dwoan daonwu — CS2 Stats

76561199000363038[U:1:1040097310]

2,165Tracked matches50%Win rate2020Tracked since

CSDB Rating6.7 SolidAggressive Rifler

Premier CS Rating21,455Top 50% of 17,162 CSDB-tracked playersPink band · top ~6.9% of ranked players (population est.)

CSDB Leaderboard#10362 of 31874 tracked

FaceitLevel 10 · 2,619 ELOTop 12.4% of ranked FACEIT players

WingmanLegendary Eagle Master

Ladder ranks via Leetify

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Performance scores

Aim84

Positioning58

Utility66

0–100 skill scores via Leetify.

Recent form

STEADY46–49–5Last 10046%Win rateLLWLLLWWWW

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

Player DNA

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

Aim8.4

Aggression6.8

Utility6.6

Positioning5.8

Opening Duels3.0

Clutch4.2

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 Twistzz 94% playstyle similarity

Most alike: positioning profile, opening-fight frequency.

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 55% on CT to 29% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 562ms 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

Aim8.4

Positioning5.8

Utility6.6

Mechanics7.1

Opening Duels3.2

Win Impact5.0

Composite 6.7/10 (Solid), a weighted mean of the bars with a small opposition adjustment (×1.02 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating0.01+0.00

first ⅓ avg 0.01 → last ⅓ avg 0.01

Reaction time570ms+7ms

first ⅓ avg 564ms → last ⅓ avg 570ms

Headshot accuracy23.6%+0.9%

first ⅓ avg 22.7% → last ⅓ avg 23.6%

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

0.15Best rating — train 12–12

13–1Biggest win — train

5Longest win streak

L2Current streak

5–10In matches decided by ≤2 rounds

7Overtime games

Map breakdown

trainBest map · 80% over 5infernoWeakest map · 30% over 10

Map	Grade	Played	Record	Win rate	Avg rating
dust2	C	22	9–13	41%	0.01
ancient	A	16	10–6	63%	0.03
anubis	B	15	7–8	47%	0.01
inferno	D	10	3–7	30%	-0.01
mirage	C	10	4–6	40%	0.02
nuke	C	7	3–4	43%	-0.01
train	S	5	4–1	80%	0.08
vertigo	C	5	2–3	40%	0.02
office	A	5	3–2	60%	0.05
cache	—	3	0–3	0%	-0.02
overpass	—	2	1–1	50%	-0.01

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (30% over 10). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

2,657Matches

51%Win rate

1.10Avg K/D

73.3ADR

47%Headshot %

Clutches & streaks

41%1v1 clutch win

22%1v2 clutch win

10Longest win streak

Recent Faceit resultsLWWWL

Map	Matches	Win rate	Avg K/D	Avg kills
Ancient	284	53%	1.07	14.7
Anubis	230	56%	1.13	14.8
Mirage	228	43%	1.04	14.2
Dust2	181	49%	1.01	14.6
Inferno	118	50%	1.12	15.2
Nuke	80	52%	0.98	13.8
Overpass	57	53%	1.11	14.1
Vertigo	36	36%	0.96	15.1

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.

22.9%Headshot accuracy

36.6%Accuracy (enemy spotted)

40.5%Spray accuracy

81.8%Counter-strafing

8.9°Preaim

562msReaction time

28.9%T opening success

55.2%CT opening success

0.65Enemies flashed / flash

11.2%Flash assists

8.34HE damage / grenade

11.31Flashes / 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 28.9467% — 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.

30% win rate across 10 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
nuke	5–8	-0.01	13%	28 Aug →
dust2	13–16	-0.05	19%	27 Aug →
ancient	13–10	0.05	29%	26 Aug →
dust2	8–13	-0.03	36%	18 Aug →
inferno	10–13	-0.08	13%	18 Aug →
inferno	8–13	-0.07	24%	17 Aug →
train	9–4	0.06	24%	15 Aug →
train	13–1	0.09	57%	15 Aug →
dust2	13–10	0.03	24%	13 Aug →
ancient	22–20	-0.03	25%	1 Aug →
ancient	13–6	-0.02	13%	30 Jul →
nuke	0–12	-0.05	10%	27 Jul →
dust2	8–13	-0.03	15%	25 Jul →
dust2	13–8	0.07	32%	24 Jul →
anubis	13–6	0.03	8%	23 Jul →
mirage	13–8	0.08	19%	23 Jul →
ancient	3–13	0.07	25%	19 Jul →
anubis	13–4	-0.02	14%	18 Jul →
mirage	2–13	-0.04	35%	18 Jul →
cache	2–13	-0.06	32%	18 Jul →
anubis	13–8	0.01	33%	10 Jul →
mirage	11–13	0.08	24%	10 Jul →
ancient	13–4	0.09	14%	9 Jul →
anubis	8–13	0.03	12%	27 Jun →
mirage	5–13	-0.05	30%	23 Jun →
ancient	13–10	0.05	13%	23 Jun →
dust2	9–13	-0.03	22%	18 Jun →
ancient	13–2	0.09	29%	16 Jun →
vertigo	2–13	-0.02	17%	16 Jun →
mirage	13–8	0.08	32%	8 Jun →
mirage	13–3	0.05	26%	7 Jun →
anubis	10–13	-0.01	25%	4 Jun →
inferno	4–13	0.00	35%	4 Jun →
anubis	7–13	-0.05	8%	31 May →
dust2	13–11	0.12	18%	27 May →
dust2	4–13	0.00	25%	27 May →
anubis	8–13	0.00	22%	27 May →
dust2	13–4	0.08	21%	26 May →
dust2	16–13	0.02	21%	24 May →
dust2	13–7	0.02	18%	20 May →
ancient	4–13	0.03	33%	20 May →
office	13–7	0.05	31%	15 May →
nuke	13–9	0.08	25%	12 May →
vertigo	13–6	0.07	15%	10 May →
cache	11–13	0.02	25%	6 May →
ancient	5–13	0.03	13%	5 May →
overpass	13–5	-0.00	12%	1 May →
inferno	13–8	0.07	20%	1 May →
anubis	13–5	-0.02	10%	1 May →
cache	11–13	-0.03	21%	30 Apr →
inferno	9–13	-0.05	36%	26 Apr →
dust2	15–15	-0.01	26%	22 Apr →
train	12–12	0.15	19%	22 Apr →
ancient	13–7	0.09	13%	21 Apr →
dust2	14–16	-0.05	9%	21 Apr →
ancient	8–13	0.06	19%	21 Apr →
dust2	6–13	0.02	28%	20 Apr →
inferno	13–10	-0.02	21%	19 Apr →
anubis	13–4	0.00	18%	5 Apr →
anubis	12–12	0.04	18%	5 Apr →
mirage	11–13	0.01	13%	31 Mar →
mirage	13–6	0.02	7%	29 Mar →
dust2	11–13	0.01	23%	25 Mar →
office	11–13	0.07	18%	23 Mar →
inferno	7–13	-0.05	19%	12 Mar →
inferno	13–3	0.00	20%	10 Mar →
anubis	6–13	0.05	14%	8 Mar →
mirage	2–13	-0.04	26%	4 Mar →
dust2	11–13	0.01	23%	2 Mar →
dust2	11–13	-0.04	22%	27 Feb →
anubis	6–13	-0.05	20%	24 Feb →
ancient	13–6	0.05	13%	23 Feb →
vertigo	9–13	-0.03	24%	17 Feb →
anubis	13–11	0.00	30%	5 Feb →
nuke	5–13	-0.02	35%	5 Feb →
anubis	13–9	0.00	39%	4 Feb →
dust2	9–13	-0.03	24%	3 Feb →
nuke	13–6	-0.04	24%	24 Jan →
office	13–9	0.05	30%	24 Jan →
office	9–13	0.03	32%	24 Jan →
nuke	10–13	-0.02	22%	19 Jan →
dust2	13–11	0.06	13%	5 Jan →
vertigo	12–12	0.01	23%	19 Dec →
office	13–1	0.07	26%	1 Dec →
inferno	10–13	0.02	33%	9 Nov →
vertigo	13–4	0.08	23%	25 Oct →
inferno	4–13	0.03	25%	17 Oct →
anubis	8–13	0.05	21%	19 Sept →
dust2	13–8	-0.03	23%	17 Sept →
train	13–10	-0.03	18%	13 Sept →
train	13–1	0.10	22%	13 Sept →
ancient	20–22	-0.01	17%	9 Sept →
nuke	13–11	-0.02	24%	23 Aug →
ancient	7–13	-0.04	11%	18 Aug →
dust2	13–6	0.04	20%	6 Aug →
ancient	13–8	0.04	30%	31 Jul →
overpass	15–15	-0.02	13%	29 Jul →
ancient	13–10	-0.02	18%	27 Jul →
mirage	7–13	0.03	13%	27 Jul →
dust2	10–13	0.01	15%	27 Jul →