Koalis

Koalis — CS2 Stats

LV76561199772837621[U:1:1812571893]Steam profile ↗1 game ban

137Tracked matches64%Win rate2025Tracked since
143Hours in CS
CSDB Rating3.8 LearningClutch Specialist
WingmanSilver Elite Master
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 21 Sep 2026 (11 days ago). Ranks are recorded once per day this page is viewed.

No change since then — still 137 tracked matches. Play, then come back: the next observation lands here.

Rating over time

Premier CS Rating: 5,174 +1,990 1 Apr – 14 May · 7 days played
3,1845,749peak 5,7491 Apr14 May
5,749Peak Premier in tracked matches
45Days played since 2026-02-24

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

Compare periods

Last 30 days vs previous 30

No matches recorded between 3 Sep 2026 and 2 Oct 2026.

Measured 3 Sep 2026 → 2 Oct 2026; no observation near 60 days ago, so there is no previous window yet.

Differences between Valve's lifetime totals on the days CSDB observed this profile — every mode Valve counts, not only ranked. A window appears only when an observation sits within a few days of each end.

Performance scores

Aim31
Positioning45
Utility7

0–100 skill scores via Leetify.

Recent form

STEADY43–53–4Last 10043%Win rateLWWWLWWLLT

Last 10 vs previous 10: +10pp win rate · −0.04 avg rating · −6.5pp headshot accuracy · +17ms 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

  • 3–2 · 60% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 8%
  • Avg reaction 744ms

Last 10

  • 5–4–1 · 50% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 10%
  • Avg reaction 736ms

Last 20

  • 9–9–2 · 45% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 13%
  • Avg reaction 728ms

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: Clutch Specialist — Late-round 1vX conversion stands above the rest of this profile (+2.5 against its own average).

Aim3.1
Utility0.7
Positioning4.5
Opening Duels3.0
Clutch6.0

Strong CT-side openerLimited 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

donk

Plays most like donk 81% playstyle similarity

Most alike: positioning profile, utility contribution.

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

T-side openings. Opening success drops from 55% on CT to 29% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 725ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 67% 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

Aim3.1
Positioning4.5
Utility0.7
Mechanics3.8
Opening Duels3.1
Win Impact9.8

Composite 3.8/10 (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-0.01−0.00
first ⅓ avg -0.01 → last ⅓ avg -0.01
Reaction time709ms−13ms
first ⅓ avg 722ms → last ⅓ avg 709ms
Headshot accuracy15.0%+1.5%
first ⅓ avg 13.4% → 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

0.15Best match rating · 3–2 · vertigo, 10 Mar →
43%Best headshot accuracy · 9–7 · mirage, 16 May →
523msFastest reaction time · 0–13 · inferno, 1 Apr →
13–1Biggest win · dust2, 21 May →

Across the last 100 tracked matches.

Highlights

6Longest win streak
6–5In matches decided by ≤2 rounds

Map breakdown

officeBest map · 60% over 5infernoWeakest map · 27% over 11
MapGradePlayedRecordWin rateAvg rating
dust2D155–1033%-0.01
mirageC145–936%-0.01
infernoD113–827%-0.01
trainC104–640%-0.01
overpassB105–550%-0.00
ancientC94–544%-0.06
cacheA95–456%-0.02
nukeC94–544%-0.01
officeA53–260%-0.01
vertigo—43–175%0.03
anubis—32–167%-0.05
alpine—10–10%0.01

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (27% over 11). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

6,275Lifetime kills
0.94K/D
440Matches
44.1%Match win rate
37.3%Headshot %
0.0%Shot accuracy
1,046MVPs
143Hours (in match)
363Bombs planted
91Bombs defused

Most-used weapons

Lifetime map wins

793dust2
425inferno
332nuke
299train
134vertigo
92office
13italy
2ar_shoots

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

Faceit stats

Combat

11Matches
18%Win rate
0.60Avg K/D
60.5ADR
41%Headshot %

Clutches & streaks

50%1v1 clutch win
17%1v2 clutch win
1Longest win streak

Recent Faceit resultsWLLLL

MapMatchesWin rateAvg K/DAvg kills
Mirage425%0.7010.8
Cache333%0.4912.0
Anubis10%0.8713.0
Nuke10%0.6210.0
Overpass10%0.365.0
Dust210%0.508.0

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.

15.4%Headshot accuracy
31.5%Accuracy (enemy spotted)
30.3%Spray accuracy
67.0%Counter-strafing
10.6°Preaim
725msReaction time
28.7%T opening success
55.0%CT opening success
0.18Enemies flashed / flash
0.0%Flash assists
2.68HE damage / grenade
1.33Flashes / 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 Settings

    Slow first shots are as often a setup problem as a reflex one — framerate, sensitivity and crosshair visibility all move this number.

    Reaction time 724.7596ms — above the 700ms mark we flag

    Aim Training →
  2. Grenades & Utility

    Most of your flashes are blinding nobody. A handful of reliable pop-flash lineups fixes this faster than anything else.

    Enemies flashed per flash 0.1791 — below the 0.5 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 28.6793% — below the 40% mark we flag

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.

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

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
dust210–13-0.109%23 Aug →
ancient13–10-0.077%8 Aug →
dust213–40.0511%30 Jul →
anubis13–4-0.090%29 Jul →
cache10–13-0.0512%4 Jun →
cache13–60.026%26 May →
anubis13–80.0421%26 May →
dust22–7-0.0414%24 May →
ancient0–13-0.138%24 May →
dust212–12-0.0414%24 May →
mirage4–13-0.0626%24 May →
mirage13–80.1118%23 May →
dust211–13-0.0212%23 May →
dust28–130.0112%23 May →
train13–50.0117%21 May →
dust213–10.0011%21 May →
mirage5–13-0.0222%21 May →
cache8–13-0.0214%20 May →
cache13–100.0420%19 May →
office12–12-0.0415%17 May →
office13–60.0624%17 May →
mirage9–7-0.0443%16 May →
overpass13–30.0623%16 May →
cache13–50.0012%16 May →
overpass13–7-0.059%14 May →
dust213–4-0.0315%14 May →
ancient0–20.000%14 May →
vertigo13–70.0815%14 May →
nuke13–11-0.0215%13 May →
cache13–4-0.0421%9 May →
cache7–130.0013%9 May →
cache5–13-0.0913%6 May →
cache13–8-0.0121%6 May →
inferno6–9-0.1228%24 Apr →
overpass4–13-0.049%24 Apr →
mirage13–11-0.0427%24 Apr →
overpass8–8-0.0612%24 Apr →
inferno8–80.0314%24 Apr →
office13–5-0.0515%23 Apr →
nuke3–13-0.0818%23 Apr →
mirage10–130.0411%23 Apr →
mirage11–13-0.0217%23 Apr →
train13–8-0.0117%22 Apr →
train13–30.1424%22 Apr →
train4–130.0318%22 Apr →
inferno9–50.0918%22 Apr →
train7–0-0.0517%22 Apr →
nuke4–13-0.0229%20 Apr →
nuke8–8-0.0515%19 Apr →
mirage5–13-0.0524%19 Apr →
ancient6–13-0.0711%19 Apr →
overpass13–10.1315%19 Apr →
inferno13–30.026%19 Apr →
train10–13-0.0817%19 Apr →
nuke13–110.0516%15 Apr →
inferno5–130.0116%15 Apr →
ancient13–3-0.0512%15 Apr →
train10–130.0014%14 Apr →
ancient13–9-0.095%14 Apr →
mirage13–60.0810%12 Apr →
train11–13-0.035%8 Apr →
dust28–130.0217%1 Apr →
mirage11–13-0.0618%1 Apr →
inferno0–13-0.1517%1 Apr →
inferno6–9-0.094%20 Mar →
dust24–130.0114%20 Mar →
vertigo9–6-0.074%19 Mar →
overpass10–13-0.0519%18 Mar →
mirage10–13-0.0212%18 Mar →
dust24–13-0.0524%16 Mar →
mirage6–13-0.0618%16 Mar →
nuke13–40.039%15 Mar →
nuke8–130.007%14 Mar →
overpass13–10.079%13 Mar →
office4–13-0.040%12 Mar →
overpass6–13-0.007%12 Mar →
overpass2–13-0.116%12 Mar →
train10–13-0.1013%12 Mar →
mirage10–130.0229%11 Mar →
inferno8–13-0.0113%11 Mar →
overpass13–60.0217%11 Mar →
dust24–13-0.0112%11 Mar →
nuke10–13-0.0311%10 Mar →
dust26–130.0114%10 Mar →
vertigo3–20.156%10 Mar →
ancient9–13-0.0411%4 Mar →
anubis6–13-0.1013%3 Mar →
alpine5–130.0128%3 Mar →
inferno4–13-0.0420%1 Mar →
dust213–60.0612%1 Mar →
ancient13–7-0.0117%1 Mar →
nuke4–13-0.0113%28 Feb →
inferno13–40.0622%28 Feb →
office13–3-0.0113%28 Feb →
mirage11–20.0111%28 Feb →
train7–13-0.056%27 Feb →
ancient9–13-0.086%26 Feb →
inferno8–130.0816%25 Feb →
dust213–100.0416%25 Feb →
vertigo2–13-0.0414%24 Feb →

Match data via Leetify.

Recent teammates

Milestones

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

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