CG NAT3 — CS2 Stats

76561199686160927[U:1:1725895199]Steam profile ↗

315Tracked matches50%Win rate2024Tracked since

CSDB Rating3.8 Learning

Premier CS Rating7,914Light Blue band · top ~64.9% of ranked players (population est.)

Ladder ranks via Leetify

Share this profile

The image is a snapshot; the link keeps updating. Nothing here is published anywhere — it is generated in your browser when you click.

Performance scores

Aim51

Positioning44

Utility15

0–100 skill scores via Leetify.

Recent form

COLD42–51–7Last 10042%Win rateLWLLLWTWLT

Last 10 vs previous 10: -30pp win rate · +0.00 avg rating

Player DNA

Aim5.1

Aggression5.2

Utility1.5

Positioning4.4

Opening Duels1.4

Clutch0.0

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

Plays most like NiKo 75% playstyle similarity

Most alike: opening-fight frequency, opening-duel success.

Where you differ: lower aim profile; lower utility contribution.

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.

T-side openings. Opening success drops from 46% on CT to 25% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 590ms 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

Aim5.1

Positioning4.4

Utility1.5

Mechanics5.8

Opening Duels2.0

Win Impact5.0

Composite 3.8/10 (Learning), a weighted mean of the bars with a small opposition adjustment (×0.90 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.02+0.01

first ⅓ avg -0.04 → last ⅓ avg -0.02

Reaction time586ms−41ms

first ⅓ avg 627ms → last ⅓ avg 586ms

Headshot accuracy9.6%−0.5%

first ⅓ avg 10.1% → last ⅓ avg 9.6%

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.

Highlights

0.09Best rating — anubis 13–6

13–0Biggest win — mirage

6Longest win streak

3–7In matches decided by ≤2 rounds

4Overtime games

Map breakdown

dust2Best map · 48% over 23infernoWeakest map · 30% over 20

Map	Grade	Played	Record	Win rate	Avg rating
dust2	B	23	11–12	48%	-0.04
inferno	D	20	6–14	30%	-0.03
nuke	B	13	6–7	46%	-0.02
mirage	C	12	5–7	42%	-0.03
ancient	C	11	4–7	36%	-0.03
anubis	C	8	3–5	38%	0.01
overpass	—	4	2–2	50%	-0.04
vertigo	—	4	3–1	75%	-0.03
train	—	2	1–1	50%	-0.03
stronghold	—	2	1–1	50%	-0.01
warden	—	1	0–1	0%	-0.06

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (30% over 20). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

4Matches

25%Win rate

0.62Avg K/D

55.3ADR

32%Headshot %

Clutches & streaks

0%1v1 clutch win

33%1v2 clutch win

1Longest win streak

Recent Faceit resultsLLLWL

Map	Matches	Win rate	Avg K/D	Avg kills
Anubis	2	50%	0.79	10.0
Mirage	1	0%	0.27	4.0
Nuke	1	0%	0.62	10.0

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.

9.9%Headshot accuracy

32.1%Accuracy (enemy spotted)

35.5%Spray accuracy

76.0%Counter-strafing

9.7°Preaim

590msReaction time

25.3%T opening success

45.9%CT opening success

0.41Enemies flashed / flash

0.0%Flash assists

5.25HE damage / grenade

1.53Flashes / 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 9.9077% — 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 0.4124 — 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 25.3031% — below the 40% mark we flag

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.

30% win rate across 20 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
mirage	4–13	-0.12	6%	17 Jun →
inferno	13–5	0.05	11%	17 Jun →
mirage	5–13	-0.03	3%	15 Jun →
dust2	10–13	-0.02	11%	15 Jun →
inferno	7–13	-0.02	9%	13 Jun →
nuke	13–8	-0.01	5%	13 Jun →
ancient	12–12	-0.01	12%	13 Jun →
nuke	13–8	0.02	9%	13 Jun →
inferno	10–13	-0.07	7%	8 Mar →
inferno	12–12	-0.02	20%	7 Mar →
train	7–12	-0.07	0%	7 Mar →
inferno	13–5	-0.04	8%	6 Mar →
ancient	13–11	0.00	15%	6 Mar →
nuke	13–7	0.05	27%	6 Mar →
dust2	9–13	-0.07	2%	6 Mar →
overpass	13–9	-0.03	14%	6 Mar →
mirage	13–6	0.01	9%	6 Mar →
vertigo	6–13	-0.03	7%	2 Mar →
dust2	13–2	-0.02	6%	2 Mar →
inferno	11–13	-0.05	10%	22 Feb →
ancient	10–13	-0.01	17%	18 Feb →
mirage	8–13	-0.02	22%	16 Feb →
anubis	13–6	0.09	7%	16 Feb →
dust2	13–6	0.02	9%	16 Feb →
anubis	8–13	0.02	7%	16 Feb →
ancient	6–13	-0.10	13%	14 Feb →
inferno	11–13	-0.06	8%	14 Feb →
overpass	13–4	-0.02	4%	14 Feb →
nuke	13–8	-0.03	8%	14 Feb →
ancient	13–11	-0.02	6%	13 Feb →
inferno	7–13	-0.05	4%	13 Feb →
dust2	13–4	-0.08	0%	13 Feb →
nuke	11–13	-0.06	17%	12 Feb →
inferno	7–13	-0.05	6%	12 Feb →
dust2	4–13	-0.05	4%	12 Feb →
anubis	15–15	-0.08	2%	12 Feb →
dust2	13–7	0.02	11%	11 Feb →
inferno	13–3	0.06	8%	11 Feb →
dust2	13–3	-0.06	7%	11 Feb →
inferno	13–8	-0.05	6%	10 Feb →
dust2	13–4	-0.04	0%	9 Feb →
vertigo	13–3	-0.04	10%	9 Feb →
nuke	2–13	-0.12	20%	8 Feb →
dust2	7–13	-0.09	0%	8 Feb →
anubis	10–13	0.03	18%	8 Feb →
inferno	13–8	0.01	12%	6 Feb →
anubis	13–8	0.03	9%	6 Feb →
dust2	4–13	-0.05	12%	28 Jan →
dust2	11–13	-0.02	6%	28 Jan →
nuke	2–8	-0.04	8%	28 Jan →
dust2	13–6	0.02	11%	27 Jan →
mirage	13–6	-0.01	2%	27 Jan →
nuke	9–13	-0.06	7%	27 Jan →
nuke	13–10	0.04	13%	27 Jan →
ancient	8–13	-0.05	13%	26 Jan →
nuke	4–13	0.01	18%	26 Jan →
mirage	13–0	0.03	0%	26 Jan →
dust2	7–13	-0.04	6%	26 Jan →
ancient	8–13	-0.05	4%	26 Jan →
vertigo	13–8	0.02	6%	25 Jan →
ancient	5–13	0.01	8%	25 Jan →
nuke	13–5	0.00	6%	25 Jan →
anubis	7–13	0.06	11%	25 Jan →
nuke	2–13	-0.09	3%	25 Jan →
stronghold	13–10	0.02	6%	25 Jan →
inferno	2–13	-0.04	6%	24 Jan →
dust2	0–13	-0.10	9%	24 Jan →
dust2	12–12	-0.02	7%	24 Jan →
inferno	13–7	-0.07	8%	24 Jan →
warden	0–13	-0.06	14%	24 Jan →
mirage	9–13	-0.07	20%	24 Jan →
inferno	12–12	0.03	12%	24 Jan →
dust2	12–12	-0.06	11%	24 Jan →
mirage	5–13	-0.01	11%	24 Jan →
dust2	13–8	-0.03	7%	24 Jan →
inferno	14–16	-0.03	15%	23 Jan →
dust2	13–1	0.01	9%	23 Jan →
anubis	13–11	-0.07	3%	22 Jan →
stronghold	12–12	-0.05	11%	22 Jan →
mirage	13–2	0.00	4%	7 Jan →
inferno	9–13	-0.07	5%	7 Jan →
mirage	11–13	0.01	10%	7 Jan →
dust2	13–9	0.04	13%	7 Jan →
mirage	10–13	-0.09	10%	7 Jan →
anubis	5–13	-0.04	5%	7 Jan →
inferno	0–13	-0.09	11%	7 Jan →
vertigo	13–9	-0.06	2%	7 Jan →
inferno	9–13	-0.10	0%	6 Jan →
ancient	13–10	-0.04	13%	6 Jan →
overpass	14–16	-0.02	15%	6 Jan →
dust2	2–6	-0.06	8%	6 Jan →
dust2	13–9	-0.04	15%	6 Jan →
mirage	13–6	-0.05	12%	5 Jan →
ancient	16–13	-0.06	8%	5 Jan →
dust2	3–13	-0.08	6%	5 Jan →
overpass	5–13	-0.09	13%	5 Jan →
ancient	3–13	-0.02	25%	27 Dec →
train	13–4	0.01	11%	19 Sept →
inferno	9–13	0.02	7%	19 Sept →
nuke	5–13	0.04	13%	19 Sept →