-D0m1Xx_ — CS2 Stats

CZ76561199550643973[U:1:1590378245]Steam profile ↗✓ No bans

2,060Tracked matches37%Win rate2023Tracked since

CSDB Rating5.9 SolidAggressive Rifler

Premier CS Rating25,603Top 25% of 29,473 CSDB-tracked playersRed band · top ~2.7% of ranked players (population est.)

CSDB Leaderboard#5204 of 56666 tracked

FaceitLevel 10 · 2,171 ELOTop 12.4% of ranked FACEIT players

WingmanLegendary Eagle

Ladder ranks via Leetify

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Performance scores

Aim77

Positioning55

Utility54

0–100 skill scores via Leetify.

Recent form

STEADY52–48Last 10052%Win rateLWWLWWLLLL

Last 10 vs previous 10: +0pp win rate · -0.00 avg rating

Player DNA

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

Aim7.7

Aggression7.0

Utility5.4

Positioning5.5

Opening Duels3.4

Clutch3.2

Strong CT-side opener

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 91% playstyle similarity

Most alike: opening-fight frequency, utility contribution.

Where you differ: 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

T-side openings. Opening success drops from 58% on CT to 29% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 580ms 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

Aim7.7

Positioning5.5

Utility5.4

Mechanics6.8

Opening Duels3.5

Win Impact0.6

Composite 5.9/10 (Solid), a weighted mean of the bars with a small opposition adjustment (×1.05 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating0.01−0.03

first ⅓ avg 0.04 → last ⅓ avg 0.01

Reaction time587ms−25ms

first ⅓ avg 612ms → last ⅓ avg 587ms

Headshot accuracy21.3%−2.1%

first ⅓ avg 23.3% → last ⅓ avg 21.3%

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.

Highlights

0.21Best rating — inferno 13–3

13–0Biggest win — overpass

8Longest win streak

12–13In matches decided by ≤2 rounds

16Overtime games

Map breakdown

nukeBest map · 82% over 11anubisWeakest map · 32% over 19

Map	Grade	Played	Record	Win rate	Avg rating
inferno	A	25	16–9	64%	0.03
anubis	D	19	6–13	32%	-0.01
mirage	C	18	8–10	44%	0.04
overpass	B	15	8–7	53%	0.04
nuke	S	11	9–2	82%	0.03
ancient	B	6	3–3	50%	0.02
cache	—	3	0–3	0%	-0.04
dust2	—	2	1–1	50%	0.04
train	—	1	1–0	100%	0.02

Across the last 100 tracked matches.

Anubis is currently your weakest sufficiently-sampled map (32% over 19). Start with the 6 essential Anubis lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

2,626Matches

50%Win rate

1.15Avg K/D

80.7ADR

50%Headshot %

Clutches & streaks

36%1v1 clutch win

24%1v2 clutch win

8Longest win streak

Recent Faceit resultsWWLLL

Map	Matches	Win rate	Avg K/D	Avg kills
Inferno	605	55%	1.16	16.7
Anubis	538	52%	1.17	17.0
Mirage	482	46%	1.12	16.4
Ancient	246	44%	1.11	16.7
Nuke	234	49%	1.18	16.3
Overpass	180	51%	1.22	17.5
Dust2	130	44%	1.00	14.8
Train	98	46%	1.14	17.2

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.

22.2%Headshot accuracy

34.7%Accuracy (enemy spotted)

39.8%Spray accuracy

80.6%Counter-strafing

10.0°Preaim

580msReaction time

29.1%T opening success

58.3%CT opening success

0.50Enemies flashed / flash

6.3%Flash assists

7.08HE damage / grenade

10.66Flashes / 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.

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 29.071% — below the 40% mark we flag

Spend your practice time on Anubis

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

32% win rate across 19 tracked games — your weakest map with enough games to be worth reading into.

Anubis callouts & strategy →Anubis grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
anubis	1–13	-0.08	8%	17 Aug →
nuke	16–14	-0.03	33%	13 Aug →
ancient	13–8	0.05	36%	13 Aug →
dust2	16–19	0.05	32%	12 Aug →
anubis	13–10	-0.02	16%	6 Aug →
inferno	19–16	0.00	28%	5 Aug →
ancient	10–13	-0.01	22%	3 Aug →
inferno	6–13	-0.05	45%	2 Aug →
anubis	10–13	0.06	13%	30 Jul →
anubis	7–13	-0.00	13%	22 Jul →
anubis	10–13	-0.06	19%	19 Jul →
cache	2–13	-0.09	19%	19 Jul →
cache	6–13	-0.02	11%	18 Jul →
mirage	11–13	0.01	28%	4 Jul →
mirage	13–5	0.11	21%	24 Jun →
mirage	3–13	-0.00	28%	24 Jun →
mirage	13–2	0.04	20%	23 Jun →
inferno	13–7	0.01	9%	14 Jun →
anubis	16–13	-0.04	20%	6 Jun →
overpass	10–13	0.01	19%	5 Jun →
mirage	14–16	0.04	22%	22 May →
mirage	13–6	0.12	24%	22 May →
mirage	10–13	0.06	20%	14 May →
inferno	13–11	0.05	22%	11 May →
nuke	9–2	0.10	39%	3 May →
nuke	14–16	0.02	21%	1 May →
anubis	2–13	0.02	12%	1 May →
overpass	13–9	-0.00	22%	28 Apr →
inferno	8–13	0.02	26%	27 Apr →
cache	8–13	-0.00	18%	26 Apr →
inferno	8–13	-0.03	18%	18 Apr →
inferno	5–13	0.01	8%	18 Apr →
overpass	10–13	0.15	10%	17 Apr →
mirage	13–11	0.01	20%	15 Apr →
mirage	10–13	0.01	13%	11 Apr →
anubis	9–13	0.01	22%	7 Apr →
overpass	11–13	0.00	20%	6 Apr →
anubis	8–13	-0.06	12%	6 Apr →
mirage	13–11	0.00	24%	6 Apr →
anubis	17–19	-0.02	26%	3 Apr →
nuke	13–7	0.10	30%	2 Apr →
mirage	8–13	-0.05	20%	2 Apr →
mirage	9–13	0.12	29%	31 Mar →
overpass	11–13	-0.00	30%	30 Mar →
inferno	13–9	-0.01	20%	30 Mar →
overpass	13–8	0.05	16%	30 Mar →
inferno	9–13	0.01	23%	29 Mar →
nuke	16–14	-0.01	27%	29 Mar →
overpass	8–13	0.02	15%	29 Mar →
overpass	16–14	0.01	23%	27 Mar →
anubis	13–11	0.01	12%	27 Mar →
nuke	10–13	0.06	17%	26 Mar →
anubis	9–13	-0.01	20%	25 Mar →
inferno	16–12	0.00	24%	25 Mar →
anubis	6–13	-0.03	13%	24 Mar →
inferno	9–5	0.02	11%	24 Mar →
anubis	11–13	-0.01	11%	23 Mar →
ancient	11–13	-0.01	31%	23 Mar →
inferno	13–3	0.05	16%	21 Mar →
inferno	13–11	0.00	16%	21 Mar →
nuke	13–9	0.01	19%	20 Mar →
inferno	13–10	0.10	27%	20 Mar →
overpass	16–14	0.03	25%	20 Mar →
overpass	13–11	-0.01	36%	16 Mar →
inferno	16–12	-0.04	30%	16 Mar →
mirage	13–7	0.04	18%	14 Mar →
anubis	5–13	-0.05	26%	12 Mar →
nuke	13–7	0.00	14%	11 Mar →
ancient	13–10	0.03	17%	8 Mar →
inferno	13–10	0.03	24%	7 Mar →
inferno	11–13	-0.02	19%	2 Mar →
inferno	7–13	-0.05	17%	1 Mar →
anubis	16–14	0.05	27%	1 Mar →
nuke	13–8	0.03	25%	1 Mar →
inferno	6–13	0.00	22%	1 Mar →
overpass	10–13	0.14	23%	28 Feb →
anubis	13–10	0.00	26%	26 Feb →
inferno	13–3	0.21	27%	26 Feb →
overpass	22–19	0.02	18%	25 Feb →
inferno	11–13	0.05	23%	22 Feb →
inferno	13–9	0.06	28%	15 Feb →
mirage	11–13	0.08	23%	11 Feb →
nuke	13–7	0.03	17%	5 Feb →
ancient	13–9	0.06	14%	3 Feb →
inferno	13–9	0.04	28%	31 Jan →
overpass	13–0	0.13	17%	31 Jan →
inferno	13–4	0.14	30%	27 Jan →
mirage	13–9	0.09	18%	22 Jan →
mirage	13–11	0.11	21%	20 Jan →
anubis	13–6	0.01	15%	17 Jan →
mirage	11–13	-0.01	17%	17 Jan →
overpass	13–7	0.11	34%	17 Jan →
overpass	8–13	0.02	30%	16 Jan →
anubis	2–13	-0.04	23%	11 Jan →
ancient	9–13	-0.00	23%	9 Jan →
dust2	13–8	0.03	21%	4 Jan →
nuke	13–9	-0.00	24%	1 Jan →
mirage	14–16	-0.01	35%	29 Dec →
inferno	13–10	0.03	48%	29 Dec →
train	16–12	0.02	26%	28 Dec →