h — CS2 Stats

76561198087814458[U:1:127548730]

376Tracked matches41%Win rate2023Tracked since

646Hours in CS

CSDB Rating3.0 Learning

WingmanGold Nova I

Ladder ranks via Leetify

Performance scores

Aim29

Positioning24

Utility47

0–100 skill scores via Leetify.

Recent form

STEADY42–50–8Last 10042%Win rateWLWLWWLTLL

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

Player DNA

Aim2.9

Aggression0.5

Utility4.7

Positioning2.4

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

Plays most like NiKo 60% playstyle similarity

Most alike: utility contribution, opening-duel success.

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. 624ms 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

Aim2.9

Positioning2.4

Utility4.7

Mechanics5.2

Opening Duels0.0

Win Impact2.1

Composite 3.0/10 (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-0.03−0.03

first ⅓ avg 0.00 → last ⅓ avg -0.03

Reaction time635ms−21ms

first ⅓ avg 656ms → last ⅓ avg 635ms

Headshot accuracy14.0%−0.1%

first ⅓ avg 14.1% → last ⅓ avg 14.0%

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

0.15Best rating — mirage 13–1

13–1Biggest win — mirage

7Longest win streak

5–3In matches decided by ≤2 rounds

10Overtime games

Map breakdown

anubisBest map · 60% over 10infernoWeakest map · 29% over 14

Map	Grade	Played	Record	Win rate	Avg rating
nuke	B	16	8–8	50%	-0.01
inferno	D	14	4–10	29%	-0.01
ancient	C	14	6–8	43%	-0.04
overpass	D	13	4–9	31%	-0.01
mirage	B	11	5–6	45%	0.02
anubis	A	10	6–4	60%	-0.02
cache	—	4	2–2	50%	-0.04
train	—	4	1–3	25%	-0.01
vertigo	—	4	0–4	0%	-0.01
office	—	4	2–2	50%	-0.00
dust2	—	4	3–1	75%	0.00
italy	—	2	1–1	50%	-0.02

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (29% over 14). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

22,997Lifetime kills

0.90K/D

1,682Matches

44.9%Match win rate

29.7%Headshot %

11.9%Shot accuracy

2,553MVPs

646Hours (in match)

2,246Bombs planted

393Bombs defused

Most-used weapons

AK-475,375

M4A4 / M4A1-S3,884

MAC-101,443

P2000 / USP-S1,373

Glock-181,042

AWP910

P250801

AUG720

UMP-45712

SG 553631

Lifetime map wins

4,425dust2

1,585inferno

1,257nuke

1,091train

561vertigo

200office

119cbble

79italy

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

Skill profile

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

13.1%Headshot accuracy

29.0%Accuracy (enemy spotted)

34.6%Spray accuracy

73.2%Counter-strafing

14.8°Preaim

624msReaction time

20.2%T opening success

17.0%CT opening success

0.43Enemies flashed / flash

3.0%Flash assists

6.97HE damage / grenade

11.52Flashes / 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.

Advanced Mechanics
Your crosshair sits further from where enemies appear than it needs to. Crosshair placement is the cheapest accuracy you can buy.
Preaim 14.7644° — above the 12° mark we flag
Aim Training →
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 13.097% — 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 0.4309 — below the 0.5 mark we flag
Grenade Lineups →

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.

29% win rate across 14 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
cache	13–5	0.00	8%	2 Aug →
anubis	5–13	-0.07	14%	27 Jul →
anubis	13–6	-0.01	16%	23 Jul →
train	10–6	0.01	19%	21 Jun →
nuke	13–10	-0.01	17%	21 Jun →
anubis	13–3	-0.04	15%	21 Jun →
vertigo	8–13	-0.10	22%	21 Jun →
overpass	15–15	-0.02	14%	17 May →
inferno	9–13	-0.05	11%	17 May →
italy	5–13	-0.01	29%	16 May →
cache	13–10	0.00	8%	15 May →
office	13–10	-0.02	13%	11 May →
cache	3–13	-0.08	11%	3 May →
cache	2–13	-0.07	22%	3 May →
anubis	5–13	-0.02	9%	4 Apr →
overpass	16–14	-0.04	16%	4 Apr →
train	1–4	-0.01	11%	4 Apr →
ancient	13–5	-0.07	0%	3 Apr →
nuke	3–13	-0.04	16%	3 Apr →
anubis	13–8	0.03	17%	3 Apr →
ancient	13–9	-0.03	0%	3 Apr →
italy	13–3	-0.03	17%	29 Mar →
ancient	8–13	-0.01	8%	29 Mar →
vertigo	3–13	0.03	11%	28 Mar →
inferno	4–7	-0.02	13%	28 Mar →
ancient	10–13	-0.06	16%	28 Mar →
inferno	9–13	-0.09	25%	23 Mar →
mirage	10–13	-0.07	5%	23 Mar →
ancient	13–7	0.02	0%	23 Mar →
ancient	6–13	-0.08	12%	22 Mar →
nuke	13–6	0.06	16%	22 Mar →
overpass	13–4	0.03	24%	22 Mar →
anubis	13–5	0.00	28%	22 Mar →
inferno	13–5	0.02	24%	22 Mar →
ancient	13–8	0.04	22%	22 Mar →
anubis	13–10	-0.04	21%	22 Mar →
nuke	13–7	-0.01	11%	22 Mar →
nuke	8–13	-0.03	13%	10 Mar →
ancient	13–6	-0.04	16%	10 Mar →
nuke	8–13	-0.03	13%	9 Mar →
inferno	3–13	0.01	12%	9 Mar →
anubis	9–13	-0.02	8%	9 Mar →
nuke	13–16	-0.02	10%	7 Mar →
dust2	6–13	0.04	19%	6 Mar →
mirage	13–1	0.15	16%	4 Mar →
office	9–13	-0.05	8%	4 Mar →
mirage	1–8	-0.07	20%	3 Mar →
inferno	13–16	-0.04	10%	3 Mar →
overpass	11–13	-0.03	15%	3 Mar →
inferno	15–15	-0.01	15%	3 Mar →
nuke	7–13	-0.04	14%	3 Mar →
anubis	13–6	0.02	9%	2 Mar →
train	13–11	-0.02	35%	2 Mar →
nuke	2–13	-0.04	14%	2 Mar →
overpass	10–13	0.03	11%	2 Mar →
mirage	13–6	-0.03	12%	2 Mar →
mirage	5–13	0.02	14%	2 Mar →
dust2	13–9	-0.02	19%	2 Mar →
ancient	6–13	-0.08	18%	2 Mar →
inferno	13–11	-0.01	12%	2 Mar →
nuke	13–16	-0.05	15%	2 Mar →
nuke	13–8	-0.01	13%	2 Mar →
ancient	2–13	-0.08	21%	2 Mar →
mirage	8–13	0.02	24%	2 Mar →
overpass	4–13	-0.07	3%	2 Mar →
ancient	3–13	-0.05	10%	2 Mar →
nuke	13–4	0.01	14%	1 Mar →
nuke	13–4	0.06	11%	1 Mar →
anubis	8–13	-0.06	12%	1 Mar →
inferno	13–10	0.02	13%	1 Mar →
inferno	11–13	0.01	15%	24 Feb →
inferno	4–13	-0.04	24%	15 Feb →
mirage	13–10	-0.02	15%	15 Feb →
inferno	9–13	-0.03	15%	3 Feb →
office	13–11	0.03	21%	6 Oct →
vertigo	12–12	0.04	21%	6 Oct →
office	12–12	0.04	17%	6 Oct →
overpass	11–13	-0.01	4%	18 Aug →
ancient	4–13	-0.05	18%	17 Aug →
nuke	8–13	0.04	17%	17 Aug →
overpass	13–8	0.04	10%	16 Aug →
inferno	13–11	0.03	12%	16 Aug →
inferno	15–15	-0.01	12%	16 Aug →
ancient	13–10	0.01	10%	13 Aug →
overpass	4–13	-0.06	15%	13 Aug →
overpass	8–13	-0.04	11%	13 Aug →
nuke	16–13	-0.04	7%	13 Aug →
train	12–12	0.00	9%	11 Aug →
dust2	13–1	0.03	9%	11 Aug →
overpass	13–3	0.09	13%	11 Aug →
mirage	13–2	0.11	22%	11 Aug →
dust2	13–1	-0.03	25%	11 Aug →
mirage	15–15	-0.03	14%	11 Aug →
nuke	13–6	-0.06	27%	11 Aug →
overpass	10–13	-0.12	7%	11 Aug →
ancient	15–15	-0.03	17%	11 Aug →
mirage	5–13	0.09	11%	7 Aug →
overpass	8–13	0.03	12%	7 Aug →
mirage	13–9	0.00	10%	7 Aug →
vertigo	1–13	-0.02	8%	7 Aug →