banuusa — CS2 Stats

76561199114171971[U:1:1153906243]✓ No bans

441Tracked matches38%Win rate2021Tracked since

CSDB Rating5.6 SolidHybrid Rifler

FaceitLevel 5Top 67.6% of ranked FACEIT players

WingmanLegendary Eagle

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

Aim63

Positioning55

Utility69

0–100 skill scores via Leetify.

Recent form

STEADY38–50–12Last 10038%Win rateLWTWTLWLTW

Last 10 vs previous 10: +0pp win rate · +0.02 avg rating

Player DNA

Primary style: Hybrid Rifler — Aim-led profile without a single dominant tendency.

Aim6.3

Aggression8.3

Utility6.9

Positioning5.5

Opening Duels3.8

Clutch5.4

Strong CT-side openerEffective flashes

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 jL 89% playstyle similarity

Most alike: opening-duel success, utility contribution.

Where you differ: 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

Strengths

Flashes. 0.79 enemies blinded per flash — utility that consistently lands.

Areas to improve

T-side openings. Opening success drops from 56% on CT to 34% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 673ms 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

Aim6.3

Positioning5.5

Utility6.9

Mechanics7.4

Opening Duels3.8

Win Impact0.8

Composite 5.6/10 (Solid), 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 rating0.03+0.02

first ⅓ avg 0.01 → last ⅓ avg 0.03

Reaction time671ms+13ms

first ⅓ avg 658ms → last ⅓ avg 671ms

Headshot accuracy21.5%−1.3%

first ⅓ avg 22.8% → last ⅓ avg 21.5%

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.

Highlights

0.12Best rating — dust2 13–7

13–1Biggest win — ancient

4Longest win streak

4–6In matches decided by ≤2 rounds

7Overtime games

Map breakdown

overpassBest map · 60% over 5infernoWeakest map · 0% over 6

Map	Grade	Played	Record	Win rate	Avg rating
dust2	C	50	22–28	44%	0.02
ancient	C	19	8–11	42%	0.01
mirage	D	13	4–9	31%	0.01
inferno	D	6	0–6	0%	-0.00
overpass	A	5	3–2	60%	0.02
nuke	—	3	0–3	0%	0.00
anubis	—	3	1–2	33%	-0.03
train	—	1	0–1	0%	-0.02

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (0% over 6). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

580Matches

52%Win rate

1.09Avg K/D

81.1ADR

52%Headshot %

Clutches & streaks

47%1v1 clutch win

23%1v2 clutch win

13Longest win streak

Recent Faceit resultsWLLLL

Map	Matches	Win rate	Avg K/D	Avg kills
Dust2	97	62%	1.23	17.4
Mirage	73	51%	1.02	15.4
Ancient	35	54%	1.04	15.7
Anubis	24	54%	1.33	16.5
Inferno	16	56%	1.14	17.8
Nuke	12	42%	0.89	12.8
Overpass	8	50%	1.13	19.6
Train	2	0%	1.06	15.5

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.

22.1%Headshot accuracy

30.8%Accuracy (enemy spotted)

32.0%Spray accuracy

83.2%Counter-strafing

9.0°Preaim

673msReaction time

34.4%T opening success

55.8%CT opening success

0.79Enemies flashed / flash

6.2%Flash assists

8.41HE damage / grenade

10.41Flashes / 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
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 34.4493% — 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.

0% 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
ancient	2–13	0.00	16%	7 Aug →
dust2	13–10	0.08	25%	6 Aug →
dust2	12–12	0.04	14%	4 Aug →
ancient	13–7	0.02	20%	3 Aug →
dust2	12–12	0.03	24%	30 Jul →
dust2	9–13	0.06	23%	26 Jul →
dust2	13–2	0.07	24%	13 Jul →
train	7–13	-0.02	29%	4 Jul →
dust2	12–12	0.11	21%	3 Jul →
dust2	9–1	-0.00	22%	2 Jul →
dust2	13–4	0.04	54%	1 Jul →
nuke	12–12	-0.02	24%	24 Jun →
dust2	7–13	0.04	22%	19 Jun →
dust2	13–8	0.01	25%	15 Jun →
dust2	11–13	0.11	23%	11 Jun →
dust2	8–13	-0.00	17%	10 Jun →
dust2	13–6	0.05	29%	10 Jun →
dust2	13–6	0.05	25%	8 Jun →
dust2	6–13	-0.06	19%	3 Jun →
dust2	11–13	0.01	24%	3 Jun →
inferno	7–9	0.01	21%	2 Jun →
mirage	11–13	0.05	17%	25 May →
ancient	13–10	0.10	16%	18 May →
dust2	12–12	0.09	25%	15 May →
inferno	8–13	-0.01	18%	15 May →
overpass	6–13	0.05	18%	15 May →
mirage	12–12	-0.03	13%	15 May →
mirage	3–13	0.02	26%	12 May →
ancient	3–13	-0.02	19%	10 May →
nuke	5–13	-0.07	14%	10 May →
dust2	10–13	0.02	22%	28 Apr →
dust2	9–13	0.06	13%	22 Apr →
dust2	9–3	0.03	7%	22 Apr →
dust2	8–13	-0.00	32%	22 Apr →
dust2	12–12	0.05	19%	20 Apr →
inferno	7–13	-0.05	9%	14 Apr →
mirage	16–14	-0.05	13%	14 Apr →
dust2	13–6	0.03	13%	12 Apr →
dust2	13–3	-0.05	6%	12 Apr →
dust2	10–13	0.03	32%	17 Mar →
dust2	13–11	0.08	14%	11 Mar →
ancient	13–1	0.09	9%	10 Mar →
ancient	10–13	-0.03	29%	10 Mar →
anubis	13–9	-0.01	14%	9 Mar →
anubis	15–15	-0.01	19%	15 Feb →
ancient	13–11	-0.00	17%	15 Feb →
overpass	13–5	0.04	34%	2 Feb →
dust2	13–2	0.03	28%	31 Jan →
dust2	13–9	-0.08	22%	31 Jan →
mirage	10–13	0.06	21%	31 Jan →
mirage	10–13	0.02	19%	27 Jan →
mirage	13–2	0.03	23%	27 Jan →
inferno	6–13	-0.03	15%	23 Jan →
ancient	1–13	-0.07	11%	19 Jan →
overpass	11–13	-0.01	15%	17 Jan →
mirage	7–13	0.01	20%	17 Jan →
ancient	8–13	-0.04	8%	16 Jan →
dust2	8–13	-0.06	14%	16 Jan →
ancient	10–13	0.04	12%	6 Jan →
dust2	13–5	0.03	16%	3 Jan →
ancient	7–13	0.04	13%	3 Jan →
dust2	2–13	-0.06	22%	2 Jan →
ancient	13–16	0.05	20%	1 Jan →
mirage	7–13	-0.08	25%	1 Jan →
dust2	12–12	0.06	28%	1 Jan →
nuke	15–15	0.09	29%	30 Dec →
anubis	3–13	-0.05	18%	30 Dec →
mirage	13–3	0.04	19%	28 Dec →
dust2	13–7	0.08	11%	28 Dec →
ancient	10–13	-0.06	21%	28 Dec →
dust2	3–13	-0.03	19%	19 Dec →
ancient	16–14	-0.01	10%	19 Dec →
dust2	16–13	-0.01	31%	17 Dec →
ancient	4–13	0.01	25%	16 Dec →
dust2	13–9	0.03	20%	9 Dec →
dust2	12–12	0.10	28%	6 Dec →
ancient	8–13	-0.03	15%	6 Dec →
dust2	13–5	0.01	22%	6 Dec →
overpass	13–1	0.06	17%	3 Dec →
ancient	13–8	0.01	24%	30 Nov →
dust2	2–13	-0.04	39%	19 Nov →
mirage	3–13	0.02	23%	19 Nov →
ancient	13–5	-0.04	34%	18 Nov →
dust2	13–7	0.12	26%	12 Nov →
dust2	13–6	0.03	27%	10 Nov →
dust2	13–10	0.00	27%	10 Nov →
inferno	8–13	0.07	30%	10 Nov →
dust2	2–13	-0.04	20%	8 Nov →
overpass	13–10	-0.05	15%	7 Nov →
ancient	16–13	0.03	17%	7 Nov →
mirage	1–13	-0.04	10%	6 Nov →
dust2	11–13	0.05	32%	6 Nov →
dust2	1–13	-0.02	33%	4 Nov →
dust2	13–8	-0.01	29%	31 Oct →
inferno	8–13	0.01	32%	26 Oct →
dust2	10–13	0.05	17%	26 Oct →
mirage	13–3	0.08	16%	17 Oct →
dust2	10–13	0.00	33%	17 Oct →
dust2	12–12	-0.06	18%	17 Oct →
dust2	5–13	-0.09	9%	2 Oct →