♛︎ Jin-w0o♛ — CS2 Stats

76561198269411551[U:1:309145823]✓ No bans

408Tracked matches43%Win rate2024Tracked since

CSDB Rating5.1 DevelopingAggressive Rifler

Premier CS Rating15,119Purple band · top ~27.9% of ranked players (population est.)

CSDB Leaderboard#20841 of 34041 tracked

FaceitLevel 5 · 1,074 ELOTop 67.6% of ranked FACEIT players

Ladder ranks via Leetify

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Performance scores

Aim70

Positioning45

Utility32

0–100 skill scores via Leetify.

Recent form

STEADY45–47–8Last 10045%Win rateLLWLWLWWWW

Last 10 vs previous 10: +40pp win rate · -0.01 avg rating

Player DNA

Primary style: Aggressive Rifler — Takes opening fights often, backed by a strong aim profile.

Aim7.0

Aggression6.7

Utility3.2

Positioning4.5

Opening Duels2.8

Clutch0.0

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.

Your pro match

Plays most like ropz 84% playstyle similarity

Most alike: opening-fight frequency, positioning profile.

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

Utility. Utility contribution runs far behind the mechanical game — the cheapest rating gain on this profile is thrown, not aimed.

Reaction time. 643ms 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

Aim7.0

Positioning4.5

Utility3.2

Mechanics7.3

Opening Duels2.8

Win Impact2.6

Composite 5.1/10 (Developing), 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 rating0.00−0.02

first ⅓ avg 0.02 → last ⅓ avg 0.00

Reaction time655ms−27ms

first ⅓ avg 681ms → last ⅓ avg 655ms

Headshot accuracy30.2%+1.2%

first ⅓ avg 29.0% → last ⅓ avg 30.2%

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.

Highlights

0.16Best rating — mirage 12–12

13–0Biggest win — inferno

5Longest win streak

L2Current streak

2–6In matches decided by ≤2 rounds

4Overtime games

Map breakdown

anubisBest map · 71% over 7dust2Weakest map · 27% over 11

Map	Grade	Played	Record	Win rate	Avg rating
mirage	C	26	10–16	38%	0.02
cache	B	15	7–8	47%	0.02
dust2	D	11	3–8	27%	0.00
nuke	A	9	5–4	56%	0.04
inferno	C	8	3–5	38%	-0.01
anubis	S	7	5–2	71%	0.03
office	D	6	2–4	33%	0.02
overpass	C	5	2–3	40%	-0.01
train	—	3	3–0	100%	0.00
ancient	—	3	2–1	67%	0.03
warden	—	2	0–2	0%	-0.00
italy	—	2	1–1	50%	0.02
boulder	—	1	1–0	100%	-0.02
alpine	—	1	0–1	0%	-0.04
vertigo	—	1	1–0	100%	0.10

Across the last 100 tracked matches.

Dust 2 is currently your weakest sufficiently-sampled map (27% over 11). Start with the 6 essential Dust 2 lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

9Matches

44%Win rate

0.68Avg K/D

65.6ADR

55%Headshot %

Clutches & streaks

20%1v1 clutch win

100%1v2 clutch win

4Longest win streak

Recent Faceit resultsWLLLL

Map	Matches	Win rate	Avg K/D	Avg kills
Ancient	3	67%	0.68	14.3
Dust2	2	50%	0.98	12.0
Mirage	2	0%	0.50	9.0
Inferno	1	100%	0.52	11.0
Nuke	1	0%	0.65	11.0

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.

28.9%Headshot accuracy

34.2%Accuracy (enemy spotted)

31.6%Spray accuracy

82.8%Counter-strafing

11.9°Preaim

643msReaction time

43.8%T opening success

38.5%CT opening success

0.46Enemies flashed / flash

7.6%Flash assists

6.04HE damage / grenade

4.34Flashes / 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.

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 0.4595 — below the 0.5 mark we flag
Grenade Lineups →
Advanced Mechanics
Losing the first CT duel repeatedly usually means holding angles that favour the peeker.
CT opening duels 38.5266% — below the 40% mark we flag

Spend your practice time on Dust 2

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.

Dust 2 callouts & strategy →Dust 2 grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
inferno	2–13	-0.03	33%	22 Aug →
nuke	6–6	0.09	28%	16 Aug →
mirage	13–4	0.04	47%	15 Aug →
overpass	10–13	-0.02	20%	14 Aug →
train	8–5	0.01	70%	13 Aug →
dust2	4–13	0.06	52%	13 Aug →
dust2	13–4	-0.05	26%	2 Aug →
mirage	13–9	-0.05	20%	2 Aug →
mirage	13–2	-0.01	21%	2 Aug →
cache	13–11	0.01	33%	1 Aug →
dust2	4–13	-0.05	21%	1 Aug →
inferno	13–0	-0.10	31%	31 Jul →
mirage	3–11	-0.04	50%	31 Jul →
office	5–13	0.08	33%	31 Jul →
nuke	10–13	0.14	30%	31 Jul →
mirage	6–13	-0.12	13%	31 Jul →
mirage	10–13	0.10	42%	31 Jul →
mirage	12–12	0.16	24%	30 Jul →
cache	13–10	0.05	27%	29 Jul →
cache	9–13	-0.05	38%	28 Jul →
mirage	13–9	0.03	31%	28 Jul →
inferno	9–13	0.04	38%	28 Jul →
mirage	5–13	0.00	18%	27 Jul →
ancient	13–9	0.06	21%	25 Jul →
overpass	13–11	-0.04	25%	24 Jul →
mirage	8–13	-0.07	26%	24 Jul →
inferno	5–9	-0.06	33%	24 Jul →
office	13–3	-0.08	17%	23 Jul →
office	12–12	-0.03	0%	19 Jul →
ancient	9–13	-0.00	34%	19 Jul →
dust2	1–13	-0.05	35%	19 Jul →
boulder	6–2	-0.02	9%	18 Jul →
mirage	4–13	0.01	54%	18 Jul →
cache	6–13	-0.08	36%	18 Jul →
mirage	3–13	0.05	30%	17 Jul →
cache	12–12	0.07	24%	21 May →
cache	8–13	0.02	28%	16 May →
mirage	8–1	0.09	8%	15 May →
dust2	0–9	-0.09	100%	15 May →
cache	8–13	-0.04	22%	14 May →
cache	11–13	0.03	60%	11 May →
cache	13–9	-0.00	28%	11 May →
cache	13–10	0.04	33%	10 May →
cache	13–7	0.06	27%	10 May →
cache	6–13	0.06	36%	8 May →
cache	13–10	0.01	26%	4 May →
cache	12–12	-0.01	26%	30 Apr →
cache	13–5	0.09	39%	30 Apr →
mirage	13–7	0.16	24%	4 Apr →
anubis	9–13	0.02	29%	2 Apr →
dust2	13–8	0.03	26%	29 Mar →
mirage	12–12	-0.04	29%	24 Mar →
mirage	13–7	-0.00	28%	23 Mar →
warden	9–13	0.08	45%	22 Mar →
anubis	13–4	0.01	46%	21 Mar →
inferno	13–7	0.02	34%	20 Mar →
nuke	12–16	-0.09	13%	19 Mar →
inferno	7–13	0.02	43%	18 Mar →
office	5–13	0.03	36%	18 Mar →
train	13–2	-0.00	20%	5 Mar →
nuke	13–8	-0.01	31%	1 Mar →
nuke	13–4	0.11	16%	27 Feb →
dust2	9–13	0.09	21%	27 Feb →
mirage	11–13	0.05	20%	26 Feb →
train	7–2	0.00	12%	26 Feb →
mirage	13–5	0.09	20%	23 Feb →
inferno	13–5	-0.01	54%	23 Feb →
overpass	4–13	-0.07	38%	23 Feb →
anubis	13–5	0.07	39%	23 Feb →
ancient	13–8	0.03	20%	18 Feb →
mirage	13–2	0.02	16%	18 Feb →
italy	13–9	0.05	24%	16 Feb →
mirage	13–3	-0.00	23%	15 Feb →
alpine	4–13	-0.04	30%	14 Feb →
dust2	10–13	0.07	21%	13 Feb →
dust2	16–13	0.02	20%	13 Feb →
vertigo	13–4	0.10	39%	12 Feb →
anubis	13–10	0.01	17%	12 Feb →
nuke	12–12	0.02	18%	11 Feb →
office	13–3	0.09	44%	11 Feb →
mirage	12–12	0.04	32%	10 Feb →
dust2	11–13	0.04	36%	10 Feb →
anubis	8–13	-0.04	24%	10 Feb →
mirage	8–13	-0.04	17%	9 Feb →
nuke	13–8	0.01	22%	7 Feb →
anubis	13–10	0.10	41%	7 Feb →
nuke	5–2	0.06	64%	6 Feb →
overpass	14–16	0.03	26%	4 Feb →
office	11–13	0.01	20%	4 Feb →
nuke	13–10	-0.01	22%	4 Feb →
mirage	12–12	0.11	36%	4 Feb →
italy	5–13	-0.02	44%	2 Feb →
dust2	12–16	-0.03	15%	1 Feb →
inferno	5–13	0.05	39%	31 Jan →
mirage	9–13	0.02	22%	31 Jan →
anubis	13–6	0.02	25%	30 Jan →
mirage	6–13	0.02	38%	28 Jan →
overpass	13–6	0.04	31%	28 Jan →
mirage	8–13	0.02	17%	28 Jan →
warden	8–13	-0.08	41%	28 Jan →