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Catrick — CS2 Stats

76561198008192583[U:1:47926855]

94Tracked matches36%Win rate2021Tracked since
CSDB Rating3.2 Learning
WingmanGold Nova Master
Ladder ranks via Leetify

Performance scores

Aim37
Positioning34
Utility35

0–100 skill scores via Leetify.

Recent form

COLD45–42–7Last 9448%Win rateLLWLLLLLWL

Last 10 vs previous 10: +0pp win rate · -0.04 avg rating

Player DNA

Aim3.7
Aggression1.9
Utility3.5
Positioning3.4
Opening Duels0.0

Style profile from tracked-match aggregates — how this player plays, not how good they are. Classification rules are deterministic and documented in code.

Your pro match

NiKo

Plays most like NiKo 68% playstyle similarity

Most alike: opening-duel success, positioning profile.

Where you differ: lower opening-fight frequency; 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. 705ms 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

Aim3.7
Positioning3.4
Utility3.5
Mechanics5.8
Opening Duels0.2
Win Impact0.2

Composite 3.2/10 (Learning), 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-0.02+0.00
first ⅓ avg -0.02 → last ⅓ avg -0.02
Reaction time703ms−41ms
first ⅓ avg 744ms → last ⅓ avg 703ms
Headshot accuracy11.7%+0.3%
first ⅓ avg 11.4% → last ⅓ avg 11.7%

Rolling 5-match average across the last 94 tracked matches, oldest to newest. The delta compares the first third of the window with the last.

Highlights

0.35Best rating — inferno 9–3
13–1Biggest win — inferno
8Longest win streak
L2Current streak
5–4In matches decided by ≤2 rounds
11Overtime games

Map breakdown

trainBest map · 67% over 12ancientWeakest map · 31% over 16
MapGradePlayedRecordWin rateAvg rating
infernoS2013–765%0.01
ancientD165–1131%0.01
nukeA169–756%-0.02
trainS128–467%0.03
overpassC83–538%0.05
mirageC73–443%-0.05
dust2—41–325%-0.05
cache—31–233%-0.03
anubis—21–150%-0.02
vertigo—20–20%-0.00
grail—10–10%-0.00
whistle—11–0100%0.18
palais—10–10%-0.04
basalt—10–10%-0.10

Across the last 94 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (31% over 16). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Skill profile

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

12.0%Headshot accuracy
34.9%Accuracy (enemy spotted)
36.0%Spray accuracy
76.0%Counter-strafing
13.1°Preaim
705msReaction time
19.3%T opening success
31.5%CT opening success
0.36Enemies flashed / flash
7.7%Flash assists
6.91HE damage / grenade
6.78Flashes / 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

    Your crosshair sits further from where enemies appear than it needs to. Crosshair placement is the cheapest accuracy you can buy.

    Preaim 13.0993° — above the 12° mark we flag

    Aim Training →
  2. 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 12.0318% — below the 15% mark we flag

    Aim Training →
  3. Best CS2 Settings

    Slow first shots are as often a setup problem as a reflex one — framerate, sensitivity and crosshair visibility all move this number.

    Reaction time 705.3541ms — above the 700ms mark we flag

    Aim Training →
Spend your practice time on Ancient

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

31% win rate across 16 tracked games — your weakest map with enough games to be worth reading into.

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

MapScoreRatingHS%Date
dust211–13-0.036%10 Aug →
ancient10–13-0.0712%10 Aug →
cache13–6-0.019%10 Aug →
mirage7–13-0.0313%10 Aug →
inferno4–13-0.093%10 Aug →
cache7–13-0.0312%9 Aug →
anubis2–13-0.116%1 Aug →
cache9–13-0.043%1 Aug →
nuke16–13-0.067%1 Aug →
ancient5–13-0.077%1 Aug →
ancient13–40.0215%21 Jun →
train12–12-0.0714%21 Jun →
dust212–12-0.0719%21 Jun →
nuke7–13-0.0714%28 Dec →
overpass3–13-0.036%28 Dec →
overpass13–7-0.0314%27 Dec →
ancient13–160.0420%4 Dec →
nuke10–130.0412%4 Dec →
nuke9–130.0310%21 Nov →
ancient10–13-0.0122%21 Nov →
inferno16–13-0.0413%19 Nov →
inferno9–13-0.0411%19 Nov →
ancient13–70.0212%19 Nov →
ancient6–130.0424%1 Nov →
ancient13–70.002%1 Nov →
nuke13–90.0719%1 Nov →
ancient9–130.0611%28 Oct →
inferno13–50.0417%28 Oct →
nuke13–110.0014%28 Oct →
ancient7–13-0.018%28 Oct →
overpass8–13-0.038%28 Oct →
ancient11–130.0311%10 Oct →
inferno16–140.0615%10 Oct →
train13–50.179%9 Oct →
overpass13–30.1712%6 Oct →
inferno13–11-0.0112%6 Oct →
nuke15–150.0912%7 Sept →
overpass13–80.1113%7 Sept →
ancient8–13-0.062%24 Aug →
ancient15–150.1311%24 Aug →
inferno13–110.0620%24 Aug →
ancient13–60.1014%30 May →
anubis13–80.078%30 May →
grail11–13-0.0013%30 May →
train10–130.1016%30 May →
train13–100.0711%29 May →
nuke13–8-0.109%16 May →
inferno13–60.0417%16 May →
nuke13–50.0216%7 May →
train13–60.1010%7 Apr →
train6–00.1120%7 Apr →
ancient13–30.0816%7 Apr →
whistle9–50.189%6 Apr →
palais5–9-0.0424%6 Apr →
inferno9–30.3524%6 Apr →
basalt8–13-0.1018%28 Mar →
dust213–6-0.048%28 Mar →
vertigo8–13-0.072%27 Mar →
inferno15–15-0.0514%25 Mar →
nuke13–2-0.026%25 Mar →
nuke8–13-0.0514%22 Mar →
inferno9–13-0.0616%22 Mar →
nuke7–13-0.092%21 Mar →
inferno7–13-0.034%21 Mar →
inferno13–50.0026%18 Mar →
mirage1–13-0.070%18 Mar →
inferno13–8-0.0411%18 Mar →
inferno6–13-0.073%18 Mar →
train4–13-0.074%16 Mar →
mirage4–13-0.103%13 Mar →
nuke13–8-0.080%13 Mar →
mirage13–10-0.0712%13 Mar →
mirage11–30.0117%12 Mar →
train13–40.013%12 Mar →
mirage16–14-0.0114%12 Mar →
inferno13–10.0318%11 Mar →
train13–2-0.038%11 Mar →
inferno10–130.0212%10 Mar →
nuke13–70.0122%10 Mar →
train13–10-0.108%10 Mar →
train13–30.083%9 Mar →
inferno13–10-0.0014%9 Mar →
inferno13–8-0.0314%8 Mar →
nuke12–16-0.074%8 Mar →
dust210–13-0.0510%8 Mar →
ancient5–13-0.095%8 Mar →
train4–13-0.0325%8 Mar →
inferno13–9-0.0312%14 Feb →
mirage2–9-0.0523%17 Sept →
vertigo7–90.0612%23 Jul →
overpass8–80.1020%14 May →
overpass3–90.0011%25 Mar →
overpass15–150.0715%13 Sept →
nuke16–90.0222%13 Sept →

Match data via Leetify.

Recent teammates

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