Chlooee — CS2 Stats

76561198077975737[U:1:117710009]Steam profile ↗✓ No bans

1,123Tracked matches33%Win rate2021Tracked since

CSDB Rating2.8 LearningPositional Player

FaceitLevel 4Top 81.0% of ranked FACEIT players

Ladder ranks via Leetify

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Performance scores

Aim24

Positioning46

Utility11

0–100 skill scores via Leetify.

Recent form

STEADY40–55–5Last 10040%Win rateLLWWWWWLLL

Last 10 vs previous 10: +40pp win rate · +0.03 avg rating · −1.2pp headshot accuracy · +115ms reaction

Win rate up 40pp 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

3–2 · 60% win rate
Avg rating -0.05
Avg headshot accuracy 13%
Avg reaction 783ms

Last 10

5–5 · 50% win rate
Avg rating -0.03
Avg headshot accuracy 12%
Avg reaction 757ms

Last 20

6–14 · 30% win rate
Avg rating -0.04
Avg headshot accuracy 13%
Avg reaction 700ms

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 (+1.7 against its own average).

Aim2.4

Utility1.1

Positioning4.6

Opening Duels3.0

Strong T-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

Plays most like s1mple 73% playstyle similarity

Most alike: positioning profile, opening-duel success.

Where you differ: lower utility contribution; 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. 704ms 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

Aim2.4

Positioning4.6

Utility1.1

Mechanics5.7

Opening Duels1.8

Win Impact0.0

Composite 2.8/10 (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-0.04−0.01

first ⅓ avg -0.03 → last ⅓ avg -0.04

Reaction time706ms+38ms

first ⅓ avg 667ms → last ⅓ avg 706ms

Headshot accuracy12.0%−3.4%

first ⅓ avg 15.3% → last ⅓ avg 12.0%

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

0.07Best match rating · 13–2 · inferno, 2 May →

33%Best headshot accuracy · 13–9 · dust2, 1 Feb →

469msFastest reaction time · 12–1 · inferno, 13 Dec →

13–1Biggest win · mirage, 5 Dec →

Across the last 100 tracked matches.

Highlights

5Longest win streak

L2Current streak

6–7In matches decided by ≤2 rounds

Map breakdown

trainBest map · 60% over 5infernoWeakest map · 27% over 15

Map	Grade	Played	Record	Win rate	Avg rating
dust2	C	20	8–12	40%	-0.04
inferno	D	15	4–11	27%	-0.05
mirage	C	12	5–7	42%	-0.05
nuke	A	12	7–5	58%	-0.03
ancient	C	10	4–6	40%	-0.03
cache	D	9	3–6	33%	-0.06
overpass	C	8	3–5	38%	-0.05
anubis	C	7	3–4	43%	0.00
train	A	5	3–2	60%	-0.03
warden	—	1	0–1	0%	-0.07
vertigo	—	1	0–1	0%	0.02

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (27% over 15). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

84Matches

42%Win rate

0.55Avg K/D

—ADR

36%Headshot %

Clutches & streaks

—1v1 clutch win

—1v2 clutch win

5Longest win streak

Recent Faceit resultsWWLLW

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

28.5%Accuracy (enemy spotted)

31.2%Spray accuracy

75.4%Counter-strafing

13.1°Preaim

704msReaction time

41.1%T opening success

33.4%CT opening success

0.10Enemies flashed / flash

0.0%Flash assists

8.06HE damage / grenade

0.34Flashes / 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.

Advanced Mechanics
Your crosshair sits further from where enemies appear than it needs to. Crosshair placement is the cheapest accuracy you can buy.
Preaim 13.0541° — above the 12° mark we flag
Aim Training →
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 12.1586% — below the 15% mark we flag
Aim Training →
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 703.5769ms — above the 700ms 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.

27% win rate across 15 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
mirage	7–13	-0.05	16%	10 May →
cache	11–13	-0.02	11%	10 May →
train	13–9	-0.01	20%	6 May →
cache	13–6	-0.10	8%	6 May →
cache	13–5	-0.06	13%	4 May →
cache	13–2	0.02	11%	4 May →
inferno	13–2	0.07	16%	2 May →
dust2	11–13	-0.05	9%	2 May →
cache	7–13	-0.02	11%	2 May →
cache	10–13	-0.06	6%	2 May →
cache	11–13	-0.08	22%	2 May →
dust2	9–13	0.04	12%	1 May →
cache	8–13	-0.10	10%	1 May →
cache	9–13	-0.08	9%	1 May →
train	6–13	-0.09	12%	28 Apr →
dust2	6–13	-0.07	9%	28 Apr →
nuke	13–3	0.01	11%	28 Apr →
dust2	8–13	-0.03	22%	21 Apr →
mirage	11–13	-0.07	4%	21 Apr →
ancient	6–13	-0.07	22%	21 Apr →
inferno	8–13	-0.04	16%	21 Apr →
nuke	13–6	-0.04	11%	14 Apr →
anubis	9–13	0.02	12%	14 Apr →
mirage	13–6	-0.04	17%	14 Apr →
ancient	13–5	-0.02	11%	14 Apr →
dust2	10–13	-0.07	3%	14 Apr →
nuke	13–4	0.05	20%	2 Apr →
ancient	5–13	-0.07	9%	2 Apr →
dust2	8–13	-0.05	12%	2 Apr →
mirage	7–13	-0.09	6%	21 Mar →
anubis	12–12	-0.02	15%	21 Mar →
dust2	7–13	-0.09	6%	21 Mar →
inferno	2–13	-0.10	6%	21 Mar →
nuke	13–9	-0.04	10%	20 Mar →
overpass	9–4	-0.03	13%	20 Mar →
mirage	9–13	-0.07	11%	20 Mar →
dust2	13–6	-0.01	25%	20 Mar →
inferno	3–13	-0.02	17%	20 Mar →
dust2	12–12	-0.01	11%	18 Mar →
ancient	12–12	-0.01	16%	18 Mar →
inferno	13–10	-0.07	7%	18 Mar →
overpass	10–13	-0.06	20%	17 Mar →
mirage	13–7	-0.11	17%	17 Mar →
dust2	13–8	-0.05	12%	17 Mar →
inferno	4–13	-0.06	21%	12 Mar →
overpass	13–5	-0.05	17%	12 Mar →
mirage	8–13	-0.05	13%	8 Mar →
inferno	11–13	-0.05	19%	8 Mar →
warden	7–13	-0.07	16%	8 Mar →
vertigo	7–13	0.02	27%	8 Mar →
inferno	8–13	-0.09	14%	7 Mar →
nuke	5–13	-0.07	20%	7 Mar →
ancient	7–13	-0.03	7%	7 Mar →
dust2	6–13	-0.07	17%	7 Mar →
overpass	12–12	-0.08	18%	7 Mar →
anubis	13–11	-0.02	18%	4 Feb →
inferno	10–13	-0.05	9%	4 Feb →
dust2	13–11	-0.01	8%	4 Feb →
nuke	13–9	-0.06	19%	3 Feb →
dust2	13–10	-0.04	15%	3 Feb →
mirage	6–13	-0.04	30%	3 Feb →
inferno	5–13	-0.05	30%	3 Feb →
dust2	13–9	-0.02	33%	1 Feb →
inferno	13–11	-0.04	9%	1 Feb →
anubis	13–5	-0.01	14%	1 Feb →
mirage	13–10	-0.00	17%	24 Jan →
dust2	7–3	0.01	25%	24 Jan →
overpass	7–13	-0.07	25%	24 Jan →
nuke	4–13	-0.05	3%	24 Jan →
ancient	10–13	-0.05	15%	24 Jan →
anubis	1–13	0.02	18%	3 Jan →
dust2	3–13	-0.03	16%	3 Jan →
dust2	11–13	0.02	28%	17 Dec →
train	13–11	-0.03	11%	17 Dec →
inferno	12–1	-0.05	10%	13 Dec →
ancient	13–11	-0.01	20%	13 Dec →
train	13–10	0.03	15%	13 Dec →
nuke	13–8	0.00	16%	13 Dec →
mirage	7–13	-0.09	19%	13 Dec →
anubis	13–3	0.06	0%	13 Dec →
dust2	11–13	-0.10	12%	13 Dec →
anubis	9–13	-0.04	15%	12 Dec →
ancient	13–6	-0.02	3%	12 Dec →
nuke	10–13	-0.02	15%	12 Dec →
inferno	4–13	-0.06	11%	8 Dec →
ancient	6–13	-0.03	24%	8 Dec →
dust2	13–11	-0.03	9%	8 Dec →
nuke	5–13	-0.04	13%	8 Dec →
mirage	13–4	0.03	14%	7 Dec →
nuke	13–9	-0.04	28%	7 Dec →
dust2	13–10	-0.05	21%	7 Dec →
inferno	12–12	0.01	26%	7 Dec →
ancient	13–5	-0.01	12%	7 Dec →
overpass	6–13	-0.02	15%	7 Dec →
overpass	10–13	-0.02	17%	5 Dec →
mirage	13–1	-0.00	24%	5 Dec →
train	9–13	-0.03	8%	5 Dec →
nuke	5–13	-0.06	30%	5 Dec →
overpass	13–5	-0.03	6%	14 Nov →
inferno	1–13	-0.09	8%	14 Nov →