XELLOW — CS2 Stats

PRO“xelulu”Nexus GamingRomaniaAWPerSettings & gear profile →

RO76561198052354821[U:1:92089093]Steam profile ↗

2,822Tracked matches50%Win rate2020Tracked since

CSDB Rating7.2 StrongAggressive Rifler

FaceitLevel 10Top 12.4% of ranked FACEIT players

Ladder ranks via Leetify

Rating over time

2Days observed since 2026-08-27

CSDB's own observations — this history builds from the day a profile is first viewed and cannot be backfilled.

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Performance scores

Aim97

Positioning67

Utility63

0–100 skill scores via Leetify.

Recent form

STEADY43–57Last 10043%Win rateWWWLLWLLWW

Last 10 vs previous 10: +0pp win rate · -0.01 avg rating

Player DNA

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

Aim9.7

Aggression8.0

Utility6.3

Positioning6.7

Opening Duels4.1

Clutch2.4

Sharp aimerStrong CT-side opener

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 b1t 98% playstyle similarity

Most alike: opening-fight frequency, utility contribution.

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 97 — the mechanical foundation is a clear strength.

Areas to improve

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

T-side openings. Opening success drops from 57% on CT to 35% on T — the same duels are being taken with worse setups on the attacking side.

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

Aim9.7

Positioning6.7

Utility6.3

Mechanics7.2

Opening Duels4.1

Win Impact5.0

Composite 7.2/10 (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 rating0.02−0.01

first ⅓ avg 0.03 → last ⅓ avg 0.02

Reaction time489ms+35ms

first ⅓ avg 455ms → last ⅓ avg 489ms

Headshot accuracy34.9%+7.0%

first ⅓ avg 27.9% → last ⅓ avg 34.9%

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

0.14Best rating — inferno 13–9

13–1Biggest win — dust2

5Longest win streak

W3Current streak

4–15In matches decided by ≤2 rounds

10Overtime games

Map breakdown

infernoBest map · 50% over 22anubisWeakest map · 29% over 7

Map	Grade	Played	Record	Win rate	Avg rating
inferno	B	22	11–11	50%	0.02
dust2	B	22	10–12	45%	0.03
ancient	C	21	8–13	38%	0.02
mirage	C	17	7–10	41%	0.03
anubis	D	7	2–5	29%	0.02
overpass	D	6	2–4	33%	0.00
nuke	—	4	2–2	50%	0.03
train	—	1	1–0	100%	0.07

Across the last 100 tracked matches.

Anubis is currently your weakest sufficiently-sampled map (29% over 7). Start with the 6 essential Anubis lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

4,377Matches

63%Win rate

1.56Avg K/D

79.0ADR

45%Headshot %

Clutches & streaks

32%1v1 clutch win

23%1v2 clutch win

32Longest win streak

Recent Faceit resultsLWWWW

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	228	64%	1.49	17.0
Ancient	196	57%	1.43	17.3
Dust2	92	61%	1.37	15.9
Anubis	89	62%	1.34	18.4
Nuke	84	60%	1.44	17.7
Inferno	46	59%	1.44	15.6
Vertigo	29	66%	1.42	16.1
Train	9	89%	2.26	19.6

Faceit-match stats via the FACEIT Data API — a separate match pool from the sections above.

Inventory

$9Inventory value9ItemsFull inventory breakdown →

Skill profile

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

34.0%Headshot accuracy

40.3%Accuracy (enemy spotted)

37.5%Spray accuracy

82.5%Counter-strafing

6.5°Preaim

489msReaction time

35.3%T opening success

57.4%CT opening success

0.64Enemies flashed / flash

9.1%Flash assists

7.10HE damage / grenade

18.71Flashes / 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 35.3369% — below the 40% mark we flag

Spend your practice time on Anubis

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 7 tracked games — your weakest map with enough games to be worth reading into.

Anubis callouts & strategy →Anubis grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
inferno	13–4	-0.03	21%	19 Jul →
mirage	13–7	0.01	100%	19 Jul →
dust2	13–9	0.03	42%	16 Jul →
nuke	5–13	0.02	46%	13 Jun →
mirage	6–13	0.06	42%	13 Jun →
dust2	13–6	-0.00	25%	13 Jun →
nuke	5–13	0.02	46%	13 Jun →
mirage	6–13	0.06	42%	13 Jun →
dust2	13–6	-0.00	25%	13 Jun →
inferno	13–9	0.14	33%	13 Jun →
nuke	13–5	0.04	36%	13 Jun →
inferno	13–9	0.14	33%	13 Jun →
nuke	13–5	0.04	36%	13 Jun →
dust2	10–13	0.00	36%	13 Jun →
mirage	11–13	0.04	30%	13 Jun →
mirage	11–13	0.04	30%	13 Jun →
dust2	10–13	0.00	36%	13 Jun →
ancient	13–10	0.03	31%	13 Jun →
inferno	13–9	0.07	25%	13 Jun →
ancient	13–10	0.03	31%	13 Jun →
mirage	8–13	0.02	14%	26 Apr →
inferno	13–5	0.01	28%	26 Apr →
ancient	11–13	0.07	38%	26 Apr →
inferno	4–13	0.02	40%	25 Apr →
dust2	8–13	-0.02	27%	25 Apr →
ancient	10–13	-0.08	29%	25 Apr →
overpass	11–13	-0.01	13%	25 Apr →
inferno	8–13	-0.03	26%	24 Apr →
mirage	13–8	0.05	39%	24 Apr →
dust2	16–13	0.02	33%	24 Apr →
inferno	6–13	-0.05	31%	23 Apr →
dust2	9–13	0.05	44%	23 Apr →
anubis	22–19	0.06	45%	22 Apr →
inferno	2–13	-0.03	27%	22 Apr →
anubis	10–13	0.06	48%	22 Apr →
dust2	13–8	0.08	30%	22 Apr →
inferno	13–6	0.04	42%	20 Apr →
mirage	17–19	0.01	24%	20 Apr →
overpass	13–11	-0.03	23%	20 Apr →
dust2	5–13	-0.00	0%	19 Apr →
inferno	7–13	-0.02	39%	19 Apr →
mirage	13–4	-0.02	19%	6 Apr →
ancient	14–16	-0.04	14%	29 Mar →
mirage	11–13	0.05	24%	29 Mar →
anubis	8–13	-0.04	22%	29 Mar →
inferno	16–14	0.02	26%	24 Mar →
dust2	11–13	0.07	39%	24 Mar →
ancient	4–13	-0.04	26%	24 Mar →
dust2	8–13	0.06	45%	24 Mar →
dust2	13–1	0.14	36%	22 Mar →
ancient	13–4	0.07	45%	19 Mar →
ancient	13–5	0.10	36%	19 Mar →
ancient	4–13	-0.04	23%	13 Mar →
ancient	4–13	-0.04	23%	13 Mar →
inferno	16–14	0.01	30%	13 Mar →
anubis	11–13	-0.02	16%	26 Feb →
overpass	13–8	0.04	32%	26 Feb →
ancient	11–13	0.07	18%	26 Feb →
mirage	6–13	-0.07	45%	26 Feb →
anubis	9–13	-0.04	29%	26 Feb →
anubis	9–4	0.03	23%	24 Feb →
ancient	13–9	0.04	21%	24 Feb →
dust2	11–13	0.07	27%	22 Feb →
overpass	7–13	0.01	29%	22 Feb →
ancient	9–13	-0.02	16%	16 Feb →
dust2	3–13	-0.09	22%	16 Feb →
mirage	13–4	0.06	29%	16 Feb →
mirage	13–3	0.09	19%	15 Feb →
inferno	13–7	0.01	44%	15 Feb →
ancient	15–19	0.03	38%	15 Feb →
ancient	11–13	0.01	43%	14 Feb →
ancient	13–6	0.04	8%	12 Feb →
anubis	10–13	0.06	14%	12 Feb →
dust2	13–1	0.13	20%	10 Feb →
inferno	7–13	0.01	39%	8 Feb →
overpass	8–13	-0.02	27%	7 Feb →
mirage	13–4	0.00	38%	7 Feb →
inferno	13–5	0.00	32%	6 Feb →
overpass	10–13	0.01	33%	6 Feb →
dust2	13–4	0.01	33%	6 Feb →
dust2	13–11	0.09	37%	4 Feb →
ancient	13–8	0.06	48%	18 Dec →
inferno	7–13	0.08	37%	18 Dec →
ancient	13–8	0.03	20%	18 Dec →
inferno	11–13	0.01	16%	18 Dec →
dust2	8–13	0.01	40%	18 Dec →
mirage	13–2	0.08	32%	17 Dec →
inferno	13–3	0.04	14%	17 Dec →
mirage	11–13	0.03	32%	17 Dec →
ancient	7–13	0.04	31%	11 Dec →
inferno	13–16	0.07	27%	11 Dec →
dust2	16–13	-0.00	17%	6 Dec →
mirage	8–13	0.02	14%	6 Dec →
ancient	7–13	0.01	33%	6 Dec →
train	13–7	0.07	16%	4 Dec →
dust2	7–13	0.03	21%	4 Dec →
inferno	14–16	0.05	18%	4 Dec →
dust2	7–13	-0.03	31%	4 Dec →
inferno	9–13	-0.01	26%	4 Dec →
ancient	5–13	-0.01	22%	3 Dec →