hax1n — CS2 Stats

BR76561199196551201[U:1:1236285473]Steam profile ↗✓ No bans

1,970Tracked matches53%Win rate2021Tracked since

CSDB Rating7.3 StrongHybrid Rifler

FaceitLevel 10 · 2,252 ELOTop 12.4% of ranked FACEIT players

WingmanGold Nova Master

Ladder ranks via Leetify

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Performance scores

Aim88

Positioning65

Utility67

0–100 skill scores via Leetify.

Recent form

STEADY59–41Last 10059%Win rateLLLWWLWLWW

Last 10 vs previous 10: -20pp win rate · -0.06 avg rating

Player DNA

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

Aim8.8

Aggression6.4

Utility6.7

Positioning6.5

Opening Duels3.1

Clutch4.0

Sharp aimerExcellent counter-strafing

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 95% playstyle similarity

Most alike: utility contribution, positioning 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

Aim. Aim score of 88 — the mechanical foundation is a clear strength.

Counter-strafing. 92% of shots taken properly stopped — movement discipline most players never reach.

Areas to improve

Reaction time. 569ms 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

Aim8.8

Positioning6.5

Utility6.7

Mechanics9.4

Opening Duels3.1

Win Impact6.1

Composite 7.3/10 (Strong), 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+0.00

first ⅓ avg 0.03 → last ⅓ avg 0.03

Reaction time568ms−25ms

first ⅓ avg 594ms → last ⅓ avg 568ms

Headshot accuracy25.5%+0.9%

first ⅓ avg 24.6% → last ⅓ avg 25.5%

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

0.24Best rating — anubis 13–5

13–1Biggest win — inferno

6Longest win streak

L3Current streak

12–5In matches decided by ≤2 rounds

10Overtime games

Map breakdown

overpassBest map · 80% over 5ancientWeakest map · 48% over 29

Map	Grade	Played	Record	Win rate	Avg rating
ancient	B	29	14–15	48%	0.02
anubis	S	26	17–9	65%	0.06
inferno	B	18	9–9	50%	0.02
dust2	S	15	10–5	67%	0.02
overpass	S	5	4–1	80%	0.03
mirage	—	4	3–1	75%	-0.04
cache	—	3	2–1	67%	0.04

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (48% over 29). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

1,821Matches

53%Win rate

1.31Avg K/D

81.8ADR

55%Headshot %

Clutches & streaks

40%1v1 clutch win

28%1v2 clutch win

12Longest win streak

Recent Faceit resultsLLLLW

Map	Matches	Win rate	Avg K/D	Avg kills
Anubis	372	58%	1.35	17.3
Ancient	352	54%	1.35	17.1
Mirage	149	44%	1.23	17.1
Dust2	147	47%	1.21	17.2
Inferno	104	51%	1.30	16.2
Vertigo	87	63%	1.52	17.9
Nuke	64	42%	1.10	15.4
Overpass	29	52%	1.05	14.4

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.

24.8%Headshot accuracy

35.6%Accuracy (enemy spotted)

38.3%Spray accuracy

92.1%Counter-strafing

8.7°Preaim

569msReaction time

36.8%T opening success

48.3%CT opening success

0.57Enemies flashed / flash

7.8%Flash assists

8.46HE damage / grenade

17.13Flashes / 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 36.8188% — below the 40% mark we flag

Spend your practice time on Ancient

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

48% win rate across 29 tracked games — your weakest map with enough games to be worth reading into.

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
inferno	8–13	0.03	32%	29 Aug →
dust2	4–13	-0.01	27%	24 Aug →
dust2	16–19	-0.03	30%	23 Aug →
mirage	13–3	0.01	28%	16 Aug →
anubis	16–12	0.01	27%	14 Aug →
mirage	1–3	-0.18	33%	14 Aug →
dust2	13–7	0.05	36%	14 Aug →
anubis	8–13	0.04	27%	12 Aug →
dust2	13–11	-0.02	29%	29 Jul →
dust2	4–3	0.02	40%	29 Jul →
dust2	10–13	0.02	20%	28 Jul →
inferno	4–13	-0.03	19%	25 Jul →
anubis	13–6	0.10	23%	17 Jul →
ancient	13–8	0.06	23%	17 Jul →
anubis	13–6	0.06	20%	17 Jul →
cache	13–10	0.04	21%	11 Jul →
anubis	13–11	0.04	17%	11 Jul →
cache	19–17	0.08	24%	1 Jul →
inferno	10–13	-0.01	27%	1 Jul →
anubis	13–10	0.13	23%	29 Jun →
ancient	13–6	0.13	23%	29 Jun →
anubis	10–13	-0.04	14%	25 Jun →
dust2	9–13	-0.01	49%	22 Jun →
ancient	13–3	0.04	14%	5 Jun →
cache	9–8	-0.01	11%	1 Jun →
inferno	13–1	0.20	28%	26 Apr →
ancient	10–13	-0.01	20%	25 Apr →
anubis	13–10	0.06	32%	21 Apr →
inferno	10–13	0.05	20%	21 Apr →
ancient	10–13	-0.00	21%	18 Apr →
ancient	13–11	-0.02	21%	17 Apr →
anubis	13–3	0.10	32%	17 Apr →
anubis	13–5	0.24	33%	14 Apr →
inferno	13–6	-0.00	33%	11 Apr →
ancient	11–13	-0.01	20%	11 Apr →
overpass	13–11	0.01	27%	5 Apr →
ancient	16–12	0.04	21%	2 Apr →
anubis	7–13	0.06	26%	22 Mar →
inferno	7–13	0.02	16%	21 Mar →
anubis	13–8	0.11	25%	21 Mar →
anubis	11–13	0.01	13%	18 Mar →
ancient	9–13	0.02	28%	18 Mar →
ancient	7–13	-0.02	26%	18 Mar →
dust2	13–8	0.02	16%	18 Mar →
inferno	13–4	0.00	27%	18 Mar →
anubis	7–13	0.08	30%	18 Mar →
anubis	5–13	0.02	18%	16 Mar →
ancient	13–10	0.00	30%	16 Mar →
dust2	13–10	-0.01	18%	13 Mar →
anubis	13–8	0.10	24%	10 Mar →
ancient	13–16	0.03	16%	8 Mar →
anubis	13–9	0.06	19%	8 Mar →
ancient	13–9	0.00	19%	8 Mar →
ancient	13–3	-0.03	13%	8 Mar →
mirage	13–10	-0.02	28%	7 Mar →
anubis	10–13	-0.01	22%	7 Mar →
ancient	22–20	0.05	15%	7 Mar →
anubis	4–13	0.04	19%	4 Mar →
overpass	8–13	0.06	29%	4 Mar →
inferno	13–8	0.03	32%	3 Mar →
anubis	8–13	-0.02	28%	3 Mar →
inferno	13–8	0.04	21%	3 Mar →
inferno	2–13	-0.08	29%	3 Mar →
ancient	13–6	0.05	33%	3 Mar →
inferno	13–4	0.09	30%	1 Mar →
ancient	13–11	0.02	19%	1 Mar →
inferno	13–10	-0.00	29%	27 Feb →
overpass	16–12	-0.02	11%	26 Feb →
ancient	8–13	-0.04	20%	26 Feb →
anubis	13–6	0.04	39%	26 Feb →
inferno	4–13	0.00	16%	26 Feb →
inferno	7–13	-0.03	21%	25 Feb →
ancient	10–13	0.02	15%	25 Feb →
anubis	13–9	0.08	24%	25 Feb →
overpass	13–11	0.03	23%	25 Feb →
inferno	13–10	0.03	35%	25 Feb →
ancient	13–8	0.08	25%	20 Feb →
ancient	2–13	0.02	32%	20 Feb →
dust2	13–11	0.07	30%	20 Feb →
ancient	10–13	0.05	27%	18 Feb →
ancient	13–8	0.04	18%	18 Feb →
mirage	13–11	0.02	21%	17 Feb →
inferno	13–2	0.04	28%	17 Feb →
ancient	13–16	0.02	28%	16 Feb →
anubis	13–9	0.05	17%	15 Feb →
dust2	13–8	0.11	41%	15 Feb →
ancient	8–13	-0.03	17%	15 Feb →
anubis	13–5	0.13	32%	14 Feb →
ancient	11–13	0.06	21%	14 Feb →
dust2	16–13	0.03	22%	14 Feb →
dust2	13–7	-0.04	31%	11 Feb →
ancient	5–13	-0.01	27%	11 Feb →
overpass	13–4	0.06	27%	11 Feb →
ancient	13–8	0.00	16%	10 Feb →
dust2	13–7	0.05	29%	10 Feb →
ancient	6–13	-0.00	14%	10 Feb →
anubis	13–2	0.08	24%	10 Feb →
dust2	7–13	0.04	32%	10 Feb →
inferno	4–13	0.01	22%	10 Feb →
anubis	16–14	-0.01	27%	9 Feb →