dippa — CS2 Stats

MZ76561198088279767[U:1:128014039]Steam profile ↗✓ No bans

714Tracked matches54%Win rate2020Tracked since

CSDB Rating5.1 Developing

FaceitLevel 6Top 54.0% of ranked FACEIT players

WingmanLegendary Eagle

Ladder ranks via Leetify

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Performance scores

Aim49

Positioning51

Utility58

0–100 skill scores via Leetify.

Recent form

COLD48–46–6Last 10048%Win rateLLLLWLTLWL

Last 10 vs previous 10: -40pp win rate · +0.01 avg rating

Player DNA

Aim4.9

Aggression5.7

Utility5.8

Positioning5.1

Opening Duels1.7

Clutch6.0

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 85% playstyle similarity

Most alike: opening-fight frequency, utility contribution.

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

T-side openings. Opening success drops from 46% on CT to 27% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 623ms 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

Aim4.9

Positioning5.1

Utility5.8

Mechanics6.0

Opening Duels2.1

Win Impact6.2

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-0.02−0.01

first ⅓ avg -0.01 → last ⅓ avg -0.02

Reaction time628ms−15ms

first ⅓ avg 643ms → last ⅓ avg 628ms

Headshot accuracy14.3%−0.6%

first ⅓ avg 14.9% → last ⅓ avg 14.3%

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

0.09Best rating — inferno 13–4

13–2Biggest win — dust2

6Longest win streak

L4Current streak

11–6In matches decided by ≤2 rounds

15Overtime games

Map breakdown

nukeBest map · 58% over 12anubisWeakest map · 33% over 9

Map	Grade	Played	Record	Win rate	Avg rating
dust2	C	22	9–13	41%	-0.00
inferno	A	21	12–9	57%	-0.00
ancient	C	16	7–9	44%	-0.04
overpass	B	13	7–6	54%	0.00
nuke	A	12	7–5	58%	-0.01
anubis	D	9	3–6	33%	-0.03
mirage	—	3	2–1	67%	-0.02
cache	—	2	0–2	0%	0.02
train	—	2	1–1	50%	-0.00

Across the last 100 tracked matches.

Anubis is currently your weakest sufficiently-sampled map (33% over 9). Start with the 6 essential Anubis lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

29Matches

55%Win rate

1.07Avg K/D

69.7ADR

36%Headshot %

Clutches & streaks

50%1v1 clutch win

0%1v2 clutch win

5Longest win streak

Recent Faceit resultsLWLLW

Map	Matches	Win rate	Avg K/D	Avg kills
Inferno	3	67%	1.22	17.0
Dust2	2	50%	1.42	14.5
Overpass	2	50%	0.62	10.5
Ancient	2	100%	0.97	16.5

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.

13.7%Headshot accuracy

31.4%Accuracy (enemy spotted)

32.3%Spray accuracy

76.9%Counter-strafing

10.1°Preaim

623msReaction time

27.0%T opening success

46.4%CT opening success

0.58Enemies flashed / flash

5.8%Flash assists

12.29HE damage / grenade

6.40Flashes / 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 13.7278% — 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 27.0261% — 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.

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