Arsenal (g dawg)

Arsenal (g dawg) — CS2 Stats

76561198955590410[U:1:995324682]Steam profile ↗✓ No bans

2,300Tracked matches31%Win rate2022Tracked since
3,097Hours in CS4Hrs last 2 wks
CSDB Rating3.8 LearningPositional Player
FaceitLevel 9 · 1,839 ELOTop 20.7% of ranked FACEIT players
WingmanGold Nova Master
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 25 Sep 2026 (23 days ago). Ranks are recorded once per day this page is viewed.

0Faceit ELO · 1,839 → 1,839
+15Tracked matches · now 2,300
−13.8ppWin rate · 44.8% → 31.0%

Rating over time

Premier CS Rating: 18,560 +2,706 25 Jun – 30 Aug · 11 days played
15,73318,681peak 18,68125 Jun30 Aug
Faceit ELO: 1,839 +0 28 Aug – 25 Sept · 3 observations
1,8391,839peak 1,83928 Aug25 Sept
18,681Peak Premier in tracked matches
1,839Highest Faceit ELO seen on CSDB
35Days played since 2026-06-25

Faceit ELO

  • At peak — 1,839
  • 0 over 30 days · steady
  • Next: Level 10 at 2,001 — 162 to go
  • Reached: Level 9 · Level 8 · Level 7 · Level 6
  • Level 9 first seen 2026-08-28
  • Level 8 first seen 2026-08-28

Premier comes from this profile’s tracked match history, so it reaches back as far as those matches do. Faceit ELO is CSDB’s own observation — no feed exposes ELO per match, so that line only has the days the profile was viewed and cannot be backfilled.

How this compares with the same rank

Median values for Level 9 among CSDB-tracked players (n=14,654), from Valve's own lifetime stats. Aim, positioning and utility scores are deliberately not benchmarked here — those are a third-party provider's derived metrics, which CSDB does not store.

MetricThis playerLevel 9 medianLevel 10 medianvs Level 10
Headshot rate40.0%48.2%50.9%10.9% short
Shot accuracy13.5%13.2%13.7%meets
Kill/death ratio1.031.071.090.06 short
Match win rate48.3%46.9%48.4%meets

This profile matches the typical Level 10 player on 2 of 4 comparable metrics.

Widest gap: Headshot rate. That is the metric furthest from the Level 10 median in relative terms — not necessarily the one holding a rank back, which no statistic here can establish.

Share this profile

CSDB.GGArsenal (g dawg)FACEITLevel 9 · 1,839 ELOSTANDINGTop 20.7% of ranked30 DAYS0 ELOPEAK ELO1,839csdb.gg/stats

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Performance scores

Aim56
Positioning38
Utility26

0–100 skill scores via Leetify.

Recent form

COLD41–55–4Last 10041%Win rateLLLLLLWTLW

Last 10 vs previous 10: −20pp win rate · −0.01 avg rating · +5.8pp headshot accuracy · +20ms reaction

Win rate down 20pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp).

Last 5 · 10 · 20 matches

Last 5

  • 0–5 · 0% win rate
  • Avg rating -0.06
  • Avg headshot accuracy 24%
  • Avg reaction 600ms

Last 10

  • 2–7–1 · 20% win rate
  • Avg rating -0.06
  • Avg headshot accuracy 23%
  • Avg reaction 620ms

Last 20

  • 6–13–1 · 30% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 20%
  • Avg reaction 609ms

Newest first, from the last 100 tracked matches. Each block is its own sample — one result moves a 5-match win rate by 20 points.

Player DNA

Primary style: Positional Player — Positioning stands above the rest of this profile (+1.2 against its own average).

Aim5.6
Utility2.6
Positioning3.8
Opening Duels0.7
Clutch3.2

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.

What this cannot see yet: which weapons you use — so CSDB cannot identify an AWPer, and no style here implies a rifle or a sniper. It also cannot see how often you take opening duels, only how often you win them, nor where you hold, so roles that depend on those (entry, lurk, anchor) are deliberately absent rather than guessed. All of it needs round-by-round demo data, which is the next thing being built.

Your pro match

iM

Plays most like iM 94% playstyle similarity

Most alike: positioning profile, utility contribution.

Where you differ: lower aim profile; lower 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

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

CT openings. Opening success drops from 47% on T to 30% on CT — first contacts on the defending side are being lost.

Reaction time. 625ms 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

Aim5.6
Positioning3.8
Utility2.6
Mechanics6.5
Opening Duels2.1
Win Impact0.0

Composite 3.8/10 (Learning), a weighted mean of the bars with a small opposition adjustment (×0.95 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.05−0.04
first ⅓ avg -0.01 → last ⅓ avg -0.05
Reaction time632ms−14ms
first ⅓ avg 646ms → last ⅓ avg 632ms
Headshot accuracy24.1%+3.6%
first ⅓ avg 20.5% → last ⅓ avg 24.1%

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.

Personal bests

0.19Best match rating · 13–1 · dust2, 7 Jul →
75%Best headshot accuracy · 0–8 · vertigo, 2 Jul →
414msFastest reaction time · 10–13 · ancient, 12 Sept →
13–0Biggest win · anubis, 28 Aug →

Across the last 100 tracked matches.

Highlights

5Longest win streak
L6Current streak
7–6In matches decided by ≤2 rounds
5Overtime games

Map breakdown

anubisBest map · 64% over 11infernoWeakest map · 25% over 16
MapGradePlayedRecordWin rateAvg rating
nukeC177–1041%-0.03
infernoD164–1225%-0.05
cacheC125–742%-0.04
ancientD124–833%-0.01
mirageC125–742%-0.03
anubisA117–464%0.01
dust2D93–633%-0.01
vertigo—42–250%-0.06
train—32–167%-0.02
overpass—21–150%-0.05
office—21–150%-0.03

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (25% over 16). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

72,381Lifetime kills
1.03K/D
3,935Matches
48.3%Match win rate · Top 50% of Level 9 players
40.0%Headshot %
13.5%Shot accuracy · Top 50% of Level 9 players
8,484MVPs
1,484Hours (in match)
1,407Bombs planted
595Bombs defused

Most-used weapons

AK-4718,898
AWP11,505
MP92,642
Knife1,733
SSG 081,689

Lifetime map wins

5,233inferno
5,147dust2
3,415nuke
2,668vertigo
1,121office
637train
79italy
57cbble

Lifetime totals via Steam — visible because this profile's game details are public. Spans CS:GO and CS2.

Faceit stats

Combat

1,266Matches
51%Win rate
1.04Avg K/D
79.0ADR
38%Headshot %

Clutches & streaks

36%1v1 clutch win
18%1v2 clutch win
9Longest win streak

Recent Faceit resultsLWLWW

MapMatchesWin rateAvg K/DAvg kills
Mirage14051%0.9715.7
Ancient9056%1.1216.2
Dust27646%0.9414.8
Inferno7352%1.0317.0
Anubis5551%1.0517.3
Nuke5555%1.1917.3
Vertigo5353%1.1616.8
Overpass3861%1.1617.5

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.

24.0%Headshot accuracy
29.5%Accuracy (enemy spotted)
30.4%Spray accuracy
79.3%Counter-strafing
11.4°Preaim
625msReaction time
46.8%T opening success
30.0%CT opening success
0.48Enemies flashed / flash
12.0%Flash assists
5.05HE damage / grenade
3.43Flashes / 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.

  1. 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 0.4772 — below the 0.5 mark we flag

    Grenade Lineups →
  2. Advanced Mechanics

    Losing the first CT duel repeatedly usually means holding angles that favour the peeker.

    CT opening duels 29.9519% — 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.

25% win rate across 16 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
nuke11–13-0.0321%17 Oct →
dust24–13-0.0824%17 Oct →
dust29–13-0.0729%4 Oct →
cache7–13-0.0621%27 Sept →
nuke4–13-0.0622%27 Sept →
train6–13-0.0520%27 Sept →
vertigo13–7-0.0619%27 Sept →
dust212–12-0.0821%23 Sept →
nuke6–13-0.1013%23 Sept →
cache13–6-0.0142%23 Sept →
vertigo5–13-0.108%23 Sept →
cache2–13-0.0814%23 Sept →
inferno6–13-0.0723%23 Sept →
nuke9–13-0.0719%22 Sept →
train13–7-0.0733%22 Sept →
vertigo13–8-0.0618%22 Sept →
ancient11–130.0319%17 Sept →
cache13–30.0115%12 Sept →
ancient10–13-0.0313%12 Sept →
mirage13–5-0.0311%12 Sept →
mirage7–13-0.0633%12 Sept →
overpass7–13-0.086%3 Sept →
nuke7–13-0.1020%3 Sept →
ancient8–13-0.0119%29 Aug →
inferno2–13-0.0633%29 Aug →
anubis13–00.0169%28 Aug →
inferno9–13-0.0429%28 Aug →
anubis9–13-0.0523%27 Aug →
nuke13–9-0.0535%20 Aug →
cache13–5-0.1063%20 Aug →
mirage11–13-0.0418%13 Aug →
ancient13–100.0117%9 Aug →
nuke7–13-0.0321%9 Aug →
mirage16–140.0213%9 Aug →
anubis13–70.0522%9 Aug →
ancient13–10-0.0225%9 Aug →
mirage13–8-0.1016%9 Aug →
inferno2–13-0.1215%4 Aug →
cache6–13-0.0722%4 Aug →
nuke6–130.0120%4 Aug →
dust212–16-0.0322%29 Jul →
anubis16–13-0.0314%23 Jul →
ancient7–130.0110%21 Jul →
dust213–8-0.0116%21 Jul →
inferno15–15-0.0430%18 Jul →
anubis5–13-0.0627%17 Jul →
dust210–130.0021%17 Jul →
cache8–13-0.0923%17 Jul →
inferno4–13-0.0213%17 Jul →
mirage4–13-0.0128%14 Jul →
inferno13–7-0.0218%14 Jul →
inferno8–13-0.0315%13 Jul →
nuke13–2-0.0513%13 Jul →
mirage9–130.0421%13 Jul →
mirage8–13-0.0810%13 Jul →
ancient5–13-0.1113%13 Jul →
mirage13–10-0.0019%13 Jul →
inferno8–13-0.0119%12 Jul →
ancient4–13-0.0416%12 Jul →
inferno10–13-0.0810%12 Jul →
anubis13–5-0.0911%12 Jul →
cache13–10-0.0321%11 Jul →
nuke13–6-0.0320%11 Jul →
nuke7–130.009%9 Jul →
anubis13–80.1229%9 Jul →
nuke13–110.0114%9 Jul →
dust213–10.1925%7 Jul →
inferno13–11-0.0120%6 Jul →
overpass13–6-0.0236%3 Jul →
cache8–13-0.0116%2 Jul →
mirage11–13-0.0521%2 Jul →
vertigo0–8-0.0275%2 Jul →
nuke13–4-0.0114%2 Jul →
office2–4-0.0325%2 Jul →
train13–00.0522%2 Jul →
cache13–4-0.0710%2 Jul →
inferno13–7-0.0928%1 Jul →
inferno6–13-0.0519%1 Jul →
nuke13–11-0.0119%1 Jul →
mirage13–110.0322%1 Jul →
ancient5–130.0113%1 Jul →
anubis13–60.1019%1 Jul →
anubis11–130.0214%30 Jun →
anubis7–13-0.0017%30 Jun →
dust213–6-0.0323%30 Jun →
inferno13–7-0.0215%30 Jun →
inferno4–13-0.0812%30 Jun →
cache9–130.0018%29 Jun →
anubis13–40.0720%29 Jun →
nuke8–13-0.0321%28 Jun →
office13–10-0.0214%28 Jun →
nuke13–11-0.0116%28 Jun →
ancient13–80.0712%28 Jun →
nuke4–130.0423%26 Jun →
ancient4–130.0222%26 Jun →
ancient13–11-0.0418%26 Jun →
cache3–13-0.0322%26 Jun →
mirage12–12-0.0817%25 Jun →
inferno8–13-0.0521%25 Jun →
dust215–15-0.0111%25 Jun →

Match data via Leetify.

Recent teammates

Milestones

1,000 Matches
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

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