10-0.0120%9 Feb →
nuke13–40.0441%7 Feb →
dust213–00.0735%7 Feb →
anubis13–20.0318%7 Feb →
ancient16–140.0415%23 Jan →
mirage13–50.1121%21 Jan →
anubis13–50.0713%21 Jan →
office8–130.0324%7 Dec →
dust212–120.0427%4 Dec →
train13–80.0218%18 Nov →
train5–13-0.0416%18 Nov →
basalt11–13-0.0729%16 Nov →
train13–60.0916%16 Nov →
edin12–120.0510%16 Nov →
inferno13–50.0618%23 Oct →

Match data via Leetify.

Recent teammates

Milestones

100 Matches10-Win Streak
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

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