Glorb — CS2 Stats

76561199177506724[U:1:1217240996]Steam profile ↗✓ No bans

917Tracked matches67%Win rate2024Tracked since

CSDB Rating6.3 SolidEntry Fragger

Ladder ranks via Leetify

Performance scores

Aim76

Positioning71

Utility5

0–100 skill scores via Leetify.

Recent form

STEADY55–37–8Last 10055%Win rateLWLWWLWLWT

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

Player DNA

Primary style: Entry Fragger — High opening-fight frequency with above-par success in them.

Aim7.6

Aggression10.0

Utility0.5

Positioning7.1

Opening Duels7.2

Clutch1.0

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 s1mple 82% 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

Strengths

CT openings. 72% CT opening-duel success — winning the first fight on the defending side is rare and valuable.

Areas to improve

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

T-side openings. Opening success drops from 72% on CT to 46% on T — the same duels are being taken with worse setups on the attacking side.

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

Aim7.6

Positioning7.1

Utility0.5

Mechanics5.5

Opening Duels7.0

Win Impact10.0

Composite 6.3/10 (Solid), 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 rating0.06+0.00

first ⅓ avg 0.06 → last ⅓ avg 0.06

Reaction time545ms−62ms

first ⅓ avg 607ms → last ⅓ avg 545ms

Headshot accuracy13.1%−2.5%

first ⅓ avg 15.6% → last ⅓ avg 13.1%

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

0.23Best rating — nuke 13–10

13–0Biggest win — nuke

6Longest win streak

5–4In matches decided by ≤2 rounds

1Overtime games

Map breakdown

trainBest map · 83% over 6infernoWeakest map · 30% over 10

Map	Grade	Played	Record	Win rate	Avg rating
nuke	A	36	23–13	64%	0.05
office	A	19	11–8	58%	0.06
dust2	B	12	6–6	50%	0.05
inferno	D	10	3–7	30%	0.08
vertigo	D	6	2–4	33%	0.04
train	S	6	5–1	83%	0.08
anubis	C	5	2–3	40%	0.04
italy	—	2	1–1	50%	-0.03
cache	—	1	0–1	0%	-0.01
ancient	—	1	1–0	100%	0.13
mirage	—	1	0–1	0%	0.11
overpass	—	1	1–0	100%	0.06

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (30% over 10). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

2Matches

50%Win rate

0.83Avg K/D

85.1ADR

40%Headshot %

Clutches & streaks

25%1v1 clutch win

0%1v2 clutch win

1Longest win streak

Recent Faceit resultsLLLLW

Map	Matches	Win rate	Avg K/D	Avg kills
Anubis	1	100%	1.13	26.0
Cache	1	0%	0.53	9.0

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.

12.9%Headshot accuracy

39.8%Accuracy (enemy spotted)

43.9%Spray accuracy

74.7%Counter-strafing

9.5°Preaim

545msReaction time

46.4%T opening success

71.6%CT opening success

0.07Enemies flashed / flash

0.0%Flash assists

5.01HE damage / grenade

0.11Flashes / 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 12.9114% — below the 15% mark we flag
Aim Training →
Grenades & Utility
Most of your flashes are blinding nobody. A handful of reliable pop-flash lineups fixes this faster than anything else.
Enemies flashed per flash 0.0702 — below the 0.5 mark we flag
Grenade Lineups →

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.

30% 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
office	4–13	0.01	25%	29 Aug →
nuke	13–3	0.13	9%	29 Aug →
cache	7–13	-0.01	3%	28 Aug →
inferno	13–4	0.07	13%	28 Aug →
nuke	13–3	0.03	6%	27 Aug →
anubis	8–13	0.06	7%	27 Aug →
office	13–10	0.15	10%	27 Aug →
nuke	6–13	-0.00	11%	25 Aug →
nuke	13–7	0.02	12%	25 Aug →
nuke	12–12	0.04	11%	25 Aug →
nuke	13–7	0.10	11%	24 Aug →
dust2	13–6	0.08	19%	24 Aug →
office	4–13	0.15	10%	23 Aug →
nuke	13–10	0.23	16%	23 Aug →
nuke	13–4	0.07	11%	22 Aug →
nuke	3–12	-0.05	13%	22 Aug →
nuke	13–8	0.03	11%	21 Aug →
vertigo	3–13	-0.05	9%	21 Aug →
nuke	13–3	0.12	20%	21 Aug →
office	13–8	0.07	20%	21 Aug →
nuke	13–7	0.13	20%	21 Aug →
nuke	13–3	-0.00	16%	21 Aug →
nuke	13–7	0.04	15%	19 Aug →
nuke	13–4	0.20	23%	19 Aug →
nuke	4–13	-0.04	16%	19 Aug →
inferno	12–12	0.11	2%	18 Aug →
dust2	5–13	0.02	16%	18 Aug →
train	13–10	0.08	5%	18 Aug →
dust2	12–12	0.05	11%	16 Aug →
ancient	13–9	0.13	19%	16 Aug →
office	13–10	0.03	15%	15 Aug →
train	13–8	0.05	6%	14 Aug →
office	6–13	0.01	21%	13 Aug →
dust2	11–13	0.10	14%	13 Aug →
italy	13–6	0.05	12%	13 Aug →
nuke	9–13	-0.04	3%	12 Aug →
inferno	10–13	0.09	9%	11 Aug →
dust2	13–9	0.07	11%	11 Aug →
train	13–9	0.02	16%	10 Aug →
anubis	5–13	-0.00	11%	10 Aug →
office	13–11	0.10	22%	10 Aug →
office	13–6	0.13	14%	8 Aug →
nuke	12–12	0.07	5%	8 Aug →
inferno	5–13	-0.03	21%	8 Aug →
nuke	7–13	0.02	16%	8 Aug →
nuke	13–5	0.09	11%	7 Aug →
nuke	12–12	-0.01	9%	7 Aug →
office	13–2	0.10	10%	7 Aug →
dust2	13–11	0.02	12%	7 Aug →
office	9–13	0.04	20%	5 Aug →
inferno	5–13	0.06	23%	5 Aug →
nuke	2–2	-0.09	0%	5 Aug →
office	13–4	0.04	24%	4 Aug →
dust2	8–13	-0.01	14%	4 Aug →
nuke	10–13	0.02	13%	4 Aug →
nuke	13–5	-0.02	8%	3 Aug →
inferno	7–13	0.06	26%	3 Aug →
vertigo	7–13	0.07	13%	3 Aug →
nuke	10–13	0.03	8%	1 Aug →
nuke	13–6	0.06	13%	1 Aug →
anubis	8–13	0.04	13%	1 Aug →
mirage	5–13	0.11	30%	30 Jul →
vertigo	13–9	0.04	15%	30 Jul →
anubis	13–5	0.11	20%	30 Jul →
nuke	4–13	-0.01	15%	29 Jul →
nuke	13–7	-0.01	11%	29 Jul →
dust2	13–8	0.07	16%	29 Jul →
inferno	11–13	0.08	24%	29 Jul →
nuke	13–9	0.06	16%	29 Jul →
nuke	13–9	0.07	18%	28 Jul →
nuke	13–4	0.00	21%	28 Jul →
nuke	13–3	0.01	10%	28 Jul →
office	10–13	0.06	24%	27 Jul →
dust2	11–13	0.06	13%	26 Jul →
office	13–2	-0.01	13%	26 Jul →
nuke	13–0	0.23	44%	26 Jul →
anubis	16–14	-0.00	13%	24 Jul →
office	11–13	0.03	6%	24 Jul →
train	13–11	0.14	21%	24 Jul →
office	13–5	0.05	9%	23 Jul →
dust2	13–6	0.10	14%	23 Jul →
vertigo	9–13	0.11	11%	23 Jul →
office	13–8	0.10	3%	20 Jul →
train	9–13	0.08	11%	20 Jul →
vertigo	9–13	-0.01	11%	19 Jul →
nuke	9–13	0.13	25%	18 Jul →
office	12–12	0.03	26%	18 Jul →
inferno	13–1	0.15	13%	18 Jul →
italy	7–13	-0.10	6%	17 Jul →
overpass	13–7	0.06	12%	16 Jul →
nuke	13–2	0.07	24%	16 Jul →
dust2	13–8	-0.00	13%	14 Jul →
dust2	12–12	0.04	18%	14 Jul →
train	13–9	0.11	14%	14 Jul →
office	13–9	-0.00	17%	13 Jul →
office	8–13	-0.02	12%	13 Jul →
vertigo	13–7	0.07	17%	13 Jul →
inferno	12–12	0.01	12%	13 Jul →
inferno	9–3	0.16	10%	13 Jul →
nuke	5–13	0.05	14%	12 Jul →