ChinnyChin01

ChinnyChin01 — CS2 Stats

US76561198095247177[U:1:134981449]Steam profile ↗✓ No bans

90Tracked matches37%Win rate2020Tracked since
CSDB Rating2.3 LearningPositional Player
Ladder ranks via Leetify

Performance scores

Aim17
Positioning35
Utility20

0–100 skill scores via Leetify.

Recent form

STEADY38–50–2Last 9042%Win rateLWWWLLWLLW

Last 10 vs previous 10: +20pp win rate · +0.00 avg rating · +1.9pp headshot accuracy · −10ms 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

  • 3–2 · 60% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 9%
  • Avg reaction 733ms

Last 10

  • 5–5 · 50% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 12%
  • Avg reaction 702ms

Last 20

  • 8–12 · 40% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 11%
  • Avg reaction 707ms

Newest first, from the last 90 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 (+1.7 against its own average).

Aim1.7
Utility2.0
Positioning3.5
Opening Duels0.0

Limited 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

jL

Plays most like jL 71% playstyle similarity

Most alike: positioning profile, utility contribution.

Where you differ: lower opening-duel success; 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. 742ms 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

Aim1.7
Positioning3.5
Utility2.0
Mechanics4.5
Opening Duels0.1
Win Impact0.6

Composite 2.3/10 (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-0.05+0.01
first ⅓ avg -0.06 → last ⅓ avg -0.05
Reaction time748ms−15ms
first ⅓ avg 763ms → last ⅓ avg 748ms
Headshot accuracy10.7%−0.0%
first ⅓ avg 10.7% → last ⅓ avg 10.7%

Rolling 5-match average across the last 90 tracked matches, oldest to newest. The delta compares the first third of the window with the last.

Personal bests

0.03Best match rating · 13–7 · dust2, 8 Jul →
27%Best headshot accuracy · 13–5 · dust2, 20 Jun →
500msFastest reaction time · 8–13 · inferno, 2 Jul →
13–2Biggest win · mirage, 8 Jul →

Across the last 90 tracked matches.

Highlights

5Longest win streak
4–7In matches decided by ≤2 rounds
6Overtime games

Map breakdown

nukeBest map · 63% over 8infernoWeakest map · 25% over 8
MapGradePlayedRecordWin rateAvg rating
dust2B3217–1553%-0.05
cacheD175–1229%-0.06
mirageB115–645%-0.05
infernoD82–625%-0.06
nukeA85–363%-0.05
ancientD72–529%-0.08
anubis—21–150%-0.02
vertigo—20–20%-0.07
office—21–150%-0.09
overpass—10–10%-0.08

Across the last 90 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (25% over 8). Start with the 6 essential Inferno 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.

11.2%Headshot accuracy
28.1%Accuracy (enemy spotted)
26.4%Spray accuracy
70.2%Counter-strafing
12.1°Preaim
742msReaction time
19.2%T opening success
31.2%CT opening success
0.42Enemies flashed / flash
5.9%Flash assists
1.59HE damage / grenade
2.13Flashes / 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 12.097° — 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 11.2022% — 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 742.4694ms — above the 700ms mark we flag

    Aim Training →
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.

25% win rate across 8 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
inferno8–13-0.044%9 Aug →
dust216–13-0.038%8 Aug →
anubis13–10-0.0611%8 Aug →
dust213–9-0.0710%8 Aug →
anubis8–130.0211%25 Jul →
mirage8–13-0.087%25 Jul →
mirage13–10-0.0722%25 Jul →
inferno11–13-0.0713%25 Jul →
vertigo5–13-0.0914%25 Jul →
nuke13–9-0.0219%25 Jul →
nuke5–13-0.100%25 Jul →
mirage13–60.018%24 Jul →
dust216–13-0.0418%24 Jul →
cache3–13-0.064%24 Jul →
mirage11–13-0.094%24 Jul →
ancient7–13-0.098%24 Jul →
inferno5–13-0.0212%15 Jul →
nuke11–13-0.0417%15 Jul →
cache12–16-0.0613%15 Jul →
nuke13–3-0.0516%13 Jul →
cache8–13-0.0611%13 Jul →
dust26–13-0.105%13 Jul →
ancient5–13-0.0911%13 Jul →
dust28–13-0.0624%13 Jul →
mirage1–13-0.085%10 Jul →
dust213–90.0122%10 Jul →
cache10–13-0.0710%10 Jul →
inferno13–3-0.0111%10 Jul →
dust29–13-0.064%10 Jul →
inferno5–3-0.060%10 Jul →
mirage8–13-0.118%10 Jul →
nuke13–90.0218%10 Jul →
mirage2–13-0.025%10 Jul →
dust213–70.0322%8 Jul →
mirage13–2-0.030%8 Jul →
dust213–6-0.0213%8 Jul →
dust28–13-0.068%8 Jul →
dust26–13-0.085%8 Jul →
dust20–13-0.0912%8 Jul →
cache7–13-0.0523%7 Jul →
dust210–13-0.0812%3 Jul →
mirage13–11-0.0314%3 Jul →
cache4–13-0.0513%3 Jul →
inferno8–13-0.1119%2 Jul →
nuke13–8-0.0619%2 Jul →
cache13–4-0.0521%2 Jul →
nuke0–7-0.120%30 Jun →
dust27–13-0.1115%27 Jun →
overpass7–13-0.0810%27 Jun →
cache4–13-0.0319%27 Jun →
dust213–11-0.076%27 Jun →
dust213–80.0311%27 Jun →
cache13–20.0214%26 Jun →
cache9–13-0.0611%20 Jun →
ancient5–13-0.0915%20 Jun →
dust213–5-0.0427%20 Jun →
inferno10–13-0.0813%20 Jun →
dust211–13-0.0311%20 Jun →
ancient10–13-0.1111%20 Jun →
dust29–13-0.136%14 Jun →
cache10–13-0.126%13 Jun →
mirage13–3-0.0516%13 Jun →
cache11–13-0.0824%13 Jun →
ancient13–50.015%12 Jun →
dust213–6-0.0611%12 Jun →
ancient13–9-0.0510%12 Jun →
cache13–6-0.0711%12 Jun →
nuke13–10-0.0511%11 Jun →
dust210–13-0.0813%11 Jun →
dust213–11-0.044%11 Jun →
cache11–13-0.0612%11 Jun →
dust213–50.0021%10 Jun →
vertigo8–13-0.054%10 Jun →
dust211–13-0.0710%10 Jun →
dust23–13-0.089%4 Jun →
cache12–12-0.0614%4 Jun →
office5–13-0.1017%28 May →
dust213–9-0.0721%26 May →
cache13–9-0.045%26 May →
office13–4-0.093%11 May →
mirage4–13-0.069%11 May →
cache13–5-0.110%11 May →
ancient9–13-0.109%14 Mar →
dust213–6-0.0710%14 Mar →
dust213–7-0.0114%3 Feb →
dust26–13-0.089%3 Feb →
dust213–6-0.0818%18 Oct →
dust29–16-0.057%4 Jun →
inferno15–15-0.0710%4 Jun →
dust216–120.009%26 Nov →

Match data via Leetify.

Recent teammates

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

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