[ FATAL ] | ꞠinꞡerꞤuŧ🥜 — CS2 Stats

DE76561198024147375[U:1:63881647]Steam profile ↗✓ No bans

749Tracked matches41%Win rate2026Tracked since

1,133Hours in CS66Hrs last 2 wks

CSDB Rating3.7 LearningClutch Specialist

Premier CS Rating10,992Blue band · top ~49.6% of ranked players (population est.)

CSDB Leaderboard#180450 of 259662 tracked

WingmanGold Nova II

Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 29 Oct 2026 (5 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: 10,992 -4,467 11 Sept – 29 Oct · 9 days played

15,459Peak Premier in tracked matches

24Days played since 2026-09-11

Premier CS Rating

4,467 below peak (15,459)
-4,007 over 30 days · declining
Next: Purple band at 15,000 — 4,008 to go
Reached: Purple band · Blue band · Light Blue band
Purple band first seen 2026-09-11
Blue band first seen 2026-09-11

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

How this compares with the same rank

Median values for Blue band among CSDB-tracked players (n=21,222), from Valve's own lifetime stats. Aim, positioning and utility scores are deliberately not benchmarked here — those are a third-party provider's derived metrics, which CSDB does not store.

Metric	This player	Blue band median	Purple band median	vs Purple band
Headshot rate	37.1%	42.0%	44.4%	7.3% short
Shot accuracy	0.3%	9.6%	11.9%	11.6% short
Kill/death ratio	0.82	0.98	1.03	0.21 short
Match win rate	39.7%	43.8%	45.2%	5.5% short

This profile sits below the typical Purple band player on every metric we can compare.

Widest gap: Shot accuracy. That is the metric furthest from the Purple band median in relative terms — not necessarily the one holding a rank back, which no statistic here can establish.

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Performance scores

Aim46

Positioning44

Utility40

0–100 skill scores via Leetify.

Recent form

COLD39–52–9Last 10039%Win rateLWLLLTLLLW

Last 10 vs previous 10: −30pp win rate · −0.03 avg rating · −4.9pp headshot accuracy · −27ms reaction

Win rate down 30pp 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

1–4 · 20% win rate
Avg rating -0.01
Avg headshot accuracy 10%
Avg reaction 653ms

Last 10

2–7–1 · 20% win rate
Avg rating -0.02
Avg headshot accuracy 12%
Avg reaction 595ms

Last 20

7–12–1 · 35% win rate
Avg rating -0.01
Avg headshot accuracy 14%
Avg reaction 608ms

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 (+3.3 against its own average).

Aim4.6

Utility4.0

Positioning4.4

Opening Duels0.0

Clutch7.2

Limited utility dependenceReliable in 1v1s

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 iM 78% 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. 589ms 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

Aim4.6

Positioning4.4

Utility4.0

Mechanics5.4

Opening Duels0.4

Win Impact2.1

Composite 3.7/10 (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-0.00+0.01

first ⅓ avg -0.02 → last ⅓ avg -0.00

Reaction time606ms+8ms

first ⅓ avg 598ms → last ⅓ avg 606ms

Headshot accuracy12.3%+2.1%

first ⅓ avg 10.2% → last ⅓ avg 12.3%

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

0.23Best match rating · 13–1 · cache, 12 Oct →

33%Best headshot accuracy · 0–2 · office, 28 Oct →

398msFastest reaction time · 1–13 · train, 8 Oct →

13–0Biggest win · dust2, 11 Oct →

Across the last 100 tracked matches.

Highlights

5Longest win streak

1–7In matches decided by ≤2 rounds

2Overtime games

Map breakdown

nukeBest map · 63% over 8infernoWeakest map · 27% over 15

Map	Grade	Played	Record	Win rate	Avg rating
dust2	B	21	10–11	48%	-0.01
cache	B	16	8–8	50%	0.02
inferno	D	15	4–11	27%	-0.00
mirage	D	10	3–7	30%	-0.04
anubis	B	10	5–5	50%	-0.00
nuke	A	8	5–3	63%	-0.00
ancient	B	6	3–3	50%	0.01
office	—	4	0–4	0%	-0.05
train	—	4	0–4	0%	-0.02
vertigo	—	3	0–3	0%	-0.01
overpass	—	3	1–2	33%	-0.07

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.

Lifetime stats

13,656Lifetime kills

0.82K/D

1,071Matches

39.7%Match win rate

37.1%Headshot %

0.3%Shot accuracy

1,627MVPs

633Hours (in match)

1,336Bombs planted

224Bombs defused

Most-used weapons

AK-474,436

M4A4 / M4A1-S2,897

AWP846

P2000 / USP-S744

FAMAS642

Galil AR582

Glock-18523

MAC-10495

MP7412

SSG 08359

Lifetime map wins

1,482inferno

1,238dust2

837vertigo

694nuke

234train

144office

5assault

4italy

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

Faceit stats

Combat

17Matches

35%Win rate

0.57Avg K/D

57.6ADR

48%Headshot %

Clutches & streaks

56%1v1 clutch win

33%1v2 clutch win

2Longest win streak

Recent Faceit resultsWLLLL

Map	Matches	Win rate	Avg K/D	Avg kills
Dust2	3	33%	0.29	4.7
Ancient	3	67%	0.69	8.7
Anubis	2	50%	1.30	18.0
Mirage	2	50%	0.40	7.0
Inferno	2	0%	0.42	6.0
Nuke	2	50%	0.40	9.5
Overpass	1	0%	0.54	14.0
Vertigo	1	0%	0.44	8.0

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.

12.2%Headshot accuracy

31.3%Accuracy (enemy spotted)

33.0%Spray accuracy

74.1%Counter-strafing

10.6°Preaim

589msReaction time

31.3%T opening success

31.8%CT opening success

0.50Enemies flashed / flash

2.7%Flash assists

9.32HE damage / grenade

3.60Flashes / 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.

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 12.1549% — below the 15% mark we flag
Aim Training →
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 0.4984 — below the 0.5 mark we flag
Grenade Lineups →
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 31.337% — 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 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
nuke	8–13	-0.06	10%	1 Nov →
mirage	13–6	-0.03	5%	1 Nov →
vertigo	4–9	0.04	6%	30 Oct →
anubis	5–13	-0.03	2%	30 Oct →
office	11–13	0.01	29%	30 Oct →
inferno	12–12	-0.00	13%	29 Oct →
dust2	6–13	-0.07	10%	29 Oct →
ancient	7–13	-0.07	21%	29 Oct →
inferno	6–13	-0.04	13%	29 Oct →
cache	13–2	0.02	9%	29 Oct →
ancient	13–5	-0.06	6%	28 Oct →
dust2	13–8	0.03	16%	28 Oct →
inferno	11–13	-0.00	18%	28 Oct →
inferno	13–8	0.05	18%	28 Oct →
anubis	13–2	0.11	16%	28 Oct →
office	7–13	-0.09	11%	28 Oct →
office	0–2	-0.01	33%	28 Oct →
ancient	7–13	0.20	25%	28 Oct →
dust2	13–6	0.02	14%	28 Oct →
overpass	3–9	-0.21	7%	27 Oct →
inferno	8–8	0.01	10%	27 Oct →
dust2	6–13	-0.04	14%	27 Oct →
anubis	20–22	-0.00	15%	27 Oct →
inferno	5–13	-0.03	5%	27 Oct →
dust2	8–13	-0.00	9%	27 Oct →
inferno	7–13	0.02	3%	26 Oct →
anubis	9–11	-0.02	11%	26 Oct →
anubis	13–5	0.06	10%	26 Oct →
dust2	2–13	-0.09	13%	26 Oct →
cache	13–8	-0.00	9%	25 Oct →
anubis	13–7	0.09	8%	25 Oct →
cache	13–8	0.06	13%	25 Oct →
nuke	9–5	0.07	4%	25 Oct →
vertigo	6–13	-0.02	3%	24 Oct →
dust2	13–7	-0.04	11%	23 Oct →
dust2	10–13	-0.02	9%	23 Oct →
inferno	10–13	0.00	8%	23 Oct →
cache	6–13	-0.02	3%	23 Oct →
mirage	11–13	0.04	9%	23 Oct →
cache	13–9	0.03	8%	22 Oct →
cache	13–9	-0.00	6%	22 Oct →
cache	12–12	0.03	13%	21 Oct →
ancient	12–12	-0.04	6%	20 Oct →
dust2	10–13	-0.00	15%	20 Oct →
cache	12–12	0.16	9%	20 Oct →
mirage	10–13	-0.05	6%	19 Oct →
overpass	7–13	-0.01	11%	18 Oct →
anubis	12–12	-0.03	10%	18 Oct →
inferno	13–4	-0.01	11%	18 Oct →
overpass	13–2	0.01	10%	17 Oct →
mirage	10–13	-0.03	7%	17 Oct →
cache	13–4	0.01	7%	17 Oct →
ancient	13–9	-0.03	10%	17 Oct →
dust2	13–5	0.01	8%	17 Oct →
anubis	5–13	-0.07	12%	17 Oct →
ancient	13–2	0.04	7%	17 Oct →
cache	13–6	-0.06	6%	16 Oct →
dust2	13–4	0.11	5%	16 Oct →
nuke	13–9	0.02	4%	16 Oct →
nuke	13–9	-0.03	9%	16 Oct →
dust2	6–13	-0.06	12%	16 Oct →
inferno	13–5	-0.01	7%	16 Oct →
dust2	8–13	-0.05	4%	15 Oct →
inferno	13–10	0.01	16%	15 Oct →
anubis	13–10	-0.04	4%	15 Oct →
mirage	7–13	0.01	10%	15 Oct →
anubis	6–13	-0.11	6%	14 Oct →
dust2	13–3	0.00	7%	14 Oct →
nuke	8–13	0.02	7%	14 Oct →
inferno	7–13	0.04	27%	13 Oct →
cache	12–12	-0.00	5%	13 Oct →
nuke	1–13	-0.08	17%	12 Oct →
mirage	13–7	-0.04	5%	12 Oct →
cache	13–1	0.23	20%	12 Oct →
office	3–13	-0.10	6%	11 Oct →
inferno	7–13	-0.05	11%	11 Oct →
dust2	13–2	-0.03	18%	11 Oct →
dust2	8–1	-0.06	11%	11 Oct →
train	12–12	-0.01	10%	11 Oct →
mirage	0–13	-0.05	9%	11 Oct →
dust2	13–0	0.06	8%	11 Oct →
cache	9–13	0.04	9%	11 Oct →
inferno	4–13	0.02	8%	10 Oct →
mirage	9–13	-0.08	4%	10 Oct →
nuke	13–8	0.08	15%	10 Oct →
nuke	13–6	-0.06	8%	10 Oct →
cache	7–13	-0.02	5%	10 Oct →
dust2	10–13	-0.01	14%	10 Oct →
train	8–13	0.02	12%	10 Oct →
mirage	16–13	-0.06	19%	9 Oct →
cache	11–13	-0.04	6%	9 Oct →
dust2	13–10	0.02	11%	9 Oct →
inferno	10–13	-0.06	4%	9 Oct →
dust2	11–13	-0.04	15%	9 Oct →
vertigo	8–13	-0.06	6%	9 Oct →
cache	8–13	-0.02	14%	9 Oct →
mirage	3–13	-0.12	20%	8 Oct →
train	6–13	-0.05	6%	8 Oct →
dust2	12–12	0.01	3%	8 Oct →
train	1–13	-0.03	0%	8 Oct →