WookiE XXXL — CS2 Stats

76561197994668429[U:1:34402701]

335Tracked matches60%Win rate2020Tracked since

3,645Hours in CS0Hrs last 2 wks

CSDB Rating5.1 DevelopingSupport

Premier CS Rating20,636Pink band · top ~7.8% of ranked players (population est.)

CSDB Leaderboard#6152 of 14960 tracked

FaceitLevel 2Top 97.9% of ranked FACEIT players

Ladder ranks via Leetify

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Performance scores

Aim52

Positioning28

Utility73

0–100 skill scores via Leetify.

Recent form

STEADY62–38Last 10062%Win rateWLLLWWLLWL

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

Player DNA

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

Aim5.2

Aggression1.4

Utility7.3

Positioning2.8

Opening Duels0.0

Effective flashes

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 72% playstyle similarity

Most alike: utility contribution, opening-duel success.

Where you differ: lower opening-fight frequency; 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. 644ms 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

Aim5.2

Positioning2.8

Utility7.3

Mechanics6.2

Opening Duels0.0

Win Impact8.3

Composite 5.1/10 (Developing), a weighted mean of the bars with a small opposition adjustment (×1.02 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.01−0.02

first ⅓ avg 0.01 → last ⅓ avg -0.01

Reaction time646ms−16ms

first ⅓ avg 662ms → last ⅓ avg 646ms

Headshot accuracy18.5%−1.4%

first ⅓ avg 20.0% → last ⅓ avg 18.5%

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.

Highlights

0.15Best rating — dust2 13–4

13–0Biggest win — overpass

7Longest win streak

6–4In matches decided by ≤2 rounds

9Overtime games

Map breakdown

ancientBest map · 90% over 10infernoWeakest map · 42% over 12

Map	Grade	Played	Record	Win rate	Avg rating
mirage	S	28	20–8	71%	-0.01
dust2	C	27	12–15	44%	-0.00
inferno	C	12	5–7	42%	0.00
ancient	S	10	9–1	90%	0.01
nuke	A	10	6–4	60%	0.01
anubis	A	7	4–3	57%	-0.01
overpass	—	4	4–0	100%	0.02
train	—	2	2–0	100%	-0.01

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (42% over 12). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

207,210Lifetime kills

1.22K/D · Top 50% of Pink band

7,178Matches

41.6%Match win rate

47.8%Headshot %

19.1%Shot accuracy · Top 10% of Pink band

28,575MVPs

3,554Hours (in match)

12,276Bombs planted

8,425Bombs defused

Most-used weapons

AK-4767,523

M4A4 / M4A1-S38,419

AWP32,174

P2000 / USP-S20,127

Desert Eagle19,751

Glock-187,036

P2504,036

Five-SeveN2,073

Tec-91,412

SG 5531,179

Lifetime map wins

15,353inferno

12,239dust2

5,162train

4,043nuke

3,631vertigo

2,514cbble

182dust

124italy

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.

17.4%Headshot accuracy

32.4%Accuracy (enemy spotted)

35.7%Spray accuracy

78.1%Counter-strafing

11.8°Preaim

644msReaction time

19.5%T opening success

24.7%CT opening success

0.75Enemies flashed / flash

6.6%Flash assists

10.48HE damage / grenade

15.67Flashes / 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
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 19.5353% — 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.

42% win rate across 12 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
mirage	13–6	0.07	11%	3 Jul →
mirage	14–16	-0.01	23%	18 Jun →
inferno	1–13	0.01	28%	7 Jun →
dust2	0–13	-0.05	8%	7 Jun →
inferno	13–8	0.02	20%	30 May →
ancient	13–7	-0.00	17%	30 May →
dust2	6–13	-0.06	15%	23 May →
mirage	3–13	-0.08	11%	23 May →
mirage	16–12	-0.02	10%	22 May →
inferno	7–13	-0.06	21%	22 May →
dust2	12–16	-0.01	19%	9 May →
nuke	13–10	0.05	10%	7 May →
ancient	4–8	0.00	25%	6 May →
ancient	13–6	0.04	22%	1 May →
ancient	13–2	0.04	15%	1 May →
mirage	16–13	-0.06	11%	1 May →
nuke	6–13	-0.02	24%	26 Apr →
inferno	14–16	-0.03	16%	27 Mar →
dust2	4–13	-0.03	18%	27 Mar →
mirage	13–11	-0.02	17%	27 Mar →
mirage	13–3	-0.01	17%	26 Mar →
dust2	13–3	0.05	23%	26 Mar →
mirage	4–0	-0.02	13%	26 Mar →
anubis	4–13	-0.03	17%	22 Mar →
overpass	13–5	0.01	24%	22 Mar →
inferno	13–5	0.06	22%	22 Mar →
mirage	13–2	0.01	17%	21 Mar →
mirage	13–6	-0.03	14%	17 Mar →
dust2	13–7	0.04	14%	17 Mar →
ancient	13–9	-0.00	23%	15 Mar →
mirage	13–4	0.03	21%	15 Mar →
dust2	5–13	-0.09	33%	14 Mar →
dust2	14–16	-0.01	31%	14 Mar →
mirage	13–10	-0.02	25%	7 Mar →
dust2	13–7	0.04	16%	28 Feb →
mirage	13–11	-0.00	17%	28 Feb →
ancient	13–3	-0.01	14%	28 Feb →
dust2	13–5	0.07	23%	28 Feb →
dust2	13–5	-0.04	17%	28 Feb →
inferno	13–2	0.04	24%	28 Feb →
dust2	8–13	-0.01	25%	25 Feb →
dust2	8–13	-0.08	15%	25 Feb →
ancient	13–3	0.07	13%	25 Feb →
mirage	5–13	-0.06	8%	25 Feb →
nuke	13–7	0.03	23%	23 Feb →
mirage	13–10	-0.01	15%	23 Feb →
ancient	13–11	-0.03	12%	23 Feb →
mirage	13–9	-0.07	13%	23 Feb →
inferno	4–13	-0.07	27%	21 Feb →
dust2	13–2	0.04	20%	21 Feb →
nuke	10–13	-0.05	14%	21 Feb →
mirage	13–10	-0.00	35%	20 Feb →
anubis	13–9	0.04	22%	20 Feb →
overpass	13–5	-0.02	21%	20 Feb →
dust2	13–10	-0.03	21%	20 Feb →
nuke	13–10	-0.02	27%	15 Feb →
anubis	13–9	0.06	14%	15 Feb →
overpass	13–7	0.02	23%	15 Feb →
nuke	10–13	0.04	29%	15 Feb →
inferno	13–7	0.00	18%	14 Feb →
mirage	13–7	-0.02	15%	14 Feb →
dust2	1–13	-0.11	4%	14 Feb →
inferno	13–5	0.06	18%	14 Feb →
ancient	16–13	-0.04	30%	14 Feb →
overpass	13–0	0.05	23%	14 Feb →
mirage	13–7	0.01	25%	14 Feb →
mirage	13–10	-0.02	17%	14 Feb →
dust2	7–13	-0.04	13%	14 Feb →
anubis	13–3	-0.02	14%	13 Feb →
mirage	13–3	0.05	14%	13 Feb →
dust2	13–11	0.02	17%	13 Feb →
inferno	10–13	0.07	20%	13 Feb →
inferno	9–13	-0.03	15%	13 Feb →
nuke	12–16	-0.02	27%	12 Feb →
anubis	8–13	-0.06	22%	12 Feb →
dust2	5–13	-0.06	14%	10 Feb →
mirage	13–10	0.05	18%	10 Feb →
anubis	13–6	-0.04	23%	9 Feb →
mirage	9–13	0.03	31%	9 Feb →
dust2	1–13	-0.06	10%	9 Feb →
dust2	10–13	0.04	27%	7 Feb →
dust2	13–5	-0.02	21%	7 Feb →
mirage	8–13	-0.02	19%	7 Feb →
anubis	7–13	-0.04	19%	7 Feb →
inferno	6–13	-0.01	21%	30 Jan →
dust2	4–13	0.04	23%	29 Jan →
nuke	13–11	0.00	33%	29 Jan →
mirage	13–3	0.04	16%	23 Jan →
dust2	11–13	-0.05	15%	22 Jan →
dust2	13–1	0.13	16%	8 Nov →
mirage	5–13	-0.05	22%	3 Nov →
dust2	13–4	0.15	26%	8 Sept →
nuke	13–9	0.02	28%	6 Jul →
ancient	13–11	0.06	22%	6 Jun →
train	13–8	-0.05	14%	2 Jun →
mirage	7–13	0.01	21%	2 Jun →
nuke	13–9	0.11	15%	29 May →
train	13–10	0.03	14%	28 May →
dust2	13–8	0.06	30%	28 May →
mirage	12–16	-0.06	19%	28 May →