Twinx — CS2 Stats

76561198075047763[U:1:114782035]✓ No bans

589Tracked matches50%Win rate2020Tracked since

CSDB Rating7.0 StrongAggressive Rifler

Premier CS Rating22,037Top 50% of 819 CSDB-tracked playersPink band · top ~6.3% of ranked players (population est.)

Ladder ranks via Leetify

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Performance scores

Aim91

Positioning61

Utility72

0–100 skill scores via Leetify.

Recent form

STEADY56–41–3Last 10056%Win rateWLWLWLLWWW

Last 10 vs previous 10: +40pp win rate · +0.04 avg rating

Player DNA

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

Aim9.1

Aggression6.9

Utility7.2

Positioning6.1

Opening Duels3.4

Clutch5.2

Sharp aimerStrong CT-side openerEffective 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 Twistzz 95% 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 91 — the mechanical foundation is a clear strength.

Areas to improve

T-side openings. Opening success drops from 55% on CT to 32% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 575ms 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

Aim9.1

Positioning6.1

Utility7.2

Mechanics6.6

Opening Duels3.4

Win Impact5.0

Composite 7.0/10 (Strong), 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 rating0.06+0.00

first ⅓ avg 0.06 → last ⅓ avg 0.06

Reaction time580ms+5ms

first ⅓ avg 575ms → last ⅓ avg 580ms

Headshot accuracy34.4%+6.0%

first ⅓ avg 28.4% → last ⅓ avg 34.4%

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

0.24Best rating — anubis 10–1

13–0Biggest win — cache

7Longest win streak

8–8In matches decided by ≤2 rounds

13Overtime games

Map breakdown

cacheBest map · 63% over 8infernoWeakest map · 17% over 6

Map	Grade	Played	Record	Win rate	Avg rating
anubis	A	21	12–9	57%	0.05
nuke	B	18	9–9	50%	0.06
ancient	A	16	9–7	56%	0.06
mirage	A	13	8–5	62%	0.05
dust2	A	10	6–4	60%	0.04
cache	A	8	5–3	63%	0.05
inferno	D	6	1–5	17%	0.02
overpass	—	4	3–1	75%	0.07
train	—	4	3–1	75%	0.07

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (17% over 6). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

2,090Matches

57%Win rate

1.22Avg K/D

75.7ADR

43%Headshot %

Clutches & streaks

46%1v1 clutch win

15%1v2 clutch win

12Longest win streak

Recent Faceit resultsWWWWW

Map	Matches	Win rate	Avg K/D	Avg kills
Ancient	31	55%	1.02	14.8
Anubis	25	52%	1.01	17.0
Dust2	18	50%	1.10	14.4
Nuke	15	73%	1.15	17.1
Inferno	12	92%	1.10	16.0
Mirage	10	70%	1.04	13.0
Train	7	43%	1.28	14.6
Overpass	6	33%	1.09	13.8

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.

34.6%Headshot accuracy

37.6%Accuracy (enemy spotted)

36.1%Spray accuracy

79.8%Counter-strafing

8.6°Preaim

575msReaction time

32.4%T opening success

54.9%CT opening success

0.73Enemies flashed / flash

19.7%Flash assists

8.17HE damage / grenade

12.57Flashes / 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 32.3796% — 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.

17% win rate across 6 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	4–0	0.10	29%	26 Aug →
ancient	8–13	0.13	29%	26 Aug →
mirage	13–6	0.03	61%	22 Aug →
dust2	14–16	-0.05	31%	22 Aug →
ancient	13–8	0.02	26%	21 Aug →
anubis	8–13	0.12	28%	15 Aug →
cache	7–13	-0.04	32%	12 Aug →
nuke	13–6	0.17	46%	12 Aug →
anubis	13–8	0.10	29%	12 Aug →
cache	13–0	0.13	36%	6 Aug →
anubis	13–2	-0.00	13%	6 Aug →
anubis	8–13	0.07	19%	3 Aug →
cache	7–13	-0.00	25%	3 Aug →
dust2	13–11	0.02	38%	29 Jul →
anubis	4–13	-0.04	56%	29 Jul →
cache	4–13	0.01	28%	29 Jul →
nuke	8–13	0.04	36%	28 Jul →
mirage	9–13	0.08	41%	13 Jul →
ancient	13–16	0.10	31%	13 Jul →
inferno	10–13	-0.02	31%	12 Jul →
cache	13–9	0.08	35%	12 Jul →
nuke	3–13	0.07	38%	10 Jul →
dust2	13–9	0.05	30%	10 Jul →
dust2	10–13	0.06	45%	9 Jul →
anubis	13–11	0.04	43%	9 Jul →
cache	13–8	0.12	47%	8 Jul →
cache	13–2	0.16	45%	8 Jul →
ancient	13–11	0.06	23%	6 Jul →
anubis	13–9	0.17	31%	5 Jul →
nuke	8–13	0.03	39%	2 Jul →
overpass	13–6	0.08	29%	2 Jul →
anubis	13–6	0.07	39%	30 Jun →
inferno	7–13	0.12	28%	30 Jun →
anubis	10–1	0.24	42%	29 Jun →
ancient	10–13	0.05	23%	28 Jun →
overpass	13–5	0.11	29%	28 Jun →
nuke	14–16	0.01	28%	25 Jun →
ancient	7–13	0.03	44%	25 Jun →
ancient	13–3	0.15	37%	22 Jun →
nuke	14–16	-0.05	30%	13 Jun →
dust2	11–13	-0.04	28%	13 Jun →
ancient	13–9	0.02	39%	12 Jun →
nuke	13–6	-0.00	35%	12 Jun →
nuke	6–13	-0.04	17%	12 Jun →
anubis	13–11	-0.02	26%	20 May →
cache	13–10	-0.04	22%	13 May →
mirage	7–13	0.04	33%	9 May →
ancient	13–8	0.14	39%	9 May →
overpass	13–7	0.05	29%	19 Mar →
dust2	12–16	-0.01	25%	19 Mar →
inferno	13–16	-0.02	29%	18 Mar →
overpass	15–15	0.06	30%	26 Dec →
nuke	3–13	-0.03	20%	23 May →
anubis	10–13	-0.02	31%	17 May →
inferno	13–4	0.04	36%	4 May →
train	8–13	0.00	6%	27 Apr →
anubis	13–4	0.07	33%	20 Apr →
anubis	13–7	0.06	28%	19 Apr →
dust2	13–5	0.16	26%	19 Apr →
inferno	11–13	0.01	30%	19 Apr →
ancient	13–8	-0.00	23%	19 Apr →
mirage	13–11	0.05	22%	19 Apr →
ancient	13–2	0.07	36%	19 Apr →
ancient	13–5	0.05	17%	18 Apr →
mirage	6–13	-0.07	32%	18 Apr →
dust2	13–10	0.03	20%	12 Apr →
mirage	9–13	0.10	29%	12 Apr →
inferno	7–13	-0.00	36%	12 Apr →
anubis	7–13	-0.03	27%	12 Apr →
nuke	13–9	0.02	34%	12 Apr →
ancient	11–13	-0.03	39%	11 Apr →
nuke	13–7	0.08	30%	11 Apr →
mirage	13–9	0.09	23%	6 Apr →
anubis	13–10	0.06	25%	4 Apr →
mirage	13–8	0.07	31%	4 Apr →
ancient	16–14	0.08	25%	4 Apr →
dust2	13–9	0.07	25%	3 Apr →
nuke	13–5	0.16	11%	3 Apr →
anubis	6–13	0.05	24%	2 Apr →
train	13–9	0.12	43%	31 Mar →
nuke	13–1	0.18	24%	31 Mar →
mirage	13–9	0.06	28%	30 Mar →
anubis	16–13	-0.01	17%	29 Mar →
nuke	13–11	0.06	32%	29 Mar →
anubis	11–13	0.08	19%	29 Mar →
nuke	11–13	0.09	30%	21 Mar →
ancient	15–15	0.02	26%	19 Mar →
nuke	13–10	0.12	37%	19 Mar →
mirage	13–7	0.07	32%	19 Mar →
train	13–8	0.08	42%	17 Mar →
mirage	15–15	0.03	28%	16 Mar →
nuke	16–12	0.12	29%	16 Mar →
mirage	13–3	-0.01	24%	15 Mar →
mirage	13–4	0.14	39%	14 Mar →
anubis	8–13	0.05	33%	10 Mar →
nuke	9–13	0.05	21%	9 Mar →
anubis	6–13	-0.04	21%	9 Mar →
train	13–11	0.06	31%	9 Mar →
ancient	8–13	0.04	28%	9 Mar →
anubis	16–13	0.09	24%	8 Mar →