chonky cat

chonky cat — CS2 Stats

FI76561198043192068[U:1:82926340]Steam profile ↗✓ No bans

889Tracked matches58%Win rate2024Tracked since
1,281Hours in CS2Hrs last 2 wks
CSDB Rating5.1 DevelopingPositional Player
Ladder ranks via Leetify

Performance scores

Aim80
Positioning42
Utility22

0–100 skill scores via Leetify.

Recent form

STEADY51–39–10Last 10051%Win rateLLTWWLWWLL

Last 10 vs previous 10: −10pp win rate · −0.05 avg rating · −3.4pp headshot accuracy · +32ms reaction

Win rate −10pp across the last 10 against the 10 before — within the normal variation of a 10-match window (±20pp), so no real shift yet.

Last 5 · 10 · 20 matches

Last 5

  • 2–2–1 · 40% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 11%
  • Avg reaction 600ms

Last 10

  • 4–5–1 · 40% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 9%
  • Avg reaction 566ms

Last 20

  • 9–8–3 · 45% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 10%
  • Avg reaction 550ms

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 (+2 against its own average).

Aim8.0
Utility2.2
Positioning4.2
Opening Duels0.1

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.

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

ropz

Plays most like ropz 83% playstyle similarity

Most alike: aim profile, positioning profile.

Where you differ: lower opening-duel success; 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

Areas to improve

Positioning. Positioning trails aim by 37 points — deaths here waste a strong aim profile.

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 42% on CT to 24% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 561ms 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

Aim8.0
Positioning4.2
Utility2.2
Mechanics4.4
Opening Duels1.5
Win Impact7.6

Composite 5.1/10 (Developing), 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+0.01
first ⅓ avg -0.02 → last ⅓ avg -0.01
Reaction time560ms−2ms
first ⅓ avg 562ms → last ⅓ avg 560ms
Headshot accuracy12.1%−1.4%
first ⅓ avg 13.5% → last ⅓ avg 12.1%

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

0.14Best match rating · 13–1 · train, 5 Oct →
38%Best headshot accuracy · 13–1 · train, 5 Oct →
406msFastest reaction time · 13–7 · anubis, 3 Sept →
13–1Biggest win · dust2, 6 Oct →

Across the last 100 tracked matches.

Highlights

7Longest win streak
L2Current streak
5–2In matches decided by ≤2 rounds

Map breakdown

trainBest map · 57% over 23dust2Weakest map · 41% over 32
MapGradePlayedRecordWin rateAvg rating
dust2C3213–1941%-0.02
trainA2313–1057%-0.01
ancientA1810–856%-0.03
anubisB137–654%-0.02
inferno—42–250%-0.03
nuke—33–0100%0.01
mirage—31–233%-0.02
vertigo—21–150%-0.09
overpass—21–150%-0.02

Across the last 100 tracked matches.

Dust 2 is currently your weakest sufficiently-sampled map (41% over 32). Start with the 6 essential Dust 2 lineups, review the callouts, then spin up a practice server.

Lifetime stats

33,849Lifetime kills
1.03K/D · Top 50% of tracked players
2,064Matches
50.9%Match win rate · Top 25% of tracked players
33.0%Headshot %
13.4%Shot accuracy · Top 50% of tracked players
4,124MVPs
779Hours (in match)
1,908Bombs planted
431Bombs defused

Most-used weapons

Lifetime map wins

4,984dust2
2,682inferno
1,217train
766nuke
394office
373vertigo
233cbble
144dust

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

Skill profile

Aggregate performance across tracked matches — stats via Leetify. Percentile context against other CSDB-tracked players arrives as our own benchmark data accumulates.

11.0%Headshot accuracy
45.9%Accuracy (enemy spotted)
44.6%Spray accuracy
69.7%Counter-strafing
8.6°Preaim
561msReaction time
24.1%T opening success
42.1%CT opening success
0.30Enemies flashed / flash
5.0%Flash assists
10.77HE damage / grenade
0.61Flashes / 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. 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 11.0308% — below the 15% mark we flag

    Aim Training →
  2. 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 0.3009 — below the 0.5 mark we flag

    Grenade Lineups →
  3. 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 24.1379% — below the 40% mark we flag

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.

41% win rate across 32 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
train10–13-0.138%4 Nov →
ancient7–13-0.0610%4 Nov →
ancient12–12-0.0416%2 Nov →
dust213–6-0.0319%2 Nov →
dust213–70.004%13 Oct →
ancient7–13-0.050%12 Oct →
anubis13–10-0.057%12 Oct →
train13–70.054%12 Oct →
anubis5–13-0.055%8 Oct →
dust28–13-0.0014%8 Oct →
anubis8–13-0.024%7 Oct →
train13–5-0.0110%7 Oct →
dust213–11-0.027%7 Oct →
dust212–120.1019%7 Oct →
ancient6–13-0.049%7 Oct →
anubis6–130.0110%7 Oct →
dust213–10.0526%6 Oct →
nuke13–50.079%6 Oct →
train12–120.0112%6 Oct →
dust213–11-0.0016%5 Oct →
train13–10.1438%5 Oct →
train0–4-0.0733%5 Oct →
vertigo0–13-0.1113%5 Oct →
ancient13–10-0.018%5 Oct →
anubis13–30.0517%5 Oct →
dust24–13-0.044%24 Sept →
dust213–2-0.0410%15 Sept →
nuke13–9-0.026%15 Sept →
ancient13–6-0.0214%15 Sept →
train12–120.0310%14 Sept →
ancient13–80.042%14 Sept →
ancient13–80.0129%9 Sept →
train13–7-0.016%9 Sept →
dust25–130.0214%9 Sept →
anubis13–5-0.004%8 Sept →
ancient8–13-0.0918%8 Sept →
train8–13-0.080%8 Sept →
train13–11-0.054%7 Sept →
dust23–13-0.090%7 Sept →
dust28–13-0.0314%3 Sept →
anubis13–70.045%3 Sept →
train4–13-0.035%3 Sept →
ancient13–10.0322%3 Sept →
anubis6–13-0.0612%1 Sept →
nuke13–5-0.023%1 Sept →
overpass6–13-0.0313%1 Sept →
train12–12-0.018%31 Aug →
dust25–13-0.0327%31 Aug →
train13–10-0.0615%28 Aug →
mirage7–13-0.078%28 Aug →
inferno12–12-0.0212%28 Aug →
anubis13–7-0.075%28 Aug →
ancient13–6-0.066%28 Aug →
train13–30.046%28 Aug →
dust27–13-0.1012%28 Aug →
dust24–13-0.1117%26 Aug →
train13–3-0.0610%26 Aug →
anubis11–13-0.036%26 Aug →
vertigo13–8-0.066%25 Aug →
dust27–13-0.105%25 Aug →
ancient5–13-0.0223%25 Aug →
train13–100.0410%25 Aug →
dust27–13-0.030%25 Aug →
anubis7–13-0.058%24 Aug →
ancient13–6-0.0710%24 Aug →
train13–110.0510%24 Aug →
dust28–13-0.0219%24 Aug →
dust22–13-0.0616%24 Aug →
ancient5–13-0.0422%21 Aug →
dust213–9-0.0017%21 Aug →
dust213–50.0216%20 Aug →
inferno13–6-0.0718%20 Aug →
anubis13–60.0514%20 Aug →
dust212–12-0.063%20 Aug →
train13–2-0.0212%18 Aug →
mirage6–130.0111%18 Aug →
dust212–120.0510%18 Aug →
dust213–100.0123%17 Aug →
overpass13–9-0.024%17 Aug →
train6–13-0.0513%17 Aug →
ancient13–80.0211%17 Aug →
dust213–8-0.0223%17 Aug →
inferno6–13-0.090%13 Aug →
dust28–13-0.064%13 Aug →
dust29–13-0.077%12 Aug →
dust24–13-0.0618%12 Aug →
ancient13–7-0.054%11 Aug →
train13–20.0311%11 Aug →
inferno13–20.0512%11 Aug →
dust213–90.0026%11 Aug →
mirage13–90.0013%10 Aug →
ancient13–100.049%10 Aug →
anubis13–7-0.057%10 Aug →
train12–120.0219%10 Aug →
dust213–10-0.0218%10 Aug →
train13–90.0111%5 Aug →
ancient12–12-0.0410%5 Aug →
dust210–13-0.0419%5 Aug →
train11–13-0.075%5 Aug →
dust213–11-0.0136%5 Aug →

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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