mylly — CS2 Stats

76561198256594307[U:1:296328579]✓ No bans

3,183Tracked matches37%Win rate2020Tracked since

CSDB Rating5.1 Developing

Premier CS Rating16,454Purple band · top ~22.6% of ranked players (population est.)

FaceitLevel 6Top 54.0% of ranked FACEIT players

Ladder ranks via Leetify

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Performance scores

Aim59

Positioning43

Utility64

0–100 skill scores via Leetify.

Recent form

STEADY47–48–5Last 10047%Win rateLLWWWLLLLW

Last 10 vs previous 10: +0pp win rate · -0.02 avg rating

Player DNA

Aim5.9

Aggression4.9

Utility6.4

Positioning4.3

Opening Duels1.2

Clutch3.2

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 NiKo 85% playstyle similarity

Most alike: utility contribution, opening-fight frequency.

Where you differ: lower positioning profile; 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

T-side openings. Opening success drops from 45% on CT to 25% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 756ms 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

Aim5.9

Positioning4.3

Utility6.4

Mechanics8.5

Opening Duels1.8

Win Impact0.6

Composite 5.1/10 (Developing), 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 rating-0.01+0.02

first ⅓ avg -0.03 → last ⅓ avg -0.01

Reaction time755ms+35ms

first ⅓ avg 719ms → last ⅓ avg 755ms

Headshot accuracy20.0%+0.1%

first ⅓ avg 20.0% → last ⅓ avg 20.0%

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

0.12Best rating — dust2 13–6

13–4Biggest win — cache

6Longest win streak

L2Current streak

8–4In matches decided by ≤2 rounds

12Overtime games

Map breakdown

trainBest map · 70% over 10infernoWeakest map · 22% over 9

Map	Grade	Played	Record	Win rate	Avg rating
dust2	B	28	13–15	46%	-0.01
mirage	A	20	11–9	55%	-0.00
ancient	D	13	4–9	31%	-0.04
train	S	10	7–3	70%	-0.01
inferno	D	9	2–7	22%	-0.01
cache	B	8	4–4	50%	-0.03
nuke	D	7	2–5	29%	-0.03
overpass	—	4	3–1	75%	-0.02
anubis	—	1	1–0	100%	0.02

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (22% over 9). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

1,577Matches

50%Win rate

1.00Avg K/D

87.3ADR

43%Headshot %

Clutches & streaks

36%1v1 clutch win

23%1v2 clutch win

13Longest win streak

Recent Faceit resultsWWWWW

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	46	54%	1.01	13.5
Inferno	22	55%	1.04	15.4
Ancient	17	41%	0.99	16.4
Dust2	16	56%	1.00	16.8
Anubis	15	60%	0.97	14.2
Vertigo	15	47%	1.02	16.8
Nuke	13	46%	0.93	14.3
Overpass	4	50%	0.91	18.0

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.

19.9%Headshot accuracy

35.3%Accuracy (enemy spotted)

33.3%Spray accuracy

88.3%Counter-strafing

10.6°Preaim

756msReaction time

25.4%T opening success

44.6%CT opening success

0.64Enemies flashed / flash

9.0%Flash assists

12.27HE damage / grenade

14.21Flashes / 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 Settings
Slow first shots are as often a setup problem as a reflex one — framerate, sensitivity and crosshair visibility all move this number.
Reaction time 756.3207ms — above the 700ms 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 25.4235% — 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.

22% win rate across 9 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
ancient	9–13	-0.04	20%	7 Jun →
inferno	6–13	0.02	30%	6 Jun →
dust2	13–6	0.12	31%	6 Jun →
nuke	13–11	0.00	13%	6 Jun →
inferno	13–8	-0.05	12%	5 Jun →
mirage	14–16	-0.01	13%	5 Jun →
ancient	3–13	-0.04	7%	5 Jun →
dust2	5–13	-0.08	13%	5 Jun →
cache	4–13	-0.10	12%	14 May →
dust2	13–10	0.01	16%	11 May →
mirage	7–13	0.05	21%	11 May →
train	13–9	0.10	19%	11 May →
dust2	13–10	0.02	34%	8 May →
cache	8–6	-0.01	31%	8 May →
cache	6–13	-0.04	18%	5 May →
cache	9–13	-0.06	16%	2 May →
dust2	5–13	-0.01	26%	2 May →
cache	13–11	0.02	14%	2 May →
mirage	14–16	0.04	17%	2 May →
ancient	5–13	-0.07	13%	2 May →
cache	13–4	-0.10	22%	2 May →
dust2	10–13	-0.04	27%	2 May →
cache	5–13	0.04	18%	30 Apr →
cache	13–9	-0.01	33%	30 Apr →
nuke	4–13	-0.03	32%	13 Mar →
overpass	16–12	-0.02	17%	13 Mar →
mirage	9–13	-0.02	25%	13 Mar →
inferno	10–13	0.00	18%	13 Mar →
dust2	8–13	-0.02	22%	13 Mar →
inferno	10–13	-0.03	13%	24 Jan →
mirage	13–7	0.03	24%	24 Jan →
anubis	13–7	0.02	17%	23 Jan →
mirage	13–7	0.01	17%	23 Jan →
dust2	13–7	-0.03	23%	23 Jan →
dust2	13–6	-0.07	25%	20 Jan →
train	13–7	0.01	14%	16 Jan →
ancient	4–13	-0.05	31%	10 Jan →
dust2	13–8	0.01	18%	7 Jan →
dust2	8–13	-0.04	24%	6 Jan →
inferno	13–5	0.07	32%	6 Jan →
mirage	13–10	-0.02	20%	6 Jan →
ancient	13–8	-0.04	24%	6 Jan →
dust2	12–16	-0.00	40%	5 Jan →
inferno	4–13	-0.06	19%	5 Jan →
ancient	7–13	-0.07	15%	5 Jan →
dust2	6–13	-0.06	24%	5 Jan →
mirage	4–13	-0.07	10%	5 Jan →
dust2	10–13	-0.03	28%	5 Jan →
mirage	14–16	-0.00	25%	5 Jan →
mirage	13–7	0.05	21%	4 Jan →
mirage	0–13	-0.08	36%	4 Jan →
train	13–9	-0.04	17%	4 Jan →
dust2	13–9	0.04	12%	4 Jan →
mirage	13–4	0.11	18%	4 Jan →
train	13–7	0.01	10%	4 Jan →
dust2	9–13	-0.04	22%	3 Jan →
ancient	13–6	0.08	20%	3 Jan →
nuke	9–13	-0.07	24%	3 Jan →
dust2	13–7	0.03	24%	3 Jan →
dust2	3–13	-0.05	18%	3 Jan →
nuke	13–6	-0.02	17%	3 Jan →
overpass	5–13	-0.03	6%	3 Jan →
dust2	13–7	-0.04	19%	2 Jan →
train	8–13	-0.02	14%	2 Jan →
ancient	10–13	0.01	18%	2 Jan →
mirage	13–9	-0.04	30%	2 Jan →
mirage	13–8	0.00	20%	2 Jan →
inferno	15–15	-0.08	24%	5 Dec →
ancient	16–13	-0.05	22%	30 Nov →
overpass	13–11	-0.04	11%	30 Nov →
mirage	11–13	-0.01	26%	30 Nov →
inferno	15–15	-0.00	20%	20 Nov →
dust2	15–15	-0.03	29%	20 Nov →
mirage	13–4	-0.00	23%	20 Nov →
dust2	13–9	-0.08	11%	19 Nov →
inferno	9–13	0.01	22%	19 Nov →
ancient	6–13	-0.12	22%	19 Nov →
train	15–15	-0.05	31%	19 Nov →
mirage	13–10	-0.03	14%	14 Nov →
ancient	13–11	-0.04	10%	14 Nov →
dust2	13–11	0.06	29%	14 Nov →
dust2	5–13	-0.06	16%	14 Nov →
dust2	13–10	0.07	26%	14 Nov →
train	13–6	0.00	16%	12 Nov →
dust2	15–15	0.05	19%	12 Nov →
train	13–10	-0.04	17%	7 Nov →
ancient	9–13	-0.08	29%	7 Nov →
mirage	13–11	-0.03	19%	18 Oct →
nuke	7–13	-0.02	19%	11 Oct →
mirage	13–5	-0.04	9%	11 Oct →
dust2	9–13	-0.02	16%	4 Oct →
ancient	9–13	0.03	17%	19 Sept →
train	9–13	-0.05	10%	19 Sept →
dust2	13–10	-0.02	22%	19 Sept →
dust2	1–13	-0.09	25%	15 Sept →
nuke	7–13	-0.04	21%	13 Sept →
train	13–11	-0.01	21%	12 Sept →
overpass	16–12	-0.00	11%	12 Sept →
nuke	1–13	-0.05	30%	12 Sept →
mirage	8–13	0.02	22%	12 Sept →