商人 — CS2 Stats

76561198196588789[U:1:236323061]Steam profile ↗✓ No bans

2,396Tracked matches54%Win rate2020Tracked since

CSDB Rating6.5 SolidAggressive Rifler

Premier CS Rating18,224Purple band · top ~15.6% of ranked players (population est.)

CSDB Leaderboard#27670 of 58728 tracked

FaceitLevel 8Top 31.2% of ranked FACEIT players

WingmanLegendary Eagle Master

Ladder ranks via Leetify

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Performance scores

Aim72

Positioning59

Utility62

0–100 skill scores via Leetify.

Recent form

STEADY52–42–6Last 10052%Win rateTWWWLLLWLL

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

Player DNA

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

Aim7.2

Aggression8.0

Utility6.2

Positioning5.9

Opening Duels3.5

Clutch4.0

Strong CT-side openerLimited utility dependence

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 iM 90% playstyle similarity

Most alike: utility contribution, positioning profile.

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 59% on CT to 29% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 644ms 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

Aim7.2

Positioning5.9

Utility6.2

Mechanics8.1

Opening Duels3.6

Win Impact6.3

Composite 6.5/10 (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 time635ms−18ms

first ⅓ avg 654ms → last ⅓ avg 635ms

Headshot accuracy16.4%−1.1%

first ⅓ avg 17.6% → last ⅓ avg 16.4%

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 — palais 9–3

13–1Biggest win — nuke

5Longest win streak

10–5In matches decided by ≤2 rounds

10Overtime games

Map breakdown

ancientBest map · 69% over 16infernoWeakest map · 33% over 6

Map	Grade	Played	Record	Win rate	Avg rating
anubis	B	19	10–9	53%	0.00
ancient	S	16	11–5	69%	-0.00
nuke	A	15	9–6	60%	0.03
mirage	B	13	6–7	46%	-0.02
train	A	12	7–5	58%	0.03
dust2	C	7	3–4	43%	-0.01
inferno	D	6	2–4	33%	-0.05
vertigo	—	4	1–3	25%	0.02
edin	—	3	1–2	33%	0.01
whistle	—	2	0–2	0%	-0.19
palais	—	1	1–0	100%	0.21
overpass	—	1	1–0	100%	0.08
basalt	—	1	0–1	0%	-0.09

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (33% over 6). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

1,391Matches

51%Win rate

1.02Avg K/D

80.2ADR

42%Headshot %

Clutches & streaks

40%1v1 clutch win

11%1v2 clutch win

10Longest win streak

Recent Faceit resultsWLLWW

Map	Matches	Win rate	Avg K/D	Avg kills
Ancient	116	53%	1.08	16.4
Mirage	86	47%	0.91	14.2
Anubis	76	50%	0.96	14.5
Nuke	71	55%	1.09	16.1
Vertigo	65	45%	0.97	15.2
Overpass	32	62%	1.09	14.7
Inferno	21	33%	0.81	13.1
Dust2	2	100%	0.68	12.0

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.

17.5%Headshot accuracy

37.7%Accuracy (enemy spotted)

39.5%Spray accuracy

86.6%Counter-strafing

10.9°Preaim

644msReaction time

29.2%T opening success

59.0%CT opening success

0.36Enemies flashed / flash

6.6%Flash assists

16.04HE damage / grenade

4.23Flashes / 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.

Grenades & Utility
Most of your flashes are blinding nobody. A handful of reliable pop-flash lineups fixes this faster than anything else.
Enemies flashed per flash 0.3637 — below the 0.5 mark we flag
Grenade Lineups →
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 29.1979% — 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.

33% win rate across 6 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
ancient	15–15	-0.04	8%	9 Dec →
inferno	13–9	0.05	22%	9 Dec →
mirage	13–11	0.03	21%	9 Dec →
ancient	13–11	-0.02	11%	9 Dec →
anubis	10–13	0.03	22%	9 Dec →
ancient	9–13	-0.06	7%	8 Dec →
nuke	2–13	0.05	21%	8 Dec →
mirage	13–8	0.05	20%	8 Dec →
inferno	6–13	-0.02	19%	8 Dec →
mirage	7–13	-0.01	21%	8 Dec →
dust2	2–2	-0.04	0%	8 Dec →
mirage	6–13	-0.08	7%	8 Dec →
train	13–11	-0.00	16%	8 Dec →
vertigo	6–13	0.02	18%	24 Nov →
anubis	13–9	0.04	20%	24 Nov →
mirage	14–16	-0.01	18%	24 Nov →
anubis	13–8	-0.03	14%	24 Nov →
dust2	13–10	0.03	21%	24 Nov →
nuke	10–13	0.10	9%	24 Nov →
train	13–11	0.02	14%	18 Nov →
train	10–0	0.19	13%	18 Nov →
inferno	2–9	-0.20	0%	18 Nov →
whistle	6–9	-0.12	18%	18 Nov →
palais	9–3	0.21	24%	18 Nov →
whistle	0–9	-0.27	0%	18 Nov →
train	13–8	-0.00	30%	17 Nov →
edin	8–13	-0.00	14%	17 Nov →
train	9–13	0.02	31%	17 Nov →
overpass	13–6	0.08	12%	17 Nov →
train	13–4	0.00	29%	17 Nov →
edin	12–12	-0.00	20%	16 Nov →
train	13–5	0.00	22%	16 Nov →
train	12–12	-0.02	20%	16 Nov →
train	12–12	-0.02	16%	16 Nov →
dust2	13–8	-0.07	18%	14 Nov →
ancient	13–2	-0.03	17%	14 Nov →
anubis	13–2	0.01	6%	14 Nov →
basalt	0–13	-0.09	9%	14 Nov →
train	12–12	0.00	17%	14 Nov →
train	13–5	0.17	17%	14 Nov →
edin	13–11	0.04	22%	14 Nov →
train	9–13	-0.04	22%	14 Nov →
dust2	11–13	-0.03	24%	13 Nov →
nuke	2–13	-0.02	46%	13 Nov →
anubis	13–3	-0.05	7%	11 Nov →
ancient	13–16	-0.06	20%	10 Nov →
anubis	4–13	0.00	32%	10 Nov →
nuke	13–16	0.02	15%	10 Nov →
anubis	13–5	0.05	14%	10 Nov →
ancient	13–6	0.11	15%	10 Nov →
mirage	16–12	0.00	23%	10 Nov →
anubis	3–13	-0.05	5%	10 Nov →
nuke	13–11	-0.00	19%	10 Nov →
anubis	11–13	0.03	12%	4 Nov →
ancient	13–5	0.06	16%	4 Nov →
anubis	2–13	-0.02	17%	4 Nov →
ancient	13–11	-0.01	12%	3 Nov →
mirage	16–12	-0.03	14%	3 Nov →
nuke	13–9	0.03	30%	3 Nov →
anubis	13–6	0.03	6%	3 Nov →
vertigo	7–13	-0.01	5%	3 Nov →
nuke	4–13	-0.02	18%	2 Nov →
mirage	9–13	-0.06	14%	2 Nov →
ancient	13–4	0.03	10%	2 Nov →
anubis	13–7	-0.04	19%	2 Nov →
nuke	13–8	0.05	17%	2 Nov →
inferno	13–6	-0.01	21%	2 Nov →
ancient	4–13	-0.10	19%	1 Nov →
anubis	8–13	0.07	6%	1 Nov →
vertigo	6–13	-0.01	24%	1 Nov →
nuke	13–1	0.19	24%	1 Nov →
nuke	13–11	0.09	18%	1 Nov →
dust2	16–14	-0.02	9%	1 Nov →
ancient	13–7	0.02	9%	1 Nov →
ancient	13–9	0.00	14%	31 Oct →
anubis	7–13	-0.02	20%	31 Oct →
nuke	13–7	-0.06	10%	31 Oct →
mirage	3–13	-0.05	27%	31 Oct →
dust2	10–13	0.13	10%	31 Oct →
mirage	13–10	0.02	11%	31 Oct →
nuke	6–13	-0.04	28%	31 Oct →
ancient	13–4	-0.08	13%	31 Oct →
anubis	13–7	0.01	29%	30 Oct →
inferno	11–13	-0.07	24%	30 Oct →
inferno	8–13	-0.03	24%	30 Oct →
anubis	7–13	-0.05	20%	30 Oct →
nuke	13–8	-0.01	21%	30 Oct →
mirage	4–13	-0.12	17%	30 Oct →
ancient	13–8	0.05	16%	30 Oct →
anubis	13–10	0.03	14%	29 Oct →
mirage	10–13	-0.01	25%	29 Oct →
nuke	13–3	0.07	21%	29 Oct →
ancient	7–0	0.03	7%	29 Oct →
anubis	6–13	-0.02	31%	29 Oct →
dust2	7–13	-0.06	9%	29 Oct →
ancient	15–15	0.04	17%	27 Oct →
nuke	16–13	-0.03	22%	27 Oct →
vertigo	16–14	0.08	23%	27 Oct →
mirage	13–6	0.05	5%	27 Oct →
anubis	13–2	0.02	13%	27 Oct →