ausland — CS2 Stats

NO76561198986440866[U:1:1026175138]Steam profile ↗✓ No bans

262Tracked matches57%Win rate2024Tracked since

CSDB Rating6.4 SolidSupport

FaceitLevel 9Top 20.7% of ranked FACEIT players

Ladder ranks via Leetify

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Performance scores

Aim76

Positioning49

Utility72

0–100 skill scores via Leetify.

Recent form

COLD51–32–1Last 8461%Win rateWLWLLLLLLL

Last 10 vs previous 10: −50pp win rate · −0.02 avg rating · +2.1pp headshot accuracy · −12ms reaction

Win rate down 50pp 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

2–3 · 40% win rate
Avg rating -0.00
Avg headshot accuracy 17%
Avg reaction 519ms

Last 10

2–8 · 20% win rate
Avg rating -0.02
Avg headshot accuracy 17%
Avg reaction 517ms

Last 20

9–11 · 45% win rate
Avg rating -0.00
Avg headshot accuracy 16%
Avg reaction 524ms

Newest first, from the last 84 tracked matches. Each block is its own sample — one result moves a 5-match win rate by 20 points.

Player DNA

Primary style: Support — Utility contribution stands above the rest of this profile (+2.4 against its own average).

Aim7.6

Utility7.2

Positioning4.9

Opening Duels1.7

Clutch5.4

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.

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 ZywOo 83% playstyle similarity

Most alike: utility contribution, positioning profile.

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 →

CSDB Rating breakdown

Aim7.6

Positioning4.9

Utility7.2

Mechanics6.9

Opening Duels3.1

Win Impact7.2

Composite 6.4/10 (Solid), 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-0.00−0.00

first ⅓ avg 0.00 → last ⅓ avg -0.00

Reaction time545ms−47ms

first ⅓ avg 591ms → last ⅓ avg 545ms

Headshot accuracy17.9%+3.1%

first ⅓ avg 14.8% → last ⅓ avg 17.9%

Rolling 5-match average across the last 84 tracked matches, oldest to newest. The delta compares the first third of the window with the last.

Personal bests

0.08Best match rating · 13–7 · anubis, 25 Oct →

37%Best headshot accuracy · 13–5 · dust2, 8 Sept →

422msFastest reaction time · 13–2 · anubis, 6 Sept →

13–1Biggest win · ancient, 6 Sept →

Across the last 84 tracked matches.

Highlights

7Longest win streak

11–7In matches decided by ≤2 rounds

11Overtime games

Map breakdown

nukeBest map · 75% over 8infernoWeakest map · 20% over 5

Map	Grade	Played	Record	Win rate	Avg rating
anubis	A	22	14–8	64%	0.00
ancient	A	14	9–5	64%	0.00
mirage	A	11	7–4	64%	0.00
dust2	A	10	6–4	60%	0.00
cache	A	10	6–4	60%	-0.01
nuke	S	8	6–2	75%	-0.00
inferno	D	5	1–4	20%	-0.03
vertigo	—	3	1–2	33%	0.00
overpass	—	1	1–0	100%	0.00

Across the last 84 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (20% over 5). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

321Matches

54%Win rate

1.08Avg K/D

81.1ADR

46%Headshot %

Clutches & streaks

47%1v1 clutch win

22%1v2 clutch win

7Longest win streak

Recent Faceit resultsWLWLW

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	89	48%	1.11	16.9
Dust2	55	56%	1.06	15.6
Anubis	48	52%	1.16	17.0
Ancient	44	50%	0.95	15.5
Inferno	27	52%	0.97	14.2
Cache	22	55%	1.06	15.3
Nuke	20	80%	1.12	16.9
Vertigo	8	38%	1.25	19.6

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.

17.9%Headshot accuracy

36.8%Accuracy (enemy spotted)

34.8%Spray accuracy

81.2%Counter-strafing

9.5°Preaim

542msReaction time

39.5%T opening success

45.3%CT opening success

0.65Enemies flashed / flash

9.7%Flash assists

11.29HE damage / grenade

19.62Flashes / 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 39.5115% — 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.

20% win rate across 5 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
anubis	13–7	0.08	27%	25 Oct →
anubis	8–13	-0.01	15%	8 Oct →
ancient	13–9	-0.02	11%	8 Oct →
vertigo	9–13	-0.02	17%	4 Oct →
anubis	11–13	-0.04	16%	4 Oct →
dust2	10–13	-0.08	21%	4 Oct →
mirage	9–13	-0.03	21%	1 Oct →
nuke	11–13	-0.01	16%	1 Oct →
cache	9–13	-0.05	13%	25 Sept →
ancient	8–13	0.03	18%	25 Sept →
mirage	13–9	0.05	19%	25 Sept →
ancient	12–16	-0.00	12%	20 Sept →
anubis	9–13	-0.01	16%	20 Sept →
cache	13–9	-0.01	21%	20 Sept →
nuke	13–7	0.00	26%	19 Sept →
cache	8–13	0.01	20%	19 Sept →
anubis	19–17	0.02	7%	18 Sept →
anubis	13–11	0.01	5%	17 Sept →
cache	13–8	-0.01	5%	12 Sept →
ancient	13–11	0.01	22%	12 Sept →
cache	13–7	0.02	26%	12 Sept →
mirage	13–8	0.07	18%	12 Sept →
dust2	23–25	0.00	17%	11 Sept →
ancient	13–9	0.04	20%	11 Sept →
cache	13–10	-0.03	18%	11 Sept →
dust2	13–11	-0.01	21%	11 Sept →
inferno	10–13	-0.01	24%	10 Sept →
mirage	16–13	0.01	31%	10 Sept →
dust2	13–9	0.07	26%	9 Sept →
vertigo	16–14	-0.02	18%	9 Sept →
inferno	12–12	0.05	19%	9 Sept →
dust2	13–5	0.01	37%	8 Sept →
mirage	9–13	0.00	21%	8 Sept →
cache	10–13	-0.02	21%	6 Sept →
ancient	13–1	-0.01	22%	6 Sept →
cache	8–13	-0.04	12%	6 Sept →
anubis	13–2	0.00	19%	6 Sept →
nuke	22–20	-0.03	19%	5 Sept →
dust2	15–19	-0.02	15%	5 Sept →
mirage	7–13	-0.04	17%	5 Sept →
ancient	11–13	0.02	23%	4 Sept →
anubis	13–8	0.01	12%	4 Sept →
nuke	13–5	-0.00	18%	4 Sept →
anubis	13–6	0.06	26%	4 Sept →
anubis	8–13	-0.05	16%	2 Sept →
ancient	2–13	-0.06	15%	1 Sept →
mirage	28–26	0.02	13%	25 Aug →
anubis	13–9	0.02	17%	9 Aug →
anubis	13–2	0.00	32%	9 Aug →
anubis	13–7	-0.03	17%	31 Jul →
anubis	13–6	0.04	9%	9 Jul →
ancient	25–23	0.05	17%	14 Jun →
dust2	13–11	-0.01	18%	13 Jun →
inferno	7–13	-0.13	18%	12 Jun →
anubis	9–13	-0.06	26%	11 Jun →
dust2	13–6	0.01	15%	10 Jun →
nuke	13–9	-0.03	10%	7 Jun →
mirage	13–9	-0.01	24%	6 Jun →
dust2	13–5	0.03	21%	6 Jun →
nuke	9–13	0.03	9%	6 Jun →
ancient	13–3	0.03	18%	6 Jun →
nuke	13–10	-0.00	17%	4 Jun →
anubis	13–2	0.01	20%	4 Jun →
cache	13–4	0.04	34%	4 Jun →
anubis	13–8	0.05	12%	4 Jun →
inferno	13–10	-0.04	16%	3 Jun →
ancient	13–9	-0.02	7%	3 Jun →
anubis	10–13	-0.04	14%	2 Jun →
anubis	11–13	-0.08	16%	30 May →
ancient	13–3	-0.00	15%	30 May →
overpass	13–3	0.00	16%	30 May →
cache	13–5	0.02	11%	30 May →
ancient	7–13	-0.02	6%	30 May →
dust2	11–13	0.02	18%	9 May →
mirage	9–13	-0.05	7%	13 Jul →
inferno	8–13	-0.05	21%	13 Jul →
mirage	13–7	-0.01	7%	13 Jul →
anubis	13–7	0.03	13%	11 Jul →
anubis	10–12	-0.03	11%	9 Jul →
anubis	16–13	0.06	21%	6 Jul →
mirage	16–14	0.04	19%	6 Jul →
ancient	13–11	-0.01	13%	30 Jun →
nuke	13–6	0.02	8%	29 Jun →
vertigo	10–13	0.04	12%	10 Jun →