Katakuna — CS2 Stats

76561199031500252[U:1:1071234524]✓ No bans

608Tracked matches43%Win rate2023Tracked since

548Hours in CS

CSDB Rating6.1 SolidAggressive Rifler

Premier CS Rating23,510Top 50% of 17,162 CSDB-tracked playersPink band · top ~4.6% of ranked players (population est.)

CSDB Leaderboard#7337 of 35928 tracked

FaceitLevel 10 · 2,081 ELOTop 12.4% of ranked FACEIT players

WingmanLegendary Eagle

Ladder ranks via Leetify

How this compares with the same rank

Median values for Level 10 among CSDB-tracked players (n=3,007), from Valve's own lifetime stats. Aim, positioning and utility scores are deliberately not benchmarked here — those are a third-party provider's derived metrics, which CSDB does not store.

Metric	This player	Level 10 median
Headshot rate	45.6%	52.0%
Shot accuracy	1.2%	13.7%
Kill/death ratio	1.60	1.08
Match win rate	51.2%	48.8%

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Performance scores

Aim78

Positioning63

Utility45

0–100 skill scores via Leetify.

Recent form

COLD43–51–6Last 10043%Win rateLTLLLWLTLL

Last 10 vs previous 10: -50pp win rate · -0.03 avg rating

Player DNA

Primary style: Aggressive Rifler — Takes opening fights often, backed by a strong aim profile.

Aim7.8

Aggression9.3

Utility4.5

Positioning6.3

Opening Duels3.9

Clutch4.0

Effective flashesLimited 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.

Your pro match

Plays most like sh1ro 90% playstyle similarity

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

Where you differ: lower utility contribution; 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

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

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

Aim7.8

Positioning6.3

Utility4.5

Mechanics6.9

Opening Duels3.9

Win Impact2.6

Composite 6.1/10 (Solid), a weighted mean of the bars with a small opposition adjustment (×1.02 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating0.03−0.01

first ⅓ avg 0.04 → last ⅓ avg 0.03

Reaction time526ms−59ms

first ⅓ avg 585ms → last ⅓ avg 526ms

Headshot accuracy17.2%−3.8%

first ⅓ avg 20.9% → last ⅓ avg 17.2%

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.

Highlights

0.20Best rating — anubis 13–4

13–1Biggest win — mirage

4Longest win streak

7–6In matches decided by ≤2 rounds

10Overtime games

Map breakdown

anubisBest map · 56% over 9infernoWeakest map · 33% over 6

Map	Grade	Played	Record	Win rate	Avg rating
mirage	B	38	19–19	50%	0.04
dust2	C	29	12–17	41%	0.03
anubis	A	9	5–4	56%	0.06
inferno	D	6	2–4	33%	0.00
ancient	—	4	1–3	25%	0.07
overpass	—	3	1–2	33%	0.04
nuke	—	3	2–1	67%	0.07
vertigo	—	2	1–1	50%	0.03
cache	—	2	0–2	0%	0.07
office	—	1	0–1	0%	0.01
alpine	—	1	0–1	0%	-0.09
italy	—	1	0–1	0%	-0.02
train	—	1	0–1	0%	0.01

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (33% over 6). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

36,559Lifetime kills

1.60K/D · Top 5% of Level 10 players

1,409Matches

51.2%Match win rate · Top 50% of Level 10 players

45.6%Headshot %

1.2%Shot accuracy

8,076MVPs

548Hours (in match)

1,763Bombs planted

1,585Bombs defused

Most-used weapons

AK-4712,549

M4A4 / M4A1-S6,080

AWP3,512

Desert Eagle2,317

P2000 / USP-S1,919

Glock-181,871

MP91,562

MAC-101,482

Knife1,071

SSG 08813

Lifetime map wins

3,077dust2

1,942inferno

1,534vertigo

725nuke

164office

159train

35italy

9lake

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

Faceit stats

Combat

378Matches

56%Win rate

1.28Avg K/D

80.7ADR

43%Headshot %

Clutches & streaks

40%1v1 clutch win

27%1v2 clutch win

11Longest win streak

Recent Faceit resultsWWLWW

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	154	55%	1.26	17.3
Inferno	43	58%	1.30	17.8
Vertigo	41	63%	1.35	18.8
Anubis	37	51%	1.25	15.5
Dust2	32	69%	1.37	16.6
Ancient	20	60%	1.16	18.1
Nuke	10	50%	1.24	17.5
Overpass	8	25%	1.08	14.5

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.

16.9%Headshot accuracy

34.7%Accuracy (enemy spotted)

34.8%Spray accuracy

81.2%Counter-strafing

8.1°Preaim

525msReaction time

49.0%T opening success

42.0%CT opening success

0.74Enemies flashed / flash

11.5%Flash assists

12.31HE damage / grenade

2.67Flashes / 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.

Grenades & Utility
You are buying and holding utility rather than using it. Unthrown flashes are wasted money every round.
Flashes per match 2.6713 — below the 4 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.

33% win rate across 6 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
vertigo	5–13	0.01	23%	18 May →
office	12–12	0.01	27%	14 May →
alpine	0–8	-0.09	30%	14 May →
mirage	6–13	-0.01	31%	12 May →
cache	5–13	-0.01	9%	12 May →
anubis	13–2	0.07	12%	12 May →
italy	1–13	-0.02	9%	8 May →
cache	12–12	0.14	12%	29 Apr →
anubis	5–13	0.01	21%	27 Apr →
inferno	11–13	-0.00	8%	21 Apr →
mirage	13–7	0.04	13%	21 Apr →
mirage	6–13	-0.03	11%	20 Apr →
dust2	13–4	0.04	16%	20 Apr →
dust2	2–13	-0.02	30%	18 Apr →
mirage	13–5	0.06	14%	17 Apr →
mirage	13–11	0.02	10%	17 Apr →
anubis	13–4	0.20	21%	17 Apr →
dust2	10–13	-0.01	22%	17 Apr →
mirage	13–6	0.05	12%	17 Apr →
dust2	5–13	0.03	34%	17 Apr →
mirage	13–6	0.08	13%	16 Apr →
dust2	11–13	-0.01	12%	16 Apr →
mirage	8–13	0.05	14%	16 Apr →
dust2	13–4	0.03	12%	16 Apr →
mirage	13–11	0.06	21%	16 Apr →
overpass	13–3	0.04	13%	8 Apr →
mirage	6–13	-0.05	11%	8 Apr →
mirage	4–13	0.00	24%	7 Apr →
ancient	13–9	0.09	16%	7 Apr →
dust2	8–13	0.15	16%	7 Apr →
dust2	13–8	0.04	20%	5 Apr →
mirage	13–10	-0.06	6%	5 Apr →
overpass	6–13	-0.00	24%	28 Mar →
dust2	13–8	0.10	12%	28 Mar →
mirage	10–13	0.03	14%	22 Mar →
ancient	1–13	-0.01	21%	22 Mar →
dust2	10–13	-0.06	20%	21 Mar →
mirage	13–1	0.07	18%	20 Mar →
mirage	13–5	0.00	21%	20 Mar →
dust2	14–16	0.06	19%	20 Mar →
mirage	5–13	-0.02	19%	20 Mar →
mirage	13–3	0.01	3%	20 Mar →
dust2	13–7	-0.01	17%	20 Mar →
dust2	13–10	0.09	23%	19 Mar →
anubis	9–13	0.07	17%	19 Mar →
mirage	7–0	0.19	6%	19 Mar →
mirage	13–16	0.09	13%	18 Mar →
anubis	13–4	0.10	13%	18 Mar →
dust2	7–13	-0.04	21%	18 Mar →
nuke	13–3	0.05	23%	17 Mar →
dust2	13–4	0.10	29%	17 Mar →
mirage	13–11	0.10	22%	12 Mar →
ancient	7–13	0.16	19%	11 Mar →
ancient	6–13	0.06	18%	5 Mar →
mirage	3–13	0.00	21%	1 Mar →
mirage	4–13	-0.04	18%	26 Feb →
dust2	8–13	0.08	17%	25 Feb →
mirage	16–14	0.07	13%	25 Feb →
inferno	13–11	0.08	14%	24 Feb →
inferno	7–13	-0.02	18%	19 Feb →
anubis	2–13	-0.03	21%	19 Feb →
mirage	15–15	0.01	13%	19 Feb →
dust2	8–13	-0.05	19%	16 Feb →
mirage	13–7	0.07	17%	16 Feb →
dust2	4–13	-0.13	16%	15 Feb →
anubis	13–2	0.05	25%	15 Feb →
mirage	11–13	0.13	38%	15 Feb →
mirage	13–3	-0.04	19%	14 Feb →
overpass	7–13	0.08	26%	10 Feb →
mirage	15–15	0.06	22%	10 Feb →
inferno	12–16	0.01	28%	10 Feb →
anubis	10–13	0.04	21%	9 Feb →
dust2	11–3	-0.03	26%	9 Feb →
nuke	13–1	0.03	29%	9 Feb →
mirage	13–7	0.15	21%	9 Feb →
mirage	8–13	0.06	16%	7 Feb →
dust2	10–13	0.03	18%	7 Feb →
inferno	16–13	-0.02	22%	6 Feb →
dust2	15–15	0.07	30%	6 Feb →
dust2	13–4	0.06	9%	6 Feb →
dust2	13–9	0.00	18%	5 Feb →
dust2	10–13	0.05	9%	5 Feb →
dust2	9–13	0.13	17%	4 Feb →
mirage	14–16	-0.03	16%	2 Feb →
mirage	13–3	0.19	16%	2 Feb →
mirage	5–13	-0.05	14%	30 Jan →
dust2	6–13	0.07	20%	29 Jan →
mirage	7–13	-0.05	18%	23 Jan →
inferno	5–13	-0.04	20%	22 Jan →
nuke	12–12	0.12	19%	21 Jan →
vertigo	13–6	0.06	9%	21 Jan →
anubis	13–11	0.07	25%	21 Jan →
dust2	1–13	-0.05	38%	19 Jan →
train	11–13	0.01	22%	19 Jan →
dust2	13–2	0.09	25%	18 Jan →
mirage	13–3	0.05	32%	18 Jan →
mirage	13–10	0.11	25%	18 Jan →
dust2	13–11	0.04	23%	17 Jan →
mirage	13–16	-0.03	18%	17 Jan →
mirage	4–13	0.04	22%	17 Jan →