MarkeyMark — CS2 Stats

76561198018515224[U:1:58249496]Steam profile ↗✓ No bans

248Tracked matches43%Win rate2020Tracked since

CSDB Rating1.7 Learning

FaceitLevel 3 · 833 ELOTop 90.6% of ranked FACEIT players

Ladder ranks via Leetify

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Performance scores

Aim7

Positioning33

Utility48

0–100 skill scores via Leetify.

Recent form

COLD41–56–3Last 10041%Win rateLLLWLLWLWL

Last 10 vs previous 10: -10pp win rate · +0.02 avg rating

Player DNA

Aim0.7

Aggression1.6

Utility4.8

Positioning3.3

Opening Duels0.0

Clutch4.0

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 NiKo 60% playstyle similarity

Most alike: utility contribution, opening-duel success.

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

Reaction time. 646ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 46% of shots are taken properly stopped — moving-shot inaccuracy is quietly taxing every duel.

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

Aim0.7

Positioning3.3

Utility4.8

Mechanics0.0

Opening Duels0.0

Win Impact2.8

Composite 1.7/10 (Learning), 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 rating-0.05−0.01

first ⅓ avg -0.04 → last ⅓ avg -0.05

Reaction time657ms+5ms

first ⅓ avg 652ms → last ⅓ avg 657ms

Headshot accuracy8.9%−0.4%

first ⅓ avg 9.3% → last ⅓ avg 8.9%

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

0.09Best rating — golden 4–0

13–1Biggest win — inferno

6Longest win streak

L3Current streak

7–4In matches decided by ≤2 rounds

6Overtime games

Map breakdown

nukeBest map · 56% over 18trainWeakest map · 18% over 11

Map	Grade	Played	Record	Win rate	Avg rating
ancient	C	19	8–11	42%	-0.04
nuke	A	18	10–8	56%	-0.05
inferno	D	13	3–10	23%	-0.05
train	D	11	2–9	18%	-0.07
overpass	B	10	5–5	50%	-0.05
anubis	B	8	4–4	50%	-0.06
dust2	D	5	1–4	20%	-0.05
vertigo	—	4	2–2	50%	-0.04
mirage	—	3	2–1	67%	-0.05
cache	—	2	0–2	0%	-0.04
warden	—	2	1–1	50%	0.01
palacio	—	2	1–1	50%	0.04
golden	—	2	2–0	100%	0.04
jura	—	1	0–1	0%	-0.03

Across the last 100 tracked matches.

Faceit stats

Combat

100Matches

44%Win rate

0.70Avg K/D

57.3ADR

34%Headshot %

Clutches & streaks

40%1v1 clutch win

0%1v2 clutch win

4Longest win streak

Recent Faceit resultsLWWWW

Map	Matches	Win rate	Avg K/D	Avg kills
Ancient	5	20%	0.43	7.4
Inferno	4	25%	0.72	12.3
Train	3	67%	0.55	8.7
Nuke	2	50%	0.38	7.0
Overpass	1	0%	0.63	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.

8.2%Headshot accuracy

23.2%Accuracy (enemy spotted)

29.3%Spray accuracy

46.1%Counter-strafing

12.1°Preaim

646msReaction time

25.2%T opening success

21.5%CT opening success

0.67Enemies flashed / flash

3.0%Flash assists

7.36HE damage / grenade

6.79Flashes / 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
Your crosshair sits further from where enemies appear than it needs to. Crosshair placement is the cheapest accuracy you can buy.
Preaim 12.1041° — above the 12° mark we flag
Aim Training →
Best CS2 Crosshair
Your shots are landing on bodies more often than heads — usually a crosshair-height and placement habit rather than raw aim.
Headshot accuracy 8.2061% — below the 15% mark we flag
Aim Training →

Spend your practice time on Train

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

18% win rate across 11 tracked games — your weakest map with enough games to be worth reading into.

Train callouts & strategy →

Recent matches

Map	Score	Rating	HS%	Date
train	4–13	-0.02	11%	22 Aug →
inferno	6–13	-0.06	16%	16 Jul →
nuke	7–13	-0.09	11%	16 Jul →
dust2	10–5	-0.02	8%	16 Jul →
anubis	5–13	-0.07	7%	16 Jul →
cache	5–13	-0.04	6%	16 Jul →
ancient	13–9	-0.04	4%	15 Jul →
ancient	8–13	-0.03	5%	15 Jul →
warden	13–11	0.03	15%	5 Jul →
cache	9–13	-0.04	7%	5 Jul →
anubis	6–13	-0.07	0%	4 Jul →
ancient	7–13	-0.05	4%	4 Jul →
nuke	13–7	-0.07	6%	4 Jul →
inferno	13–9	-0.04	12%	7 Mar →
overpass	7–13	-0.03	13%	7 Mar →
mirage	13–9	-0.02	12%	13 Feb →
nuke	9–13	-0.11	3%	11 Feb →
overpass	13–10	-0.03	10%	11 Feb →
ancient	2–13	-0.11	6%	11 Feb →
dust2	5–13	-0.08	11%	11 Feb →
anubis	16–14	-0.08	7%	11 Feb →
ancient	13–8	-0.06	9%	11 Feb →
overpass	8–13	-0.06	6%	11 Feb →
inferno	6–13	-0.06	6%	11 Feb →
ancient	8–2	-0.04	10%	11 Feb →
anubis	13–9	-0.02	9%	11 Feb →
warden	8–13	-0.01	15%	11 Feb →
ancient	13–4	-0.00	9%	11 Feb →
train	4–13	-0.09	7%	11 Feb →
anubis	13–9	-0.08	3%	11 Feb →
dust2	5–13	-0.03	15%	11 Feb →
nuke	13–6	-0.02	25%	11 Feb →
inferno	8–13	-0.07	7%	11 Feb →
nuke	5–13	-0.02	10%	24 Jan →
inferno	11–13	-0.05	17%	14 Jan →
nuke	4–5	-0.09	0%	14 Jan →
ancient	15–15	-0.04	8%	14 Jan →
inferno	1–13	-0.07	20%	14 Jan →
train	6–13	-0.09	9%	14 Jan →
nuke	13–6	0.03	13%	14 Jan →
mirage	4–13	-0.10	23%	14 Jan →
overpass	4–13	-0.13	0%	14 Jan →
nuke	13–6	-0.04	12%	14 Jan →
dust2	5–13	-0.10	14%	14 Jan →
nuke	14–16	-0.07	4%	14 Jan →
inferno	3–13	-0.07	40%	14 Jan →
overpass	13–6	-0.05	9%	23 Dec →
ancient	13–9	-0.06	11%	23 Dec →
nuke	13–9	-0.05	7%	23 Dec →
ancient	13–7	0.05	17%	23 Dec →
dust2	7–13	-0.01	7%	23 Dec →
inferno	13–6	0.01	15%	23 Dec →
anubis	1–13	-0.10	8%	23 Dec →
nuke	4–13	-0.11	12%	23 Dec →
palacio	6–13	0.01	8%	20 Dec →
golden	4–0	0.09	20%	20 Dec →
train	10–13	-0.07	6%	20 Dec →
ancient	9–13	-0.05	4%	20 Dec →
nuke	9–13	-0.08	6%	19 Dec →
ancient	7–13	-0.06	10%	19 Dec →
overpass	13–6	0.01	3%	19 Dec →
train	13–11	-0.03	8%	19 Dec →
nuke	16–12	-0.03	7%	18 Dec →
nuke	13–5	-0.06	17%	13 Dec →
ancient	13–10	-0.10	9%	13 Dec →
train	13–7	-0.03	10%	12 Dec →
inferno	8–13	-0.03	8%	12 Dec →
train	9–13	-0.09	6%	12 Dec →
ancient	3–13	-0.09	12%	12 Dec →
ancient	5–13	-0.07	4%	11 Dec →
inferno	7–13	-0.07	13%	11 Dec →
ancient	11–13	-0.01	10%	11 Dec →
inferno	12–16	-0.04	5%	11 Dec →
overpass	16–14	-0.07	2%	4 Dec →
nuke	13–11	-0.08	6%	4 Dec →
ancient	2–13	-0.09	11%	29 Nov →
palacio	13–4	0.07	6%	29 Nov →
vertigo	12–12	-0.02	6%	29 Nov →
overpass	13–8	-0.02	11%	28 Nov →
anubis	13–9	-0.07	4%	28 Nov →
nuke	13–2	0.08	13%	28 Nov →
train	12–12	-0.07	12%	28 Nov →
vertigo	5–13	-0.04	8%	28 Nov →
overpass	10–13	-0.04	12%	26 Nov →
anubis	3–13	-0.01	11%	26 Nov →
inferno	13–1	0.01	9%	26 Nov →
vertigo	13–4	-0.04	2%	26 Nov →
ancient	13–11	-0.01	6%	26 Nov →
nuke	8–13	0.01	12%	26 Nov →
train	10–13	-0.08	17%	26 Nov →
golden	13–4	-0.00	15%	22 Nov →
vertigo	13–6	-0.05	10%	22 Nov →
train	4–13	-0.09	19%	22 Nov →
mirage	13–6	-0.03	0%	11 Sept →
overpass	9–13	-0.10	9%	10 Sept →
jura	9–13	-0.03	10%	10 Sept →
train	11–13	-0.06	11%	10 Sept →
inferno	5–13	-0.11	0%	10 Sept →
nuke	8–13	-0.06	14%	23 Aug →
ancient	8–13	0.05	18%	23 Aug →