KritaEnGubbe135 — CS2 Stats

SE76561198001506896[U:1:41241168]Steam profile ↗✓ No bans

285Tracked matches53%Win rate2020Tracked since

CSDB Rating7.6 StrongAll-Rounder

WingmanGold Nova III

Ladder ranks via Leetify

Performance scores

Aim87

Positioning72

Utility71

0–100 skill scores via Leetify.

Recent form

STEADY50–50Last 10050%Win rateWLWLWWLWLW

Last 10 vs previous 10: +10pp win rate · −0.01 avg rating · +5.4pp headshot accuracy · +20ms reaction

Win rate +10pp across the last 10 against the 10 before — within the normal variation of a 10-match window (±20pp), so no real shift yet.

Last 5 · 10 · 20 matches

Last 5

3–2 · 60% win rate
Avg rating 0.02
Avg headshot accuracy 23%
Avg reaction 567ms

Last 10

6–4 · 60% win rate
Avg rating 0.01
Avg headshot accuracy 22%
Avg reaction 575ms

Last 20

11–9 · 55% win rate
Avg rating 0.01
Avg headshot accuracy 19%
Avg reaction 565ms

Newest first, from the last 100 tracked matches. Each block is its own sample — one result moves a 5-match win rate by 20 points.

Player DNA

Primary style: All-Rounder — No style dimension stands clear of the others in this profile.

Aim8.7

Utility7.1

Positioning7.2

Opening Duels6.2

Sharp aimerEffective flashes

Style profile from tracked-match aggregates — how this player plays, not how good they are. Classification rules are deterministic and documented in code.

What this cannot see yet: which weapons you use — so CSDB cannot identify an AWPer, and no style here implies a rifle or a sniper. It also cannot see how often you take opening duels, only how often you win them, nor where you hold, so roles that depend on those (entry, lurk, anchor) are deliberately absent rather than guessed. All of it needs round-by-round demo data, which is the next thing being built.

Your pro match

Plays most like m0NESY 93% playstyle similarity

Most alike: utility contribution, positioning 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

Strengths

Aim. Aim score of 87 — the mechanical foundation is a clear strength.

T openings. 70% T opening-duel success — entries that actually open the round.

Areas to improve

Preaim. Mechanical aim is strong but the crosshair sits 10.3° off target when enemies appear — placement, not flicking, is the bigger win available.

CT openings. Opening success drops from 70% on T to 54% on CT — first contacts on the defending side are being lost.

Reaction time. 568ms 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

Aim8.7

Positioning7.2

Utility7.1

Mechanics7.4

Opening Duels8.0

Win Impact6.1

Composite 7.6/10 (Strong), 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 rating0.01−0.00

first ⅓ avg 0.01 → last ⅓ avg 0.01

Reaction time565ms−61ms

first ⅓ avg 627ms → last ⅓ avg 565ms

Headshot accuracy20.5%+0.7%

first ⅓ avg 19.8% → last ⅓ avg 20.5%

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.

Personal bests

0.20Best match rating · 13–1 · inferno, 10 Oct →

36%Best headshot accuracy · 13–10 · inferno, 5 Nov →

438msFastest reaction time · 9–13 · inferno, 12 Nov →

13–1Biggest win · inferno, 8 Nov →

Across the last 100 tracked matches.

Highlights

4Longest win streak

8–17In matches decided by ≤2 rounds

15Overtime games

Map breakdown

cacheBest map · 56% over 18nukeWeakest map · 29% over 7

Map	Grade	Played	Record	Win rate	Avg rating
inferno	B	30	16–14	53%	0.02
dust2	B	28	14–14	50%	0.00
cache	A	18	10–8	56%	0.03
ancient	B	12	6–6	50%	0.02
nuke	D	7	2–5	29%	0.03
anubis	—	3	2–1	67%	0.01
mirage	—	2	0–2	0%	-0.04

Across the last 100 tracked matches.

Nuke is currently your weakest sufficiently-sampled map (29% over 7). Start with the 6 essential Nuke lineups, review the callouts, then spin up a practice server.

Skill profile

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

20.7%Headshot accuracy

41.3%Accuracy (enemy spotted)

44.9%Spray accuracy

83.4%Counter-strafing

10.3°Preaim

568msReaction time

69.9%T opening success

54.4%CT opening success

0.71Enemies flashed / flash

14.0%Flash assists

11.14HE damage / grenade

11.44Flashes / match

Recommended for you

Spend your practice time on Nuke

Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.

29% win rate across 7 tracked games — your weakest map with enough games to be worth reading into.

Nuke callouts & strategy →Nuke grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
inferno	13–3	0.03	15%	13 Nov →
dust2	14–16	0.04	26%	13 Nov →
inferno	13–10	0.02	35%	13 Nov →
cache	6–13	-0.00	22%	13 Nov →
dust2	13–8	0.02	16%	13 Nov →
cache	13–11	0.01	24%	13 Nov →
inferno	10–13	-0.05	23%	13 Nov →
dust2	13–4	-0.02	9%	12 Nov →
inferno	9–13	-0.01	26%	12 Nov →
inferno	13–4	0.03	21%	12 Nov →
inferno	19–15	0.01	15%	10 Nov →
dust2	11–13	-0.02	15%	10 Nov →
dust2	17–19	-0.00	13%	9 Nov →
nuke	14–16	-0.03	16%	9 Nov →
inferno	13–3	-0.02	20%	9 Nov →
dust2	13–10	0.06	13%	8 Nov →
inferno	13–1	0.05	16%	8 Nov →
inferno	11–13	-0.03	18%	8 Nov →
cache	16–14	0.09	21%	7 Nov →
ancient	11–13	0.01	17%	7 Nov →
inferno	11–13	-0.02	28%	7 Nov →
dust2	13–10	0.04	24%	7 Nov →
nuke	13–16	0.03	22%	7 Nov →
inferno	6–13	-0.06	24%	5 Nov →
cache	14–16	0.00	18%	5 Nov →
dust2	13–8	0.10	15%	5 Nov →
inferno	13–10	-0.01	36%	5 Nov →
cache	13–5	0.01	23%	3 Nov →
inferno	7–13	0.07	22%	3 Nov →
nuke	13–10	-0.03	29%	2 Nov →
dust2	2–13	-0.09	16%	2 Nov →
inferno	13–5	0.05	22%	2 Nov →
ancient	13–8	-0.04	16%	2 Nov →
inferno	10–13	0.09	21%	22 Oct →
dust2	3–13	-0.09	24%	22 Oct →
inferno	22–18	0.07	18%	21 Oct →
ancient	13–5	0.02	18%	21 Oct →
dust2	13–8	0.06	22%	18 Oct →
inferno	13–7	0.01	16%	18 Oct →
mirage	7–13	-0.05	18%	18 Oct →
dust2	13–10	0.02	26%	17 Oct →
dust2	3–13	-0.03	14%	17 Oct →
inferno	13–10	0.04	22%	17 Oct →
cache	13–8	-0.01	15%	17 Oct →
dust2	6–13	-0.06	8%	17 Oct →
nuke	9–13	0.03	20%	17 Oct →
cache	13–9	0.14	18%	15 Oct →
anubis	13–2	0.00	21%	15 Oct →
ancient	13–6	0.07	20%	15 Oct →
inferno	9–13	-0.01	22%	15 Oct →
cache	13–7	0.07	34%	11 Oct →
dust2	4–13	-0.03	13%	11 Oct →
inferno	10–13	-0.05	23%	10 Oct →
inferno	13–1	0.20	15%	10 Oct →
cache	13–3	0.09	15%	10 Oct →
ancient	13–10	0.03	17%	10 Oct →
dust2	17–19	-0.01	10%	10 Oct →
inferno	13–11	-0.02	27%	10 Oct →
dust2	13–9	0.08	19%	9 Oct →
ancient	10–13	0.03	22%	9 Oct →
nuke	16–14	0.05	18%	9 Oct →
inferno	13–16	-0.04	13%	9 Oct →
dust2	13–6	0.05	25%	8 Oct →
ancient	13–8	0.03	11%	8 Oct →
dust2	13–4	-0.02	20%	8 Oct →
inferno	13–9	-0.01	27%	8 Oct →
cache	9–13	-0.00	11%	7 Oct →
dust2	11–13	0.00	20%	7 Oct →
ancient	11–13	0.02	11%	7 Oct →
mirage	9–13	-0.02	13%	5 Oct →
dust2	13–1	0.07	23%	5 Oct →
anubis	9–13	0.01	4%	5 Oct →
dust2	16–13	0.01	23%	5 Oct →
inferno	6–13	-0.01	20%	4 Oct →
dust2	11–13	-0.04	19%	4 Oct →
cache	11–13	-0.03	16%	2 Oct →
cache	13–7	0.05	15%	2 Oct →
dust2	5–13	-0.07	26%	2 Oct →
ancient	16–19	0.05	15%	1 Oct →
cache	11–13	-0.01	20%	1 Oct →
ancient	13–11	0.02	27%	1 Oct →
dust2	13–6	0.01	21%	1 Oct →
ancient	3–13	-0.04	17%	1 Oct →
nuke	10–13	0.10	31%	1 Oct →
inferno	13–11	0.05	27%	1 Oct →
nuke	17–19	0.03	20%	28 Sept →
inferno	9–13	-0.04	15%	28 Sept →
dust2	6–13	-0.05	17%	28 Sept →
anubis	16–14	0.01	21%	28 Sept →
cache	5–13	0.02	35%	28 Sept →
cache	13–3	0.04	17%	27 Sept →
dust2	13–7	-0.04	13%	27 Sept →
cache	11–13	-0.01	22%	27 Sept →
inferno	7–13	0.10	19%	26 Sept →
dust2	5–13	0.05	15%	26 Sept →
ancient	7–13	0.02	16%	26 Sept →
cache	9–13	-0.03	22%	26 Sept →
inferno	13–7	0.01	21%	26 Sept →
inferno	11–13	0.01	26%	26 Sept →
cache	13–11	0.04	28%	25 Sept →