𝒥𝒐𝔂𝓑𝒐𝔂 — CS2 Stats

76561199248599880[U:1:1288334152]Steam profile ↗✓ No bans

1,382Tracked matches48%Win rate2023Tracked since

CSDB Rating6.2 SolidAggressive Rifler

Premier CS Rating15,887Purple band · top ~24.9% of ranked players (population est.)

CSDB Leaderboard#31830 of 55999 tracked

FaceitLevel 3Top 90.6% of ranked FACEIT players

WingmanSilver Elite Master

Ladder ranks via Leetify

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Performance scores

Aim79

Positioning57

Utility45

0–100 skill scores via Leetify.

Recent form

STEADY44–49–7Last 10044%Win rateWWLLLLWLWW

Last 10 vs previous 10: +10pp win rate · +0.05 avg rating

Player DNA

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

Aim7.9

Aggression7.7

Utility4.5

Positioning5.7

Opening Duels3.2

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.

Your pro match

Plays most like ropz 90% playstyle similarity

Most alike: positioning profile, utility contribution.

Where you differ: 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

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

Reaction time. 617ms 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

Aim7.9

Positioning5.7

Utility4.5

Mechanics8.4

Opening Duels3.2

Win Impact4.4

Composite 6.2/10 (Solid), 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+0.02

first ⅓ avg 0.00 → last ⅓ avg 0.02

Reaction time604ms−47ms

first ⅓ avg 651ms → last ⅓ avg 604ms

Headshot accuracy23.5%+4.1%

first ⅓ avg 19.5% → last ⅓ avg 23.5%

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

0.18Best rating — dust2 13–7

13–1Biggest win — cache

3Longest win streak

W2Current streak

4–6In matches decided by ≤2 rounds

8Overtime games

Map breakdown

infernoBest map · 55% over 11ancientWeakest map · 33% over 12

Map	Grade	Played	Record	Win rate	Avg rating
train	C	18	7–11	39%	-0.00
dust2	C	16	7–9	44%	-0.00
mirage	B	13	6–7	46%	-0.00
ancient	D	12	4–8	33%	0.01
inferno	A	11	6–5	55%	-0.00
nuke	B	11	5–6	45%	0.02
anubis	B	6	3–3	50%	0.01
office	C	5	2–3	40%	0.01
cache	—	4	2–2	50%	-0.01
overpass	—	2	1–1	50%	-0.01
vertigo	—	1	0–1	0%	0.01
shelter	—	1	1–0	100%	-0.01

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (33% over 12). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

25Matches

40%Win rate

1.00Avg K/D

87.5ADR

44%Headshot %

Clutches & streaks

35%1v1 clutch win

32%1v2 clutch win

3Longest win streak

Recent Faceit resultsWLLLL

Map	Matches	Win rate	Avg K/D	Avg kills
Inferno	4	50%	1.40	17.8
Mirage	4	25%	0.46	5.5
Vertigo	3	33%	1.12	18.3
Anubis	3	33%	1.36	20.3
Ancient	2	100%	1.32	15.0
Nuke	1	0%	1.08	14.0

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.

23.2%Headshot accuracy

33.6%Accuracy (enemy spotted)

37.8%Spray accuracy

87.7%Counter-strafing

9.3°Preaim

617msReaction time

36.9%T opening success

48.7%CT opening success

0.54Enemies flashed / flash

4.4%Flash assists

5.10HE damage / grenade

11.28Flashes / 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 36.8706% — 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.

33% win rate across 12 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
mirage	13–5	0.08	28%	2 Aug →
inferno	13–4	0.16	21%	2 Aug →
ancient	3–13	-0.02	33%	1 Aug →
ancient	11–13	0.00	22%	30 Jul →
nuke	10–13	0.01	17%	29 Jul →
vertigo	10–13	0.01	22%	29 Jul →
mirage	13–8	-0.01	27%	29 Jul →
dust2	9–13	0.01	30%	29 Jul →
dust2	13–10	0.07	26%	24 Jul →
dust2	13–7	0.18	23%	24 Jul →
ancient	13–7	0.04	26%	24 Jul →
mirage	4–13	-0.01	28%	23 Jul →
nuke	10–13	0.07	27%	23 Jul →
dust2	8–13	-0.03	15%	23 Jul →
office	13–10	0.07	52%	23 Jul →
ancient	15–15	0.03	19%	23 Jul →
nuke	13–10	0.00	24%	23 Jul →
dust2	0–13	-0.05	0%	23 Jul →
mirage	13–5	-0.02	29%	23 Jul →
cache	1–13	-0.10	25%	23 Jul →
ancient	16–13	0.06	15%	22 Jul →
anubis	11–13	0.06	19%	22 Jul →
dust2	16–14	-0.00	12%	22 Jul →
anubis	13–4	0.14	21%	22 Jul →
office	13–6	0.05	34%	22 Jul →
anubis	5–13	-0.06	14%	22 Jul →
train	9–13	-0.02	15%	22 Jul →
cache	13–5	0.03	27%	21 Jul →
inferno	6–9	0.04	16%	21 Jul →
inferno	4–9	-0.14	40%	21 Jul →
nuke	13–16	0.02	22%	21 Jul →
anubis	11–13	0.04	26%	21 Jul →
nuke	13–10	0.06	22%	21 Jul →
inferno	6–9	-0.03	14%	21 Jul →
inferno	5–9	-0.11	25%	21 Jul →
nuke	4–13	0.02	10%	21 Jul →
mirage	7–13	-0.04	13%	21 Jul →
mirage	8–13	0.03	18%	20 Jul →
overpass	13–8	0.02	28%	20 Jul →
office	5–13	-0.04	10%	20 Jul →
dust2	12–12	-0.02	19%	20 Jul →
inferno	9–5	-0.06	17%	20 Jul →
mirage	9–13	-0.01	19%	18 Jul →
ancient	13–9	0.03	8%	18 Jul →
dust2	13–9	-0.05	16%	18 Jul →
ancient	13–2	0.04	20%	18 Jul →
ancient	5–13	-0.07	6%	17 Jul →
inferno	8–8	0.00	15%	17 Jul →
dust2	7–13	-0.03	17%	17 Jul →
anubis	13–8	-0.05	20%	16 Jul →
cache	10–13	0.03	21%	16 Jul →
cache	13–1	0.01	13%	14 Jul →
dust2	12–12	-0.02	18%	14 Jul →
shelter	13–7	-0.01	24%	13 Jul →
office	6–13	-0.04	29%	13 Jul →
office	8–13	0.01	18%	12 Jul →
nuke	13–5	0.03	21%	12 Apr →
dust2	12–12	0.02	13%	8 Nov →
train	8–13	-0.05	35%	7 Nov →
ancient	5–13	-0.10	9%	2 Nov →
train	7–13	-0.00	21%	2 Nov →
train	13–11	0.02	26%	2 Nov →
anubis	13–4	-0.08	19%	1 Nov →
train	5–13	-0.06	24%	1 Nov →
nuke	13–10	-0.02	20%	1 Nov →
mirage	4–12	0.03	21%	1 Nov →
nuke	13–9	0.04	10%	31 Oct →
inferno	13–3	0.00	21%	31 Oct →
dust2	12–12	0.01	26%	31 Oct →
nuke	0–5	-0.06	9%	30 Oct →
train	12–12	-0.06	19%	26 Oct →
train	13–11	0.03	19%	26 Oct →
dust2	13–7	-0.01	15%	25 Oct →
ancient	14–16	0.05	21%	25 Oct →
train	8–13	0.06	22%	24 Oct →
train	13–3	0.02	18%	23 Oct →
nuke	3–13	0.04	36%	21 Oct →
train	13–2	-0.02	20%	19 Oct →
dust2	6–13	-0.09	25%	19 Oct →
inferno	13–8	-0.00	17%	19 Oct →
train	13–9	0.02	26%	18 Oct →
train	7–13	-0.10	20%	17 Oct →
mirage	13–7	-0.01	14%	17 Oct →
train	10–13	-0.01	20%	17 Oct →
ancient	8–13	0.06	16%	17 Oct →
train	7–13	-0.08	19%	17 Oct →
train	11–13	-0.02	26%	16 Oct →
train	13–3	0.10	21%	16 Oct →
train	13–5	0.09	16%	16 Oct →
mirage	8–13	-0.03	12%	16 Oct →
mirage	16–14	-0.02	17%	16 Oct →
train	11–13	0.04	16%	16 Oct →
ancient	12–16	0.02	21%	16 Oct →
inferno	13–9	-0.03	24%	16 Oct →
dust2	13–6	-0.02	18%	15 Oct →
inferno	13–10	0.14	14%	15 Oct →
overpass	12–16	-0.03	16%	15 Oct →
dust2	11–5	-0.01	18%	15 Oct →
mirage	13–7	0.05	24%	15 Oct →
mirage	5–13	-0.04	17%	14 Oct →