𝓑𝓪𝔂𝓵𝓪 — CS2 Stats

ZA76561198398549625[U:1:438283897]Steam profile ↗✓ No bans

3,400Tracked matches52%Win rate2020Tracked since

5,990Hours in CS17Hrs last 2 wks

CSDB Rating3.7 Learning

Premier CS Rating12,415Blue band · top ~42.1% of ranked players (population est.)

CSDB Leaderboard#9078 of 12904 tracked

Ladder ranks via Leetify

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Performance scores

Aim36

Positioning42

Utility56

0–100 skill scores via Leetify.

Recent form

STEADY51–48–1Last 10051%Win rateLLLTWWLWWW

Last 10 vs previous 10: -20pp win rate · -0.00 avg rating

Player DNA

Aim3.6

Aggression4.5

Utility5.6

Positioning4.2

Opening Duels0.6

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 NiKo 80% playstyle similarity

Most alike: opening-fight frequency, utility contribution.

Where you differ: lower positioning profile; 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. 609ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 65% 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

Aim3.6

Positioning4.2

Utility5.6

Mechanics3.2

Opening Duels0.6

Win Impact5.6

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 rating0.02−0.01

first ⅓ avg 0.03 → last ⅓ avg 0.02

Reaction time611ms−40ms

first ⅓ avg 651ms → last ⅓ avg 611ms

Headshot accuracy13.5%−0.3%

first ⅓ avg 13.7% → last ⅓ avg 13.5%

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

0.18Best rating — cache 13–4

13–1Biggest win — nuke

8Longest win streak

L3Current streak

9–6In matches decided by ≤2 rounds

10Overtime games

Map breakdown

dust2Best map · 65% over 17infernoWeakest map · 13% over 8

Map	Grade	Played	Record	Win rate	Avg rating
nuke	A	23	14–9	61%	0.03
dust2	S	17	11–6	65%	0.02
cache	C	14	6–8	43%	0.03
anubis	B	12	6–6	50%	0.02
ancient	B	12	6–6	50%	0.02
mirage	C	9	4–5	44%	0.02
inferno	D	8	1–7	13%	0.01
train	—	2	1–1	50%	0.03
overpass	—	1	1–0	100%	-0.01
vertigo	—	1	0–1	0%	0.01
office	—	1	1–0	100%	0.02

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (13% over 8). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

143,320Lifetime kills

1.14K/D · Top 50% of tracked players

7,409Matches

45.5%Match win rate

34.1%Headshot %

15.3%Shot accuracy · Top 50% of tracked players

14,370MVPs

3,289Hours (in match)

6,210Bombs planted

1,934Bombs defused

Most-used weapons

AK-4729,238

AWP23,634

M4A4 / M4A1-S18,422

P2000 / USP-S8,336

Glock-187,342

Galil AR4,648

P2504,453

Desert Eagle4,411

MAC-103,860

P903,610

Lifetime map wins

18,556dust2

11,738inferno

8,758nuke

2,333vertigo

2,055train

467office

291cbble

131lake

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.

13.7%Headshot accuracy

28.5%Accuracy (enemy spotted)

29.8%Spray accuracy

64.5%Counter-strafing

9.5°Preaim

609msReaction time

32.6%T opening success

32.5%CT opening success

0.55Enemies flashed / flash

7.4%Flash assists

7.83HE damage / grenade

15.83Flashes / 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 13.6549% — below the 15% mark we flag
Aim Training →
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 32.611% — 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.

13% win rate across 8 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
dust2	12–16	0.06	10%	27 Aug →
nuke	11–13	0.08	17%	27 Aug →
dust2	6–13	0.00	11%	26 Aug →
inferno	15–15	-0.01	15%	26 Aug →
anubis	13–10	0.01	9%	24 Aug →
ancient	13–4	0.04	4%	24 Aug →
dust2	9–13	-0.05	21%	21 Aug →
nuke	13–6	0.07	21%	21 Aug →
anubis	13–3	0.02	13%	21 Aug →
dust2	13–5	0.02	11%	21 Aug →
mirage	14–16	0.02	21%	20 Aug →
dust2	13–5	0.02	27%	20 Aug →
cache	9–13	-0.01	9%	18 Aug →
ancient	13–9	0.06	18%	18 Aug →
dust2	13–4	0.07	15%	17 Aug →
inferno	5–13	0.05	22%	17 Aug →
nuke	13–8	0.07	16%	17 Aug →
overpass	13–6	-0.01	7%	16 Aug →
nuke	13–7	0.04	8%	15 Aug →
inferno	13–10	-0.03	13%	15 Aug →
ancient	8–13	-0.00	21%	13 Aug →
train	13–3	0.08	13%	12 Aug →
ancient	7–13	-0.03	0%	12 Aug →
dust2	13–11	-0.04	13%	12 Aug →
inferno	7–13	-0.03	4%	10 Aug →
nuke	13–6	0.00	16%	10 Aug →
cache	8–13	-0.06	14%	10 Aug →
ancient	10–13	0.04	7%	8 Aug →
anubis	0–13	-0.03	6%	8 Aug →
mirage	8–13	0.02	23%	8 Aug →
dust2	5–13	0.12	11%	8 Aug →
nuke	8–13	-0.00	18%	7 Aug →
inferno	8–13	0.05	11%	7 Aug →
nuke	4–13	-0.01	16%	7 Aug →
cache	16–14	0.03	13%	6 Aug →
cache	6–13	-0.01	17%	6 Aug →
mirage	13–10	0.03	12%	6 Aug →
nuke	13–8	-0.02	11%	3 Aug →
nuke	10–13	0.02	15%	3 Aug →
anubis	13–11	0.01	8%	3 Aug →
dust2	13–4	-0.03	13%	3 Aug →
cache	10–13	0.09	10%	2 Aug →
dust2	13–3	0.07	14%	2 Aug →
nuke	13–16	0.01	5%	1 Aug →
dust2	13–9	-0.05	7%	1 Aug →
ancient	4–13	0.04	10%	1 Aug →
nuke	5–13	-0.04	18%	1 Aug →
ancient	8–13	-0.04	10%	30 Jul →
ancient	13–10	-0.02	6%	30 Jul →
cache	10–13	-0.01	14%	28 Jul →
nuke	13–11	-0.02	5%	28 Jul →
anubis	3–13	0.01	16%	26 Jul →
anubis	13–16	0.02	13%	26 Jul →
inferno	11–13	0.01	11%	24 Jul →
anubis	10–13	0.03	16%	24 Jul →
cache	13–10	0.02	9%	24 Jul →
nuke	13–5	0.09	19%	23 Jul →
ancient	13–6	0.04	13%	23 Jul →
nuke	13–1	0.08	21%	23 Jul →
inferno	9–13	0.03	19%	22 Jul →
nuke	13–10	0.07	4%	22 Jul →
mirage	13–8	0.05	6%	22 Jul →
dust2	16–14	0.12	32%	22 Jul →
anubis	14–16	0.00	19%	21 Jul →
cache	13–10	0.08	20%	21 Jul →
anubis	13–11	0.04	13%	21 Jul →
ancient	13–6	-0.02	16%	20 Jul →
dust2	13–6	-0.02	14%	20 Jul →
mirage	13–2	-0.03	9%	20 Jul →
cache	13–7	0.08	13%	20 Jul →
anubis	13–3	0.07	16%	18 Jul →
nuke	13–5	0.11	4%	18 Jul →
nuke	7–13	0.15	15%	18 Jul →
cache	9–13	-0.05	19%	18 Jul →
nuke	13–8	-0.02	14%	17 Jul →
nuke	13–10	-0.02	15%	16 Jul →
cache	13–3	0.10	13%	16 Jul →
dust2	13–9	0.03	18%	16 Jul →
mirage	6–13	-0.01	3%	16 Jul →
ancient	9–13	0.06	14%	16 Jul →
ancient	13–7	0.08	13%	15 Jul →
mirage	13–10	0.04	10%	15 Jul →
dust2	7–13	-0.04	17%	15 Jul →
mirage	14–16	0.01	12%	14 Jul →
anubis	13–6	0.01	8%	14 Jul →
cache	5–13	0.04	15%	14 Jul →
dust2	2–13	-0.01	12%	13 Jul →
nuke	13–11	0.03	13%	13 Jul →
anubis	11–13	0.01	24%	12 Jul →
nuke	10–13	0.00	18%	12 Jul →
nuke	7–13	0.02	9%	11 Jul →
inferno	5–13	0.05	22%	11 Jul →
nuke	13–11	0.04	15%	11 Jul →
cache	1–13	-0.07	7%	11 Jul →
dust2	13–10	0.03	23%	11 Jul →
mirage	12–16	0.05	12%	11 Jul →
train	6–13	-0.02	12%	8 Jul →
vertigo	3–13	0.01	13%	8 Jul →
cache	13–4	0.18	17%	8 Jul →
office	13–11	0.02	17%	7 Jul →