ÙwÚ

ÙwÚ — CS2 Stats

DE76561198299228633[U:1:338962905]Steam profile ↗✓ No bans

2,796Tracked matches63%Win rate2020Tracked since
CSDB Rating2.7 LearningPositional Player
Premier CS Rating12,619Blue band · top ~41% of ranked players (population est.)
FaceitLevel 1Top 100.0% of ranked FACEIT players
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 13 Sep 2026 (19 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: 12,619 +7,504 7 May – 13 Sept · 8 days played
5,00012,619peak 12,6197 May13 Sept
12,619Peak Premier in tracked matches
532Highest Faceit ELO seen on CSDB
35Days played since 2026-05-06

Premier CS Rating

  • At peak — 12,619
  • Next: Purple band at 15,000 — 2,381 to go
  • Reached: Blue band · Light Blue band
  • Blue band first seen 2026-09-06
  • Light Blue band first seen 2026-09-06

Premier comes from this profile’s tracked match history, so it reaches back as far as those matches do. Faceit ELO is CSDB’s own observation — no feed exposes ELO per match, so that line only has the days the profile was viewed and cannot be backfilled.

Share this profile

CSDB.GGÙwÚFACEITLevel 1STANDINGTop 100.0% of rankedPREMIER12,619 · Blue bandPEAK ELO532csdb.gg/stats

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Performance scores

Aim19
Positioning30
Utility26

0–100 skill scores via Leetify.

Recent form

STEADY41–56–3Last 10041%Win rateWWWLWWWLLL

Last 10 vs previous 10: 0pp win rate · +0.02 avg rating · −0.5pp headshot accuracy · +55ms reaction

Win rate 0pp across the last 10 against the 10 before — within the normal variation of a 10-match window (±20pp), so no real shift yet.

Last 5 · 10 · 20 matches

Last 5

  • 4–1 · 80% win rate
  • Avg rating -0.06
  • Avg headshot accuracy 11%
  • Avg reaction 731ms

Last 10

  • 6–4 · 60% win rate
  • Avg rating -0.06
  • Avg headshot accuracy 11%
  • Avg reaction 693ms

Last 20

  • 12–8 · 60% win rate
  • Avg rating -0.07
  • Avg headshot accuracy 12%
  • Avg reaction 666ms

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: Positional Player — Positioning stands above the rest of this profile (+1.2 against its own average).

Aim1.9
Utility2.6
Positioning3.0
Opening Duels0.3
Clutch1.2

Limited utility dependence

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

jL

Plays most like jL 73% 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. 682ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 58% 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

Aim1.9
Positioning3.0
Utility2.6
Mechanics1.8
Opening Duels0.4
Win Impact9.4

Composite 2.7/10 (Learning), a weighted mean of the bars with a small opposition adjustment (×0.95 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.06+0.01
first ⅓ avg -0.07 → last ⅓ avg -0.06
Reaction time688ms−93ms
first ⅓ avg 781ms → last ⅓ avg 688ms
Headshot accuracy10.7%+0.7%
first ⅓ avg 10.0% → last ⅓ avg 10.7%

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

0.18Best match rating · 9–0 · overpass, 18 Jun →
24%Best headshot accuracy · 7–9 · debris, 29 Aug →
438msFastest reaction time · 9–2 · anubis, 4 Sept →
13–1Biggest win · inferno, 7 Sept →

Across the last 100 tracked matches.

Highlights

5Longest win streak
W3Current streak
3–10In matches decided by ≤2 rounds
3Overtime games

Map breakdown

ancientBest map · 60% over 5nukeWeakest map · 25% over 12
MapGradePlayedRecordWin rateAvg rating
mirageA1710–759%-0.06
cacheD155–1033%-0.06
infernoC156–940%-0.06
nukeD123–925%-0.08
anubisA116–555%-0.05
dust2B105–550%-0.05
officeD62–433%-0.06
ancientA53–260%-0.05
overpass—31–233%-0.03
eldorado—10–10%-0.26
debris—10–10%-0.05
shelter—10–10%-0.06
fachwerk—10–10%-0.03
italy—10–10%-0.06
alpine—10–10%-0.06

Across the last 100 tracked matches.

Nuke is currently your weakest sufficiently-sampled map (25% over 12). Start with the 6 essential Nuke lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

155Matches
35%Win rate
0.55Avg K/D
48.8ADR
36%Headshot %

Clutches & streaks

26%1v1 clutch win
9%1v2 clutch win
5Longest win streak

Recent Faceit resultsLLLLL

MapMatchesWin rateAvg K/DAvg kills
Ancient4628%0.477.7
Anubis3241%0.589.4
Inferno2836%0.549.1
Mirage2250%0.8210.4
Nuke825%0.356.3
Dust2540%0.6810.6
Train560%0.366.2
Overpass425%0.447.5

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.

11.5%Headshot accuracy
28.0%Accuracy (enemy spotted)
31.8%Spray accuracy
58.3%Counter-strafing
11.1°Preaim
682msReaction time
20.8%T opening success
33.4%CT opening success
0.55Enemies flashed / flash
0.5%Flash assists
10.99HE damage / grenade
2.48Flashes / 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 11.465% — below the 15% mark we flag

    Aim Training →
  2. Grenades & Utility

    You are buying and holding utility rather than using it. Unthrown flashes are wasted money every round.

    Flashes per match 2.4753 — below the 4 mark we flag

    Grenade Lineups →
  3. Advanced Mechanics

    You are losing most of the first duels you take on T side, which is usually a peeking and spacing problem, not aim.

    T opening duels 20.7892% — below the 40% mark we flag

Spend your practice time on Nuke

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

25% win rate across 12 tracked games — your weakest map with enough games to be worth reading into.

Nuke callouts & strategy →Nuke grenade lineups →

Recent matches

MapScoreRatingHS%Date
mirage13–8-0.0510%13 Sept →
cache13–8-0.0514%10 Sept →
mirage13–10-0.0812%10 Sept →
inferno8–13-0.098%10 Sept →
mirage13–9-0.0414%8 Sept →
dust29–1-0.026%8 Sept →
dust213–7-0.0420%8 Sept →
mirage3–13-0.0812%8 Sept →
dust25–13-0.034%8 Sept →
inferno9–13-0.0816%8 Sept →
inferno13–1-0.0617%7 Sept →
dust213–11-0.1211%7 Sept →
nuke6–13-0.087%7 Sept →
inferno10–13-0.095%7 Sept →
ancient11–13-0.0716%7 Sept →
inferno13–7-0.1012%7 Sept →
anubis13–8-0.0811%6 Sept →
anubis11–13-0.1113%6 Sept →
dust213–7-0.0210%5 Sept →
mirage13–10-0.0318%5 Sept →
anubis13–8-0.0214%5 Sept →
inferno13–2-0.0213%5 Sept →
inferno1–8-0.100%5 Sept →
inferno4–130.006%5 Sept →
dust213–9-0.036%4 Sept →
cache1–13-0.0713%4 Sept →
mirage12–9-0.0215%4 Sept →
anubis9–2-0.0620%4 Sept →
mirage11–0-0.020%4 Sept →
nuke13–6-0.0212%4 Sept →
anubis16–12-0.119%3 Sept →
anubis2–13-0.035%31 Aug →
mirage4–13-0.106%31 Aug →
anubis13–40.1121%31 Aug →
mirage13–10.0112%29 Aug →
cache7–13-0.065%29 Aug →
cache7–13-0.0315%29 Aug →
cache13–40.0312%29 Aug →
eldorado3–9-0.267%29 Aug →
debris7–9-0.0524%29 Aug →
inferno3–9-0.0613%29 Aug →
mirage13–10-0.0613%28 Aug →
nuke6–13-0.090%27 Aug →
cache9–13-0.110%24 Aug →
office5–13-0.1018%13 Aug →
cache13–8-0.0510%13 Aug →
ancient12–20.030%13 Aug →
office10–13-0.0410%10 Aug →
mirage11–13-0.098%10 Aug →
dust212–12-0.069%5 Aug →
mirage11–13-0.076%3 Aug →
inferno13–10.0312%30 Jul →
mirage9–13-0.0713%30 Jul →
cache13–4-0.0716%30 Jul →
nuke13–10-0.129%29 Jul →
anubis10–13-0.0610%29 Jul →
shelter7–13-0.068%25 Jul →
fachwerk12–12-0.0318%25 Jul →
anubis13–11-0.0115%7 Jul →
dust24–13-0.058%1 Jul →
inferno13–4-0.0918%1 Jul →
anubis11–13-0.1015%24 Jun →
mirage13–2-0.0519%24 Jun →
dust26–130.0311%24 Jun →
cache4–13-0.0513%24 Jun →
mirage6–13-0.068%24 Jun →
ancient3–13-0.0911%18 Jun →
nuke3–9-0.0315%18 Jun →
inferno5–9-0.136%18 Jun →
overpass9–00.1813%18 Jun →
italy6–13-0.067%13 Jun →
office13–8-0.058%13 Jun →
ancient13–11-0.039%13 Jun →
cache6–13-0.0612%2 Jun →
mirage13–5-0.0513%2 Jun →
nuke4–9-0.174%24 May →
nuke1–9-0.089%23 May →
overpass3–9-0.184%23 May →
inferno3–90.0210%23 May →
nuke7–13-0.052%22 May →
dust23–13-0.1218%22 May →
nuke9–6-0.1218%15 May →
nuke7–9-0.0511%15 May →
inferno3–9-0.019%15 May →
office11–13-0.0610%15 May →
office13–7-0.043%15 May →
mirage4–13-0.096%13 May →
cache4–13-0.113%13 May →
office3–13-0.0722%13 May →
nuke7–9-0.124%13 May →
cache9–13-0.087%11 May →
anubis5–13-0.039%9 May →
alpine2–13-0.068%9 May →
inferno16–12-0.100%7 May →
overpass15–15-0.097%7 May →
ancient13–7-0.0817%7 May →
nuke7–9-0.0522%7 May →
cache13–5-0.0611%7 May →
cache2–13-0.0611%7 May →
cache6–13-0.0921%6 May →

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 →

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