グジュルドング — CS2 Stats

76561199151758577[U:1:1191492849]Steam profile ↗✓ No bans

3,119Tracked matches57%Win rate2021Tracked since

CSDB Rating6.9 SolidAggressive Rifler

CSDB Leaderboard#48349 of 55291 tracked

FaceitLevel 10 · 2,266 ELOTop 12.4% of ranked FACEIT players

Ladder ranks via Leetify

Rating over time

Faceit ELO: 2,266 +0 28 Aug – 31 Aug · 3 observations

2,266Peak Faceit ELO on CSDB

3Days observed since 2026-08-28

Faceit ELO

At peak — 2,266
Next: 2,500 ELO at 2,500 — 234 to go
Reached: Level 10 · Level 9 · Level 8 · Level 7
Level 10 first seen 2026-08-28
Level 9 first seen 2026-08-28

CSDB's own observations — this history builds from the day a profile is first viewed and cannot be backfilled.

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Performance scores

Aim85

Positioning63

Utility56

0–100 skill scores via Leetify.

Recent form

STEADY56–44Last 10056%Win rateWLLWLWWWLL

Last 10 vs previous 10: -10pp win rate · +0.01 avg rating

Player DNA

Primary style: Aggressive Rifler — Takes opening fights often, backed by a strong aim profile.

Aim8.5

Aggression9.4

Utility5.6

Positioning6.3

Opening Duels4.5

Clutch4.0

Sharp aimerStrong CT-side opener

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 sh1ro 95% playstyle similarity

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

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

T-side openings. Opening success drops from 57% on CT to 40% 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

Aim8.5

Positioning6.3

Utility5.6

Mechanics6.9

Opening Duels4.5

Win Impact7.2

Composite 6.9/10 (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 rating0.01+0.01

first ⅓ avg 0.01 → last ⅓ avg 0.01

Reaction time537ms−38ms

first ⅓ avg 576ms → last ⅓ avg 537ms

Headshot accuracy21.1%+0.0%

first ⅓ avg 21.1% → last ⅓ avg 21.1%

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

0.15Best rating — anubis 13–2

13–2Biggest win — anubis

5Longest win streak

10–9In matches decided by ≤2 rounds

14Overtime games

Map breakdown

mirageBest map · 76% over 17anubisWeakest map · 46% over 13

Map	Grade	Played	Record	Win rate	Avg rating
inferno	B	29	15–14	52%	-0.00
mirage	S	17	13–4	76%	0.01
anubis	B	13	6–7	46%	0.04
ancient	A	12	7–5	58%	0.00
overpass	A	9	5–4	56%	0.02
dust2	B	8	4–4	50%	-0.00
cache	A	5	3–2	60%	0.02
nuke	—	4	1–3	25%	-0.03
train	—	2	2–0	100%	0.02
vertigo	—	1	0–1	0%	0.01

Across the last 100 tracked matches.

Anubis is currently your weakest sufficiently-sampled map (46% over 13). Start with the 6 essential Anubis lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

2,871Matches

52%Win rate

1.08Avg K/D

76.8ADR

51%Headshot %

Clutches & streaks

40%1v1 clutch win

18%1v2 clutch win

11Longest win streak

Recent Faceit resultsWWLWL

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	312	54%	1.07	15.9
Ancient	210	50%	1.07	15.1
Inferno	174	55%	1.09	15.3
Anubis	171	49%	1.05	15.1
Overpass	121	60%	1.06	16.0
Nuke	112	43%	1.06	15.7
Dust2	84	43%	0.99	14.6
Vertigo	84	52%	1.03	14.9

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.

20.1%Headshot accuracy

35.8%Accuracy (enemy spotted)

41.7%Spray accuracy

80.9%Counter-strafing

8.3°Preaim

531msReaction time

39.8%T opening success

56.5%CT opening success

0.56Enemies flashed / flash

3.7%Flash assists

8.02HE damage / grenade

9.77Flashes / 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
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 39.7548% — below the 40% mark we flag

Spend your practice time on Anubis

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

46% win rate across 13 tracked games — your weakest map with enough games to be worth reading into.

Anubis callouts & strategy →Anubis grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
inferno	13–11	-0.02	22%	22 Aug →
anubis	10–13	0.04	20%	14 Aug →
mirage	11–13	-0.03	29%	8 Aug →
cache	13–9	-0.04	11%	8 Aug →
vertigo	14–16	0.01	18%	8 Aug →
inferno	13–9	0.02	17%	7 Aug →
anubis	13–2	0.15	21%	25 Jul →
inferno	13–11	-0.04	31%	16 Jul →
cache	6–13	-0.04	24%	14 Jul →
cache	5–13	0.06	23%	14 Jul →
mirage	16–13	-0.01	11%	13 Jul →
mirage	13–5	-0.01	21%	12 Jul →
inferno	4–13	-0.06	21%	12 Jul →
cache	13–2	0.10	30%	11 Jul →
cache	16–14	0.01	19%	2 Jul →
ancient	13–9	-0.01	18%	30 Jun →
inferno	7–13	0.05	13%	29 Jun →
anubis	7–13	-0.02	12%	29 Jun →
mirage	13–8	-0.00	21%	28 Jun →
overpass	7–13	-0.06	27%	25 Jun →
inferno	0–13	-0.07	33%	25 Jun →
overpass	19–17	0.00	11%	16 Jun →
anubis	6–13	0.03	33%	14 Jun →
inferno	13–5	0.14	26%	12 Jun →
anubis	13–2	0.11	18%	12 Jun →
anubis	7–13	-0.04	16%	31 May →
anubis	13–8	0.07	18%	27 Apr →
overpass	13–10	0.02	10%	26 Apr →
inferno	13–9	-0.04	27%	26 Apr →
overpass	10–13	0.09	21%	19 Apr →
inferno	16–14	0.06	18%	14 Apr →
inferno	10–13	0.01	34%	12 Apr →
dust2	11–13	-0.04	21%	12 Apr →
inferno	9–13	-0.02	25%	9 Apr →
nuke	8–13	-0.06	13%	7 Apr →
anubis	10–13	0.03	24%	6 Apr →
ancient	10–13	0.03	16%	6 Apr →
dust2	13–10	0.05	22%	6 Apr →
anubis	13–6	0.05	17%	6 Apr →
mirage	16–14	0.01	22%	2 Apr →
ancient	9–13	-0.02	17%	22 Mar →
dust2	10–13	-0.02	23%	14 Mar →
ancient	13–10	0.05	20%	14 Mar →
inferno	13–10	-0.02	41%	12 Mar →
mirage	13–7	-0.06	26%	12 Mar →
dust2	16–12	0.07	32%	12 Mar →
inferno	13–11	0.00	21%	12 Mar →
overpass	7–13	-0.00	20%	11 Mar →
inferno	13–10	-0.03	19%	10 Mar →
mirage	13–7	0.07	27%	7 Mar →
anubis	13–8	0.01	16%	7 Mar →
overpass	13–6	-0.04	17%	6 Mar →
ancient	13–8	-0.01	21%	3 Mar →
inferno	2–13	-0.05	14%	27 Feb →
mirage	13–7	0.03	16%	21 Feb →
dust2	13–5	0.06	9%	20 Feb →
dust2	13–10	-0.01	23%	18 Feb →
mirage	13–7	0.02	24%	18 Feb →
inferno	7–13	0.00	32%	17 Feb →
ancient	13–10	0.00	16%	9 Feb →
inferno	14–16	-0.02	26%	6 Feb →
ancient	13–10	-0.02	22%	4 Feb →
nuke	13–9	0.05	29%	22 Jan →
anubis	11–13	0.02	32%	20 Jan →
mirage	19–16	0.06	19%	17 Jan →
mirage	13–7	-0.05	21%	15 Jan →
nuke	1–13	-0.07	10%	13 Jan →
inferno	2–13	-0.05	10%	6 Jan →
ancient	13–8	0.04	19%	11 Dec →
dust2	8–13	-0.06	17%	10 Dec →
inferno	13–9	-0.01	15%	27 Oct →
nuke	7–13	-0.02	30%	11 Aug →
mirage	4–13	-0.05	18%	5 Aug →
mirage	13–11	0.01	25%	4 Aug →
mirage	16–12	0.04	15%	24 Jul →
inferno	13–10	-0.02	24%	24 Jul →
ancient	5–13	-0.07	24%	20 Jul →
overpass	13–10	0.00	14%	20 Jul →
overpass	9–13	0.02	36%	17 Jul →
overpass	13–4	0.10	30%	16 Jul →
inferno	12–16	-0.01	19%	13 Jul →
ancient	13–2	0.11	22%	10 Jul →
inferno	13–11	0.01	31%	7 Jul →
train	13–10	0.02	19%	6 Jul →
inferno	11–13	-0.03	20%	5 Jul →
ancient	20–22	-0.02	15%	1 Jul →
mirage	8–13	0.03	21%	29 Jun →
train	13–11	0.03	20%	28 Jun →
mirage	13–4	0.08	23%	28 Jun →
inferno	4–13	-0.00	20%	28 Jun →
inferno	13–6	0.03	14%	27 Jun →
inferno	11–13	0.03	35%	26 Jun →
dust2	6–13	-0.07	17%	25 Jun →
anubis	10–13	-0.04	16%	23 Jun →
anubis	16–13	0.07	14%	22 Jun →
inferno	9–13	-0.00	20%	21 Jun →
inferno	13–9	0.02	10%	20 Jun →
inferno	13–9	0.06	25%	20 Jun →
mirage	2–13	-0.01	32%	23 May →
ancient	14–16	-0.04	23%	11 May →