LilySoftpaw — CS2 Stats

76561199061852190[U:1:1101586462]✓ No bans

319Tracked matches41%Win rate2025Tracked since

396Hours in CS0Hrs last 2 wks

CSDB Rating1.7 Learning

WingmanSilver III

Ladder ranks via Leetify

Performance scores

Aim12

Positioning33

Utility4

0–100 skill scores via Leetify.

Recent form

STEADY40–54–6Last 10040%Win rateWWLWWLLLLL

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

Player DNA

Aim1.2

Aggression2.1

Utility0.4

Positioning3.3

Opening Duels0.0

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 NiKo 57% playstyle similarity

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

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. 790ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 63% of shots are taken properly stopped — moving-shot inaccuracy is quietly taxing every duel.

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

Aim1.2

Positioning3.3

Utility0.4

Mechanics2.8

Opening Duels0.0

Win Impact2.1

Composite 1.7/10 (Learning), 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-0.05−0.01

first ⅓ avg -0.04 → last ⅓ avg -0.05

Reaction time800ms−6ms

first ⅓ avg 806ms → last ⅓ avg 800ms

Headshot accuracy11.9%+0.3%

first ⅓ avg 11.6% → 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

0.12Best rating — inferno 9–6

13–0Biggest win — mirage

5Longest win streak

W2Current streak

4–4In matches decided by ≤2 rounds

Map breakdown

mirageBest map · 60% over 5ancientWeakest map · 0% over 5

Map	Grade	Played	Record	Win rate	Avg rating
inferno	C	14	6–8	43%	-0.06
dust2	C	14	5–9	36%	-0.05
train	B	13	6–7	46%	-0.04
vertigo	C	10	4–6	40%	-0.08
cache	A	9	5–4	56%	-0.03
office	B	8	4–4	50%	-0.05
nuke	D	8	2–6	25%	-0.04
poseidon	C	7	3–4	43%	-0.06
mirage	A	5	3–2	60%	-0.04
ancient	D	5	0–5	0%	-0.03
overpass	—	2	0–2	0%	-0.02
alpine	—	2	1–1	50%	-0.02
anubis	—	1	1–0	100%	0.01
warden	—	1	0–1	0%	-0.09
rooftop	—	1	0–1	0%	0.01

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (0% over 5). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Lifetime stats

7,156Lifetime kills

0.75K/D

517Matches

41.0%Match win rate

42.5%Headshot %

0.0%Shot accuracy

1,830MVPs

153Hours (in match)

914Bombs planted

173Bombs defused

Most-used weapons

M4A4 / M4A1-S1,536

AK-471,475

Galil AR868

Glock-18455

P2000 / USP-S360

AWP328

MAC-10281

Desert Eagle277

P90267

FAMAS242

Lifetime map wins

616inferno

566dust2

515nuke

421train

354office

283vertigo

15italy

1dust

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

Skill profile

Aggregate performance across tracked matches — stats via Leetify. Percentile context against other CSDB-tracked players arrives as our own benchmark data accumulates.

11.7%Headshot accuracy

27.4%Accuracy (enemy spotted)

29.5%Spray accuracy

62.7%Counter-strafing

12.1°Preaim

790msReaction time

15.9%T opening success

30.3%CT opening success

0.07Enemies flashed / flash

0.0%Flash assists

0.64HE damage / grenade

0.14Flashes / 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
Your crosshair sits further from where enemies appear than it needs to. Crosshair placement is the cheapest accuracy you can buy.
Preaim 12.0742° — above the 12° mark we flag
Aim Training →
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 11.6957% — below the 15% mark we flag
Aim Training →
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 789.8413ms — above the 700ms mark we flag
Aim Training →

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.

0% win rate across 5 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
anubis	13–8	0.01	8%	10 Aug →
mirage	13–5	-0.02	7%	9 Aug →
train	4–13	-0.02	17%	9 Aug →
office	13–8	-0.07	8%	9 Aug →
cache	13–6	0.02	16%	9 Aug →
vertigo	10–13	-0.09	7%	4 Aug →
vertigo	4–13	-0.11	5%	4 Aug →
vertigo	7–13	-0.09	16%	4 Aug →
inferno	3–13	-0.13	9%	4 Aug →
dust2	9–13	-0.04	16%	4 Aug →
overpass	8–13	-0.01	10%	3 Aug →
dust2	7–13	0.01	26%	3 Aug →
cache	13–9	0.07	9%	3 Aug →
vertigo	13–7	-0.02	20%	3 Aug →
vertigo	8–13	-0.05	5%	3 Aug →
vertigo	2–13	-0.11	6%	3 Aug →
train	13–8	-0.03	11%	18 Jun →
train	12–12	-0.14	17%	18 Jun →
train	13–10	-0.04	6%	17 Jun →
train	13–5	-0.05	4%	17 Jun →
train	6–13	-0.08	2%	17 Jun →
train	13–9	0.04	9%	17 Jun →
mirage	8–13	-0.07	10%	15 Jun →
nuke	6–13	-0.04	20%	15 Jun →
inferno	6–13	-0.07	9%	15 Jun →
inferno	2–1	-0.08	0%	15 Jun →
ancient	3–13	-0.05	27%	15 Jun →
cache	0–13	-0.12	22%	15 Jun →
ancient	6–13	-0.11	13%	15 Jun →
inferno	13–6	-0.05	11%	15 Jun →
inferno	13–10	-0.00	13%	15 Jun →
inferno	4–13	-0.11	12%	15 Jun →
alpine	9–13	-0.03	23%	12 Jun →
dust2	9–13	-0.04	16%	12 Jun →
ancient	8–13	-0.01	9%	12 Jun →
train	3–13	-0.08	10%	11 Jun →
inferno	13–4	-0.06	7%	11 Jun →
office	2–13	-0.09	8%	11 Jun →
nuke	8–13	-0.00	17%	11 Jun →
dust2	3–13	-0.14	12%	11 Jun →
cache	13–9	-0.00	12%	11 Jun →
vertigo	13–6	-0.07	21%	9 Jun →
vertigo	13–9	-0.06	19%	9 Jun →
vertigo	1–13	-0.13	0%	7 Jun →
office	7–13	-0.06	3%	17 May →
cache	6–13	-0.08	5%	11 May →
cache	13–4	-0.02	13%	11 May →
cache	8–13	-0.04	17%	10 May →
cache	5–13	-0.09	8%	28 Apr →
cache	13–3	-0.05	8%	28 Apr →
ancient	10–13	0.04	12%	18 Apr →
inferno	10–13	-0.03	5%	18 Apr →
vertigo	13–8	-0.07	4%	15 Apr →
train	11–13	-0.04	17%	15 Apr →
ancient	10–13	-0.01	15%	15 Apr →
nuke	12–12	-0.04	12%	15 Apr →
office	3–13	-0.11	6%	15 Apr →
office	13–8	0.05	10%	27 Mar →
office	8–13	0.02	15%	20 Mar →
office	13–7	-0.07	20%	19 Mar →
office	13–8	-0.02	7%	19 Mar →
nuke	13–7	-0.05	17%	18 Mar →
dust2	13–9	-0.10	3%	17 Mar →
poseidon	9–4	-0.04	15%	17 Mar →
poseidon	1–9	-0.16	24%	17 Mar →
poseidon	9–6	-0.04	9%	17 Mar →
poseidon	8–8	-0.04	14%	17 Mar →
poseidon	3–9	-0.21	12%	17 Mar →
poseidon	8–8	0.04	22%	17 Mar →
poseidon	9–3	0.04	9%	17 Mar →
mirage	11–13	-0.06	6%	14 Mar →
inferno	13–11	0.00	14%	14 Mar →
inferno	3–9	-0.06	14%	14 Mar →
nuke	10–13	0.02	9%	14 Mar →
inferno	6–9	-0.17	21%	14 Mar →
inferno	9–6	0.12	18%	14 Mar →
warden	8–13	-0.09	11%	14 Mar →
inferno	12–12	-0.08	7%	2 Mar →
alpine	13–7	-0.01	15%	2 Mar →
overpass	9–13	-0.03	13%	2 Mar →
mirage	13–11	-0.07	15%	24 Jan →
nuke	13–7	-0.01	5%	24 Jan →
dust2	13–5	0.04	10%	24 Jan →
dust2	13–4	0.02	10%	24 Jan →
dust2	5–13	-0.07	15%	24 Jan →
train	13–11	-0.05	5%	24 Jan →
dust2	11–13	-0.08	12%	24 Jan →
dust2	9–13	-0.04	26%	22 Jan →
train	7–13	-0.05	15%	22 Jan →
nuke	5–9	-0.12	9%	19 Jan →
rooftop	7–9	0.01	6%	19 Jan →
train	12–12	-0.03	10%	12 Dec →
inferno	4–9	-0.14	13%	12 Dec →
dust2	13–7	-0.06	9%	12 Dec →
dust2	8–13	-0.07	5%	12 Dec →
dust2	13–6	-0.05	8%	10 Dec →
mirage	13–0	0.01	12%	10 Dec →
dust2	9–13	-0.06	8%	10 Dec →
nuke	8–13	-0.10	4%	9 Dec →
train	8–1	0.01	14%	9 Dec →