comandos — CS2 Stats

PT76561198080053621[U:1:119787893]Steam profile ↗✓ No bans

660Tracked matches41%Win rate2020Tracked since

CSDB Rating6.2 SolidAggressive Rifler

Premier CS Rating21,433Top 50% of 17,162 CSDB-tracked playersPink band · top ~6.9% of ranked players (population est.)

CSDB Leaderboard#10790 of 33159 tracked

FaceitLevel 8Top 31.2% of ranked FACEIT players

WingmanLegendary Eagle Master

Ladder ranks via Leetify

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Performance scores

Aim76

Positioning65

Utility51

0–100 skill scores via Leetify.

Recent form

HOT41–52–7Last 10041%Win rateWWLLWWWLWW

Last 10 vs previous 10: +30pp win rate · +0.03 avg rating

Player DNA

Primary style: Aggressive Rifler — Takes opening fights often, backed by a strong aim profile.

Aim7.6

Aggression9.3

Utility5.1

Positioning6.5

Opening Duels3.9

Clutch5.8

Strong CT-side opener

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 sh1ro 91% playstyle similarity

Most alike: opening-fight frequency, positioning profile.

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 56% on CT to 34% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 607ms 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

Aim7.6

Positioning6.5

Utility5.1

Mechanics7.4

Opening Duels3.9

Win Impact2.1

Composite 6.2/10 (Solid), 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 rating0.02+0.03

first ⅓ avg -0.01 → last ⅓ avg 0.02

Reaction time592ms−30ms

first ⅓ avg 622ms → last ⅓ avg 592ms

Headshot accuracy19.3%−1.7%

first ⅓ avg 21.0% → last ⅓ avg 19.3%

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

0.28Best rating — inferno 9–1

13–0Biggest win — mirage

4Longest win streak

W2Current streak

3–14In matches decided by ≤2 rounds

11Overtime games

Map breakdown

vertigoBest map · 60% over 5infernoWeakest map · 32% over 25

Map	Grade	Played	Record	Win rate	Avg rating
inferno	D	25	8–17	32%	0.01
dust2	B	20	9–11	45%	0.00
mirage	C	17	7–10	41%	-0.00
nuke	A	12	7–5	58%	0.04
ancient	D	9	3–6	33%	-0.02
cache	C	8	3–5	38%	0.01
vertigo	A	5	3–2	60%	0.01
anubis	—	2	0–2	0%	-0.04
office	—	1	1–0	100%	0.02
train	—	1	0–1	0%	0.04

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (32% over 25). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

917Matches

51%Win rate

1.17Avg K/D

84.5ADR

42%Headshot %

Clutches & streaks

49%1v1 clutch win

25%1v2 clutch win

9Longest win streak

Recent Faceit resultsLWLLL

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	37	46%	1.19	17.1
Inferno	32	66%	1.43	19.7
Vertigo	21	48%	1.22	19.2
Dust2	15	67%	1.36	17.4
Ancient	14	50%	0.97	15.8
Anubis	11	45%	1.35	21.0
Nuke	5	80%	1.26	19.8
Overpass	3	33%	1.12	20.3

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.

17.1%Headshot accuracy

39.3%Accuracy (enemy spotted)

38.9%Spray accuracy

83.3%Counter-strafing

10.2°Preaim

607msReaction time

34.5%T opening success

56.4%CT opening success

0.58Enemies flashed / flash

3.9%Flash assists

9.75HE damage / grenade

5.99Flashes / 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 34.4536% — 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.

32% win rate across 25 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	13–8	0.04	13%	29 Aug →
dust2	13–5	-0.03	14%	28 Aug →
ancient	8–13	-0.07	7%	28 Aug →
dust2	8–13	-0.02	16%	28 Aug →
vertigo	9–6	0.03	35%	28 Aug →
inferno	9–5	-0.07	23%	28 Aug →
inferno	9–1	0.28	25%	28 Aug →
inferno	11–13	0.09	16%	27 Aug →
dust2	13–6	0.00	20%	27 Aug →
nuke	13–6	0.02	16%	26 Aug →
dust2	13–3	0.04	10%	26 Aug →
nuke	8–8	0.08	23%	26 Aug →
inferno	2–9	-0.09	20%	23 Aug →
vertigo	1–9	-0.22	27%	23 Aug →
nuke	9–0	0.17	14%	22 Aug →
inferno	9–6	-0.06	9%	22 Aug →
inferno	14–16	0.05	15%	17 Aug →
dust2	7–13	-0.02	19%	17 Aug →
mirage	13–0	0.13	26%	17 Aug →
mirage	6–13	-0.06	19%	16 Aug →
anubis	7–13	-0.04	10%	15 Aug →
mirage	6–13	-0.08	10%	15 Aug →
dust2	10–13	-0.03	20%	15 Aug →
ancient	13–9	0.03	9%	6 Aug →
inferno	1–13	-0.02	21%	3 Aug →
mirage	13–16	0.06	17%	31 Jul →
nuke	10–13	0.02	11%	31 Jul →
nuke	13–9	-0.02	26%	28 Jul →
inferno	13–6	0.11	38%	28 Jul →
mirage	3–13	-0.01	29%	27 Jul →
vertigo	6–9	-0.03	27%	27 Jul →
nuke	9–2	0.13	31%	27 Jul →
inferno	9–5	0.12	20%	27 Jul →
vertigo	9–0	0.15	17%	27 Jul →
dust2	4–13	0.01	16%	26 Jul →
cache	14–16	0.03	19%	26 Jul →
dust2	6–13	0.02	7%	26 Jul →
cache	5–13	-0.02	33%	26 Jul →
dust2	13–10	0.06	19%	26 Jul →
nuke	13–5	0.10	13%	25 Jul →
mirage	13–10	0.04	23%	25 Jul →
vertigo	9–4	0.14	20%	25 Jul →
inferno	8–8	-0.02	12%	25 Jul →
mirage	15–15	0.00	9%	23 Jul →
ancient	9–13	0.00	14%	23 Jul →
inferno	15–15	0.06	21%	23 Jul →
mirage	9–4	-0.05	18%	22 Jul →
inferno	11–13	-0.03	15%	22 Jul →
nuke	7–9	-0.00	23%	14 Jul →
dust2	13–11	-0.04	20%	13 Jul →
inferno	13–5	0.05	12%	13 Jul →
dust2	10–13	-0.03	30%	12 Jul →
cache	13–10	-0.03	14%	10 Jul →
dust2	13–4	0.02	17%	10 Jul →
inferno	10–13	0.01	16%	9 Jul →
office	13–10	0.02	17%	8 Jul →
cache	11–13	0.00	8%	7 Jul →
cache	11–13	0.00	20%	6 Jul →
cache	13–4	0.12	17%	6 Jul →
dust2	11–13	-0.02	18%	5 Jul →
ancient	13–16	0.05	19%	5 Jul →
cache	7–13	-0.03	20%	2 Jul →
nuke	13–5	-0.05	20%	16 Jun →
mirage	8–13	-0.04	9%	15 Jun →
ancient	16–14	0.03	25%	15 Jun →
dust2	13–2	0.14	17%	14 Jun →
dust2	4–13	-0.04	13%	10 Jun →
inferno	11–13	-0.06	25%	10 Jun →
nuke	13–7	-0.02	12%	9 Jun →
mirage	12–16	-0.02	4%	8 Jun →
mirage	13–8	0.01	10%	8 Jun →
ancient	1–13	-0.11	25%	2 Jun →
mirage	13–5	-0.03	37%	1 Jun →
nuke	3–13	-0.01	16%	30 May →
mirage	13–2	0.00	27%	30 May →
mirage	10–13	-0.05	20%	30 May →
mirage	11–13	0.03	16%	28 May →
inferno	10–13	-0.03	15%	27 May →
inferno	11–13	-0.00	21%	26 May →
inferno	0–5	-0.03	0%	24 May →
dust2	10–13	-0.01	29%	24 May →
dust2	13–6	-0.02	29%	24 May →
inferno	13–6	0.02	20%	15 May →
cache	13–4	0.02	35%	13 May →
nuke	10–13	0.03	28%	4 May →
ancient	13–6	-0.03	16%	4 May →
dust2	13–9	0.01	20%	26 Apr →
anubis	8–13	-0.04	19%	26 Apr →
inferno	13–11	0.07	23%	25 Apr →
mirage	4–13	0.02	22%	24 Apr →
inferno	15–15	0.00	28%	23 Apr →
dust2	15–15	0.06	15%	22 Apr →
ancient	4–13	-0.05	18%	22 Apr →
train	12–12	0.04	20%	22 Apr →
inferno	7–13	-0.04	26%	20 Apr →
ancient	11–13	-0.00	7%	19 Apr →
inferno	14–16	-0.02	24%	19 Apr →
inferno	2–13	-0.06	38%	17 Apr →
dust2	5–13	-0.06	25%	17 Apr →
inferno	11–13	0.02	26%	16 Apr →