MOOSCLES — CS2 Stats

76561198040430988[U:1:80165260]Steam profile ↗✓ No bans

313Tracked matches40%Win rate2023Tracked since

CSDB Rating3.8 Learning

Premier CS Rating9,280Light Blue band · top ~58.3% of ranked players (population est.)

CSDB Leaderboard#28169 of 38425 tracked

FaceitLevel 5Top 67.6% of ranked FACEIT players

Ladder ranks via Leetify

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Performance scores

Aim41

Positioning59

Utility46

0–100 skill scores via Leetify.

Recent form

COLD40–52–8Last 10040%Win rateTLTLWLLLWT

Last 10 vs previous 10: -40pp win rate · -0.04 avg rating

Player DNA

Aim4.1

Aggression10.0

Utility4.6

Positioning5.9

Opening Duels5.3

Clutch7.0

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 device 81% playstyle similarity

Most alike: opening-fight frequency, positioning profile.

Where you differ: lower utility contribution; 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

Strengths

CT openings. 63% CT opening-duel success — winning the first fight on the defending side is rare and valuable.

Areas to improve

T-side openings. Opening success drops from 63% on CT to 39% on T — the same duels are being taken with worse setups on the attacking side.

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 63% 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

Aim4.1

Positioning5.9

Utility4.6

Mechanics2.9

Opening Duels5.3

Win Impact1.7

Composite 3.8/10 (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 rating0.03+0.01

first ⅓ avg 0.02 → last ⅓ avg 0.03

Reaction time655ms+40ms

first ⅓ avg 615ms → last ⅓ avg 655ms

Headshot accuracy14.9%−1.6%

first ⅓ avg 16.5% → last ⅓ avg 14.9%

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

0.22Best rating — dust2 13–2

13–2Biggest win — mirage

6Longest win streak

3–7In matches decided by ≤2 rounds

1Overtime games

Map breakdown

anubisBest map · 67% over 6infernoWeakest map · 20% over 10

Map	Grade	Played	Record	Win rate	Avg rating
dust2	D	41	13–28	32%	0.02
mirage	C	18	8–10	44%	0.03
nuke	B	15	7–8	47%	0.02
inferno	D	10	2–8	20%	0.02
anubis	S	6	4–2	67%	0.03
cache	D	6	2–4	33%	-0.02
ancient	—	2	2–0	100%	0.06
vertigo	—	1	1–0	100%	0.11
train	—	1	1–0	100%	0.03

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (20% over 10). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

22Matches

50%Win rate

0.99Avg K/D

81.4ADR

33%Headshot %

Clutches & streaks

55%1v1 clutch win

25%1v2 clutch win

2Longest win streak

Recent Faceit resultsWLWLW

Map	Matches	Win rate	Avg K/D	Avg kills
Dust2	7	57%	1.00	17.1
Inferno	4	50%	1.32	18.5
Mirage	3	67%	0.93	13.0
Nuke	2	100%	1.10	16.0
Anubis	2	0%	0.54	9.5
Overpass	1	0%	0.44	7.0
Cache	1	0%	0.75	18.0
Ancient	1	100%	1.47	22.0

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.

14.3%Headshot accuracy

33.5%Accuracy (enemy spotted)

30.5%Spray accuracy

63.1%Counter-strafing

9.4°Preaim

646msReaction time

39.0%T opening success

63.5%CT opening success

0.61Enemies flashed / flash

4.5%Flash assists

6.90HE damage / grenade

7.02Flashes / 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.

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 14.2581% — below the 15% mark we flag
Aim Training →
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 38.9673% — 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.

20% 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
dust2	12–12	0.04	19%	15 Aug →
anubis	3–13	0.01	26%	9 Aug →
mirage	12–12	0.08	16%	9 Aug →
dust2	2–13	0.02	37%	9 Aug →
nuke	13–11	0.05	7%	5 Jul →
dust2	8–13	0.01	6%	5 Jul →
dust2	8–13	0.01	11%	4 Jul →
dust2	8–13	0.02	19%	28 Jun →
dust2	13–9	0.06	15%	28 Jun →
nuke	12–12	-0.06	13%	20 Jun →
mirage	2–13	-0.03	4%	20 Jun →
dust2	13–3	0.13	18%	20 Jun →
anubis	13–7	0.14	10%	15 Jun →
cache	13–10	0.02	15%	15 Jun →
mirage	13–2	0.07	12%	15 Jun →
dust2	5–0	0.20	19%	15 Jun →
nuke	13–8	0.03	21%	15 Jun →
inferno	5–13	0.03	11%	15 Jun →
dust2	12–12	0.09	10%	15 Jun →
dust2	9–13	-0.05	6%	14 Jun →
nuke	11–13	-0.02	4%	14 Jun →
dust2	12–12	-0.02	25%	14 Jun →
inferno	7–13	-0.01	10%	14 Jun →
dust2	13–7	0.02	15%	14 Jun →
mirage	8–13	0.04	13%	14 Jun →
dust2	13–10	0.11	22%	14 Jun →
dust2	7–13	-0.05	10%	13 Jun →
nuke	7–13	-0.02	18%	13 Jun →
dust2	10–13	-0.01	10%	13 Jun →
dust2	8–13	-0.02	17%	13 Jun →
nuke	13–3	0.01	22%	13 Jun →
anubis	13–3	-0.05	12%	13 Jun →
dust2	4–13	0.10	19%	13 Jun →
nuke	11–3	0.10	10%	12 Jun →
dust2	8–13	0.11	13%	12 Jun →
mirage	10–13	0.00	19%	7 Jun →
anubis	13–9	0.15	27%	7 Jun →
dust2	3–13	0.01	19%	7 Jun →
cache	6–7	-0.08	10%	6 Jun →
mirage	13–4	0.09	18%	6 Jun →
dust2	9–13	-0.07	9%	6 Jun →
nuke	10–13	0.04	44%	6 Jun →
dust2	13–10	0.01	8%	6 Jun →
dust2	5–13	-0.05	18%	1 Jun →
dust2	3–13	-0.12	8%	29 May →
dust2	9–13	0.02	23%	29 May →
dust2	13–10	-0.04	32%	28 May →
mirage	16–14	0.02	11%	27 May →
mirage	6–13	0.04	23%	27 May →
nuke	2–9	-0.14	10%	26 May →
nuke	9–1	0.02	5%	26 May →
nuke	9–7	0.04	19%	26 May →
mirage	4–13	0.05	17%	26 May →
mirage	13–8	-0.03	16%	26 May →
dust2	12–12	-0.02	18%	25 May →
cache	12–12	-0.06	13%	25 May →
dust2	7–13	0.01	15%	25 May →
inferno	11–13	0.11	13%	25 May →
mirage	13–3	0.06	14%	25 May →
nuke	13–3	0.16	18%	25 May →
dust2	13–2	0.20	37%	25 May →
dust2	11–13	-0.08	8%	24 May →
inferno	11–13	0.04	13%	24 May →
nuke	7–13	-0.00	20%	23 May →
dust2	13–2	0.22	18%	23 May →
mirage	13–9	-0.04	8%	23 May →
mirage	13–10	-0.03	6%	23 May →
inferno	5–13	-0.06	19%	22 May →
dust2	11–13	0.02	9%	22 May →
anubis	8–13	-0.08	5%	20 May →
ancient	13–9	0.07	19%	17 May →
dust2	10–13	0.05	8%	16 May →
ancient	13–2	0.06	21%	16 May →
inferno	13–5	0.01	9%	16 May →
dust2	4–13	-0.02	5%	16 May →
cache	5–13	-0.00	14%	16 May →
dust2	12–12	-0.00	29%	16 May →
mirage	6–13	-0.02	21%	16 May →
mirage	3–13	-0.04	17%	16 May →
inferno	13–8	0.06	10%	16 May →
vertigo	13–7	0.11	16%	16 May →
dust2	13–8	0.01	28%	16 May →
dust2	13–7	0.10	15%	16 May →
dust2	9–13	-0.02	30%	15 May →
inferno	8–13	-0.00	43%	15 May →
mirage	13–10	0.07	7%	15 May →
dust2	13–8	0.13	24%	15 May →
nuke	8–13	0.07	22%	14 May →
train	13–6	0.03	19%	14 May →
mirage	10–13	0.00	13%	14 May →
dust2	3–13	-0.01	5%	14 May →
anubis	13–3	0.03	28%	14 May →
nuke	10–13	0.03	18%	14 May →
mirage	9–13	0.13	13%	14 May →
cache	13–6	0.02	19%	14 May →
inferno	5–13	-0.04	6%	14 May →
dust2	3–13	-0.04	21%	14 May →
inferno	11–13	0.01	7%	12 May →
dust2	13–8	-0.04	13%	12 May →
cache	6–13	-0.02	13%	11 May →