arrozdoce — CS2 Stats

PRO“zecarocho”OG EsportsPortugalRiflerSettings & gear profile →

PT76561197973757802[U:1:13492074]Steam profile ↗✓ No bans

1,910Tracked matches63%Win rate2020Tracked since

CSDB Rating8.0 StrongAggressive Rifler

CSDB Leaderboard#6378 of 7811 tracked

FaceitLevel 10 · 3,364 ELOTop 12.4% of ranked FACEIT players

WingmanSupreme Master First Class

Ladder ranks via Leetify

Rating over time

3,364Peak Faceit ELO on CSDB

1Days observed since 2026-08-28

Faceit ELO

At peak — 3,364
Next: 3,500 ELO at 3,500 — 136 to go
Reached: 3,000 ELO · 2,500 ELO · Level 10 · Level 9
3,000 ELO first seen 2026-08-28
2,500 ELO first seen 2026-08-28

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

Aim92

Positioning63

Utility80

0–100 skill scores via Leetify.

Recent form

HOT60–40Last 10060%Win rateWWWLWWWWWW

Last 10 vs previous 10: +40pp win rate · +0.08 avg rating

Player DNA

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

Aim9.2

Aggression8.5

Utility8.0

Positioning6.3

Opening Duels5.0

Clutch4.8

Sharp aimerStrong CT-side openerHigh utility contribution

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 93% playstyle similarity

Most alike: aim profile, 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 92 — the mechanical foundation is a clear strength.

Utility. Utility score of 80 — grenade impact well above the norm.

Areas to improve

T-side openings. Opening success drops from 60% on CT to 41% 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

Aim9.2

Positioning6.3

Utility8.0

Mechanics6.6

Opening Duels5.0

Win Impact9.4

Composite 8.0/10 (Strong), 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 rating0.02+0.02

first ⅓ avg 0.01 → last ⅓ avg 0.02

Reaction time529ms+3ms

first ⅓ avg 526ms → last ⅓ avg 529ms

Headshot accuracy25.6%−0.3%

first ⅓ avg 25.9% → last ⅓ avg 25.6%

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 — mirage 13–8

13–0Biggest win — dust2

8Longest win streak

W3Current streak

6–13In matches decided by ≤2 rounds

12Overtime games

Map breakdown

infernoBest map · 77% over 22trainWeakest map · 40% over 5

Map	Grade	Played	Record	Win rate	Avg rating
ancient	S	24	16–8	67%	0.02
inferno	S	22	17–5	77%	0.02
dust2	C	14	6–8	43%	0.01
mirage	A	11	6–5	55%	0.03
nuke	B	10	5–5	50%	-0.01
overpass	B	10	5–5	50%	0.00
train	C	5	2–3	40%	-0.03
cache	—	2	2–0	100%	0.07
boulder	—	1	1–0	100%	0.08
anubis	—	1	0–1	0%	-0.00

Across the last 100 tracked matches.

Faceit stats

Combat

2,536Matches

61%Win rate

1.27Avg K/D

86.5ADR

54%Headshot %

Clutches & streaks

44%1v1 clutch win

27%1v2 clutch win

11Longest win streak

Recent Faceit resultsLLWWL

Map	Matches	Win rate	Avg K/D	Avg kills
Ancient	171	70%	1.36	17.6
Dust2	125	63%	1.44	18.2
Mirage	109	58%	1.30	16.1
Anubis	108	58%	1.26	17.2
Nuke	84	70%	1.41	18.4
Inferno	64	58%	1.39	18.2
Vertigo	31	61%	1.48	15.6
Overpass	20	75%	1.35	16.4

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

Inventory

$18,166Inventory value79ItemsFull inventory breakdown →

$1,810

$1,808

$1,666

$1,445

$1,241

$1,025

Skill profile

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

26.4%Headshot accuracy

41.0%Accuracy (enemy spotted)

46.0%Spray accuracy

79.6%Counter-strafing

10.0°Preaim

519msReaction time

40.7%T opening success

59.6%CT opening success

0.62Enemies flashed / flash

12.1%Flash assists

12.71HE damage / grenade

15.75Flashes / match

Recommended for you

Spend your practice time on Train

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

40% win rate across 5 tracked games — your weakest map with enough games to be worth reading into.

Train callouts & strategy →

Recent matches

Map	Score	Rating	HS%	Date
ancient	13–11	0.03	27%	28 Jul →
boulder	13–1	0.08	45%	18 Jul →
cache	13–8	-0.02	19%	18 Jul →
dust2	5–13	0.02	19%	10 Jul →
mirage	13–8	0.20	24%	10 Jul →
cache	13–6	0.16	37%	10 Jul →
dust2	13–5	0.03	37%	10 Jul →
ancient	13–4	0.08	27%	29 Jun →
inferno	16–12	0.10	24%	22 Apr →
inferno	16–13	0.07	36%	6 Apr →
ancient	13–10	0.01	52%	6 Apr →
anubis	9–13	-0.00	36%	6 Apr →
ancient	10–13	-0.00	28%	1 Apr →
nuke	13–9	-0.00	38%	1 Apr →
inferno	13–2	0.05	24%	31 Mar →
ancient	9–13	-0.04	13%	30 Mar →
inferno	8–13	0.00	26%	30 Mar →
overpass	13–11	-0.00	14%	29 Mar →
ancient	13–9	0.02	20%	29 Mar →
overpass	6–13	-0.06	30%	29 Mar →
inferno	19–15	-0.01	20%	24 Mar →
nuke	11–13	0.01	22%	24 Mar →
ancient	6–13	-0.04	22%	24 Mar →
inferno	13–4	0.03	32%	24 Mar →
dust2	13–1	0.06	12%	24 Mar →
dust2	10–13	-0.03	32%	24 Mar →
nuke	3–13	-0.07	12%	15 Mar →
dust2	10–13	-0.02	20%	15 Mar →
overpass	13–7	0.02	11%	15 Mar →
ancient	13–8	0.08	33%	15 Mar →
dust2	23–25	-0.05	19%	15 Mar →
inferno	13–6	0.03	24%	15 Mar →
dust2	13–8	-0.02	10%	14 Mar →
inferno	13–11	0.00	28%	14 Mar →
overpass	11–13	-0.01	24%	13 Mar →
ancient	13–4	0.07	30%	13 Mar →
ancient	13–4	0.07	30%	13 Mar →
mirage	13–7	0.01	32%	21 Feb →
mirage	13–6	0.14	29%	13 Feb →
inferno	13–1	0.08	44%	13 Feb →
dust2	13–0	0.10	17%	13 Feb →
inferno	5–13	-0.04	28%	11 Feb →
overpass	8–13	-0.01	22%	11 Feb →
ancient	13–5	-0.04	29%	11 Feb →
nuke	19–17	0.01	27%	11 Feb →
ancient	20–22	0.01	13%	11 Feb →
inferno	13–7	-0.01	31%	11 Feb →
dust2	9–13	0.04	31%	10 Feb →
inferno	6–13	-0.08	40%	10 Feb →
nuke	13–10	-0.01	20%	8 Feb →
inferno	13–0	0.03	24%	8 Feb →
inferno	13–0	0.03	24%	8 Feb →
inferno	13–8	0.08	21%	7 Feb →
inferno	13–7	-0.00	26%	7 Feb →
ancient	13–2	0.13	19%	7 Feb →
ancient	13–11	0.02	15%	7 Feb →
ancient	13–0	0.00	50%	7 Feb →
overpass	14–16	-0.03	19%	4 Feb →
ancient	11–13	-0.03	15%	13 Jan →
mirage	11–13	-0.03	28%	13 Jan →
mirage	13–6	-0.01	16%	25 Dec →
dust2	11–13	-0.04	57%	12 Dec →
inferno	13–10	0.00	29%	12 Dec →
mirage	1–13	-0.07	6%	12 Dec →
inferno	13–9	0.01	22%	30 Nov →
dust2	11–13	-0.05	45%	30 Nov →
mirage	9–13	-0.02	25%	30 Nov →
nuke	10–13	-0.02	13%	29 Nov →
mirage	13–5	-0.03	20%	29 Nov →
dust2	13–7	0.03	32%	29 Nov →
mirage	13–2	0.14	26%	29 Nov →
dust2	14–16	-0.00	21%	29 Nov →
inferno	13–6	0.07	27%	29 Nov →
dust2	13–4	0.04	17%	17 Nov →
nuke	8–13	0.01	23%	16 Oct →
ancient	16–19	-0.06	39%	16 Oct →
train	13–7	0.05	28%	16 Oct →
nuke	10–13	0.02	31%	15 Oct →
inferno	9–13	-0.07	21%	15 Oct →
inferno	13–6	0.06	41%	12 Oct →
ancient	13–11	0.01	10%	5 Oct →
train	10–13	-0.03	15%	5 Oct →
nuke	13–4	-0.01	18%	5 Oct →
mirage	8–13	-0.06	21%	5 Oct →
ancient	11–13	0.06	35%	5 Oct →
overpass	13–9	0.09	29%	4 Oct →
ancient	13–3	-0.03	21%	4 Oct →
inferno	3–13	0.05	41%	4 Oct →
ancient	13–7	0.00	32%	4 Oct →
overpass	13–1	0.11	30%	4 Oct →
overpass	14–16	-0.04	19%	30 Sept →
overpass	13–9	-0.04	14%	30 Sept →
ancient	9–13	-0.02	23%	30 Sept →
mirage	13–16	0.02	24%	30 Sept →
train	3–13	-0.11	45%	28 Sept →
nuke	13–6	-0.03	23%	28 Sept →
ancient	13–9	0.01	33%	28 Sept →
ancient	16–13	0.04	29%	27 Sept →
train	13–10	0.00	38%	27 Sept →
train	11–13	-0.06	18%	26 Sept →