Sever1ty — CS2 Stats

76561197980244571[U:1:19978843]

592Tracked matches46%Win rate2023Tracked since

1,184Hours in CS9Hrs last 2 wks

CSDB Rating3.3 Learning

Ladder ranks via Leetify

Performance scores

Aim32

Positioning44

Utility42

0–100 skill scores via Leetify.

Recent form

STEADY43–47–10Last 10043%Win rateWLLLLLWWLW

Last 10 vs previous 10: +0pp win rate · -0.01 avg rating

Player DNA

Aim3.2

Aggression4.0

Utility4.2

Positioning4.4

Opening Duels0.7

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 76% playstyle similarity

Most alike: opening-fight frequency, 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. 644ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 60% 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

Aim3.2

Positioning4.4

Utility4.2

Mechanics2.2

Opening Duels1.1

Win Impact3.7

Composite 3.3/10 (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-0.02−0.02

first ⅓ avg 0.00 → last ⅓ avg -0.02

Reaction time659ms−56ms

first ⅓ avg 714ms → last ⅓ avg 659ms

Headshot accuracy12.7%−1.0%

first ⅓ avg 13.7% → last ⅓ avg 12.7%

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 — dust2 13–3

13–1Biggest win — inferno

3Longest win streak

4–4In matches decided by ≤2 rounds

Map breakdown

nukeBest map · 63% over 16infernoWeakest map · 15% over 13

Map	Grade	Played	Record	Win rate	Avg rating
dust2	C	23	9–14	39%	-0.02
nuke	A	16	10–6	63%	-0.01
inferno	D	13	2–11	15%	0.02
mirage	B	11	5–6	45%	0.00
cache	C	9	4–5	44%	-0.02
overpass	C	8	3–5	38%	-0.01
ancient	B	6	3–3	50%	-0.01
anubis	A	5	3–2	60%	0.02
vertigo	C	5	2–3	40%	0.01
train	—	2	0–2	0%	-0.02
office	—	2	2–0	100%	-0.01

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (15% over 13). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

33,919Lifetime kills

1.15K/D · Top 50% of tracked players

1,908Matches

35.8%Match win rate

35.6%Headshot %

13.3%Shot accuracy · Top 50% of tracked players

2,279MVPs

532Hours (in match)

1,228Bombs planted

264Bombs defused

Most-used weapons

AK-478,111

M4A4 / M4A1-S6,967

AWP2,145

P2000 / USP-S1,786

Desert Eagle1,320

Glock-181,242

Galil AR1,185

P90993

MP7883

UMP-45664

Lifetime map wins

2,921dust2

1,288inferno

1,226nuke

695vertigo

458train

300office

153lake

105cbble

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.

12.2%Headshot accuracy

30.5%Accuracy (enemy spotted)

30.1%Spray accuracy

60.0%Counter-strafing

9.0°Preaim

644msReaction time

26.6%T opening success

38.7%CT opening success

0.67Enemies flashed / flash

2.5%Flash assists

8.09HE damage / grenade

5.67Flashes / 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 12.2092% — 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 26.574% — 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.

15% win rate across 13 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
inferno	13–1	0.09	10%	26 Aug →
dust2	6–13	-0.03	15%	22 Aug →
dust2	8–13	-0.09	14%	22 Aug →
dust2	5–13	-0.02	14%	21 Aug →
inferno	10–13	0.02	12%	21 Aug →
cache	10–13	-0.08	13%	21 Aug →
ancient	7–0	0.04	17%	21 Aug →
dust2	13–6	0.02	11%	19 Aug →
dust2	3–10	-0.03	8%	19 Aug →
cache	13–7	-0.07	7%	16 Aug →
mirage	13–11	0.06	17%	15 Aug →
dust2	3–13	-0.08	10%	14 Aug →
nuke	13–10	0.00	17%	13 Aug →
inferno	4–13	-0.08	15%	8 Aug →
cache	10–13	0.02	5%	8 Aug →
nuke	12–12	-0.00	11%	8 Aug →
cache	4–13	-0.07	9%	8 Aug →
ancient	13–4	0.00	5%	7 Aug →
dust2	10–13	-0.01	15%	31 Jul →
cache	13–5	0.07	7%	31 Jul →
mirage	12–12	-0.03	16%	29 Jul →
nuke	13–6	-0.07	9%	28 Jul →
mirage	11–13	-0.04	16%	28 Jul →
nuke	1–13	-0.08	33%	28 Jul →
ancient	7–13	-0.01	10%	28 Jul →
cache	11–2	0.03	14%	28 Jul →
nuke	13–9	0.00	9%	28 Jul →
cache	12–12	0.03	15%	25 Jul →
inferno	12–12	-0.08	11%	25 Jul →
nuke	13–6	-0.03	11%	25 Jul →
dust2	13–9	-0.02	26%	15 Jul →
inferno	6–13	-0.01	9%	15 Jul →
dust2	13–5	0.01	9%	14 Jul →
cache	13–1	-0.04	21%	12 Jul →
mirage	13–2	0.06	13%	11 Jul →
inferno	12–12	0.09	14%	9 Jul →
overpass	4–12	0.09	3%	8 Jul →
mirage	11–13	-0.01	17%	8 Jul →
ancient	6–13	-0.07	8%	7 Jul →
mirage	6–13	0.02	13%	7 Jul →
mirage	12–12	-0.02	12%	4 Jul →
inferno	2–13	0.03	14%	4 Jul →
anubis	13–3	0.05	14%	30 Jun →
anubis	10–13	-0.01	16%	29 Jun →
train	4–13	-0.07	15%	23 Jun →
overpass	6–13	-0.05	18%	19 Jun →
inferno	8–13	0.06	17%	14 Jun →
mirage	11–3	-0.02	25%	14 Jun →
dust2	13–5	0.00	19%	9 Jun →
dust2	2–13	-0.07	16%	9 Jun →
dust2	2–13	-0.09	14%	8 Jun →
overpass	13–8	0.15	18%	4 Jun →
nuke	13–11	-0.02	11%	4 Jun →
dust2	2–13	-0.12	22%	4 Jun →
nuke	13–2	-0.02	12%	4 Jun →
vertigo	12–12	0.01	3%	3 Jun →
vertigo	13–8	0.02	14%	3 Jun →
nuke	13–4	0.04	14%	30 May →
anubis	10–13	-0.03	11%	30 May →
nuke	10–13	0.01	9%	25 May →
overpass	4–13	-0.08	8%	25 May →
nuke	9–13	-0.03	8%	24 May →
mirage	10–13	-0.03	12%	24 May →
nuke	5–13	-0.01	13%	24 May →
ancient	7–0	0.05	6%	24 May →
mirage	13–3	-0.01	13%	24 May →
inferno	6–13	-0.07	13%	24 May →
dust2	13–11	-0.06	17%	23 May →
inferno	6–13	0.08	5%	23 May →
vertigo	8–13	-0.03	8%	14 May →
inferno	13–11	0.05	6%	14 May →
dust2	5–13	-0.04	18%	9 May →
cache	10–13	-0.06	9%	8 May →
dust2	13–10	0.01	18%	20 Apr →
nuke	11–13	-0.03	11%	14 Apr →
overpass	2–13	-0.06	8%	14 Mar →
overpass	7–13	-0.08	11%	14 Mar →
inferno	9–13	-0.02	16%	5 Feb →
nuke	13–7	0.09	11%	27 Jan →
dust2	6–13	-0.09	12%	14 Jan →
dust2	7–1	0.02	36%	14 Jan →
nuke	13–5	-0.02	9%	23 Dec →
vertigo	13–7	0.01	16%	23 Dec →
vertigo	12–12	0.04	9%	20 Dec →
nuke	13–2	0.02	4%	14 Dec →
anubis	13–3	0.06	9%	14 Dec →
anubis	5–2	0.02	0%	14 Dec →
dust2	2–7	-0.03	28%	14 Dec →
dust2	13–3	0.16	20%	9 Dec →
inferno	8–13	0.08	25%	9 Dec →
dust2	13–9	-0.01	9%	9 Dec →
office	13–4	-0.02	14%	2 Dec →
office	13–4	-0.00	26%	27 Nov →
dust2	10–13	0.04	12%	25 Nov →
overpass	13–4	-0.01	18%	24 Nov →
overpass	13–8	-0.07	18%	19 Nov →
dust2	12–12	0.03	12%	15 Nov →
mirage	13–4	0.04	11%	14 Nov →
ancient	12–12	-0.05	12%	12 Nov →
train	11–13	0.04	14%	8 Nov →