NEXT — CS2 Stats

76561199556572065[U:1:1596306337]Steam profile ↗✓ No bans

716Tracked matches59%Win rate2024Tracked since

CSDB Rating4.9 DevelopingHybrid Rifler

Premier CS Rating7,139Light Blue band · top ~68.6% of ranked players (population est.)

CSDB Leaderboard#32009 of 42608 tracked

FaceitLevel 4 · 800 ELOTop 81.0% of ranked FACEIT players

Ladder ranks via Leetify

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Performance scores

Aim63

Positioning52

Utility50

0–100 skill scores via Leetify.

Recent form

STEADY54–39–7Last 10054%Win rateLWWLWLWWTL

Last 10 vs previous 10: +10pp win rate · -0.03 avg rating

Player DNA

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

Aim6.3

Aggression6.6

Utility5.0

Positioning5.2

Opening Duels1.8

Clutch2.8

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 86% playstyle similarity

Most alike: utility contribution, opening-fight frequency.

Where you differ: lower opening-duel success; 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. 556ms 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

Aim6.3

Positioning5.2

Utility5.0

Mechanics5.2

Opening Duels1.8

Win Impact8.1

Composite 4.9/10 (Developing), 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 rating0.02+0.02

first ⅓ avg 0.00 → last ⅓ avg 0.02

Reaction time577ms−64ms

first ⅓ avg 641ms → last ⅓ avg 577ms

Headshot accuracy11.9%+0.0%

first ⅓ avg 11.8% → last ⅓ avg 11.9%

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

0.23Best rating — train 9–1

13–0Biggest win — dust2

6Longest win streak

8–8In matches decided by ≤2 rounds

4Overtime games

Map breakdown

trainBest map · 71% over 7anubisWeakest map · 0% over 7

Map	Grade	Played	Record	Win rate	Avg rating
dust2	A	29	17–12	59%	0.01
mirage	A	20	12–8	60%	0.01
nuke	A	13	8–5	62%	0.02
ancient	B	8	4–4	50%	0.02
inferno	B	8	4–4	50%	-0.00
anubis	D	7	0–7	0%	-0.01
train	S	7	5–2	71%	0.06
cache	—	4	2–2	50%	0.05
overpass	—	2	1–1	50%	0.07
office	—	2	1–1	50%	0.05

Across the last 100 tracked matches.

Anubis is currently your weakest sufficiently-sampled map (0% over 7). Start with the 6 essential Anubis lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

507Matches

50%Win rate

0.82Avg K/D

61.4ADR

33%Headshot %

Clutches & streaks

34%1v1 clutch win

17%1v2 clutch win

10Longest win streak

Recent Faceit resultsWLLLW

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	184	58%	0.85	11.2
Dust2	126	47%	0.79	11.5
Inferno	73	48%	0.86	12.7
Ancient	66	48%	0.79	12.6
Nuke	20	45%	0.86	12.3
Anubis	19	32%	0.72	11.5
Overpass	9	56%	0.67	10.3
Train	5	40%	0.63	10.6

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.

11.8%Headshot accuracy

38.9%Accuracy (enemy spotted)

39.3%Spray accuracy

73.3%Counter-strafing

11.6°Preaim

556msReaction time

37.0%T opening success

37.6%CT opening success

0.59Enemies flashed / flash

3.7%Flash assists

4.20HE damage / grenade

8.59Flashes / 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.

Best CS2 Crosshair
Your shots are landing on bodies more often than heads — usually a crosshair-height and placement habit rather than raw aim.
Headshot accuracy 11.7553% — below the 15% mark we flag
Aim Training →
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 37.0222% — 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.

0% 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
cache	12–16	0.07	11%	30 Aug →
ancient	13–11	-0.03	15%	28 Aug →
nuke	13–4	0.04	16%	27 Aug →
anubis	9–13	-0.04	14%	27 Aug →
ancient	13–11	-0.01	8%	27 Aug →
cache	10–13	0.01	15%	26 Aug →
inferno	13–5	0.05	4%	26 Aug →
dust2	13–3	0.12	13%	26 Aug →
inferno	15–15	0.02	8%	25 Aug →
mirage	14–16	0.02	8%	25 Aug →
ancient	9–13	0.03	7%	24 Aug →
dust2	13–10	0.03	23%	24 Aug →
dust2	7–13	0.13	21%	23 Aug →
dust2	8–13	0.06	12%	23 Aug →
ancient	13–10	0.07	18%	23 Aug →
ancient	13–1	0.04	7%	23 Aug →
cache	13–5	0.10	10%	22 Aug →
mirage	12–12	0.08	10%	10 May →
dust2	12–12	0.00	12%	10 May →
nuke	11–13	0.01	10%	10 May →
inferno	13–4	-0.03	10%	10 May →
train	9–1	0.23	6%	10 May →
dust2	13–8	-0.03	10%	8 May →
cache	13–3	0.03	11%	7 May →
mirage	13–6	-0.03	17%	7 May →
dust2	13–3	-0.09	3%	7 May →
train	11–13	-0.01	19%	6 May →
nuke	13–4	-0.03	8%	4 May →
anubis	1–13	-0.03	13%	4 May →
inferno	7–13	-0.04	6%	3 May →
nuke	13–11	0.06	11%	2 May →
dust2	13–5	0.02	17%	2 May →
dust2	7–13	-0.04	17%	2 May →
dust2	13–1	-0.03	17%	2 May →
dust2	13–7	-0.02	7%	1 May →
dust2	13–11	0.01	13%	1 May →
mirage	13–3	0.11	3%	1 May →
mirage	13–11	-0.04	6%	29 Apr →
mirage	0–10	-0.05	0%	29 Apr →
nuke	9–13	0.05	7%	28 Apr →
anubis	12–12	0.02	4%	27 Apr →
dust2	13–9	-0.02	4%	27 Apr →
dust2	5–13	-0.05	8%	26 Apr →
inferno	13–7	0.01	2%	25 Apr →
dust2	13–9	0.02	8%	25 Apr →
inferno	13–6	0.01	8%	24 Apr →
train	13–7	-0.04	10%	24 Apr →
ancient	5–13	-0.00	14%	23 Apr →
anubis	7–13	-0.06	8%	22 Apr →
train	10–13	-0.02	2%	22 Apr →
nuke	13–1	0.06	3%	20 Apr →
anubis	8–13	0.00	7%	20 Apr →
dust2	8–13	0.04	16%	20 Apr →
mirage	8–13	0.02	14%	19 Apr →
mirage	13–11	-0.05	11%	19 Apr →
dust2	10–13	0.06	15%	18 Apr →
nuke	13–9	0.03	5%	18 Apr →
dust2	13–0	0.08	14%	18 Apr →
dust2	13–10	0.01	9%	18 Apr →
dust2	13–3	0.07	6%	18 Apr →
overpass	13–2	0.12	17%	16 Apr →
dust2	11–13	-0.08	2%	15 Apr →
mirage	13–8	0.08	12%	14 Apr →
inferno	10–13	-0.01	5%	13 Apr →
nuke	13–8	0.10	16%	12 Apr →
train	13–1	0.21	21%	11 Apr →
train	13–3	0.11	13%	11 Apr →
dust2	6–13	-0.03	8%	9 Apr →
mirage	13–10	-0.06	12%	9 Apr →
dust2	13–11	-0.01	17%	8 Apr →
inferno	5–13	-0.05	4%	8 Apr →
nuke	7–4	-0.03	11%	8 Apr →
mirage	13–9	-0.03	16%	8 Apr →
nuke	8–1	0.03	6%	6 Apr →
ancient	11–13	0.05	14%	6 Apr →
anubis	11–13	0.05	12%	5 Apr →
train	13–6	-0.06	17%	5 Apr →
dust2	16–12	-0.03	15%	3 Apr →
mirage	13–6	0.05	12%	1 Apr →
dust2	13–5	-0.04	5%	31 Mar →
nuke	1–13	-0.04	9%	31 Mar →
mirage	5–13	-0.02	9%	31 Mar →
mirage	7–13	-0.01	10%	29 Mar →
mirage	10–13	-0.02	14%	29 Mar →
office	12–12	0.12	14%	24 Mar →
office	13–6	-0.02	11%	24 Mar →
mirage	13–2	0.02	23%	23 Mar →
nuke	3–13	-0.01	2%	23 Mar →
ancient	12–12	0.03	15%	22 Mar →
mirage	13–10	-0.01	16%	22 Mar →
mirage	13–5	0.06	14%	22 Mar →
overpass	11–13	0.03	8%	22 Mar →
mirage	13–11	0.11	10%	21 Mar →
dust2	6–13	0.05	14%	20 Mar →
nuke	11–13	0.03	7%	19 Mar →
anubis	7–13	-0.03	10%	18 Mar →
mirage	7–13	-0.04	15%	18 Mar →
dust2	12–12	0.01	13%	17 Mar →
dust2	10–13	0.01	14%	16 Mar →
dust2	13–2	-0.01	15%	16 Mar →