Goose

Goose — CS2 Stats

AU76561198089923339[U:1:129657611]Steam profile ↗✓ No bans

693Tracked matches66%Win rate2020Tracked since
1,659Hours in CS
CSDB Rating6.0 SolidPositional Player
FaceitLevel 9Top 20.7% of ranked FACEIT players
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 2 Sep 2026 (39 days ago). Ranks are recorded once per day this page is viewed.

No change since then — still 693 tracked matches. Play, then come back: the next observation lands here.

Rating over time

Premier CS Rating: 24,604 +1,662 27 Jun – 20 Jun · 15 days played
21,75024,604peak 24,60427 Jun20 Jun
24,604Peak Premier in tracked matches
56Days played since 2025-06-27

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=14,332), 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 rate44.5%48.2%50.9%6.5% short
Shot accuracy13.2%13.3%13.7%0.5% short
Kill/death ratio1.031.071.090.05 short
Match win rate49.1%46.9%48.4%meets

This profile matches the typical Level 10 player on 1 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.GGGooseFACEITLevel 9STANDINGTop 20.7% of rankedcsdb.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

Aim58
Positioning61
Utility53

0–100 skill scores via Leetify.

Recent form

STEADY63–32–5Last 10063%Win rateWWLWLWLWLT

Last 10 vs previous 10: −40pp win rate · −0.05 avg rating · −3.7pp headshot accuracy · +60ms reaction

Win rate down 40pp 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

  • 3–2 · 60% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 18%
  • Avg reaction 608ms

Last 10

  • 5–4–1 · 50% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 18%
  • Avg reaction 673ms

Last 20

  • 14–5–1 · 70% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 20%
  • Avg reaction 643ms

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 (+1.5 against its own average).

Aim5.8
Utility5.3
Positioning6.1
Opening Duels4.8
Clutch2.4

Strong 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.

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 81% playstyle similarity

Most alike: utility contribution, opening-duel success.

Where you differ: lower positioning profile; 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

CT openings. 60% CT opening-duel success — winning the first fight on the defending side is rare and valuable.

Areas to improve

T-side openings. Opening success drops from 60% on CT to 31% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 635ms 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

Aim5.8
Positioning6.1
Utility5.3
Mechanics5.4
Opening Duels3.9
Win Impact10.0

Composite 6.0/10 (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 rating-0.01−0.02
first ⅓ avg 0.01 → last ⅓ avg -0.01
Reaction time635ms−2ms
first ⅓ avg 637ms → last ⅓ avg 635ms
Headshot accuracy19.6%+1.7%
first ⅓ avg 17.9% → last ⅓ avg 19.6%

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

0.15Best match rating · 13–6 · ancient, 9 Jan →
37%Best headshot accuracy · 13–4 · mirage, 11 Jul →
469msFastest reaction time · 13–6 · mirage, 21 Feb →
13–1Biggest win · ancient, 21 Jul →

Across the last 100 tracked matches.

Highlights

6Longest win streak
W2Current streak
11–6In matches decided by ≤2 rounds
10Overtime games

Map breakdown

nukeBest map · 100% over 5infernoWeakest map · 40% over 5
MapGradePlayedRecordWin rateAvg rating
dust2A2816–1257%-0.01
mirageA1710–759%-0.01
ancientS128–467%0.02
cacheS107–370%0.01
overpassS108–280%-0.01
trainB63–350%0.00
infernoC52–340%-0.01
nukeS55–0100%0.05
anubis—42–250%0.01
alpine—11–0100%0.01
vertigo—10–10%0.00
jura—11–0100%0.10

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (40% over 5). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

76,725Lifetime kills
1.03K/D
3,037Matches
49.1%Match win rate · Top 50% of Level 9 players
44.5%Headshot %
13.2%Shot accuracy
7,161MVPs
1,659Hours (in match)
3,381Bombs planted
1,022Bombs defused

Most-used weapons

AK-4728,757
AWP7,788
P2502,439
P901,257
Tec-91,000

Lifetime map wins

7,456dust2
2,367inferno
1,945nuke
1,118cbble
1,010train
791vertigo
573lake
121office

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

Faceit stats

Combat

118Matches
62%Win rate
1.20Avg K/D
83.4ADR
44%Headshot %

Clutches & streaks

32%1v1 clutch win
23%1v2 clutch win
10Longest win streak

Recent Faceit resultsLLLWL

MapMatchesWin rateAvg K/DAvg kills
Mirage2665%1.1115.0
Dust22167%1.1617.1
Anubis1942%1.1218.2
Ancient1362%1.1617.9
Inferno825%0.9816.6
Nuke683%1.4216.3
Vertigo475%0.8214.8
Train2100%1.0414.0

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.

20.2%Headshot accuracy
30.6%Accuracy (enemy spotted)
39.1%Spray accuracy
74.2%Counter-strafing
10.3°Preaim
635msReaction time
31.1%T opening success
60.4%CT opening success
0.57Enemies flashed / flash
7.3%Flash assists
8.68HE damage / grenade
7.10Flashes / 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. 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 31.071% — 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.

40% win rate across 5 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
ancient13–30.0119%29 Aug →
cache13–8-0.0530%29 Aug →
mirage5–13-0.0813%29 Aug →
dust213–8-0.0310%21 Aug →
ancient10–13-0.0317%26 Jul →
dust213–11-0.0527%11 Jul →
dust29–13-0.0515%3 Jul →
dust213–11-0.0412%20 Jun →
dust28–13-0.0224%20 Jun →
cache12–120.0012%11 Jun →
dust213–80.0318%1 Jun →
cache13–110.0323%1 Jun →
cache13–8-0.0124%21 May →
dust24–8-0.0922%15 May →
mirage13–40.0637%15 May →
dust213–8-0.0411%15 May →
cache13–50.0523%14 May →
ancient13–90.0713%14 May →
cache13–100.0520%11 May →
cache13–100.0424%10 May →
ancient14–16-0.0219%3 May →
cache10–13-0.0617%1 May →
cache6–130.0125%30 Apr →
cache13–80.0222%30 Apr →
alpine13–60.0118%15 Apr →
mirage5–13-0.0914%9 Apr →
overpass13–11-0.0117%7 Apr →
ancient7–30.0221%6 Apr →
dust213–11-0.0325%6 Apr →
dust212–16-0.0732%4 Apr →
ancient16–140.0016%4 Apr →
mirage16–13-0.0313%27 Mar →
dust213–30.0712%27 Mar →
mirage13–6-0.0117%21 Feb →
dust28–130.0319%10 Feb →
mirage13–30.0611%9 Jan →
ancient13–60.1514%9 Jan →
overpass13–3-0.0428%17 Dec →
dust213–110.0314%18 Nov →
overpass13–3-0.0112%5 Sept →
overpass10–1-0.004%5 Sept →
inferno11–13-0.036%28 Aug →
nuke16–14-0.0212%24 Aug →
dust215–15-0.0025%23 Aug →
mirage7–13-0.0212%23 Aug →
dust212–120.0225%19 Aug →
nuke13–40.0926%17 Aug →
dust27–13-0.089%6 Aug →
overpass13–11-0.0519%6 Aug →
nuke13–70.0618%6 Aug →
overpass13–80.0923%5 Aug →
dust213–50.0511%5 Aug →
dust213–110.0117%5 Aug →
inferno11–13-0.0322%5 Aug →
mirage7–13-0.0324%5 Aug →
dust25–13-0.0614%4 Aug →
train13–40.0831%4 Aug →
ancient20–22-0.0319%4 Aug →
mirage3–13-0.0627%4 Aug →
dust213–50.0321%3 Aug →
ancient13–9-0.077%3 Aug →
overpass3–13-0.0226%2 Aug →
vertigo9–130.0027%2 Aug →
mirage13–40.0012%2 Aug →
dust213–80.0123%2 Aug →
inferno13–40.0017%31 Jul →
overpass11–13-0.015%31 Jul →
dust213–7-0.0212%28 Jul →
dust24–13-0.0213%28 Jul →
mirage13–80.0320%26 Jul →
train4–130.0124%26 Jul →
overpass13–50.0312%26 Jul →
overpass13–8-0.0315%25 Jul →
mirage16–120.0324%25 Jul →
dust27–13-0.0719%23 Jul →
train13–10-0.0212%23 Jul →
dust216–130.0224%22 Jul →
nuke13–40.0421%21 Jul →
ancient13–10.0713%21 Jul →
train5–13-0.0316%21 Jul →
ancient10–130.0619%20 Jul →
dust213–8-0.0026%15 Jul →
anubis13–2-0.0218%15 Jul →
inferno5–130.0211%14 Jul →
mirage13–60.0311%14 Jul →
anubis12–120.0621%11 Jul →
mirage13–4-0.0537%11 Jul →
jura13–80.1019%10 Jul →
anubis13–90.0113%10 Jul →
ancient13–100.029%9 Jul →
nuke13–50.0817%9 Jul →
mirage13–30.0417%9 Jul →
dust212–120.0223%8 Jul →
dust23–10.0320%8 Jul →
anubis12–16-0.029%3 Jul →
train13–60.0027%2 Jul →
mirage7–13-0.0415%2 Jul →
inferno13–3-0.0414%28 Jun →
train9–13-0.0227%28 Jun →
mirage11–13-0.0213%27 Jun →

Match data via Leetify.

Recent teammates

Milestones

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

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