UniqueUser — CS2 Stats

DE76561198375269031[U:1:415003303]Steam profile ↗✓ No bans

927Tracked matches45%Win rate2023Tracked since

978Hours in CS7Hrs last 2 wks

CSDB Rating5.0 DevelopingPositional Player

WingmanGold Nova III

Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 11 Sep 2026 (47 days ago). Ranks are recorded once per day this page is viewed.

+81Tracked matches · now 927

+11.5ppWin rate · 33.3% → 44.8%

Rating over time

33Days played since 2026-09-03

Premier is CSDB’s own observation — it builds from the day a profile is first viewed and cannot be backfilled.

Performance scores

Aim59

Positioning59

Utility25

0–100 skill scores via Leetify.

Recent form

STEADY43–44–13Last 10043%Win rateWLWLLLLWWW

Last 10 vs previous 10: 0pp win rate · +0.04 avg rating · +2.0pp headshot accuracy · −101ms reaction

Win rate 0pp 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

2–3 · 40% win rate
Avg rating 0.01
Avg headshot accuracy 18%
Avg reaction 612ms

Last 10

5–5 · 50% win rate
Avg rating -0.01
Avg headshot accuracy 14%
Avg reaction 581ms

Last 20

10–10 · 50% win rate
Avg rating -0.03
Avg headshot accuracy 13%
Avg reaction 632ms

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 (+1.9 against its own average).

Aim5.9

Utility2.5

Positioning5.9

Opening Duels3.7

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.

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 donk 84% playstyle similarity

Most alike: positioning profile, utility contribution.

Where you differ: lower aim profile; lower opening-duel success.

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. 635ms 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

Aim5.9

Positioning5.9

Utility2.5

Mechanics4.9

Opening Duels5.9

Win Impact3.3

Composite 5.0/10 (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 rating-0.03−0.01

first ⅓ avg -0.02 → last ⅓ avg -0.03

Reaction time626ms−35ms

first ⅓ avg 661ms → last ⅓ avg 626ms

Headshot accuracy12.8%−4.5%

first ⅓ avg 17.4% → last ⅓ avg 12.8%

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

0.10Best match rating · 13–2 · cache, 22 Oct →

43%Best headshot accuracy · 12–12 · train, 6 Sept →

422msFastest reaction time · 3–13 · ancient, 21 Oct →

13–2Biggest win · cache, 22 Oct →

Across the last 100 tracked matches.

Highlights

5Longest win streak

7–4In matches decided by ≤2 rounds

Map breakdown

anubisBest map · 67% over 6trainWeakest map · 20% over 5

Map	Grade	Played	Record	Win rate	Avg rating
cache	A	33	20–13	61%	-0.02
dust2	D	27	9–18	33%	-0.02
inferno	C	11	4–7	36%	-0.04
mirage	C	7	3–4	43%	-0.04
anubis	S	6	4–2	67%	-0.03
train	D	5	1–4	20%	-0.04
ancient	—	4	0–4	0%	-0.06
nuke	—	3	0–3	0%	-0.02
vertigo	—	2	1–1	50%	0.00
rush_001	—	1	1–0	100%	-0.02
office	—	1	0–1	0%	-0.02

Across the last 100 tracked matches.

Lifetime stats

27,316Lifetime kills

0.85K/D

2,384Matches

39.5%Match win rate

32.2%Headshot %

8.9%Shot accuracy

2,502MVPs

564Hours (in match)

577Bombs planted

258Bombs defused

Most-used weapons

AWP3,713

AK-472,570

XM10142,335

Desert Eagle2,157

Nova2,087

M4A4 / M4A1-S2,035

SSG 081,992

Negev1,238

P2000 / USP-S1,007

Glock-18810

Lifetime map wins

3,203dust2

2,390inferno

793nuke

782train

698vertigo

530office

250lake

207militia

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

Skill profile

Aggregate performance across tracked matches — stats via Leetify. Percentile context against other CSDB-tracked players arrives as our own benchmark data accumulates.

13.1%Headshot accuracy

33.8%Accuracy (enemy spotted)

43.1%Spray accuracy

72.0%Counter-strafing

8.8°Preaim

635msReaction time

59.0%T opening success

48.3%CT opening success

0.40Enemies flashed / flash

4.4%Flash assists

8.22HE damage / grenade

0.90Flashes / 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 13.148% — 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 0.3992 — below the 0.5 mark we flag
Grenade Lineups →

Spend your practice time on Train

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

20% win rate across 5 tracked games — your weakest map with enough games to be worth reading into.

Train callouts & strategy →

Recent matches

Map	Score	Rating	HS%	Date
cache	13–3	-0.07	13%	27 Oct →
dust2	9–13	-0.05	8%	27 Oct →
cache	13–2	0.10	9%	22 Oct →
cache	8–13	0.04	33%	22 Oct →
dust2	6–13	0.04	25%	22 Oct →
ancient	3–13	-0.11	7%	21 Oct →
dust2	6–13	-0.07	5%	21 Oct →
cache	9–1	0.02	0%	21 Oct →
inferno	13–11	-0.04	17%	20 Oct →
dust2	13–10	0.02	20%	20 Oct →
dust2	13–11	-0.07	0%	17 Oct →
cache	13–8	-0.02	2%	17 Oct →
cache	7–13	-0.07	5%	13 Oct →
cache	13–10	-0.04	3%	13 Oct →
inferno	7–13	-0.07	20%	13 Oct →
mirage	13–6	-0.08	21%	13 Oct →
inferno	11–13	-0.07	29%	12 Oct →
cache	13–7	0.00	19%	12 Oct →
inferno	9–13	-0.06	10%	11 Oct →
anubis	6–13	-0.08	8%	10 Oct →
cache	3–13	-0.01	14%	9 Oct →
inferno	7–13	-0.08	20%	9 Oct →
dust2	13–3	0.02	13%	9 Oct →
cache	10–13	-0.06	6%	8 Oct →
anubis	13–11	-0.02	19%	8 Oct →
train	11–13	-0.09	5%	8 Oct →
inferno	12–12	-0.06	7%	7 Oct →
dust2	10–13	0.00	5%	7 Oct →
anubis	9–13	-0.08	19%	7 Oct →
inferno	13–9	-0.01	26%	6 Oct →
dust2	13–5	-0.02	17%	6 Oct →
ancient	12–12	-0.00	9%	4 Oct →
inferno	7–13	-0.08	12%	3 Oct →
cache	13–6	0.00	7%	3 Oct →
cache	13–4	-0.04	10%	3 Oct →
mirage	11–13	-0.03	15%	2 Oct →
nuke	7–13	0.01	10%	2 Oct →
dust2	13–6	0.01	8%	2 Oct →
train	13–3	-0.06	29%	1 Oct →
anubis	13–7	0.01	10%	1 Oct →
cache	13–11	0.00	13%	1 Oct →
cache	3–0	-0.06	0%	1 Oct →
cache	8–13	-0.04	0%	30 Sept →
dust2	12–12	-0.07	15%	30 Sept →
rush_001	8–6	-0.02	0%	30 Sept →
cache	8–13	-0.09	9%	30 Sept →
vertigo	13–9	0.03	30%	28 Sept →
inferno	3–13	-0.06	12%	28 Sept →
dust2	6–13	-0.08	29%	28 Sept →
dust2	3–13	-0.03	13%	27 Sept →
nuke	4–13	-0.05	24%	27 Sept →
cache	13–9	-0.03	4%	27 Sept →
dust2	13–8	0.01	5%	27 Sept →
cache	13–2	-0.03	0%	26 Sept →
dust2	12–12	-0.02	12%	26 Sept →
cache	13–5	0.05	29%	26 Sept →
dust2	7–13	-0.02	17%	26 Sept →
cache	9–13	-0.06	22%	24 Sept →
mirage	13–10	-0.04	19%	24 Sept →
inferno	13–8	0.03	16%	24 Sept →
dust2	1–13	-0.05	24%	24 Sept →
cache	13–6	0.09	23%	23 Sept →
dust2	12–12	-0.03	18%	23 Sept →
cache	12–12	-0.07	13%	23 Sept →
dust2	5–13	-0.06	42%	23 Sept →
cache	6–13	-0.03	25%	22 Sept →
dust2	12–12	0.01	17%	22 Sept →
cache	12–12	0.05	17%	22 Sept →
anubis	13–6	-0.02	27%	22 Sept →
train	1–13	-0.09	0%	13 Sept →
mirage	13–9	-0.02	10%	13 Sept →
dust2	9–13	0.04	11%	13 Sept →
dust2	13–9	-0.05	17%	13 Sept →
anubis	13–9	-0.00	11%	12 Sept →
dust2	7–13	-0.03	17%	12 Sept →
cache	11–6	-0.03	12%	12 Sept →
dust2	13–11	-0.03	3%	12 Sept →
cache	13–5	-0.04	17%	12 Sept →
cache	13–6	-0.04	24%	12 Sept →
mirage	12–12	-0.04	5%	11 Sept →
dust2	12–12	-0.01	16%	11 Sept →
cache	13–6	-0.02	8%	10 Sept →
cache	13–8	-0.04	19%	10 Sept →
dust2	9–13	-0.03	35%	10 Sept →
cache	8–13	-0.02	16%	7 Sept →
cache	7–13	-0.03	19%	6 Sept →
mirage	5–13	0.03	12%	6 Sept →
vertigo	6–13	-0.03	21%	6 Sept →
ancient	1–13	-0.06	11%	6 Sept →
train	12–12	0.04	43%	6 Sept →
ancient	12–12	-0.07	33%	5 Sept →
train	10–13	0.00	24%	4 Sept →
cache	13–3	0.08	28%	4 Sept →
inferno	13–4	0.00	14%	4 Sept →
office	12–12	-0.02	8%	3 Sept →
nuke	11–13	-0.02	23%	3 Sept →
dust2	2–13	-0.09	30%	3 Sept →
cache	2–13	-0.09	9%	3 Sept →
mirage	3–13	-0.10	0%	3 Sept →
dust2	13–11	0.01	33%	3 Sept →