meowmeowcow

meowmeowcow — CS2 Stats

CA76561198000813202[U:1:40547474]Steam profile ↗✓ No bans

605Tracked matches50%Win rate2021Tracked since
CSDB Rating5.8 SolidSupport
Ladder ranks via Leetify

Performance scores

Aim69
Positioning50
Utility52

0–100 skill scores via Leetify.

Recent form

STEADY45–44–11Last 10045%Win rateLLWLLTWWLW

Last 10 vs previous 10: 0pp win rate · −0.02 avg rating · −1.8pp headshot accuracy · +61ms reaction

Win rate 0pp 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

  • 1–4 · 20% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 12%
  • Avg reaction 667ms

Last 10

  • 4–5–1 · 40% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 14%
  • Avg reaction 663ms

Last 20

  • 8–11–1 · 40% win rate
  • Avg rating 0.02
  • Avg headshot accuracy 15%
  • Avg reaction 633ms

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: Support — Utility contribution stands above the rest of this profile (+1 against its own average).

Aim6.9
Utility5.2
Positioning5.0
Opening Duels2.5

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

ZywOo

Plays most like ZywOo 94% playstyle similarity

Most alike: opening-duel success, positioning profile.

Where you differ: lower utility contribution; 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

Areas to improve

Reaction time. 638ms 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

Aim6.9
Positioning5.0
Utility5.2
Mechanics8.2
Opening Duels2.0
Win Impact5.0

Composite 5.8/10 (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 rating0.02+0.01
first ⅓ avg 0.01 → last ⅓ avg 0.02
Reaction time649ms−11ms
first ⅓ avg 661ms → last ⅓ avg 649ms
Headshot accuracy14.6%−4.0%
first ⅓ avg 18.5% → last ⅓ avg 14.6%

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

0.27Best match rating · 13–1 · cache, 14 May →
35%Best headshot accuracy · 7–2 · inferno, 5 Aug →
492msFastest reaction time · 2–13 · cache, 4 May →
13–1Biggest win · cache, 14 May →

Across the last 100 tracked matches.

Highlights

5Longest win streak
L2Current streak
3–3In matches decided by ≤2 rounds
3Overtime games

Map breakdown

ancientBest map · 73% over 11trainWeakest map · 20% over 5
MapGradePlayedRecordWin rateAvg rating
dust2B3817–2145%0.02
cacheB209–1145%0.00
ancientS118–373%0.03
infernoC73–443%0.04
nukeB63–350%-0.03
mirageD62–433%-0.00
trainD51–420%0.02
overpass—31–233%-0.01
anubis—20–20%-0.00
grail—11–0100%-0.00
jura—10–10%-0.03

Across the last 100 tracked matches.

Skill profile

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

15.0%Headshot accuracy
34.7%Accuracy (enemy spotted)
37.5%Spray accuracy
87.1%Counter-strafing
9.0°Preaim
638msReaction time
41.6%T opening success
34.0%CT opening success
0.57Enemies flashed / flash
9.2%Flash assists
5.75HE damage / grenade
7.55Flashes / 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 14.996% — below the 15% mark we flag

    Aim Training →
  2. Advanced Mechanics

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

    CT opening duels 33.9937% — below the 40% mark we flag

Spend your practice time on Train

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

20% win rate across 5 tracked games — your weakest map with enough games to be worth reading into.

Train callouts & strategy →

Recent matches

MapScoreRatingHS%Date
dust25–13-0.025%13 Sept →
anubis14–16-0.0112%8 Sept →
ancient7–1-0.0219%28 Jul →
dust20–40.010%28 Jul →
dust25–13-0.0027%28 Jul →
dust215–150.0215%17 Jun →
dust213–60.0919%16 Jun →
dust213–110.0821%13 Jun →
cache6–13-0.0010%13 Jun →
nuke13–5-0.0610%2 Jun →
ancient13–50.1116%2 Jun →
cache5–13-0.0523%1 Jun →
dust29–13-0.0019%1 Jun →
dust213–80.1418%1 Jun →
dust25–130.0616%28 May →
cache13–70.0214%28 May →
dust213–40.0513%25 May →
anubis6–130.0116%20 May →
cache11–13-0.0212%20 May →
dust210–13-0.038%19 May →
cache13–11-0.0110%14 May →
dust213–80.0529%14 May →
cache13–10.2720%14 May →
cache9–130.0113%13 May →
inferno8–130.0616%13 May →
cache13–8-0.058%12 May →
dust212–12-0.0417%12 May →
cache13–3-0.0013%9 May →
dust213–80.0414%8 May →
dust28–13-0.0113%8 May →
dust211–130.0212%8 May →
cache5–13-0.0813%7 May →
dust213–5-0.0412%7 May →
mirage8–13-0.0614%5 May →
ancient16–120.0322%5 May →
dust212–12-0.0014%4 May →
cache0–13-0.0714%4 May →
cache2–13-0.0221%4 May →
inferno10–40.1020%2 May →
dust27–2-0.0315%2 May →
cache12–12-0.0116%2 May →
ancient13–60.0012%2 May →
mirage12–120.0412%2 May →
nuke13–20.0214%2 May →
cache3–13-0.0312%2 May →
cache13–100.0316%2 May →
cache6–130.0113%2 May →
train13–7-0.0326%1 May →
cache13–11-0.0010%1 May →
cache13–40.0719%1 May →
cache13–6-0.0110%30 Apr →
cache10–130.0211%30 Apr →
nuke2–13-0.0720%24 Jan →
ancient12–120.0518%23 Jan →
dust29–130.0321%21 Jan →
mirage12–12-0.0115%18 Jan →
dust23–130.0112%10 Jan →
dust24–13-0.0412%7 Jan →
nuke3–13-0.0417%7 Jan →
dust213–80.0421%8 Oct →
dust22–130.0017%8 Oct →
overpass13–7-0.0217%8 Oct →
ancient12–12-0.0311%17 Sept →
nuke5–13-0.0118%17 Sept →
dust21–13-0.0714%16 Sept →
train12–120.0715%14 Aug →
dust213–10.1019%14 Aug →
dust213–100.0216%14 Aug →
train9–130.0926%11 Aug →
ancient13–90.0617%9 Aug →
dust213–50.1029%9 Aug →
mirage13–50.0313%7 Aug →
grail13–2-0.0014%5 Aug →
inferno7–20.0335%5 Aug →
dust210–130.0214%5 Aug →
inferno9–130.0216%5 Aug →
overpass2–13-0.0219%4 Aug →
inferno10–130.0121%4 Aug →
jura4–13-0.0318%3 Aug →
ancient13–90.0726%3 Aug →
inferno13–30.0719%2 Aug →
ancient13–90.0324%2 Aug →
nuke13–9-0.039%1 Aug →
train12–12-0.0213%1 Aug →
dust213–10-0.0220%1 Aug →
dust27–13-0.0318%31 Jul →
inferno6–13-0.0215%31 Jul →
mirage9–130.0414%31 Jul →
overpass12–120.0211%31 Jul →
dust26–130.0118%31 Jul →
dust213–40.0320%30 Jul →
dust27–130.0431%28 Jul →
dust213–90.0111%28 Jul →
ancient6–130.0122%28 Jul →
mirage13–7-0.0715%26 Jul →
dust213–90.0021%26 Jul →
train10–13-0.0322%26 Jul →
dust213–60.0017%21 Jun →
dust28–13-0.0411%7 Jun →
ancient13–80.0314%7 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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