f1ene_ (Allahu) — CS2 Stats

76561198169564242[U:1:209298514]Steam profile ↗✓ No bans

2,339Tracked matches46%Win rate2022Tracked since

CSDB Rating6.9 SolidPositional Player

Premier CS Rating25,756Red band · top ~2.6% of ranked players (population est.)

WingmanDistinguished Master Guardian

Ladder ranks via Leetify

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Performance scores

Aim79

Positioning61

Utility52

0–100 skill scores via Leetify.

Recent form

STEADY58–37–5Last 10058%Win rateLWLLLLWLWW

Last 10 vs previous 10: 0pp win rate · +0.03 avg rating · +2.9pp headshot accuracy · −34ms reaction

Win rate 0pp 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

1–4 · 20% win rate
Avg rating 0.01
Avg headshot accuracy 16%
Avg reaction 492ms

Last 10

4–6 · 40% win rate
Avg rating 0.01
Avg headshot accuracy 16%
Avg reaction 496ms

Last 20

8–11–1 · 40% win rate
Avg rating -0.01
Avg headshot accuracy 15%
Avg reaction 513ms

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 (+1.4 against its own average).

Aim7.9

Utility5.2

Positioning6.1

Opening Duels4.6

Clutch3.0

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 m0NESY 91% playstyle similarity

Most alike: aim profile, positioning profile.

Where you differ: lower utility contribution; 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 →

CSDB Rating breakdown

Aim7.9

Positioning6.1

Utility5.2

Mechanics8.3

Opening Duels5.7

Win Impact3.8

Composite 6.9/10 (Solid), a weighted mean of the bars with a small opposition adjustment (×1.05 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating0.00−0.07

first ⅓ avg 0.07 → last ⅓ avg 0.00

Reaction time514ms−17ms

first ⅓ avg 531ms → last ⅓ avg 514ms

Headshot accuracy18.8%−6.4%

first ⅓ avg 25.2% → last ⅓ avg 18.8%

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

0.22Best match rating · 13–3 · inferno, 14 Jul →

53%Best headshot accuracy · 13–10 · dust2, 21 Jul →

406msFastest reaction time · 13–9 · anubis, 29 Aug →

13–1Biggest win · ancient, 1 Sept →

Across the last 100 tracked matches.

Highlights

7Longest win streak

6–7In matches decided by ≤2 rounds

7Overtime games

Map breakdown

anubisBest map · 82% over 11mirageWeakest map · 31% over 16

Map	Grade	Played	Record	Win rate	Avg rating
inferno	S	23	18–5	78%	0.06
mirage	D	16	5–11	31%	-0.00
dust2	B	16	8–8	50%	0.02
cache	D	15	5–10	33%	0.03
ancient	A	11	7–4	64%	0.02
anubis	S	11	9–2	82%	0.05
nuke	S	8	6–2	75%	0.03

Across the last 100 tracked matches.

Mirage is currently your weakest sufficiently-sampled map (31% over 16). Start with the 5 essential Mirage lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

475Matches

55%Win rate

1.22Avg K/D

79.5ADR

39%Headshot %

Clutches & streaks

35%1v1 clutch win

17%1v2 clutch win

11Longest win streak

Recent Faceit resultsWLWWL

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	107	53%	1.14	16.8
Dust2	74	55%	1.13	16.9
Ancient	54	50%	1.13	17.7
Inferno	39	51%	1.10	15.9
Anubis	30	60%	1.09	15.5
Nuke	19	42%	0.97	15.0
Overpass	9	67%	1.17	16.8
Vertigo	4	25%	1.05	20.5

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.

17.7%Headshot accuracy

34.9%Accuracy (enemy spotted)

32.9%Spray accuracy

87.3%Counter-strafing

9.4°Preaim

510msReaction time

58.0%T opening success

47.7%CT opening success

0.63Enemies flashed / flash

5.8%Flash assists

8.42HE damage / grenade

7.21Flashes / match

Recommended for you

Spend your practice time on Mirage

Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.

31% win rate across 16 tracked games — your weakest map with enough games to be worth reading into.

Mirage callouts & strategy →Mirage grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
inferno	9–13	-0.03	25%	12 Nov →
ancient	13–6	0.00	10%	12 Nov →
cache	14–16	-0.00	13%	10 Nov →
mirage	14–16	0.02	14%	9 Nov →
cache	11–13	0.04	20%	9 Nov →
dust2	8–13	-0.05	20%	8 Nov →
anubis	13–6	0.03	22%	8 Nov →
ancient	3–13	-0.05	15%	2 Nov →
inferno	13–6	0.08	10%	2 Nov →
inferno	13–6	0.01	14%	2 Nov →
mirage	12–16	-0.01	16%	2 Nov →
inferno	4–13	-0.05	0%	2 Nov →
cache	13–9	-0.03	13%	2 Nov →
anubis	15–15	0.03	15%	29 Oct →
anubis	13–8	-0.03	14%	28 Oct →
cache	13–9	-0.02	18%	28 Oct →
mirage	6–13	-0.09	9%	22 Oct →
dust2	3–13	-0.00	24%	22 Oct →
ancient	13–6	-0.02	19%	22 Oct →
dust2	11–13	-0.02	4%	19 Oct →
dust2	13–10	0.10	22%	19 Oct →
ancient	12–12	-0.03	23%	14 Oct →
mirage	11–13	0.02	24%	13 Oct →
cache	10–13	0.07	25%	13 Oct →
anubis	16–14	0.05	18%	13 Oct →
dust2	13–7	-0.00	25%	13 Oct →
mirage	5–13	-0.05	17%	7 Sept →
dust2	6–13	0.03	30%	2 Sept →
ancient	13–1	-0.02	20%	1 Sept →
inferno	13–11	0.07	35%	1 Sept →
cache	8–13	0.02	24%	31 Aug →
nuke	13–9	0.03	32%	31 Aug →
inferno	13–6	0.01	33%	31 Aug →
inferno	13–3	0.08	25%	30 Aug →
anubis	13–7	0.01	33%	30 Aug →
cache	7–1	-0.01	21%	30 Aug →
nuke	13–2	0.14	35%	29 Aug →
anubis	13–9	0.07	33%	29 Aug →
cache	12–12	0.10	38%	29 Aug →
inferno	13–6	0.06	33%	29 Aug →
anubis	13–10	0.03	26%	28 Aug →
nuke	13–3	-0.00	21%	28 Aug →
mirage	4–13	-0.06	28%	28 Aug →
mirage	19–17	-0.03	25%	25 Aug →
cache	7–10	-0.04	17%	15 Aug →
dust2	9–13	-0.01	27%	12 Aug →
inferno	13–8	0.06	41%	12 Aug →
nuke	13–8	0.02	23%	11 Aug →
dust2	12–12	0.05	38%	11 Aug →
inferno	13–8	0.06	25%	11 Aug →
inferno	13–2	0.14	21%	8 Aug →
dust2	13–10	0.03	44%	7 Aug →
mirage	1–13	-0.03	0%	7 Aug →
anubis	13–5	0.09	26%	7 Aug →
ancient	9–13	0.02	18%	6 Aug →
ancient	13–11	0.04	24%	5 Aug →
nuke	11–13	0.00	19%	4 Aug →
inferno	13–8	-0.00	11%	4 Aug →
nuke	6–13	-0.01	24%	4 Aug →
cache	13–4	0.03	32%	4 Aug →
anubis	13–8	-0.02	28%	3 Aug →
nuke	13–9	0.04	29%	3 Aug →
dust2	5–13	-0.02	24%	2 Aug →
inferno	11–0	0.03	50%	2 Aug →
inferno	13–11	0.04	40%	2 Aug →
cache	12–16	-0.01	21%	1 Aug →
mirage	13–6	0.03	27%	31 Jul →
dust2	13–10	-0.02	19%	31 Jul →
inferno	6–13	-0.02	22%	31 Jul →
mirage	4–13	-0.05	20%	30 Jul →
mirage	10–13	0.02	18%	29 Jul →
inferno	8–13	0.05	25%	28 Jul →
inferno	13–8	0.05	23%	24 Jul →
inferno	13–4	0.13	21%	23 Jul →
ancient	13–3	0.08	36%	23 Jul →
anubis	13–7	0.14	36%	23 Jul →
nuke	13–6	0.04	22%	23 Jul →
cache	13–1	0.12	24%	23 Jul →
mirage	7–13	0.01	24%	23 Jul →
ancient	13–11	0.06	25%	22 Jul →
dust2	13–10	0.16	53%	21 Jul →
ancient	8–13	0.06	31%	21 Jul →
cache	4–13	0.02	13%	20 Jul →
anubis	11–13	0.19	21%	18 Jul →
inferno	13–3	0.11	24%	18 Jul →
ancient	13–4	0.07	22%	17 Jul →
dust2	8–13	-0.14	0%	17 Jul →
inferno	13–2	0.11	28%	17 Jul →
mirage	13–2	0.02	37%	17 Jul →
mirage	10–7	0.01	16%	17 Jul →
inferno	13–9	0.12	25%	16 Jul →
dust2	13–7	0.21	42%	16 Jul →
dust2	13–9	0.01	19%	16 Jul →
dust2	13–8	0.01	27%	15 Jul →
mirage	5–13	0.06	26%	15 Jul →
cache	6–13	0.04	28%	14 Jul →
mirage	13–8	0.11	27%	14 Jul →
inferno	13–3	0.22	24%	14 Jul →
cache	5–13	0.16	30%	14 Jul →
inferno	12–12	0.11	27%	9 Jul →