slim chuddy — CS2 Stats

PL76561198134651860[U:1:174386132]Steam profile ↗✓ No bans

460Tracked matches41%Win rate2020Tracked since

784Hours in CS11Hrs last 2 wks

CSDB Rating4.2 DevelopingPositional Player

Ladder ranks via Leetify

Performance scores

Aim64

Positioning47

Utility27

0–100 skill scores via Leetify.

Recent form

COLD35–56–9Last 10035%Win rateLLLLLWWLWL

Last 10 vs previous 10: −10pp win rate · −0.01 avg rating · −1.1pp headshot accuracy · −44ms 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

0–5 · 0% win rate
Avg rating -0.01
Avg headshot accuracy 14%
Avg reaction 547ms

Last 10

3–7 · 30% win rate
Avg rating -0.01
Avg headshot accuracy 12%
Avg reaction 563ms

Last 20

7–12–1 · 35% win rate
Avg rating -0.00
Avg headshot accuracy 13%
Avg reaction 585ms

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 (+1.5 against its own average).

Aim6.4

Utility2.7

Positioning4.7

Opening Duels2.2

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

Plays most like donk 95% playstyle similarity

Most alike: aim profile, utility contribution.

Where you differ: lower positioning profile; lower opening-duel success.

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

Utility. Utility contribution runs far behind the mechanical game — the cheapest rating gain on this profile is thrown, not aimed.

Reaction time. 585ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 65% 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

Aim6.4

Positioning4.7

Utility2.7

Mechanics3.4

Opening Duels2.3

Win Impact2.1

Composite 4.2/10 (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-0.01−0.02

first ⅓ avg 0.01 → last ⅓ avg -0.01

Reaction time597ms−23ms

first ⅓ avg 620ms → last ⅓ avg 597ms

Headshot accuracy13.2%+1.1%

first ⅓ avg 12.1% → last ⅓ avg 13.2%

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

0.17Best match rating · 13–4 · boulder, 6 Sept →

31%Best headshot accuracy · 13–4 · ancient, 11 Sept →

438msFastest reaction time · 7–13 · office, 12 Sept →

13–0Biggest win · nuke, 29 Sept →

Across the last 100 tracked matches.

Highlights

3Longest win streak

L5Current streak

4–11In matches decided by ≤2 rounds

1Overtime games

Map breakdown

dust2Best map · 50% over 6italyWeakest map · 0% over 6

Map	Grade	Played	Record	Win rate	Avg rating
office	D	41	13–28	32%	0.01
cache	C	8	3–5	38%	-0.00
mirage	C	7	3–4	43%	-0.02
italy	D	6	0–6	0%	-0.04
dust2	B	6	3–3	50%	0.01
train	D	6	0–6	0%	-0.00
anubis	D	6	2–4	33%	-0.01
nuke	C	5	2–3	40%	0.04
vertigo	—	4	2–2	50%	0.04
overpass	—	3	2–1	67%	0.00
ancient	—	3	3–0	100%	0.00
inferno	—	3	1–2	33%	0.04
boulder	—	1	1–0	100%	0.17
alpine	—	1	0–1	0%	0.05

Across the last 100 tracked matches.

Lifetime stats

16,883Lifetime kills

0.99K/D

1,031Matches

39.6%Match win rate

36.4%Headshot %

10.8%Shot accuracy

1,580MVPs

363Hours (in match)

864Bombs planted

168Bombs defused

Most-used weapons

AK-474,293

M4A4 / M4A1-S3,388

Desert Eagle1,165

AWP1,000

P2000 / USP-S716

Galil AR535

P90517

Five-SeveN446

P250420

MP9405

Lifetime map wins

1,548dust2

897office

788nuke

761inferno

218vertigo

184train

147italy

54cbble

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

Faceit stats

Combat

2Matches

0%Win rate

0.26Avg K/D

—ADR

50%Headshot %

Clutches & streaks

—1v1 clutch win

—1v2 clutch win

0Longest win streak

Recent Faceit resultsLLLLL

Faceit-match stats via the FACEIT Data API — a separate match pool from the sections above.

Skill profile

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

12.6%Headshot accuracy

36.3%Accuracy (enemy spotted)

38.0%Spray accuracy

65.4%Counter-strafing

7.2°Preaim

585msReaction time

37.6%T opening success

41.2%CT opening success

0.30Enemies flashed / flash

7.0%Flash assists

3.50HE damage / grenade

5.28Flashes / 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.

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 12.6306% — below the 15% mark we flag
Aim Training →
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 0.2978 — below the 0.5 mark we flag
Grenade Lineups →
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 37.5823% — 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.

0% win rate across 6 tracked games — your weakest map with enough games to be worth reading into.

Train callouts & strategy →

Recent matches

Map	Score	Rating	HS%	Date
cache	3–13	-0.02	31%	11 Oct →
italy	10–13	0.02	2%	11 Oct →
office	8–13	-0.00	11%	11 Oct →
office	11–13	-0.01	16%	11 Oct →
dust2	6–13	-0.03	11%	9 Oct →
mirage	13–5	-0.07	7%	6 Oct →
overpass	12–3	0.02	3%	5 Oct →
mirage	6–13	0.01	19%	5 Oct →
office	13–7	-0.01	12%	3 Oct →
office	9–13	-0.02	10%	3 Oct →
italy	8–13	-0.01	11%	3 Oct →
office	9–13	0.04	13%	1 Oct →
dust2	14–16	-0.02	11%	29 Sept →
nuke	13–0	0.08	11%	29 Sept →
office	9–13	0.04	15%	28 Sept →
office	13–10	-0.02	3%	28 Sept →
dust2	12–12	0.02	12%	24 Sept →
vertigo	13–8	-0.03	17%	24 Sept →
ancient	7–1	-0.04	25%	24 Sept →
mirage	9–13	-0.05	15%	23 Sept →
italy	10–13	-0.04	9%	23 Sept →
office	5–13	-0.03	18%	18 Sept →
train	2–13	-0.06	8%	14 Sept →
overpass	13–11	0.00	12%	14 Sept →
dust2	13–7	0.03	15%	13 Sept →
inferno	13–3	0.11	15%	13 Sept →
office	6–13	-0.09	18%	13 Sept →
office	13–9	-0.04	14%	13 Sept →
italy	11–13	-0.01	9%	13 Sept →
anubis	13–11	-0.04	16%	12 Sept →
office	9–13	-0.02	14%	12 Sept →
office	12–12	0.05	14%	12 Sept →
office	7–13	0.02	18%	12 Sept →
train	8–13	-0.01	10%	11 Sept →
ancient	13–4	0.02	31%	11 Sept →
office	13–9	0.05	12%	9 Sept →
office	13–7	0.14	16%	7 Sept →
anubis	5–13	-0.02	11%	7 Sept →
cache	2–12	0.01	17%	7 Sept →
office	13–3	0.03	5%	7 Sept →
vertigo	12–12	0.03	12%	6 Sept →
boulder	13–4	0.17	10%	6 Sept →
ancient	13–8	0.03	7%	2 Sept →
anubis	12–12	0.06	16%	1 Sept →
nuke	7–13	0.05	18%	1 Sept →
office	13–8	0.00	6%	25 Aug →
office	12–12	0.12	15%	25 Aug →
office	7–2	0.02	24%	25 Aug →
anubis	3–13	-0.07	12%	24 Aug →
nuke	13–9	-0.02	15%	24 Aug →
office	2–13	-0.03	13%	16 Aug →
office	8–13	0.08	4%	3 Jul →
office	13–6	0.09	16%	3 Jul →
office	5–13	-0.04	10%	2 Jul →
office	4–13	-0.06	7%	2 Jul →
office	10–13	-0.05	8%	30 Jun →
dust2	13–3	0.04	20%	30 Jun →
mirage	11–13	-0.06	16%	30 Jun →
mirage	13–6	-0.03	13%	30 Jun →
office	12–12	0.01	13%	27 Jun →
cache	13–6	0.02	15%	27 Jun →
office	6–13	-0.08	6%	27 Jun →
cache	12–12	-0.05	14%	25 Jun →
office	11–13	0.06	12%	25 Jun →
office	11–13	0.01	11%	25 Jun →
office	3–13	-0.02	13%	23 Jun →
alpine	3–13	0.05	6%	23 Jun →
office	6–13	-0.00	8%	22 Jun →
office	13–9	0.07	12%	22 Jun →
italy	3–13	-0.09	6%	21 Jun →
office	13–9	-0.03	9%	21 Jun →
anubis	12–12	0.00	8%	21 Jun →
train	10–13	0.01	13%	21 Jun →
vertigo	11–13	0.03	13%	21 Jun →
office	12–12	-0.02	4%	21 Jun →
cache	4–13	0.01	11%	21 Jun →
cache	13–6	0.02	8%	21 Jun →
office	4–13	-0.05	11%	21 Jun →
overpass	6–13	-0.01	15%	20 Jun →
mirage	13–4	0.02	6%	20 Jun →
nuke	11–13	0.01	13%	20 Jun →
train	11–13	0.06	12%	19 Jun →
inferno	5–13	-0.02	27%	19 Jun →
nuke	7–13	0.06	12%	19 Jun →
cache	13–3	0.03	9%	18 Jun →
office	10–13	0.06	8%	18 Jun →
cache	11–13	-0.01	20%	18 Jun →
office	8–13	0.06	16%	17 Jun →
train	7–13	0.06	6%	17 Jun →
train	10–13	-0.09	13%	17 Jun →
anubis	13–11	0.04	15%	16 Jun →
dust2	13–5	0.04	19%	16 Jun →
vertigo	13–5	0.12	8%	16 Jun →
inferno	4–13	0.03	10%	16 Jun →
office	13–4	0.13	20%	16 Jun →
mirage	10–13	0.03	7%	16 Jun →
office	11–13	0.02	15%	15 Jun →
office	13–11	-0.00	16%	15 Jun →
italy	5–13	-0.11	10%	15 Jun →
office	5–13	-0.03	18%	15 Jun →