da shoes — CS2 Stats

NO76561197961325431[U:1:1059703]Steam profile ↗✓ No bans

1,726Tracked matches50%Win rate2020Tracked since

CSDB Rating5.9 SolidSupport

Premier CS Rating25,120Top 25% of 819 CSDB-tracked playersRed band · top ~2.9% of ranked players (population est.)

CSDB Leaderboard#1228 of 7921 tracked

WingmanGold Nova Master

Ladder ranks via Leetify

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Performance scores

Aim83

Positioning35

Utility71

0–100 skill scores via Leetify.

Recent form

HOT49–39–12Last 10049%Win rateWWLTLWWWWW

Last 10 vs previous 10: +40pp win rate · +0.02 avg rating

Player DNA

Primary style: Support — Utility contribution leads the profile while opening activity stays low.

Aim8.3

Aggression1.7

Utility7.1

Positioning3.5

Opening Duels0.0

Clutch4.8

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 NiKo 81% playstyle similarity

Most alike: utility contribution, aim profile.

Where you differ: lower positioning profile; lower opening-fight frequency.

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

Positioning. Positioning trails aim by 48 points — deaths here waste a strong aim profile.

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

Aim8.3

Positioning3.5

Utility7.1

Mechanics5.5

Opening Duels0.4

Win Impact5.0

Composite 5.9/10 (Solid), a weighted mean of the bars with a small opposition adjustment (×1.05 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating0.01−0.01

first ⅓ avg 0.02 → last ⅓ avg 0.01

Reaction time542ms−42ms

first ⅓ avg 584ms → last ⅓ avg 542ms

Headshot accuracy12.9%−4.2%

first ⅓ avg 17.1% → last ⅓ avg 12.9%

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

0.15Best rating — nuke 5–0

13–0Biggest win — dust2

5Longest win streak

W2Current streak

7–8In matches decided by ≤2 rounds

5Overtime games

Map breakdown

ancientBest map · 64% over 11infernoWeakest map · 25% over 8

Map	Grade	Played	Record	Win rate	Avg rating
dust2	A	25	14–11	56%	0.02
mirage	B	13	6–7	46%	0.00
nuke	B	13	7–6	54%	0.02
ancient	A	11	7–4	64%	0.01
anubis	B	10	5–5	50%	0.02
cache	C	10	4–6	40%	-0.01
inferno	D	8	2–6	25%	0.00
train	—	4	2–2	50%	0.05
vertigo	—	3	1–2	33%	0.03
overpass	—	2	1–1	50%	0.05
debris	—	1	0–1	0%	0.11

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (25% over 8). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

845Matches

50%Win rate

1.16Avg K/D

77.0ADR

32%Headshot %

Clutches & streaks

44%1v1 clutch win

26%1v2 clutch win

8Longest win streak

Recent Faceit resultsLLLLL

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	73	44%	1.06	14.4
Dust2	65	62%	1.18	15.5
Ancient	57	44%	1.09	14.3
Inferno	40	45%	1.06	15.5
Anubis	33	39%	1.06	15.2
Nuke	31	61%	1.23	15.2
Vertigo	22	36%	0.94	15.3
Overpass	19	79%	1.26	16.4

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.

12.8%Headshot accuracy

42.3%Accuracy (enemy spotted)

46.2%Spray accuracy

74.9%Counter-strafing

8.6°Preaim

539msReaction time

18.9%T opening success

33.4%CT opening success

0.58Enemies flashed / flash

9.6%Flash assists

10.57HE damage / grenade

11.36Flashes / 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 12.8347% — 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 18.8588% — 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.

25% win rate across 8 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
dust2	13–10	-0.05	13%	28 Aug →
anubis	13–4	0.11	20%	28 Aug →
dust2	1–13	-0.05	11%	28 Aug →
inferno	12–12	0.01	13%	24 Aug →
vertigo	3–13	-0.02	9%	24 Aug →
ancient	13–10	0.05	14%	23 Aug →
mirage	13–6	0.02	13%	23 Aug →
train	13–6	0.00	12%	23 Aug →
dust2	13–0	0.04	18%	23 Aug →
nuke	13–5	0.04	14%	23 Aug →
inferno	12–12	0.01	12%	13 Aug →
ancient	12–12	0.01	13%	13 Aug →
dust2	11–13	0.04	17%	13 Aug →
mirage	10–13	-0.07	15%	12 Aug →
cache	12–12	-0.01	16%	12 Aug →
inferno	15–15	0.03	11%	11 Aug →
ancient	16–13	-0.02	9%	9 Aug →
nuke	13–8	-0.04	8%	9 Aug →
nuke	13–7	-0.02	7%	9 Aug →
anubis	3–13	0.00	26%	9 Aug →
cache	8–13	-0.04	0%	8 Aug →
cache	4–13	-0.08	17%	6 Aug →
nuke	8–13	0.01	9%	6 Aug →
anubis	13–7	0.14	12%	5 Aug →
dust2	12–12	-0.02	20%	5 Aug →
dust2	10–13	0.09	12%	2 Aug →
anubis	4–13	-0.01	3%	2 Aug →
dust2	6–13	-0.02	19%	31 Jul →
mirage	13–11	-0.04	15%	31 Jul →
anubis	11–13	-0.06	12%	30 Jul →
dust2	12–12	0.00	17%	30 Jul →
train	13–2	0.13	10%	30 Jul →
inferno	13–11	0.03	10%	29 Jul →
train	8–13	0.07	13%	29 Jul →
inferno	7–13	-0.04	10%	28 Jul →
mirage	11–13	0.11	15%	28 Jul →
dust2	1–13	-0.03	0%	27 Jul →
mirage	13–0	0.06	17%	27 Jul →
dust2	13–6	0.10	15%	27 Jul →
dust2	13–9	0.03	14%	26 Jul →
nuke	5–13	-0.01	12%	26 Jul →
dust2	13–8	-0.01	9%	26 Jul →
cache	13–3	0.04	12%	26 Jul →
dust2	13–11	0.04	12%	25 Jul →
nuke	13–10	-0.01	4%	25 Jul →
mirage	5–13	-0.07	19%	24 Jul →
dust2	1–13	-0.09	25%	24 Jul →
ancient	13–4	0.02	11%	24 Jul →
cache	5–13	-0.00	21%	23 Jul →
inferno	13–3	-0.06	11%	23 Jul →
vertigo	5–13	0.02	11%	22 Jul →
ancient	13–11	0.01	10%	22 Jul →
cache	13–11	0.01	15%	20 Jul →
dust2	13–11	0.01	11%	20 Jul →
anubis	13–10	0.06	13%	20 Jul →
ancient	8–13	-0.05	13%	20 Jul →
cache	11–13	0.03	16%	18 Jul →
anubis	13–9	0.04	21%	18 Jul →
cache	13–9	-0.01	6%	18 Jul →
mirage	11–13	-0.01	11%	18 Jul →
nuke	14–16	0.07	10%	17 Jul →
anubis	13–10	0.02	9%	17 Jul →
ancient	16–13	0.01	11%	17 Jul →
dust2	13–4	0.13	16%	17 Jul →
mirage	6–13	-0.00	19%	15 Jul →
dust2	8–13	-0.01	17%	14 Jul →
debris	8–8	0.11	16%	13 Jul →
mirage	13–3	0.10	24%	12 Jul →
cache	13–9	-0.01	28%	12 Jul →
dust2	8–3	0.04	12%	12 Jul →
dust2	13–9	0.01	20%	12 Jul →
inferno	10–13	0.04	22%	8 Jul →
ancient	2–13	-0.05	13%	8 Jul →
nuke	12–12	-0.02	14%	8 Jul →
anubis	12–12	-0.03	17%	7 Jul →
ancient	6–1	0.05	29%	7 Jul →
vertigo	13–10	0.09	13%	7 Jul →
dust2	13–9	0.06	18%	7 Jul →
nuke	4–13	-0.04	17%	4 Jul →
mirage	13–11	0.02	19%	4 Jul →
train	6–13	-0.01	21%	4 Jul →
nuke	13–10	0.04	17%	2 Jul →
dust2	5–13	0.09	24%	2 Jul →
cache	7–13	-0.06	18%	2 Jul →
mirage	4–13	-0.00	24%	27 Jun →
nuke	15–15	-0.00	10%	27 Jun →
dust2	13–10	0.02	21%	27 Jun →
overpass	12–12	0.03	16%	24 Jun →
dust2	13–4	0.00	13%	24 Jun →
dust2	10–13	0.07	16%	23 Jun →
inferno	11–13	-0.01	2%	23 Jun →
mirage	13–7	-0.04	9%	14 Jun →
ancient	11–13	0.02	14%	14 Jun →
anubis	10–13	-0.03	15%	14 Jun →
nuke	5–0	0.15	25%	14 Jun →
ancient	13–10	0.02	10%	13 Jun →
dust2	13–8	0.03	16%	13 Jun →
overpass	13–6	0.08	18%	13 Jun →
nuke	13–4	0.04	15%	13 Jun →
mirage	8–13	-0.03	17%	13 Jun →