𐂃 — CS2 Stats

76561198328300608[U:1:368034880]Steam profile ↗✓ No bans

2,584Tracked matches47%Win rate2020Tracked since

CSDB Rating5.0 DevelopingPositional Player

CSDB Leaderboard#251866 of 259659 tracked

FaceitLevel 6 · 1,205 ELOTop 54.0% of ranked FACEIT players

WingmanGold Nova Master

Ladder ranks via Leetify

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Performance scores

Aim68

Positioning59

Utility56

0–100 skill scores via Leetify.

Recent form

STEADY45–54–1Last 10045%Win rateLLWWWLLLWL

Last 10 vs previous 10: −20pp win rate · +0.00 avg rating · −7.2pp headshot accuracy · +15ms reaction

Win rate down 20pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp). Average match rating moved the other way (+0.00), so results shifted more than performance did.

Last 5 · 10 · 20 matches

Last 5

3–2 · 60% win rate
Avg rating 0.03
Avg headshot accuracy 21%
Avg reaction 658ms

Last 10

4–6 · 40% win rate
Avg rating 0.03
Avg headshot accuracy 21%
Avg reaction 614ms

Last 20

10–10 · 50% win rate
Avg rating 0.02
Avg headshot accuracy 25%
Avg reaction 607ms

Newest first, from the last 100 tracked matches. Each block is its own sample — one result moves a 5-match win rate by 20 points.

Player DNA

Primary style: Positional Player — Positioning stands above the rest of this profile (+1.1 against its own average).

Aim6.8

Utility5.6

Positioning5.9

Opening Duels5.0

Clutch2.8

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 device 86% playstyle similarity

Most alike: positioning profile, utility contribution.

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 →

Strengths & areas to improve

Areas to improve

T-side openings. Opening success drops from 55% on CT to 37% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 610ms 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

Aim6.8

Positioning5.9

Utility5.6

Mechanics4.5

Opening Duels4.1

Win Impact3.9

Composite 5.0/10 (Developing), a weighted mean of the bars with a small opposition adjustment (×0.90 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating0.01+0.01

first ⅓ avg 0.00 → last ⅓ avg 0.01

Reaction time613ms−70ms

first ⅓ avg 683ms → last ⅓ avg 613ms

Headshot accuracy25.2%+2.8%

first ⅓ avg 22.4% → last ⅓ avg 25.2%

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.

Personal bests

0.29Best match rating · 5–1 · rush_001, 10 Oct →

40%Best headshot accuracy · 3–13 · inferno, 22 Jul →

438msFastest reaction time · 11–13 · inferno, 14 Jun →

13–0Biggest win · ancient, 9 May →

Across the last 100 tracked matches.

Highlights

6Longest win streak

L2Current streak

5–14In matches decided by ≤2 rounds

12Overtime games

Map breakdown

nukeBest map · 71% over 7infernoWeakest map · 21% over 19

Map	Grade	Played	Record	Win rate	Avg rating
ancient	B	23	11–12	48%	0.00
inferno	D	19	4–15	21%	0.01
mirage	D	17	5–12	29%	-0.01
dust2	B	15	8–7	53%	-0.01
nuke	S	7	5–2	71%	-0.01
anubis	C	5	2–3	40%	-0.01
rush_001	—	4	3–1	75%	0.12
cache	—	4	2–2	50%	0.01
office	—	2	2–0	100%	0.01
overpass	—	2	2–0	100%	-0.03
vertigo	—	1	0–1	0%	0.02
alpine	—	1	1–0	100%	-0.01

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (21% over 19). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

2,016Matches

49%Win rate

0.94Avg K/D

80.3ADR

50%Headshot %

Clutches & streaks

34%1v1 clutch win

22%1v2 clutch win

9Longest win streak

Recent Faceit resultsLLLWL

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	429	43%	0.92	15.0
Ancient	427	54%	0.99	17.4
Dust2	359	50%	0.97	15.4
Inferno	206	52%	0.92	15.6
Anubis	171	49%	0.91	15.4
Nuke	100	53%	0.93	15.2
Overpass	67	54%	0.95	15.5
Vertigo	34	44%	0.79	14.4

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.

24.7%Headshot accuracy

34.4%Accuracy (enemy spotted)

37.8%Spray accuracy

70.3%Counter-strafing

10.3°Preaim

610msReaction time

37.1%T opening success

55.4%CT opening success

0.54Enemies flashed / flash

10.8%Flash assists

8.91HE damage / grenade

9.37Flashes / 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 37.095% — 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.

21% win rate across 19 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	4–13	-0.03	26%	31 Oct →
ancient	9–13	0.03	25%	31 Oct →
mirage	13–3	0.12	22%	31 Oct →
office	13–8	0.03	16%	31 Oct →
office	8–7	0.00	15%	31 Oct →
mirage	13–16	-0.05	24%	18 Oct →
inferno	6–9	0.01	29%	16 Oct →
ancient	11–13	-0.08	4%	11 Oct →
rush_001	5–1	0.29	24%	10 Oct →
rush_001	7–8	-0.06	27%	10 Oct →
rush_001	8–5	0.02	25%	10 Oct →
rush_001	4–0	0.21	27%	10 Oct →
dust2	14–16	0.04	33%	30 Aug →
dust2	11–13	-0.05	33%	28 Aug →
ancient	16–14	-0.02	17%	22 Aug →
dust2	13–8	0.03	32%	21 Aug →
inferno	14–16	-0.05	32%	20 Aug →
inferno	12–16	-0.01	34%	19 Aug →
ancient	25–22	-0.01	24%	18 Aug →
dust2	13–1	0.09	28%	16 Aug →
dust2	13–7	0.05	28%	16 Aug →
anubis	13–7	0.02	36%	14 Aug →
cache	11–13	-0.03	16%	13 Aug →
dust2	13–6	-0.05	25%	13 Aug →
cache	13–10	0.01	30%	12 Aug →
ancient	6–13	-0.03	19%	12 Aug →
dust2	13–4	0.04	33%	12 Aug →
ancient	13–4	0.10	20%	11 Aug →
dust2	6–13	0.01	31%	9 Aug →
ancient	11–13	-0.03	26%	2 Aug →
ancient	5–13	-0.04	29%	2 Aug →
dust2	7–13	-0.06	19%	2 Aug →
ancient	13–7	0.01	25%	2 Aug →
mirage	10–13	-0.01	26%	1 Aug →
inferno	0–13	-0.10	0%	31 Jul →
nuke	13–7	-0.03	26%	30 Jul →
dust2	13–8	-0.03	20%	22 Jul →
inferno	3–13	0.03	40%	22 Jul →
mirage	13–10	0.00	20%	22 Jul →
ancient	10–13	0.02	22%	21 Jul →
dust2	13–16	-0.06	20%	19 Jul →
ancient	17–19	0.05	24%	19 Jul →
mirage	9–13	-0.04	20%	11 Jul →
mirage	13–8	0.05	25%	11 Jul →
mirage	13–9	0.00	40%	5 Jul →
dust2	22–19	0.01	34%	4 Jul →
nuke	13–5	0.03	38%	29 Jun →
nuke	13–3	0.02	17%	28 Jun →
ancient	10–13	0.04	13%	27 Jun →
inferno	14–16	0.03	19%	27 Jun →
mirage	11–13	-0.03	27%	22 Jun →
ancient	7–13	-0.03	22%	19 Jun →
inferno	16–13	0.02	22%	15 Jun →
inferno	11–13	-0.00	30%	14 Jun →
ancient	13–6	0.00	24%	4 Jun →
nuke	6–13	0.04	17%	4 Jun →
cache	13–8	0.03	23%	14 May →
mirage	9–13	-0.01	22%	10 May →
mirage	11–13	-0.02	15%	10 May →
inferno	10–13	-0.01	23%	9 May →
ancient	13–0	-0.02	10%	9 May →
overpass	13–4	-0.02	16%	9 May →
ancient	13–11	0.03	18%	9 May →
anubis	13–5	0.01	8%	9 May →
dust2	13–11	-0.04	15%	9 May →
nuke	13–6	-0.07	18%	9 May →
mirage	7–13	-0.04	23%	9 May →
ancient	16–12	0.05	30%	9 May →
mirage	7–13	-0.08	19%	9 May →
ancient	5–13	-0.01	12%	2 May →
inferno	6–13	-0.07	14%	2 May →
cache	6–13	0.02	20%	29 Apr →
inferno	11–13	-0.05	30%	26 Apr →
inferno	12–12	0.02	27%	24 Apr →
ancient	13–9	-0.02	33%	24 Apr →
vertigo	10–13	0.02	25%	24 Apr →
dust2	6–13	-0.04	17%	24 Apr →
mirage	7–13	-0.04	21%	24 Apr →
nuke	13–6	0.01	15%	19 Apr →
mirage	10–13	-0.00	12%	18 Apr →
overpass	13–11	-0.03	10%	18 Apr →
nuke	9–13	-0.05	19%	18 Apr →
ancient	13–5	0.05	25%	18 Apr →
ancient	13–5	0.06	25%	11 Apr →
ancient	3–13	-0.02	12%	10 Apr →
ancient	9–13	-0.07	10%	9 Apr →
inferno	13–4	-0.06	31%	5 Apr →
anubis	4–13	0.02	24%	5 Apr →
anubis	4–13	-0.05	15%	5 Apr →
mirage	8–13	-0.05	24%	5 Apr →
inferno	11–13	0.06	32%	5 Apr →
inferno	13–9	0.05	30%	4 Apr →
inferno	5–13	-0.04	26%	3 Apr →
anubis	8–13	-0.05	16%	3 Apr →
mirage	13–10	0.04	28%	30 Mar →
inferno	10–13	0.13	32%	23 Mar →
alpine	13–10	-0.01	22%	23 Mar →
inferno	8–13	0.04	36%	22 Mar →
mirage	10–13	-0.01	16%	22 Mar →
inferno	9–1	0.26	32%	22 Mar →