grum — CS2 Stats

76561198964153141[U:1:1003887413]Steam profile ↗✓ No bans

822Tracked matches41%Win rate2021Tracked since

CSDB Rating5.0 DevelopingHybrid Rifler

Premier CS Rating18,098Purple band · top ~16.1% of ranked players (population est.)

CSDB Leaderboard#15889 of 31471 tracked

WingmanGold Nova Master

Ladder ranks via Leetify

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Performance scores

Aim78

Positioning47

Utility26

0–100 skill scores via Leetify.

Recent form

COLD46–44–10Last 10046%Win rateWLLLLLLLLW

Last 10 vs previous 10: -40pp win rate · +0.02 avg rating

Player DNA

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

Aim7.8

Aggression4.5

Utility2.6

Positioning4.7

Opening Duels0.9

Limited 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 NiKo 84% playstyle similarity

Most alike: opening-fight frequency, opening-duel success.

Where you differ: lower aim profile; lower utility contribution.

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

Positioning. Positioning trails aim by 30 points — deaths here waste a strong aim profile.

Utility. Utility contribution runs far behind the mechanical game — the cheapest rating gain on this profile is thrown, not aimed.

Reaction time. 658ms 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

Aim7.8

Positioning4.7

Utility2.6

Mechanics6.8

Opening Duels1.3

Win Impact2.1

Composite 5.0/10 (Developing), 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 rating0.01−0.02

first ⅓ avg 0.03 → last ⅓ avg 0.01

Reaction time656ms−81ms

first ⅓ avg 737ms → last ⅓ avg 656ms

Headshot accuracy28.8%−0.6%

first ⅓ avg 29.4% → last ⅓ avg 28.8%

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

0.20Best rating — mirage 13–8

13–1Biggest win — train

5Longest win streak

6–6In matches decided by ≤2 rounds

8Overtime games

Map breakdown

cacheBest map · 60% over 5nukeWeakest map · 17% over 6

Map	Grade	Played	Record	Win rate	Avg rating
mirage	B	31	16–15	52%	0.01
dust2	B	24	11–13	46%	0.03
train	A	11	6–5	55%	0.03
inferno	B	10	5–5	50%	0.00
nuke	D	6	1–5	17%	-0.01
ancient	B	6	3–3	50%	0.00
overpass	D	5	1–4	20%	0.01
cache	A	5	3–2	60%	-0.00
anubis	—	1	0–1	0%	-0.06
vertigo	—	1	0–1	0%	0.07

Across the last 100 tracked matches.

Nuke is currently your weakest sufficiently-sampled map (17% over 6). Start with the 6 essential Nuke lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

1Matches

0%Win rate

0.75Avg K/D

—ADR

33%Headshot %

Clutches & streaks

—1v1 clutch win

—1v2 clutch win

0Longest win streak

Recent Faceit resultsLLLLL

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.

29.3%Headshot accuracy

34.2%Accuracy (enemy spotted)

35.9%Spray accuracy

80.4%Counter-strafing

9.1°Preaim

658msReaction time

26.4%T opening success

40.7%CT opening success

0.51Enemies flashed / flash

5.7%Flash assists

6.09HE damage / grenade

2.41Flashes / 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 2.407 — 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 26.4494% — below the 40% mark we flag

Spend your practice time on Nuke

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

17% win rate across 6 tracked games — your weakest map with enough games to be worth reading into.

Nuke callouts & strategy →Nuke grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
mirage	13–9	0.08	21%	29 Jul →
train	9–13	0.05	34%	12 Jul →
mirage	7–13	-0.04	22%	24 Jun →
nuke	6–13	0.00	20%	24 Jun →
mirage	7–13	-0.12	14%	24 Jun →
dust2	9–13	-0.00	15%	23 Jun →
mirage	9–13	0.05	29%	18 Jun →
anubis	14–16	-0.06	30%	18 Jun →
dust2	2–6	0.11	26%	18 Jun →
mirage	13–6	0.08	50%	17 Jun →
dust2	12–12	0.06	39%	17 Jun →
ancient	5–13	-0.01	30%	16 Jun →
ancient	13–6	0.05	15%	16 Jun →
ancient	13–7	-0.03	19%	16 Jun →
train	11–7	-0.00	43%	16 Jun →
train	13–1	0.03	25%	16 Jun →
mirage	10–13	-0.06	35%	15 Jun →
mirage	13–9	-0.01	22%	13 Jun →
dust2	6–13	-0.05	44%	13 Jun →
inferno	13–11	0.02	34%	13 Jun →
inferno	10–13	0.01	29%	13 Jun →
dust2	13–11	0.02	17%	11 Jun →
nuke	10–13	-0.01	38%	11 Jun →
mirage	13–7	-0.02	29%	10 Jun →
overpass	10–13	-0.03	47%	10 Jun →
dust2	13–7	-0.02	39%	10 Jun →
mirage	9–13	-0.01	28%	10 Jun →
nuke	5–13	-0.04	17%	9 Jun →
mirage	8–13	0.03	30%	9 Jun →
dust2	16–14	-0.03	32%	9 Jun →
mirage	13–8	0.20	40%	9 Jun →
mirage	11–13	-0.01	15%	8 Jun →
train	4–13	-0.07	23%	8 Jun →
inferno	3–13	-0.06	19%	6 Jun →
nuke	9–13	-0.04	44%	6 Jun →
ancient	13–7	0.04	36%	6 Jun →
nuke	13–1	0.02	24%	6 Jun →
inferno	15–15	-0.06	33%	6 Jun →
ancient	2–13	-0.02	28%	6 Jun →
overpass	15–15	0.06	31%	6 Jun →
nuke	9–13	0.04	38%	6 Jun →
overpass	11–13	-0.05	34%	3 Jun →
ancient	15–15	-0.02	31%	3 Jun →
dust2	10–13	0.02	39%	2 Jun →
dust2	16–12	-0.07	23%	2 Jun →
inferno	13–11	-0.03	30%	2 Jun →
dust2	15–15	-0.02	27%	2 Jun →
mirage	11–13	-0.01	31%	2 Jun →
mirage	7–13	-0.07	13%	31 May →
dust2	13–7	-0.01	17%	31 May →
dust2	11–13	-0.01	31%	31 May →
dust2	13–7	0.09	25%	31 May →
dust2	13–10	0.01	24%	30 May →
train	13–10	0.02	35%	30 May →
mirage	13–8	0.05	22%	30 May →
dust2	13–6	0.10	36%	30 May →
mirage	12–12	0.01	30%	30 May →
dust2	2–13	0.06	24%	29 May →
dust2	13–2	0.14	59%	29 May →
cache	13–10	0.05	25%	29 May →
dust2	9–13	-0.03	24%	29 May →
inferno	13–8	-0.02	23%	29 May →
overpass	13–1	0.03	18%	29 May →
mirage	13–11	-0.01	19%	28 May →
mirage	13–6	0.03	27%	28 May →
dust2	7–13	-0.01	19%	28 May →
dust2	10–13	0.04	25%	26 May →
mirage	6–13	0.06	16%	26 May →
dust2	8–13	0.06	22%	25 May →
mirage	13–7	0.02	23%	25 May →
dust2	12–12	0.10	33%	23 May →
mirage	9–13	0.02	23%	14 May →
mirage	13–8	0.04	36%	10 May →
mirage	13–9	-0.04	19%	2 May →
train	13–10	0.11	21%	2 May →
cache	3–13	-0.01	35%	1 May →
cache	13–7	0.04	22%	30 Apr →
cache	13–10	-0.06	36%	29 Apr →
cache	5–13	-0.04	36%	29 Apr →
inferno	10–13	-0.04	27%	28 Apr →
mirage	13–11	0.03	30%	27 Apr →
train	13–2	0.00	52%	27 Apr →
inferno	13–8	0.07	31%	27 Apr →
mirage	13–5	0.01	28%	27 Apr →
train	6–13	0.06	39%	27 Apr →
mirage	12–12	0.04	33%	26 Apr →
mirage	13–8	0.02	31%	26 Apr →
train	9–13	-0.02	28%	26 Apr →
train	13–8	0.17	44%	26 Apr →
inferno	13–8	0.02	41%	22 Apr →
mirage	13–7	-0.00	18%	21 Apr →
mirage	5–11	0.05	42%	20 Apr →
vertigo	12–12	0.07	21%	20 Apr →
train	7–13	-0.05	32%	12 Apr →
mirage	7–13	-0.08	17%	3 Apr →
inferno	11–13	0.10	32%	12 Mar →
dust2	13–10	0.09	23%	10 Mar →
mirage	13–9	0.08	23%	27 Feb →
overpass	15–15	0.03	28%	27 Feb →
dust2	13–4	-0.04	27%	27 Feb →