flamerje^ — CS2 Stats

PT76561198181682333[U:1:221416605]Steam profile ↗✓ No bans

2,313Tracked matches50%Win rate2020Tracked since

5,174Hours in CS58Hrs last 2 wks

CSDB Rating5.9 SolidAggressive Rifler

WingmanSupreme Master First Class

Ladder ranks via Leetify

Performance scores

Aim75

Positioning55

Utility51

0–100 skill scores via Leetify.

Recent form

STEADY55–45Last 10055%Win rateLLWLLWWLLW

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

Player DNA

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

Aim7.5

Aggression7.2

Utility5.1

Positioning5.5

Opening Duels2.5

Clutch3.0

Limited utility dependence

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 90% playstyle similarity

Most alike: opening-fight frequency, opening-duel success.

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

Reaction time. 563ms 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

Aim7.5

Positioning5.5

Utility5.1

Mechanics6.4

Opening Duels2.5

Win Impact5.0

Composite 5.9/10 (Solid), a weighted mean of the bars with a small opposition adjustment (×1.02 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.01−0.01

first ⅓ avg -0.00 → last ⅓ avg -0.01

Reaction time577ms−4ms

first ⅓ avg 581ms → last ⅓ avg 577ms

Headshot accuracy23.8%−0.3%

first ⅓ avg 24.1% → last ⅓ avg 23.8%

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

0.15Best rating — ancient 13–6

13–3Biggest win — inferno

5Longest win streak

L2Current streak

10–8In matches decided by ≤2 rounds

13Overtime games

Map breakdown

nukeBest map · 65% over 17trainWeakest map · 20% over 10

Map	Grade	Played	Record	Win rate	Avg rating
ancient	A	33	19–14	58%	-0.01
nuke	S	17	11–6	65%	-0.00
inferno	A	15	9–6	60%	0.00
anubis	A	13	8–5	62%	0.01
train	D	10	2–8	20%	-0.00
overpass	C	5	2–3	40%	-0.01
mirage	—	4	1–3	25%	0.03
dust2	—	3	3–0	100%	0.00

Across the last 100 tracked matches.

Lifetime stats

116,755Lifetime kills

1.09K/D · Top 50% of tracked players

5,291Matches

51.1%Match win rate · Top 25% of tracked players

51.9%Headshot % · Top 25% of tracked players

11.9%Shot accuracy

18,474MVPs

2,163Hours (in match)

7,126Bombs planted

1,331Bombs defused

Most-used weapons

AK-4743,910

M4A4 / M4A1-S18,305

AWP17,167

Desert Eagle7,883

P2000 / USP-S5,541

Glock-184,052

Knife2,097

Galil AR1,917

FAMAS1,910

SSG 081,450

Lifetime map wins

8,335dust2

5,722inferno

3,147nuke

2,860train

2,441vertigo

1,048cbble

110safehouse

104lake

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

Faceit stats

Combat

2,031Matches

52%Win rate

1.07Avg K/D

78.5ADR

56%Headshot %

Clutches & streaks

35%1v1 clutch win

19%1v2 clutch win

10Longest win streak

Recent Faceit resultsLWLLL

Map	Matches	Win rate	Avg K/D	Avg kills
Ancient	450	54%	1.05	16.8
Anubis	342	54%	1.13	17.5
Nuke	192	46%	1.12	15.3
Inferno	170	51%	1.05	15.8
Vertigo	104	54%	1.19	18.5
Overpass	104	60%	1.10	16.5
Train	70	47%	1.06	16.9
Dust2	59	49%	0.97	15.8

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.

23.9%Headshot accuracy

32.6%Accuracy (enemy spotted)

33.7%Spray accuracy

78.9%Counter-strafing

9.1°Preaim

563msReaction time

34.4%T opening success

45.3%CT opening success

0.64Enemies flashed / flash

8.2%Flash assists

7.38HE damage / grenade

4.74Flashes / 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 34.3844% — below the 40% mark we flag

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.

20% win rate across 10 tracked games — your weakest map with enough games to be worth reading into.

Train callouts & strategy →

Recent matches

Map	Score	Rating	HS%	Date
anubis	11–13	-0.03	15%	18 Aug →
inferno	6–13	-0.04	23%	17 Aug →
ancient	13–9	-0.01	25%	24 May →
overpass	5–13	-0.03	21%	24 May →
ancient	3–13	-0.06	24%	24 May →
ancient	13–7	-0.02	14%	10 May →
inferno	13–3	0.03	31%	9 May →
inferno	8–13	-0.01	15%	1 May →
anubis	13–16	-0.02	25%	21 Mar →
ancient	13–10	-0.02	25%	20 Mar →
ancient	13–8	-0.02	28%	12 Mar →
anubis	9–13	-0.01	19%	5 Mar →
nuke	13–7	0.03	24%	4 Mar →
ancient	9–13	-0.03	19%	3 Mar →
ancient	13–7	-0.02	38%	3 Mar →
anubis	4–13	-0.03	37%	3 Mar →
ancient	13–7	0.03	31%	2 Mar →
nuke	9–13	-0.03	33%	18 Jan →
train	13–6	-0.01	15%	17 Jan →
ancient	4–13	-0.05	25%	17 Jan →
anubis	13–5	0.04	15%	17 Jan →
anubis	19–17	0.02	22%	12 Jan →
train	11–13	0.01	31%	11 Jan →
anubis	11–13	-0.06	25%	11 Jan →
train	13–7	0.06	31%	9 Jan →
train	21–25	-0.04	17%	4 Jan →
ancient	13–6	-0.05	23%	4 Jan →
dust2	13–11	0.02	39%	4 Jan →
overpass	6–13	-0.01	16%	4 Jan →
nuke	13–9	0.04	21%	4 Jan →
train	10–13	-0.01	20%	4 Jan →
overpass	16–13	0.02	17%	4 Jan →
train	10–13	-0.06	24%	4 Jan →
ancient	10–13	-0.02	32%	2 Jan →
ancient	6–13	-0.07	15%	2 Jan →
inferno	7–13	-0.02	29%	31 Dec →
ancient	13–6	0.15	23%	31 Dec →
nuke	9–13	-0.00	26%	30 Dec →
ancient	3–13	0.05	42%	6 Dec →
inferno	13–6	-0.01	35%	6 Dec →
train	11–13	-0.00	35%	6 Dec →
dust2	13–9	0.05	30%	23 Oct →
mirage	6–8	-0.03	14%	19 Oct →
dust2	13–10	-0.07	17%	18 Oct →
ancient	13–11	-0.00	31%	17 Oct →
ancient	13–11	0.02	37%	17 Oct →
train	9–13	-0.03	23%	16 Oct →
nuke	8–13	0.02	25%	10 Oct →
ancient	16–14	-0.06	18%	6 Oct →
overpass	13–8	-0.04	15%	6 Oct →
nuke	16–13	0.02	23%	5 Oct →
ancient	13–5	0.01	25%	5 Oct →
ancient	13–11	0.02	31%	5 Oct →
ancient	8–13	0.05	22%	4 Oct →
train	5–13	0.07	30%	29 Sept →
nuke	13–6	0.05	32%	28 Sept →
inferno	11–13	0.05	32%	27 Sept →
ancient	19–17	-0.00	23%	24 Sept →
nuke	13–8	0.02	28%	23 Sept →
inferno	13–9	-0.03	17%	23 Sept →
ancient	10–13	-0.04	24%	21 Sept →
nuke	13–9	0.02	33%	21 Sept →
ancient	13–6	0.03	27%	18 Sept →
ancient	13–4	0.05	28%	18 Sept →
ancient	13–11	-0.01	25%	14 Sept →
mirage	13–10	0.04	35%	13 Sept →
train	11–13	-0.01	24%	13 Sept →
overpass	7–12	-0.01	13%	12 Sept →
nuke	16–12	-0.01	25%	2 Sept →
anubis	13–7	0.03	38%	13 Jul →
ancient	10–13	-0.06	18%	29 Jun →
ancient	2–13	-0.09	5%	22 Jun →
mirage	10–13	0.07	19%	14 Jun →
anubis	13–6	0.02	18%	7 Jun →
nuke	1–13	-0.09	12%	28 May →
nuke	8–13	-0.03	16%	18 May →
inferno	16–14	-0.02	38%	12 May →
inferno	13–8	-0.01	22%	1 May →
nuke	13–7	-0.09	22%	28 Apr →
nuke	13–7	-0.04	22%	10 Apr →
ancient	6–13	-0.07	21%	25 Mar →
nuke	13–8	-0.02	27%	18 Mar →
inferno	16–14	-0.02	37%	17 Mar →
ancient	13–10	0.03	27%	17 Mar →
ancient	13–3	-0.00	29%	9 Mar →
anubis	13–7	0.06	26%	9 Mar →
ancient	9–13	0.05	24%	8 Mar →
inferno	13–8	0.08	23%	8 Mar →
anubis	13–3	0.05	14%	7 Mar →
mirage	11–13	0.03	27%	5 Mar →
inferno	13–9	-0.04	17%	4 Mar →
ancient	4–13	-0.04	20%	4 Mar →
nuke	7–13	0.01	30%	4 Mar →
nuke	13–5	0.09	33%	4 Mar →
inferno	13–9	0.04	25%	4 Mar →
anubis	16–12	0.05	35%	27 Feb →
anubis	13–7	-0.02	30%	25 Feb →
ancient	7–13	-0.08	22%	24 Feb →
inferno	12–16	-0.02	26%	24 Feb →
inferno	12–16	0.01	35%	23 Feb →