Tweeckle — CS2 Stats

76561199802688190[U:1:1842422462]✓ No bans

1,053Tracked matches60%Win rate2024Tracked since

CSDB Rating7.1 StrongAggressive Rifler

FaceitLevel 10 · 2,887 ELOTop 12.4% of ranked FACEIT players

WingmanGold Nova III

Ladder ranks via Leetify

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Performance scores

Aim90

Positioning58

Utility55

0–100 skill scores via Leetify.

Recent form

STEADY58–42Last 10058%Win rateWLWLLWWLLW

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

Player DNA

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

Aim9.0

Aggression6.9

Utility5.5

Positioning5.8

Opening Duels3.6

Clutch4.6

Sharp aimerStrong 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 b1t 95% playstyle similarity

Most alike: opening-duel success, utility contribution.

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 90 — the mechanical foundation is a clear strength.

Areas to improve

Positioning. Positioning trails aim by 32 points — deaths here waste a strong aim profile.

Utility. Utility contribution runs far behind the mechanical game — the cheapest rating gain on this profile is thrown, not aimed.

T-side openings. Opening success drops from 54% on CT to 35% on T — the same duels are being taken with worse setups on the attacking side.

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

Aim9.0

Positioning5.8

Utility5.5

Mechanics7.4

Opening Duels3.6

Win Impact8.3

Composite 7.1/10 (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 rating0.01−0.02

first ⅓ avg 0.04 → last ⅓ avg 0.01

Reaction time561ms−35ms

first ⅓ avg 595ms → last ⅓ avg 561ms

Headshot accuracy23.1%+1.9%

first ⅓ avg 21.3% → last ⅓ avg 23.1%

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

0.13Best rating — inferno 13–8

13–3Biggest win — dust2

9Longest win streak

11–7In matches decided by ≤2 rounds

14Overtime games

Map breakdown

infernoBest map · 75% over 12overpassWeakest map · 40% over 5

Map	Grade	Played	Record	Win rate	Avg rating
dust2	A	28	17–11	61%	0.03
ancient	B	17	8–9	47%	0.01
nuke	A	15	9–6	60%	0.01
anubis	B	13	7–6	54%	0.02
inferno	S	12	9–3	75%	0.02
mirage	S	6	4–2	67%	0.01
overpass	C	5	2–3	40%	0.02
train	—	2	1–1	50%	0.07
cache	—	1	0–1	0%	0.01
poseidon	—	1	1–0	100%	0.10

Across the last 100 tracked matches.

Overpass is currently your weakest sufficiently-sampled map (40% over 5). Start with the 6 essential Overpass lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

1,590Matches

53%Win rate

1.33Avg K/D

86.4ADR

48%Headshot %

Clutches & streaks

43%1v1 clutch win

27%1v2 clutch win

11Longest win streak

Recent Faceit resultsWWLWW

Map	Matches	Win rate	Avg K/D	Avg kills
Dust2	367	56%	1.39	18.6
Ancient	348	53%	1.31	19.1
Anubis	204	50%	1.30	17.9
Nuke	176	53%	1.34	19.6
Mirage	164	49%	1.21	18.0
Inferno	158	56%	1.32	18.6
Train	83	54%	1.41	19.1
Overpass	68	51%	1.37	19.2

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.

22.7%Headshot accuracy

38.8%Accuracy (enemy spotted)

43.9%Spray accuracy

83.4%Counter-strafing

8.5°Preaim

547msReaction time

34.8%T opening success

53.7%CT opening success

0.61Enemies flashed / flash

7.8%Flash assists

6.77HE damage / grenade

14.52Flashes / 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 34.7751% — below the 40% mark we flag

Spend your practice time on Overpass

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

40% win rate across 5 tracked games — your weakest map with enough games to be worth reading into.

Overpass callouts & strategy →Overpass grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
dust2	16–13	0.05	9%	25 Aug →
ancient	10–13	0.02	20%	8 Aug →
mirage	13–11	0.01	24%	26 Jul →
cache	8–13	0.01	18%	26 Jul →
dust2	3–13	-0.00	36%	26 Jul →
nuke	13–8	-0.04	20%	15 Jul →
dust2	13–8	0.05	13%	2 Jul →
ancient	4–13	-0.06	17%	9 Jun →
mirage	7–13	-0.01	22%	4 Jun →
dust2	13–8	0.01	27%	23 May →
dust2	13–9	0.10	18%	22 May →
dust2	13–5	0.02	21%	17 May →
nuke	13–8	0.02	23%	17 May →
ancient	13–10	-0.11	19%	15 May →
mirage	13–10	0.04	22%	6 May →
mirage	13–11	0.08	19%	2 May →
ancient	13–9	-0.03	33%	23 Apr →
dust2	13–8	0.03	17%	23 Apr →
mirage	5–13	-0.04	17%	19 Apr →
ancient	13–9	0.04	33%	7 Apr →
dust2	13–3	0.11	17%	5 Apr →
dust2	13–10	0.07	19%	29 Mar →
dust2	2–8	-0.01	43%	26 Mar →
dust2	7–13	-0.03	20%	22 Mar →
nuke	10–13	-0.01	27%	20 Mar →
dust2	13–9	0.01	29%	20 Mar →
dust2	8–13	-0.02	37%	18 Mar →
ancient	11–13	0.01	26%	15 Mar →
dust2	8–2	0.12	25%	11 Mar →
ancient	8–13	-0.01	25%	8 Mar →
poseidon	9–7	0.10	21%	7 Mar →
dust2	13–11	-0.02	27%	15 Feb →
anubis	13–16	-0.03	20%	14 Feb →
ancient	16–13	0.03	27%	8 Feb →
anubis	10–13	0.02	23%	1 Feb →
inferno	13–9	0.06	37%	25 Jan →
anubis	13–10	-0.00	30%	24 Jan →
dust2	11–13	-0.01	18%	23 Jan →
dust2	7–13	-0.07	28%	9 Jan →
dust2	8–13	0.02	31%	6 Jan →
overpass	9–13	0.02	26%	6 Jan →
inferno	6–13	-0.05	38%	27 Dec →
inferno	16–14	0.01	30%	8 Nov →
nuke	13–11	-0.01	32%	2 Nov →
anubis	13–5	-0.01	25%	2 Nov →
ancient	16–13	0.05	27%	19 Oct →
inferno	13–9	-0.01	17%	18 Oct →
nuke	11–13	-0.03	18%	18 Oct →
nuke	13–9	0.04	13%	9 Oct →
ancient	5–13	0.01	8%	5 Oct →
inferno	25–23	0.02	24%	4 Oct →
dust2	6–13	0.05	22%	29 Sept →
nuke	16–13	0.04	29%	29 Sept →
inferno	13–11	-0.02	19%	20 Sept →
overpass	13–9	0.05	18%	20 Sept →
dust2	13–9	0.06	22%	17 Sept →
nuke	13–10	0.04	28%	14 Sept →
nuke	7–12	-0.04	16%	5 Sept →
ancient	10–13	0.07	17%	3 Sept →
dust2	9–13	-0.00	10%	29 Aug →
train	9–13	0.04	19%	29 Aug →
inferno	16–13	0.02	16%	26 Aug →
overpass	13–11	0.03	39%	13 Aug →
overpass	10–12	-0.01	27%	11 Aug →
overpass	14–16	0.02	23%	9 Aug →
nuke	13–6	0.01	20%	8 Aug →
mirage	13–9	-0.01	24%	10 Jul →
inferno	5–13	0.04	30%	10 Jul →
nuke	7–13	0.04	21%	29 Jun →
nuke	13–7	0.02	24%	29 Jun →
dust2	6–13	0.10	23%	24 Jun →
inferno	13–8	0.02	31%	23 Jun →
inferno	13–7	0.03	21%	23 Jun →
dust2	13–5	-0.00	29%	15 Jun →
nuke	13–7	0.09	19%	8 Jun →
anubis	11–13	0.01	12%	7 Jun →
nuke	3–13	-0.10	17%	5 Jun →
dust2	13–4	0.08	20%	4 Jun →
train	13–4	0.09	29%	2 Jun →
ancient	13–6	0.01	16%	2 Jun →
dust2	17–18	0.03	25%	29 May →
dust2	13–7	0.06	17%	25 May →
anubis	22–20	0.02	13%	25 May →
ancient	3–13	0.04	16%	16 May →
dust2	13–11	0.03	24%	11 May →
nuke	10–13	0.01	18%	11 May →
ancient	9–13	-0.01	18%	11 May →
anubis	9–13	0.03	23%	4 May →
anubis	9–13	-0.02	19%	27 Apr →
anubis	13–8	0.06	28%	24 Apr →
ancient	7–13	0.01	22%	23 Apr →
anubis	13–5	0.06	7%	4 Apr →
anubis	16–19	0.01	17%	31 Mar →
inferno	13–8	0.13	19%	23 Mar →
dust2	22–18	0.06	18%	20 Mar →
anubis	13–10	0.01	20%	16 Mar →
inferno	12–16	0.03	27%	13 Mar →
ancient	13–7	0.08	30%	12 Mar →
anubis	13–10	0.08	25%	10 Mar →
ancient	13–10	0.04	24%	9 Mar →