The Great Khan — CS2 Stats

76561199381089141[U:1:1420823413]Steam profile ↗✓ No bans

2,405Tracked matches32%Win rate2024Tracked since

CSDB Rating4.3 DevelopingHybrid Rifler

FaceitLevel 5 · 1,053 ELOTop 67.6% of ranked FACEIT players

WingmanSilver III

Ladder ranks via Leetify

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Performance scores

Aim70

Positioning43

Utility47

0–100 skill scores via Leetify.

Recent form

STEADY35–49–16Last 10035%Win rateWWLLLLLWWL

Last 10 vs previous 10: +20pp win rate · +0.03 avg rating

Player DNA

Primary style: Hybrid Rifler — Aim-led profile without a single dominant tendency.

Aim7.0

Aggression4.7

Utility4.7

Positioning4.3

Opening Duels1.8

Clutch6.0

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 b1t 85% playstyle similarity

Most alike: opening-duel success, utility contribution.

Where you differ: lower opening-fight frequency; 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 52% on CT to 22% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 655ms 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

Aim7.0

Positioning4.3

Utility4.7

Mechanics5.9

Opening Duels2.7

Win Impact0.0

Composite 4.3/10 (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 0.01 → last ⅓ avg 0.01

Reaction time656ms+4ms

first ⅓ avg 652ms → last ⅓ avg 656ms

Headshot accuracy25.4%+3.9%

first ⅓ avg 21.6% → last ⅓ avg 25.4%

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

0.12Best rating — train 7–13

13–2Biggest win — anubis

3Longest win streak

W2Current streak

6–7In matches decided by ≤2 rounds

Map breakdown

infernoBest map · 56% over 9dust2Weakest map · 29% over 34

Map	Grade	Played	Record	Win rate	Avg rating
dust2	D	34	10–24	29%	0.02
mirage	D	17	5–12	29%	0.02
cache	B	15	8–7	53%	0.01
inferno	A	9	5–4	56%	0.02
anubis	B	8	4–4	50%	0.03
vertigo	C	5	2–3	40%	0.03
train	—	4	0–4	0%	0.04
nuke	—	3	1–2	33%	-0.04
fachwerk	—	2	0–2	0%	0.03
shelter	—	1	0–1	0%	0.03
boulder	—	1	0–1	0%	-0.02
ancient	—	1	0–1	0%	0.05

Across the last 100 tracked matches.

Dust 2 is currently your weakest sufficiently-sampled map (29% over 34). Start with the 6 essential Dust 2 lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

4Matches

50%Win rate

0.88Avg K/D

71.0ADR

44%Headshot %

Clutches & streaks

50%1v1 clutch win

0%1v2 clutch win

2Longest win streak

Recent Faceit resultsLLWWL

Map	Matches	Win rate	Avg K/D	Avg kills
Dust2	1	0%	0.75	12.0
Ancient	1	100%	1.27	28.0
Mirage	1	100%	0.81	13.0
Nuke	1	0%	0.67	29.0

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.

25.6%Headshot accuracy

34.6%Accuracy (enemy spotted)

35.1%Spray accuracy

76.4%Counter-strafing

9.6°Preaim

655msReaction time

22.5%T opening success

51.5%CT opening success

0.67Enemies flashed / flash

12.0%Flash assists

9.21HE damage / grenade

2.73Flashes / 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
You are buying and holding utility rather than using it. Unthrown flashes are wasted money every round.
Flashes per match 2.734 — below the 4 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 22.4805% — below the 40% mark we flag

Spend your practice time on Dust 2

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

29% win rate across 34 tracked games — your weakest map with enough games to be worth reading into.

Dust 2 callouts & strategy →Dust 2 grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
dust2	13–5	0.03	13%	29 Aug →
inferno	13–11	0.01	14%	29 Aug →
mirage	6–13	-0.01	31%	29 Aug →
mirage	5–13	0.04	23%	29 Aug →
inferno	5–13	-0.00	24%	28 Aug →
cache	8–13	-0.03	27%	20 Aug →
dust2	8–13	-0.00	26%	20 Aug →
dust2	9–9	0.03	29%	15 Aug →
dust2	13–11	0.03	47%	15 Aug →
mirage	11–13	-0.03	35%	13 Aug →
dust2	12–12	0.01	24%	10 Aug →
dust2	9–13	-0.01	23%	8 Aug →
anubis	9–13	0.03	19%	7 Aug →
dust2	13–7	0.05	13%	1 Aug →
mirage	9–13	-0.05	19%	1 Aug →
dust2	6–13	-0.07	10%	31 Jul →
cache	13–7	0.00	14%	31 Jul →
dust2	12–12	-0.02	14%	26 Jul →
inferno	5–10	-0.07	24%	24 Jul →
dust2	6–13	-0.08	53%	18 Jul →
mirage	8–13	0.01	35%	18 Jul →
mirage	13–4	0.01	35%	17 Jul →
anubis	13–2	0.09	20%	17 Jul →
anubis	3–13	-0.01	21%	17 Jul →
anubis	8–13	0.03	34%	17 Jul →
dust2	8–13	0.03	38%	17 Jul →
train	7–13	0.12	29%	17 Jul →
dust2	13–4	0.05	29%	17 Jul →
cache	3–13	-0.09	15%	17 Jul →
train	5–13	-0.01	28%	16 Jul →
dust2	13–7	0.11	19%	16 Jul →
cache	13–5	0.09	30%	16 Jul →
fachwerk	7–13	0.00	26%	16 Jul →
dust2	13–4	0.10	29%	16 Jul →
mirage	4–13	0.02	31%	16 Jul →
dust2	1–13	-0.02	29%	16 Jul →
dust2	11–13	0.08	40%	16 Jul →
mirage	12–12	0.09	22%	16 Jul →
mirage	13–4	0.09	12%	16 Jul →
dust2	12–12	0.03	24%	15 Jul →
dust2	11–13	0.04	11%	15 Jul →
nuke	6–13	-0.08	25%	15 Jul →
mirage	11–13	0.02	18%	15 Jul →
mirage	13–2	0.06	29%	14 Jul →
anubis	13–8	0.06	37%	14 Jul →
train	12–12	0.09	43%	14 Jul →
dust2	13–5	-0.02	15%	14 Jul →
cache	13–8	0.05	20%	14 Jul →
nuke	13–10	0.01	31%	13 Jul →
mirage	12–12	0.00	29%	13 Jul →
dust2	12–12	0.08	22%	10 Jul →
mirage	13–11	0.08	20%	10 Jul →
inferno	12–12	0.02	24%	10 Jul →
cache	13–4	0.03	23%	10 Jul →
cache	13–10	0.05	18%	10 Jul →
mirage	13–4	0.11	21%	10 Jul →
dust2	4–13	-0.02	20%	9 Jul →
inferno	8–1	0.12	26%	9 Jul →
dust2	12–12	-0.03	8%	9 Jul →
dust2	12–12	0.11	20%	9 Jul →
fachwerk	9–13	0.05	12%	9 Jul →
shelter	1–13	0.03	13%	9 Jul →
boulder	9–13	-0.02	10%	8 Jul →
anubis	12–12	-0.03	12%	8 Jul →
cache	13–5	-0.02	24%	8 Jul →
vertigo	13–10	-0.01	11%	7 Jul →
cache	4–13	-0.03	12%	7 Jul →
inferno	13–7	0.11	20%	5 Jul →
cache	9–13	-0.00	41%	5 Jul →
dust2	3–13	0.04	24%	5 Jul →
dust2	13–3	0.02	15%	4 Jul →
vertigo	13–8	0.11	16%	4 Jul →
anubis	4–1	0.04	43%	4 Jul →
inferno	7–13	0.09	16%	1 Jul →
vertigo	10–13	-0.05	10%	29 Jun →
dust2	11–13	-0.06	28%	21 Jun →
dust2	12–12	0.12	22%	21 Jun →
dust2	12–12	-0.01	26%	21 Jun →
mirage	1–8	-0.04	12%	21 Jun →
inferno	13–11	-0.04	29%	11 Jun →
train	5–13	-0.03	19%	11 Jun →
cache	12–12	-0.02	12%	10 Jun →
dust2	4–13	-0.06	32%	1 Jun →
nuke	3–13	-0.06	6%	1 Jun →
dust2	12–12	0.07	18%	1 Jun →
cache	2–3	0.05	25%	30 May →
mirage	10–13	-0.01	24%	30 May →
dust2	6–13	-0.04	22%	30 May →
mirage	6–13	-0.05	7%	29 May →
anubis	13–11	0.08	19%	29 May →
inferno	13–7	-0.05	28%	29 May →
dust2	13–6	0.03	15%	29 May →
vertigo	5–13	0.10	20%	29 May →
cache	13–6	0.04	20%	27 May →
ancient	11–13	0.05	11%	27 May →
dust2	5–13	-0.01	27%	27 May →
cache	9–13	-0.00	26%	26 May →
cache	13–6	0.05	31%	26 May →
dust2	12–12	-0.01	27%	26 May →
vertigo	8–13	0.01	21%	26 May →