jeegz — CS2 Stats

76561199002121567[U:1:1041855839]

184Tracked matches50%Win rate2020Tracked since

CSDB Rating4.6 DevelopingPassive Rifler

Premier CS Rating12,758Blue band · top ~40.3% of ranked players (population est.)

FaceitLevel 5Top 67.6% of ranked FACEIT players

Ladder ranks via Leetify

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Performance scores

Aim67

Positioning34

Utility35

0–100 skill scores via Leetify.

Recent form

STEADY57–41–2Last 10057%Win rateWLLLWWWLLL

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

Player DNA

Primary style: Passive Rifler — Aim-led profile without a single dominant tendency.

Aim6.7

Aggression2.6

Utility3.5

Positioning3.4

Opening Duels0.0

Clutch4.0

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 75% playstyle similarity

Most alike: opening-duel success, opening-fight frequency.

Where you differ: lower aim profile; lower 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

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.

Reaction time. 651ms 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

Aim6.7

Positioning3.4

Utility3.5

Mechanics7.7

Opening Duels0.0

Win Impact5.0

Composite 4.6/10 (Developing), a weighted mean of the bars with a small opposition adjustment (×0.95 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.02−0.03

first ⅓ avg 0.02 → last ⅓ avg -0.02

Reaction time657ms−12ms

first ⅓ avg 669ms → last ⅓ avg 657ms

Headshot accuracy23.3%+2.7%

first ⅓ avg 20.6% → last ⅓ avg 23.3%

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

0.15Best rating — vertigo 10–1

13–1Biggest win — train

6Longest win streak

9–6In matches decided by ≤2 rounds

6Overtime games

Map breakdown

anubisBest map · 67% over 12ancientWeakest map · 40% over 10

Map	Grade	Played	Record	Win rate	Avg rating
mirage	A	23	13–10	57%	0.01
inferno	B	13	7–6	54%	0.03
anubis	S	12	8–4	67%	-0.01
nuke	A	11	7–4	64%	0.02
ancient	C	10	4–6	40%	-0.01
train	A	8	5–3	63%	-0.01
cache	S	6	4–2	67%	-0.03
dust2	B	6	3–3	50%	-0.00
vertigo	S	6	4–2	67%	0.00
office	—	2	1–1	50%	-0.09
edin	—	2	1–1	50%	-0.02
shelter	—	1	0–1	0%	-0.06

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (40% over 10). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

31Matches

58%Win rate

1.26Avg K/D

67.1ADR

49%Headshot %

Clutches & streaks

40%1v1 clutch win

20%1v2 clutch win

6Longest win streak

Recent Faceit resultsLLWLL

Map	Matches	Win rate	Avg K/D	Avg kills
Vertigo	9	100%	0.96	15.0
Mirage	4	25%	0.91	14.0
Dust2	3	33%	0.98	12.7
Anubis	3	33%	0.70	9.7
Ancient	2	50%	0.66	10.5
Inferno	2	50%	2.06	13.0
Train	1	100%	1.82	20.0
Nuke	1	0%	0.62	10.0

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.

21.6%Headshot accuracy

31.2%Accuracy (enemy spotted)

36.1%Spray accuracy

84.7%Counter-strafing

9.7°Preaim

651msReaction time

24.1%T opening success

24.1%CT opening success

0.63Enemies flashed / flash

4.2%Flash assists

6.31HE damage / grenade

6.90Flashes / 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 24.1434% — below the 40% mark we flag

Spend your practice time on Ancient

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 10 tracked games — your weakest map with enough games to be worth reading into.

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
mirage	13–10	0.00	32%	29 Aug →
ancient	6–13	-0.06	20%	29 Aug →
mirage	5–13	-0.07	31%	12 Aug →
mirage	8–13	0.00	24%	11 Aug →
anubis	13–5	0.03	27%	9 Aug →
ancient	13–9	-0.01	21%	9 Aug →
cache	13–11	-0.04	24%	9 Aug →
dust2	13–16	-0.04	21%	9 Aug →
ancient	4–13	-0.07	19%	9 Aug →
mirage	14–16	-0.00	12%	6 Aug →
cache	11–13	-0.02	16%	6 Aug →
mirage	13–2	0.01	18%	6 Aug →
ancient	13–2	0.05	23%	6 Aug →
anubis	13–4	0.00	20%	5 Aug →
shelter	4–13	-0.06	28%	3 Aug →
nuke	2–13	-0.05	25%	31 Jul →
cache	8–13	-0.01	19%	31 Jul →
anubis	13–6	0.06	24%	28 Jul →
cache	13–7	-0.02	24%	26 Jul →
anubis	7–13	-0.04	20%	9 Jul →
anubis	2–1	0.02	100%	9 Jul →
anubis	7–13	0.00	29%	6 Jul →
cache	13–6	-0.06	22%	7 Jun →
cache	13–11	-0.01	29%	3 Jun →
inferno	5–13	-0.00	5%	2 Jun →
anubis	13–6	-0.02	15%	1 Jun →
mirage	10–13	-0.04	14%	28 Nov →
inferno	13–3	0.01	32%	28 Nov →
train	9–13	-0.03	22%	27 May →
anubis	13–8	-0.01	9%	25 May →
train	10–13	-0.08	24%	25 May →
train	13–7	0.06	10%	25 May →
ancient	3–13	-0.08	10%	22 Apr →
inferno	13–8	-0.04	50%	20 Apr →
mirage	13–9	0.05	14%	20 Apr →
nuke	10–8	0.04	56%	20 Apr →
mirage	9–13	0.06	21%	19 Apr →
anubis	13–11	0.00	20%	19 Apr →
inferno	13–5	0.06	21%	19 Apr →
dust2	13–8	-0.01	14%	19 Apr →
nuke	13–4	0.04	23%	18 Apr →
mirage	13–10	0.01	18%	18 Apr →
inferno	13–8	0.12	20%	18 Apr →
ancient	7–13	-0.05	31%	18 Apr →
dust2	13–5	0.01	28%	18 Apr →
nuke	13–8	-0.02	41%	17 Apr →
mirage	13–3	0.03	41%	17 Apr →
ancient	13–5	0.03	12%	17 Apr →
office	13–4	0.01	24%	15 Apr →
edin	13–7	-0.01	19%	15 Apr →
inferno	12–12	0.08	36%	11 Apr →
mirage	0–2	-0.09	0%	11 Apr →
ancient	9–13	-0.01	13%	11 Apr →
inferno	7–13	-0.02	30%	7 Apr →
ancient	4–13	0.03	24%	7 Apr →
mirage	13–3	0.00	29%	5 Apr →
nuke	6–13	-0.01	29%	4 Apr →
train	13–1	-0.01	17%	4 Apr →
inferno	13–2	0.09	42%	4 Apr →
mirage	13–11	-0.02	23%	3 Apr →
vertigo	13–11	-0.06	21%	3 Apr →
train	13–8	-0.01	24%	3 Apr →
nuke	13–9	0.03	34%	22 Mar →
vertigo	8–13	-0.07	35%	22 Mar →
anubis	13–10	-0.07	22%	25 Feb →
office	0–5	-0.18	50%	25 Feb →
vertigo	12–12	0.01	23%	25 Feb →
mirage	13–9	-0.04	17%	25 Feb →
ancient	13–2	0.09	14%	25 Feb →
nuke	13–3	0.07	26%	23 Feb →
mirage	13–8	0.03	32%	23 Feb →
dust2	5–13	-0.04	12%	23 Feb →
vertigo	13–11	-0.05	30%	23 Feb →
dust2	0–13	-0.03	29%	22 Feb →
mirage	16–12	0.04	9%	22 Feb →
inferno	2–13	-0.01	35%	22 Feb →
mirage	13–7	0.03	23%	21 Feb →
train	6–13	-0.00	16%	21 Feb →
mirage	13–8	0.03	15%	10 Feb →
nuke	7–13	-0.03	8%	10 Feb →
nuke	16–12	0.08	20%	9 Feb →
mirage	6–13	0.07	13%	9 Feb →
nuke	14–16	-0.02	14%	8 Feb →
nuke	13–10	0.06	14%	8 Feb →
vertigo	13–10	0.02	19%	8 Feb →
anubis	8–13	-0.06	16%	6 Feb →
train	13–6	-0.02	33%	6 Feb →
edin	7–13	-0.03	29%	6 Feb →
mirage	13–16	0.03	24%	6 Feb →
inferno	11–13	-0.00	24%	5 Feb →
inferno	13–8	0.12	29%	4 Feb →
mirage	11–13	0.02	28%	4 Feb →
mirage	8–13	-0.03	23%	4 Feb →
mirage	13–6	0.02	10%	1 Feb →
inferno	10–13	0.02	22%	30 Jan →
train	13–7	0.03	21%	30 Jan →
anubis	2–13	-0.08	12%	30 Jan →
dust2	13–4	0.09	24%	29 Jan →
inferno	13–11	0.01	15%	26 Jan →
vertigo	10–1	0.15	25%	25 Jan →