Gravida 17 luni — CS2 Stats

76561198352019831[U:1:391754103]Steam profile ↗✓ No bans

418Tracked matches25%Win rate2020Tracked since

1,261Hours in CS

CSDB Rating3.0 LearningSupport

CSDB Leaderboard#173629 of 222028 tracked

FaceitLevel 4 · 1,000 ELOTop 81.0% of ranked FACEIT players

WingmanGold Nova II

Ladder ranks via Leetify

Track this profile

CSDB reads this profile's Premier rating from its tracked match history, and records Faceit ELO once on every day the page is viewed. 37 days played since 20 Apr 2026. Come back after the next session and the change shows above.

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Rating over time

Premier CS Rating: 4,244 +707 3 May – 23 Jun · 22 days played

4,769Peak Premier in tracked matches

1,000Highest Faceit ELO seen on CSDB

37Days played since 2026-04-20

Faceit ELO

At peak — 1,000
Next: Level 5 at 1,051 — 51 to go
Reached: Level 4 · Level 3 · Level 2

Premier comes from this profile’s tracked match history, so it reaches back as far as those matches do. Faceit ELO is CSDB’s own observation — no feed exposes ELO per match, so that line only has the days the profile was viewed and cannot be backfilled.

How this compares with the same rank

Median values for Level 4 among CSDB-tracked players (n=11,988), from Valve's own lifetime stats. Aim, positioning and utility scores are deliberately not benchmarked here — those are a third-party provider's derived metrics, which CSDB does not store.

Metric	This player	Level 4 median	Level 5 median	vs Level 5
Headshot rate	45.1%	42.7%	43.8%	above
Shot accuracy	12.9%	11.2%	12.0%	above
Kill/death ratio	0.88	0.99	1.02	0.14 short
Match win rate	42.6%	43.9%	44.7%	2.1% short

This profile matches the typical Level 5 player on 2 of 4 comparable metrics.

Widest gap: Kill/death ratio. That is the metric furthest from the Level 5 median in relative terms — not necessarily the one holding a rank back, which no statistic here can establish.

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Performance scores

Aim41

Positioning35

Utility45

0–100 skill scores via Leetify.

Recent form

COLD30–65–5Last 10030%Win rateWWLLLLWLLL

Last 10 vs previous 10: +10pp win rate · +0.03 avg rating · +1.5pp headshot accuracy · +86ms reaction

Win rate +10pp 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

2–3 · 40% win rate
Avg rating -0.01
Avg headshot accuracy 24%
Avg reaction 686ms

Last 10

3–7 · 30% win rate
Avg rating -0.03
Avg headshot accuracy 20%
Avg reaction 719ms

Last 20

5–15 · 25% win rate
Avg rating -0.04
Avg headshot accuracy 20%
Avg reaction 676ms

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: Support — Utility contribution stands above the rest of this profile (+2.5 against its own average).

Aim4.1

Utility4.5

Positioning3.5

Opening Duels0.0

Clutch0.0

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 Twistzz 75% playstyle similarity

Most alike: positioning profile, opening-duel success.

Where you differ: lower utility contribution; 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

Areas to improve

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

Aim4.1

Positioning3.5

Utility4.5

Mechanics4.5

Opening Duels0.0

Win Impact0.0

Composite 3.0/10 (Learning), a weighted mean of the bars with a small opposition adjustment (×0.90 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.05−0.01

first ⅓ avg -0.04 → last ⅓ avg -0.05

Reaction time686ms−8ms

first ⅓ avg 694ms → last ⅓ avg 686ms

Headshot accuracy21.2%+1.1%

first ⅓ avg 20.1% → last ⅓ avg 21.2%

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

0.08Best match rating · 13–2 · mirage, 4 May →

57%Best headshot accuracy · 1–13 · overpass, 27 May →

469msFastest reaction time · 2–13 · nuke, 23 Apr →

13–1Biggest win · dust2, 26 Apr →

Across the last 100 tracked matches.

Highlights

5Longest win streak

W2Current streak

5–5In matches decided by ≤2 rounds

6Overtime games

Map breakdown

mirageBest map · 35% over 17infernoWeakest map · 17% over 6

Map	Grade	Played	Record	Win rate	Avg rating
dust2	D	43	14–29	33%	-0.04
mirage	C	17	6–11	35%	-0.04
cache	D	14	4–10	29%	-0.04
nuke	D	7	2–5	29%	-0.06
inferno	D	6	1–5	17%	-0.04
ancient	D	5	1–4	20%	-0.05
anubis	—	3	0–3	0%	-0.06
overpass	—	3	1–2	33%	-0.04
train	—	2	1–1	50%	-0.05

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (17% over 6). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

56,151Lifetime kills

0.88K/D

3,000Matches

42.6%Match win rate

45.1%Headshot % · Top 50% of Level 4 players

12.9%Shot accuracy · Top 50% of Level 4 players

7,008MVPs

1,261Hours (in match)

1,993Bombs planted

468Bombs defused

Most-used weapons

AK-477,187

AWP6,933

P905,570

M4A4 / M4A1-S4,500

Desert Eagle3,807

P2000 / USP-S2,700

SG 5532,441

AUG2,176

Glock-182,018

Negev1,578

Lifetime map wins

15,517dust2

3,457office

2,979inferno

1,088nuke

565vertigo

334train

84cbble

29safehouse

Lifetime totals via Steam — visible because this profile's game details are public. Spans CS:GO and CS2.

Faceit stats

Combat

437Matches

48%Win rate

0.95Avg K/D

52.3ADR

45%Headshot %

Clutches & streaks

0%1v1 clutch win

33%1v2 clutch win

7Longest win streak

Recent Faceit resultsWLLWL

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	2	0%	0.54	7.5
Overpass	1	100%	1.13	17.0
Anubis	1	0%	0.29	4.0
Cache	1	100%	0.65	11.0

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.

21.2%Headshot accuracy

27.0%Accuracy (enemy spotted)

27.4%Spray accuracy

70.5%Counter-strafing

9.4°Preaim

673msReaction time

22.7%T opening success

29.5%CT opening success

0.65Enemies flashed / flash

2.8%Flash assists

7.08HE damage / grenade

9.93Flashes / 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 22.6577% — 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.

17% 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
dust2	13–7	0.05	19%	3 Sept →
dust2	13–8	-0.02	17%	3 Sept →
cache	5–13	-0.03	23%	2 Sept →
anubis	5–13	-0.02	33%	2 Sept →
dust2	11–13	-0.03	28%	2 Sept →
dust2	1–13	-0.10	23%	22 Jul →
dust2	13–2	-0.01	21%	21 Jul →
cache	5–13	-0.03	17%	10 Jul →
ancient	9–13	-0.04	11%	23 Jun →
cache	6–13	-0.03	11%	23 Jun →
dust2	3–13	0.01	17%	23 Jun →
mirage	2–13	-0.09	12%	17 Jun →
dust2	4–13	-0.08	18%	11 Jun →
dust2	16–14	-0.03	36%	11 Jun →
nuke	0–13	-0.10	7%	11 Jun →
inferno	2–13	-0.05	28%	10 Jun →
dust2	6–13	-0.06	17%	10 Jun →
dust2	13–10	-0.08	4%	10 Jun →
mirage	9–13	0.01	30%	9 Jun →
mirage	13–16	-0.06	21%	9 Jun →
mirage	10–13	-0.05	35%	8 Jun →
nuke	1–13	-0.08	28%	8 Jun →
dust2	13–11	-0.03	28%	1 Jun →
inferno	6–13	-0.04	18%	1 Jun →
dust2	9–13	-0.08	32%	1 Jun →
cache	3–13	-0.04	11%	1 Jun →
cache	13–7	-0.04	23%	1 Jun →
cache	12–12	0.00	13%	28 May →
anubis	2–13	-0.10	18%	28 May →
dust2	12–12	-0.06	21%	27 May →
cache	12–12	-0.09	15%	27 May →
overpass	1–13	-0.08	57%	27 May →
cache	2–13	-0.10	7%	27 May →
dust2	13–8	0.00	15%	26 May →
nuke	13–5	-0.05	10%	26 May →
cache	3–13	-0.06	8%	25 May →
dust2	2–13	-0.09	10%	25 May →
mirage	13–5	0.01	11%	25 May →
dust2	8–13	-0.01	7%	21 May →
dust2	2–13	-0.07	18%	21 May →
cache	4–13	-0.07	15%	21 May →
nuke	6–13	0.01	20%	21 May →
mirage	8–13	-0.06	13%	21 May →
mirage	7–13	-0.04	14%	20 May →
dust2	5–13	-0.05	17%	20 May →
dust2	4–13	-0.04	25%	18 May →
dust2	15–15	-0.04	24%	18 May →
dust2	1–13	-0.09	33%	18 May →
mirage	4–13	-0.09	17%	18 May →
overpass	9–13	-0.03	15%	18 May →
dust2	2–13	-0.04	15%	17 May →
cache	13–9	-0.06	18%	17 May →
cache	12–6	0.01	7%	14 May →
dust2	13–9	-0.07	12%	14 May →
dust2	13–11	-0.07	11%	13 May →
dust2	16–14	0.03	14%	12 May →
dust2	5–13	0.00	24%	8 May →
ancient	13–16	-0.01	13%	8 May →
mirage	8–13	-0.08	21%	7 May →
dust2	3–13	-0.11	10%	7 May →
mirage	6–13	-0.07	0%	6 May →
mirage	13–2	0.08	27%	4 May →
anubis	2–13	-0.07	14%	4 May →
mirage	13–6	-0.00	22%	3 May →
dust2	7–13	-0.02	23%	3 May →
mirage	13–4	0.01	13%	3 May →
overpass	13–5	-0.03	17%	3 May →
inferno	13–10	-0.05	23%	3 May →
ancient	13–16	-0.10	25%	3 May →
dust2	11–13	-0.04	17%	3 May →
cache	13–9	0.01	27%	3 May →
cache	6–13	0.02	16%	29 Apr →
dust2	8–13	-0.07	20%	29 Apr →
ancient	13–5	-0.07	0%	29 Apr →
inferno	2–13	-0.09	25%	28 Apr →
dust2	9–13	-0.01	17%	28 Apr →
inferno	11–13	0.00	23%	28 Apr →
train	5–13	-0.03	25%	26 Apr →
dust2	11–13	-0.07	22%	26 Apr →
dust2	13–1	-0.02	0%	26 Apr →
dust2	1–13	-0.02	10%	26 Apr →
dust2	3–13	-0.04	15%	26 Apr →
dust2	13–7	0.01	18%	25 Apr →
dust2	13–10	0.04	17%	25 Apr →
nuke	7–13	-0.07	14%	24 Apr →
nuke	13–11	-0.04	18%	24 Apr →
dust2	7–13	-0.00	22%	24 Apr →
mirage	7–13	-0.07	22%	24 Apr →
mirage	13–5	-0.08	19%	23 Apr →
nuke	2–13	-0.10	20%	23 Apr →
mirage	7–13	-0.06	21%	23 Apr →
train	13–5	-0.07	33%	22 Apr →
dust2	4–13	-0.13	43%	22 Apr →
dust2	13–7	-0.02	11%	22 Apr →
inferno	11–13	-0.02	23%	21 Apr →
ancient	9–13	-0.02	24%	21 Apr →
mirage	13–4	0.02	15%	21 Apr →
dust2	9–13	-0.09	25%	21 Apr →
dust2	5–13	-0.06	27%	21 Apr →
dust2	12–12	0.03	27%	20 Apr →