Y35 Cr4ck Fl0ppy

Y35 Cr4ck Fl0ppy — CS2 Stats

TJ76561198442441422[U:1:482175694]Steam profile ↗✓ No bans

190Tracked matches30%Win rate2020Tracked since
CSDB Rating2.7 LearningPositional Player
WingmanGold Nova III
Ladder ranks via Leetify

Rating over time

Premier CS Rating: 4,487 -2,152 22 Feb – 17 Apr · 14 days played
4,4876,639peak 6,63922 Feb17 Apr
6,639Peak Premier in tracked matches
55Days played since 2024-09-16

Premier comes from this profile’s tracked match history, so it reaches back as far as those matches do.

Performance scores

Aim22
Positioning40
Utility28

0–100 skill scores via Leetify.

Recent form

STEADY41–53–6Last 10041%Win rateWLWLWLLWLL

Last 10 vs previous 10: 0pp win rate · −0.01 avg rating · −3.6pp headshot accuracy · −55ms 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

  • 3–2 · 60% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 13%
  • Avg reaction 758ms

Last 10

  • 4–6 · 40% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 10%
  • Avg reaction 638ms

Last 20

  • 8–12 · 40% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 12%
  • Avg reaction 665ms

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 (+1.4 against its own average).

Aim2.2
Utility2.8
Positioning4.0
Opening Duels0.9

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

T-side openings. Opening success drops from 40% on CT to 23% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 687ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 70% of shots are taken properly stopped — moving-shot inaccuracy is quietly taxing every duel.

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

Aim2.2
Positioning4.0
Utility2.8
Mechanics4.4
Opening Duels1.2
Win Impact0.0

Composite 2.7/10 (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-0.03−0.01
first ⅓ avg -0.02 → last ⅓ avg -0.03
Reaction time679ms−39ms
first ⅓ avg 718ms → last ⅓ avg 679ms
Headshot accuracy11.5%+0.1%
first ⅓ avg 11.4% → last ⅓ avg 11.5%

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

0.12Best match rating · 5–0 · vertigo, 5 Dec →
38%Best headshot accuracy · 2–13 · mirage, 2 Apr →
391msFastest reaction time · 2–13 · mirage, 2 Apr →
13–1Biggest win · mirage, 22 Feb →

Across the last 100 tracked matches.

Highlights

5Longest win streak
4–6In matches decided by ≤2 rounds
1Overtime games

Map breakdown

mirageBest map · 50% over 6trainWeakest map · 17% over 6
MapGradePlayedRecordWin rateAvg rating
dust2C5122–2943%-0.02
infernoB157–847%-0.01
anubisD103–730%-0.04
mirageB63–350%-0.03
ancientD62–433%-0.04
trainD61–517%-0.05
vertigo—31–233%-0.01
nuke—10–10%0.00
edin—11–0100%-0.01
basalt—11–0100%-0.01

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.

12.1%Headshot accuracy
27.5%Accuracy (enemy spotted)
32.2%Spray accuracy
69.7%Counter-strafing
12.8°Preaim
687msReaction time
22.5%T opening success
40.0%CT opening success
0.60Enemies flashed / flash
9.2%Flash assists
8.15HE damage / grenade
2.99Flashes / 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 12.8217° — 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 12.0889% — below the 15% mark we flag

    Aim Training →
  3. Grenades & Utility

    You are buying and holding utility rather than using it. Unthrown flashes are wasted money every round.

    Flashes per match 2.9929 — below the 4 mark we flag

    Grenade Lineups →
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.

17% win rate across 6 tracked games — your weakest map with enough games to be worth reading into.

Train callouts & strategy →

Recent matches

MapScoreRatingHS%Date
anubis13–5-0.0613%21 May →
dust29–13-0.0217%21 May →
dust213–60.0010%20 May →
dust23–13-0.075%20 May →
mirage13–50.0318%15 May →
dust29–13-0.0215%15 May →
ancient7–13-0.0213%15 May →
inferno13–110.000%14 May →
dust211–13-0.013%14 May →
dust28–13-0.108%11 May →
dust213–5-0.0213%19 Apr →
inferno11–130.0513%17 Apr →
mirage2–13-0.0338%2 Apr →
inferno13–8-0.0314%28 Mar →
inferno13–2-0.0010%28 Mar →
anubis10–13-0.0313%25 Mar →
dust27–13-0.056%24 Mar →
anubis13–30.037%22 Mar →
ancient11–13-0.0712%16 Mar →
dust29–13-0.0412%16 Mar →
ancient6–13-0.1410%8 Mar →
mirage2–13-0.0810%7 Mar →
inferno10–13-0.038%7 Mar →
anubis6–13-0.044%6 Mar →
dust24–13-0.0411%6 Mar →
anubis8–13-0.0731%4 Mar →
mirage10–13-0.069%2 Mar →
inferno3–13-0.058%23 Feb →
mirage13–1-0.0611%22 Feb →
train3–13-0.0911%22 Feb →
dust213–40.023%22 Feb →
ancient13–40.1013%21 Feb →
dust213–30.0610%20 Feb →
ancient11–13-0.0812%19 Feb →
dust213–3-0.0511%19 Feb →
train10–13-0.076%17 Feb →
dust24–13-0.1010%17 Feb →
train13–30.048%17 Feb →
inferno3–13-0.0618%16 Feb →
dust213–80.0318%16 Feb →
anubis7–130.0112%16 Feb →
anubis13–7-0.0312%16 Feb →
dust213–70.019%16 Feb →
inferno7–13-0.0718%12 Feb →
dust28–13-0.1024%12 Feb →
dust29–13-0.0014%12 Feb →
dust24–13-0.0810%11 Feb →
nuke14–160.008%9 Feb →
inferno10–130.0314%9 Feb →
dust21–13-0.0416%9 Feb →
mirage13–80.0111%9 Feb →
inferno13–90.029%2 Feb →
inferno10–130.0211%2 Feb →
ancient13–4-0.033%30 Jan →
dust213–10.0220%22 Jan →
dust213–10.058%22 Jan →
dust213–8-0.087%18 Jan →
dust27–13-0.0513%8 Jan →
dust210–13-0.0410%6 Jan →
inferno8–13-0.0010%5 Jan →
dust23–13-0.0820%5 Jan →
dust213–110.047%5 Jan →
dust213–70.089%23 Dec →
dust28–130.1015%22 Dec →
dust212–12-0.008%22 Dec →
dust20–13-0.0811%19 Dec →
dust24–13-0.105%16 Dec →
dust212–120.009%11 Dec →
anubis6–13-0.038%9 Dec →
dust26–13-0.057%9 Dec →
dust213–5-0.048%9 Dec →
dust210–130.0320%8 Dec →
train4–13-0.0718%8 Dec →
dust213–100.0011%8 Dec →
vertigo12–12-0.0411%8 Dec →
inferno13–7-0.013%8 Dec →
dust213–10-0.017%7 Dec →
vertigo3–13-0.115%6 Dec →
inferno13–90.039%6 Dec →
dust213–110.0010%6 Dec →
dust211–130.039%5 Dec →
dust212–12-0.0213%5 Dec →
vertigo5–00.1221%5 Dec →
dust213–60.0114%2 Dec →
dust213–60.009%2 Dec →
inferno13–7-0.1118%1 Dec →
dust213–110.0717%1 Dec →
train9–13-0.0215%24 Nov →
dust29–13-0.077%24 Nov →
anubis1–13-0.1016%24 Nov →
dust213–6-0.0014%24 Nov →
train4–13-0.1019%21 Nov →
dust23–13-0.0714%19 Nov →
edin13–8-0.0111%14 Nov →
basalt5–2-0.015%14 Nov →
dust213–20.0713%20 Oct →
dust213–6-0.0115%10 Oct →
dust212–12-0.0610%19 Sept →
anubis12–12-0.076%19 Sept →
dust26–13-0.056%16 Sept →

Match data via Leetify.

Recent teammates

Milestones

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

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