Glep — CS2 Stats

US76561199073947800[U:1:1113682072]Steam profile ↗✓ No bans

240Tracked matches38%Win rate2025Tracked since

CSDB Rating3.7 Learning

Ladder ranks via Leetify

Performance scores

Aim42

Positioning33

Utility48

0–100 skill scores via Leetify.

Recent form

COLD36–57–7Last 10036%Win rateLWLLWLLLLL

Last 10 vs previous 10: -20pp win rate · -0.02 avg rating

Player DNA

Aim4.2

Aggression1.2

Utility4.8

Positioning3.3

Opening Duels0.0

Clutch6.0

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 70% 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

Strengths

Flashes. 0.80 enemies blinded per flash — utility that consistently lands.

Areas to improve

Reaction time. 673ms 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

Aim4.2

Positioning3.3

Utility4.8

Mechanics6.5

Opening Duels0.0

Win Impact1.0

Composite 3.7/10 (Learning), 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-0.04+0.01

first ⅓ avg -0.05 → last ⅓ avg -0.04

Reaction time675ms−61ms

first ⅓ avg 736ms → last ⅓ avg 675ms

Headshot accuracy12.3%−3.2%

first ⅓ avg 15.5% → last ⅓ avg 12.3%

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

0.13Best rating — rooftop 9–4

13–1Biggest win — dust2

6Longest win streak

4–7In matches decided by ≤2 rounds

1Overtime games

Map breakdown

dust2Best map · 40% over 43infernoWeakest map · 29% over 17

Map	Grade	Played	Record	Win rate	Avg rating
dust2	C	43	17–26	40%	-0.03
nuke	C	21	8–13	38%	-0.07
inferno	D	17	5–12	29%	-0.04
mirage	—	4	1–3	25%	-0.09
train	—	4	1–3	25%	-0.06
rooftop	—	3	1–2	33%	-0.06
ancient	—	2	2–0	100%	0.01
poseidon	—	2	0–2	0%	-0.11
vertigo	—	2	1–1	50%	0.05
cache	—	1	0–1	0%	-0.12
overpass	—	1	0–1	0%	-0.07

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (29% over 17). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

1Matches

0%Win rate

0.21Avg K/D

32.0ADR

67%Headshot %

Clutches & streaks

50%1v1 clutch win

0%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.

11.7%Headshot accuracy

31.7%Accuracy (enemy spotted)

33.7%Spray accuracy

79.1%Counter-strafing

10.9°Preaim

673msReaction time

20.7%T opening success

23.0%CT opening success

0.80Enemies flashed / flash

17.0%Flash assists

1.23HE damage / grenade

3.83Flashes / 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 11.6945% — below the 15% mark we flag
Aim Training →
Grenades & Utility
You are buying and holding utility rather than using it. Unthrown flashes are wasted money every round.
Flashes per match 3.8255 — 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 20.7269% — 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.

29% win rate across 17 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	7–13	-0.05	13%	18 Aug →
inferno	13–10	-0.02	10%	14 Aug →
dust2	8–13	-0.07	19%	30 Jul →
nuke	10–13	-0.03	7%	27 Jul →
dust2	13–7	0.01	9%	27 Jul →
dust2	7–13	-0.04	9%	12 Jun →
nuke	11–13	-0.09	21%	12 Jun →
dust2	9–13	-0.03	11%	26 May →
dust2	4–13	-0.09	13%	26 May →
cache	1–13	-0.12	8%	4 May →
dust2	7–13	-0.03	17%	19 Apr →
dust2	4–13	-0.10	9%	11 Apr →
nuke	4–13	-0.07	14%	4 Apr →
mirage	3–13	-0.07	11%	4 Apr →
inferno	12–12	0.02	8%	29 Mar →
dust2	6–13	-0.06	8%	29 Mar →
dust2	13–7	0.04	10%	28 Mar →
dust2	13–9	-0.04	14%	28 Mar →
ancient	13–8	-0.01	9%	22 Mar →
ancient	7–4	0.02	21%	22 Mar →
inferno	5–13	-0.04	11%	22 Mar →
dust2	13–10	-0.05	18%	20 Mar →
nuke	9–0	-0.01	20%	20 Mar →
mirage	11–13	-0.10	6%	20 Mar →
dust2	13–3	0.05	25%	19 Mar →
dust2	13–9	-0.01	7%	17 Mar →
mirage	2–13	-0.09	11%	17 Mar →
nuke	13–2	-0.03	8%	17 Mar →
inferno	6–13	-0.10	5%	17 Mar →
dust2	8–13	-0.00	5%	16 Mar →
dust2	10–13	-0.04	14%	16 Mar →
inferno	6–13	-0.04	22%	16 Mar →
inferno	1–13	-0.05	10%	14 Mar →
dust2	7–13	-0.06	16%	13 Mar →
dust2	13–1	0.10	21%	13 Mar →
dust2	7–13	-0.01	18%	13 Mar →
dust2	13–5	-0.03	18%	13 Mar →
nuke	6–13	-0.07	3%	9 Mar →
dust2	11–13	-0.02	18%	8 Mar →
dust2	4–13	-0.07	6%	2 Mar →
nuke	11–13	-0.07	2%	2 Mar →
overpass	8–13	-0.07	9%	28 Feb →
nuke	13–11	-0.10	29%	28 Feb →
inferno	13–9	0.02	11%	26 Feb →
dust2	13–5	0.02	4%	23 Feb →
nuke	13–2	0.00	9%	23 Feb →
nuke	9–6	0.05	21%	23 Feb →
inferno	9–3	0.05	14%	23 Feb →
inferno	11–13	-0.01	8%	21 Feb →
nuke	13–9	-0.07	12%	21 Feb →
inferno	5–9	-0.18	8%	4 Feb →
inferno	3–9	-0.16	30%	4 Feb →
inferno	8–8	-0.03	9%	1 Feb →
nuke	11–13	-0.09	19%	27 Jan →
dust2	7–13	-0.07	24%	27 Jan →
poseidon	4–9	-0.08	13%	27 Jan →
poseidon	1–9	-0.15	25%	27 Jan →
dust2	16–12	-0.09	10%	26 Jan →
dust2	8–13	-0.05	15%	26 Jan →
inferno	7–13	-0.06	14%	22 Jan →
nuke	13–10	-0.04	4%	22 Jan →
dust2	8–2	0.03	20%	22 Jan →
inferno	13–4	0.02	22%	13 Jan →
vertigo	9–7	0.08	11%	13 Jan →
dust2	9–13	-0.00	25%	10 Jan →
mirage	13–11	-0.09	12%	10 Jan →
dust2	13–6	-0.04	22%	6 Jan →
nuke	8–8	-0.06	19%	6 Jan →
nuke	4–9	-0.05	18%	3 Jan →
rooftop	6–9	-0.12	19%	3 Jan →
nuke	2–9	-0.16	13%	3 Jan →
dust2	13–4	0.07	21%	2 Jan →
train	7–13	-0.14	7%	2 Jan →
nuke	12–12	-0.05	6%	31 Dec →
dust2	5–13	-0.03	7%	31 Dec →
nuke	1–9	-0.31	0%	31 Dec →
train	8–13	-0.06	9%	29 Dec →
dust2	5–13	-0.10	15%	29 Dec →
rooftop	9–4	0.13	20%	29 Dec →
dust2	10–13	0.03	22%	27 Dec →
train	9–0	-0.05	40%	27 Dec →
nuke	10–13	-0.09	23%	26 Dec →
dust2	12–12	0.00	18%	26 Dec →
dust2	13–1	0.11	6%	26 Dec →
dust2	5–13	-0.05	22%	24 Dec →
nuke	10–13	-0.12	20%	24 Dec →
dust2	9–13	-0.13	11%	20 Dec →
dust2	5–13	0.00	11%	18 Dec →
inferno	13–11	-0.06	9%	17 Dec →
inferno	9–13	-0.03	29%	13 Dec →
dust2	13–7	-0.03	24%	13 Dec →
dust2	12–12	-0.04	6%	8 Dec →
vertigo	8–8	0.01	8%	8 Dec →
rooftop	1–9	-0.21	14%	8 Dec →
nuke	13–7	0.01	21%	7 Dec →
train	9–13	-0.01	13%	7 Dec →
dust2	13–4	-0.07	22%	7 Dec →
dust2	8–13	-0.06	8%	6 Dec →
dust2	13–2	-0.01	24%	6 Dec →
inferno	11–13	-0.06	8%	1 Dec →