✪Noonga

✪Noonga — CS2 Stats

AU76561198255865101[U:1:295599373]Steam profile ↗✓ No bans

1,175Tracked matches44%Win rate2020Tracked since
CSDB Rating5.6 SolidPositional Player
FaceitLevel 3Top 90.6% of ranked FACEIT players
Ladder ranks via Leetify

Rating over time

Premier CS Rating: 12,107 -225 30 May – 22 Jun · 2 days played
12,10712,332peak 12,33230 May22 Jun
12,332Peak Premier in tracked matches
50Days played since 2025-05-30

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

Share this profile

CSDB.GG✪NoongaFACEITLevel 3STANDINGTop 90.6% of rankedcsdb.gg/stats

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Performance scores

Aim61
Positioning59
Utility50

0–100 skill scores via Leetify.

Recent form

COLD41–52–7Last 10041%Win rateLLLLLLWWWT

Last 10 vs previous 10: −10pp win rate · −0.04 avg rating · +4.1pp headshot accuracy · +81ms reaction

Win rate −10pp 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

  • 0–5 · 0% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 15%
  • Avg reaction 566ms

Last 10

  • 3–6–1 · 30% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 17%
  • Avg reaction 614ms

Last 20

  • 7–10–3 · 35% win rate
  • Avg rating 0.00
  • Avg headshot accuracy 14%
  • Avg reaction 574ms

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 (+1.5 against its own average).

Aim6.1
Utility5.0
Positioning5.9
Opening Duels3.9
Clutch2.6

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

donk

Plays most like donk 84% 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. 556ms 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

Aim6.1
Positioning5.9
Utility5.0
Mechanics6.6
Opening Duels5.8
Win Impact3.1

Composite 5.6/10 (Solid), 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 rating0.01+0.00
first ⅓ avg 0.00 → last ⅓ avg 0.01
Reaction time572ms−38ms
first ⅓ avg 610ms → last ⅓ avg 572ms
Headshot accuracy15.0%+0.6%
first ⅓ avg 14.3% → last ⅓ avg 15.0%

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

0.21Best match rating · 13–6 · inferno, 1 Jan →
33%Best headshot accuracy · 5–13 · nuke, 19 Jun →
422msFastest reaction time · 11–13 · mirage, 13 Oct →
13–1Biggest win · mirage, 26 Sept →

Across the last 100 tracked matches.

Highlights

4Longest win streak
L6Current streak
3–4In matches decided by ≤2 rounds
1Overtime games

Map breakdown

dust2Best map · 62% over 26infernoWeakest map · 17% over 12
MapGradePlayedRecordWin rateAvg rating
dust2A2616–1062%0.00
mirageC2410–1442%0.02
infernoD122–1017%-0.00
nukeD82–625%-0.03
cacheB84–450%0.02
ancientB63–350%-0.01
anubisD62–433%0.01
vertigoD51–420%-0.03
train—20–20%0.05
agency—11–0100%0.06
jura—10–10%-0.02
overpass—10–10%-0.00

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (17% over 12). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

6Matches
33%Win rate
1.93Avg K/D
100.6ADR
36%Headshot %

Clutches & streaks

33%1v1 clutch win
100%1v2 clutch win
1Longest win streak

Recent Faceit resultsWLLWL

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.

14.3%Headshot accuracy
32.1%Accuracy (enemy spotted)
32.3%Spray accuracy
79.9%Counter-strafing
9.7°Preaim
556msReaction time
58.2%T opening success
48.4%CT opening success
0.71Enemies flashed / flash
7.1%Flash assists
5.02HE damage / grenade
7.73Flashes / 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 14.2738% — below the 15% mark we flag

    Aim Training →
Spend your practice time on Inferno

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

17% win rate across 12 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
mirage11–13-0.046%13 Oct →
dust29–13-0.0520%13 Oct →
ancient9–13-0.0817%12 Oct →
nuke8–13-0.0813%12 Oct →
inferno3–13-0.0018%12 Oct →
dust210–130.0415%12 Oct →
dust213–20.0126%12 Oct →
nuke13–70.0212%8 Oct →
dust213–70.0125%8 Oct →
mirage12–12-0.0314%4 Oct →
dust213–90.0113%4 Oct →
anubis8–13-0.0318%4 Oct →
dust213–60.156%4 Oct →
inferno10–13-0.0120%3 Oct →
dust23–13-0.1411%3 Oct →
mirage11–13-0.0111%3 Oct →
nuke12–120.0516%26 Sept →
dust212–12-0.0214%26 Sept →
dust213–50.048%26 Sept →
mirage13–10.197%26 Sept →
vertigo1–10-0.0117%26 Sept →
mirage13–100.137%25 Sept →
inferno7–130.0422%25 Sept →
ancient7–13-0.046%24 Sept →
dust25–13-0.1111%24 Sept →
dust213–50.0622%18 Sept →
anubis10–130.0214%18 Sept →
mirage13–90.0414%23 Jul →
anubis13–30.0915%23 Jul →
dust213–40.0415%23 Jul →
dust213–80.0421%23 Jun →
nuke5–13-0.0333%19 Jun →
inferno11–13-0.099%12 Jun →
mirage13–100.0914%11 Jun →
dust29–13-0.0212%11 Jun →
dust213–80.089%11 Jun →
inferno2–10-0.045%8 Jun →
cache11–130.0113%7 Jun →
ancient4–130.0510%7 Jun →
anubis12–120.0220%7 Jun →
dust213–30.0312%7 Jun →
mirage3–13-0.0910%5 Jun →
vertigo5–130.0611%5 Jun →
mirage8–13-0.0022%31 May →
inferno13–40.0313%31 May →
anubis8–13-0.109%31 May →
cache7–130.038%29 May →
mirage2–130.0125%16 May →
dust28–13-0.0928%7 May →
mirage13–110.0317%7 May →
cache13–9-0.0211%6 May →
inferno8–13-0.0131%3 May →
mirage4–130.0426%3 May →
dust213–80.0419%2 May →
cache13–70.0313%1 May →
cache13–90.0821%1 May →
cache13–50.0811%1 May →
cache8–130.0117%29 Apr →
mirage13–40.0925%29 Apr →
cache12–12-0.0311%29 Apr →
vertigo9–13-0.0716%5 Apr →
mirage3–13-0.064%22 Mar →
mirage13–3-0.0218%15 Mar →
inferno4–13-0.0717%25 Jan →
inferno10–13-0.0229%25 Jan →
dust213–7-0.0311%25 Jan →
vertigo13–7-0.064%3 Jan →
mirage9–13-0.0410%1 Jan →
inferno13–60.2121%1 Jan →
dust26–13-0.056%16 Nov →
mirage13–90.0622%16 Nov →
dust29–13-0.039%16 Nov →
nuke9–13-0.0411%3 Nov →
inferno4–9-0.0814%3 Nov →
mirage5–130.0412%24 Oct →
ancient13–80.0618%24 Oct →
dust25–13-0.0410%24 Oct →
dust213–8-0.0316%17 Oct →
mirage10–130.0312%17 Oct →
inferno12–12-0.0211%17 Oct →
train9–130.0818%12 Sept →
anubis13–100.0221%21 Aug →
dust213–80.088%21 Aug →
mirage9–13-0.0119%20 Aug →
mirage13–6-0.0413%20 Aug →
dust213–20.0124%26 Jul →
mirage3–13-0.0310%26 Jul →
ancient13–110.016%25 Jul →
mirage13–80.0614%23 Jul →
train8–130.0114%4 Jul →
nuke2–13-0.104%4 Jul →
ancient13–10-0.0322%22 Jun →
dust213–110.0517%22 Jun →
mirage2–13-0.0415%20 Jun →
nuke13–6-0.0327%13 Jun →
agency13–10.0612%12 Jun →
vertigo9–13-0.0614%6 Jun →
jura12–12-0.028%6 Jun →
overpass4–13-0.0016%31 May →
nuke12–16-0.0322%30 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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