ScoobX

ScoobX — CS2 Stats

US76561197960368797[U:1:103069]Steam profile ↗✓ No bans

1,351Tracked matches34%Win rate2020Tracked since
11,982Hours in CS6Hrs last 2 wks
CSDB Rating5.7 SolidPositional Player
Premier CS Rating20,000Top 50% of 213,399 CSDB-tracked playersPink band · top ~8.5% of ranked players (population est.)
CSDB Leaderboard#83814 of 260014 tracked
FaceitLevel 9Top 20.7% of ranked FACEIT players
WingmanDistinguished Master Guardian
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 14 Oct 2026 (22 days ago). Ranks are recorded once per day this page is viewed.

No change since then. Play, then come back: the next observation lands here.

Rating over time

Premier CS Rating: 20,000 -1,363 3 Jul – 14 Oct · 3 days played
19,69321,363peak 21,3633 Jul14 Oct
21,604Peak Premier in tracked matches
69Days played since 2025-07-18

Premier CS Rating

  • 1,363 below peak (21,363)
  • -1,363 over 90 days
  • Next: Red band at 25,000 — 5,000 to go
  • Reached: Pink band · Purple band · Blue band · Light Blue band

Premier comes from this profile’s tracked match history, so it reaches back as far as those matches do.

How this compares with the same rank

Median values for Level 9 among CSDB-tracked players (n=15,132), 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 rate59.5%48.2%50.9%above
Shot accuracy20.0%13.2%13.7%above
Kill/death ratio1.261.071.09above
Match win rate26.2%46.9%48.4%22.2% short

This profile matches the typical Level 10 player on 3 of 4 comparable metrics.

Widest gap: Match win 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.GGScoobXFACEITLevel 9STANDINGTop 20.7% of rankedPREMIER20,000 · Pink bandcsdb.gg/stats

The image is a snapshot; the link keeps updating. Nothing here is published anywhere — it is generated in your browser when you click.

Performance scores

Aim73
Positioning59
Utility53

0–100 skill scores via Leetify.

Recent form

STEADY53–46–1Last 10053%Win rateLLLLWWWLLW

Last 10 vs previous 10: +20pp win rate · +0.03 avg rating · −3.7pp headshot accuracy · +35ms reaction

Win rate up 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

  • 1–4 · 20% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 19%
  • Avg reaction 538ms

Last 10

  • 4–6 · 40% win rate
  • Avg rating 0.02
  • Avg headshot accuracy 19%
  • Avg reaction 543ms

Last 20

  • 6–14 · 30% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 21%
  • Avg reaction 526ms

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 against its own average).

Aim7.3
Utility5.3
Positioning5.9
Opening Duels3.8
Clutch4.6

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

jL

Plays most like jL 92% playstyle similarity

Most alike: utility contribution, positioning profile.

Where you differ: lower aim profile.

Similarity of playstyle shape across shared dimensions — it says how you play, not that you play at their level. Full comparison →

CSDB Rating breakdown

Aim7.3
Positioning5.9
Utility5.3
Mechanics6.6
Opening Duels4.0
Win Impact0.0

Composite 5.7/10 (Solid), a weighted mean of the bars with a small opposition adjustment (×1.02 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating0.01−0.00
first ⅓ avg 0.01 → last ⅓ avg 0.01
Reaction time540ms−16ms
first ⅓ avg 555ms → last ⅓ avg 540ms
Headshot accuracy22.4%+6.6%
first ⅓ avg 15.8% → last ⅓ avg 22.4%

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

0.16Best match rating · 9–3 · inferno, 9 Jul →
100%Best headshot accuracy · 0–13 · mirage, 14 Jul →
438msFastest reaction time · 4–13 · overpass, 24 Nov →
13–0Biggest win · inferno, 3 Jul →

Across the last 100 tracked matches.

Highlights

6Longest win streak
L4Current streak
7–6In matches decided by ≤2 rounds
9Overtime games

Map breakdown

nukeBest map · 75% over 8dust2Weakest map · 42% over 19
MapGradePlayedRecordWin rateAvg rating
infernoA2212–1055%0.03
dust2C198–1142%-0.00
ancientA169–756%0.01
mirageB147–750%0.02
cacheB105–550%0.02
nukeS86–275%-0.00
overpassB63–350%-0.00
anubis—43–175%0.01
eldorado—10–10%-0.03

Across the last 100 tracked matches.

Dust 2 is currently your weakest sufficiently-sampled map (42% over 19). Start with the 6 essential Dust 2 lineups, review the callouts, then spin up a practice server.

Lifetime stats

780,398Lifetime kills
1.26K/D · Top 25% of Level 9 players
12,113Matches
26.2%Match win rate
59.5%Headshot % · Top 25% of Level 9 players
20.0%Shot accuracy · Top 5% of Level 9 players
22,752MVPs
24,551Hours (in match)
12,166Bombs planted
4,491Bombs defused

Most-used weapons

AK-47375,862
AWP51,853
Glock-1820,937
P25015,395
Knife4,878
MP94,445

Lifetime map wins

23,219inferno
20,827dust2
8,435nuke
4,824vertigo
4,285train
2,380cbble
214office
122lake

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

Faceit stats

Combat

562Matches
47%Win rate
1.06Avg K/D
71.6ADR
45%Headshot %

Clutches & streaks

43%1v1 clutch win
20%1v2 clutch win
8Longest win streak

Recent Faceit resultsLWWWL

MapMatchesWin rateAvg K/DAvg kills
Ancient10345%1.0413.9
Anubis6235%0.9413.7
Inferno5133%0.9213.4
Dust24743%1.0413.5
Mirage3050%0.9915.8
Nuke2737%0.9514.3
Vertigo2450%1.2716.1
Overpass1861%1.0113.9

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.

20.0%Headshot accuracy
35.1%Accuracy (enemy spotted)
35.6%Spray accuracy
79.8%Counter-strafing
10.6°Preaim
539msReaction time
45.6%T opening success
46.1%CT opening success
0.60Enemies flashed / flash
7.7%Flash assists
10.13HE damage / grenade
9.81Flashes / match

Recommended for you

Spend your practice time on Dust 2

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

42% win rate across 19 tracked games — your weakest map with enough games to be worth reading into.

Dust 2 callouts & strategy →Dust 2 grenade lineups →

Recent matches

MapScoreRatingHS%Date
inferno7–13-0.0218%22 Oct →
inferno11–13-0.0221%20 Oct →
anubis2–130.0014%15 Oct →
cache10–130.0220%14 Oct →
ancient13–80.0721%14 Oct →
mirage13–60.0222%14 Oct →
mirage13–90.0721%13 Oct →
mirage8–130.0220%13 Oct →
mirage8–130.0312%12 Oct →
anubis13–80.0124%9 Oct →
dust24–130.0423%9 Oct →
ancient9–13-0.0729%9 Oct →
ancient7–13-0.0221%6 Oct →
inferno10–130.0016%1 Oct →
cache8–130.0227%29 Sept →
ancient13–50.0023%26 Sept →
ancient5–13-0.0135%26 Sept →
mirage12–10.0411%27 Aug →
nuke4–13-0.0421%27 Aug →
inferno8–13-0.0323%20 Aug →
cache21–25-0.0222%18 Aug →
mirage13–100.0617%13 Aug →
cache13–110.0523%12 Aug →
mirage14–16-0.0110%29 Jul →
cache6–13-0.0216%21 Jul →
dust22–13-0.0616%16 Jul →
mirage8–13-0.0316%16 Jul →
cache13–80.0520%16 Jul →
cache13–40.0421%15 Jul →
inferno15–15-0.0118%14 Jul →
mirage0–13-0.03100%14 Jul →
cache13–50.0320%10 Jul →
anubis13–80.0419%10 Jul →
ancient3–13-0.0710%10 Jul →
cache13–60.0124%10 Jul →
inferno13–50.0726%9 Jul →
dust29–130.0113%9 Jul →
mirage13–50.0623%9 Jul →
cache10–13-0.0217%9 Jul →
inferno9–30.1614%9 Jul →
eldorado7–9-0.0332%9 Jul →
dust210–130.0313%3 Jul →
mirage7–13-0.0026%3 Jul →
inferno13–00.1226%3 Jul →
mirage13–9-0.0013%1 Jul →
ancient13–100.0410%26 Jun →
dust213–10-0.0220%26 Jun →
inferno10–130.028%24 Jun →
inferno10–13-0.0215%24 Jun →
inferno8–130.0329%20 May →
dust210–13-0.0422%13 May →
inferno13–110.0515%13 May →
dust213–5-0.008%6 May →
dust211–13-0.039%30 Apr →
inferno3–130.0013%30 Apr →
ancient11–13-0.0122%29 Apr →
inferno13–50.0316%13 Apr →
inferno13–70.0322%31 Mar →
ancient13–50.1017%27 Mar →
dust213–70.0633%23 Mar →
mirage7–130.0226%22 Mar →
dust210–13-0.0518%22 Mar →
anubis13–90.0016%22 Mar →
nuke13–11-0.0214%18 Mar →
nuke16–130.0319%15 Mar →
mirage13–60.0114%15 Mar →
nuke13–6-0.0217%12 Mar →
inferno16–14-0.0712%27 Feb →
ancient12–16-0.0316%25 Feb →
inferno13–60.0619%6 Feb →
ancient13–50.0617%17 Dec →
inferno13–60.0227%16 Dec →
dust213–60.0617%24 Nov →
overpass4–13-0.0415%24 Nov →
inferno2–130.0113%23 Nov →
ancient16–130.0519%12 Nov →
overpass13–60.0521%7 Nov →
ancient13–100.0419%5 Nov →
overpass1–00.000%24 Oct →
dust213–160.0132%24 Oct →
nuke13–8-0.0111%10 Sept →
ancient13–7-0.0122%5 Sept →
overpass13–4-0.0120%29 Aug →
nuke13–70.0515%28 Aug →
nuke9–13-0.0412%27 Aug →
dust213–80.0619%27 Aug →
ancient13–110.0213%25 Aug →
dust29–13-0.0320%22 Aug →
dust213–30.0415%20 Aug →
overpass7–13-0.018%15 Aug →
ancient8–13-0.0314%15 Aug →
dust211–13-0.0411%13 Aug →
nuke13–90.0214%8 Aug →
overpass2–13-0.028%6 Aug →
dust23–13-0.0422%1 Aug →
inferno19–16-0.0415%30 Jul →
inferno13–80.1211%29 Jul →
dust213–11-0.0111%25 Jul →
inferno13–40.0919%23 Jul →
dust213–3-0.0116%18 Jul →

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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