eblan667 — CS2 Stats

EC76561198045098126[U:1:84832398]Steam profile ↗

961Tracked matches40%Win rate2020Tracked since

CSDB Rating5.5 SolidAggressive Rifler

FaceitLevel 10 · 1,905 ELOTop 12.4% of ranked FACEIT players

WingmanSupreme Master First Class

Ladder ranks via Leetify

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Performance scores

Aim81

Positioning54

Utility52

0–100 skill scores via Leetify.

Recent form

STEADY48–44–8Last 10048%Win rateLLLLWWLLWW

Last 10 vs previous 10: -10pp win rate · -0.05 avg rating

Player DNA

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

Aim8.1

Aggression7.8

Utility5.2

Positioning5.4

Opening Duels3.4

Clutch5.6

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 b1t 92% playstyle similarity

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

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

Preaim. Mechanical aim is strong but the crosshair sits 10.2° off target when enemies appear — placement, not flicking, is the bigger win available.

T-side openings. Opening success drops from 56% on CT to 31% 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

Aim8.1

Positioning5.4

Utility5.2

Mechanics6.6

Opening Duels3.4

Win Impact1.7

Composite 5.5/10 (Solid), 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-0.00−0.02

first ⅓ avg 0.02 → last ⅓ avg -0.00

Reaction time543ms−12ms

first ⅓ avg 554ms → last ⅓ avg 543ms

Headshot accuracy22.9%+1.6%

first ⅓ avg 21.3% → last ⅓ avg 22.9%

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

0.23Best rating — inferno 9–2

13–2Biggest win — cache

8Longest win streak

L4Current streak

2–9In matches decided by ≤2 rounds

4Overtime games

Map breakdown

cacheBest map · 64% over 11dust2Weakest map · 29% over 7

Map	Grade	Played	Record	Win rate	Avg rating
inferno	B	35	17–18	49%	-0.00
overpass	A	18	10–8	56%	0.03
nuke	C	12	5–7	42%	0.01
cache	A	11	7–4	64%	0.00
ancient	D	9	3–6	33%	0.04
dust2	D	7	2–5	29%	-0.01
train	—	2	2–0	100%	0.03
mirage	—	2	0–2	0%	0.02
anubis	—	1	0–1	0%	-0.06
eldorado	—	1	0–1	0%	0.04
debris	—	1	1–0	100%	0.15
alpine	—	1	1–0	100%	0.06

Across the last 100 tracked matches.

Dust 2 is currently your weakest sufficiently-sampled map (29% over 7). Start with the 6 essential Dust 2 lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

642Matches

53%Win rate

1.12Avg K/D

84.8ADR

42%Headshot %

Clutches & streaks

48%1v1 clutch win

15%1v2 clutch win

9Longest win streak

Recent Faceit resultsLLLLW

Map	Matches	Win rate	Avg K/D	Avg kills
Ancient	75	55%	1.08	16.0
Inferno	28	57%	1.18	16.6
Anubis	27	63%	1.18	16.6
Vertigo	27	52%	1.13	16.5
Nuke	26	73%	1.22	20.3
Overpass	23	74%	1.12	17.9
Mirage	16	25%	0.91	14.1
Cache	14	50%	1.08	19.0

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.

20.9%Headshot accuracy

36.7%Accuracy (enemy spotted)

40.7%Spray accuracy

79.8%Counter-strafing

10.2°Preaim

526msReaction time

31.1%T opening success

55.9%CT opening success

0.60Enemies flashed / flash

9.3%Flash assists

8.86HE damage / grenade

5.80Flashes / 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 31.1309% — below the 40% mark we flag

Spend your practice time on Dust 2

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

29% win rate across 7 tracked games — your weakest map with enough games to be worth reading into.

Dust 2 callouts & strategy →Dust 2 grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
anubis	6–13	-0.06	23%	15 Aug →
nuke	6–13	0.02	18%	15 Aug →
ancient	9–13	-0.07	21%	15 Aug →
cache	11–13	-0.03	17%	20 Jul →
cache	13–8	0.01	18%	20 Jul →
nuke	13–4	-0.08	53%	20 Jul →
inferno	3–13	0.00	27%	20 Jul →
inferno	3–9	-0.08	20%	13 Jul →
cache	13–2	0.02	24%	9 Jul →
cache	13–2	0.02	13%	9 Jul →
inferno	13–2	-0.03	21%	9 Jul →
eldorado	3–9	0.04	39%	9 Jul →
debris	9–0	0.15	25%	9 Jul →
dust2	13–9	0.04	15%	2 Jul →
dust2	9–13	-0.07	15%	2 Jul →
dust2	4–13	-0.04	42%	2 Jul →
nuke	9–5	0.05	31%	29 Jun →
overpass	9–5	0.08	33%	27 Jun →
overpass	6–13	0.05	13%	27 Jun →
ancient	13–16	0.04	14%	27 Jun →
inferno	9–13	-0.04	17%	27 Jun →
inferno	4–9	-0.07	43%	26 Jun →
inferno	8–8	0.05	24%	22 Jun →
overpass	4–9	-0.06	36%	22 Jun →
ancient	10–13	-0.02	9%	18 Jun →
overpass	13–4	0.05	15%	3 Jun →
overpass	13–11	-0.02	25%	30 May →
dust2	10–13	0.00	17%	30 May →
nuke	9–13	-0.04	12%	30 May →
cache	11–13	-0.01	14%	29 May →
inferno	13–16	0.01	17%	29 May →
nuke	13–5	0.07	16%	24 May →
cache	13–10	-0.03	25%	23 May →
inferno	13–5	0.02	54%	23 May →
nuke	13–4	0.02	15%	23 May →
inferno	1–9	-0.26	0%	23 May →
ancient	9–13	0.14	39%	23 May →
cache	11–13	0.02	20%	15 May →
cache	2–13	-0.05	12%	15 May →
inferno	2–9	-0.13	15%	15 May →
cache	16–12	0.00	13%	11 May →
nuke	13–11	0.03	17%	10 May →
inferno	7–9	-0.07	22%	9 May →
overpass	9–3	0.10	32%	9 May →
inferno	9–5	0.16	28%	9 May →
train	13–7	-0.00	11%	8 May →
cache	13–9	0.03	12%	8 May →
cache	13–9	0.07	25%	8 May →
inferno	8–8	-0.06	24%	7 May →
inferno	13–8	0.08	24%	2 May →
overpass	8–8	-0.01	33%	2 May →
inferno	0–13	-0.04	30%	24 Apr →
inferno	13–8	0.04	20%	24 Apr →
ancient	13–8	0.06	33%	24 Apr →
ancient	9–0	0.17	23%	23 Apr →
overpass	3–13	0.06	29%	20 Apr →
mirage	12–12	0.08	24%	20 Apr →
overpass	8–8	0.16	12%	18 Apr →
overpass	0–9	-0.18	21%	18 Apr →
inferno	11–13	-0.04	26%	18 Apr →
ancient	13–5	0.10	19%	18 Apr →
inferno	13–7	-0.06	13%	18 Apr →
overpass	9–2	0.16	14%	18 Apr →
inferno	4–9	-0.07	23%	18 Apr →
ancient	12–12	-0.02	15%	16 Apr →
inferno	7–9	-0.04	41%	16 Apr →
inferno	9–6	0.06	12%	13 Apr →
nuke	7–13	0.02	16%	13 Apr →
inferno	10–13	-0.02	13%	11 Apr →
dust2	13–10	0.09	18%	11 Apr →
nuke	9–13	0.01	25%	11 Apr →
dust2	10–13	-0.07	10%	9 Apr →
overpass	13–9	0.07	25%	9 Apr →
dust2	6–13	-0.02	18%	9 Apr →
mirage	5–13	-0.04	22%	9 Apr →
inferno	9–4	-0.01	18%	9 Apr →
overpass	9–3	0.04	29%	9 Apr →
nuke	4–9	-0.03	6%	9 Apr →
inferno	7–9	-0.10	41%	6 Apr →
overpass	8–8	-0.01	25%	6 Apr →
ancient	14–16	-0.02	12%	3 Apr →
inferno	9–6	0.09	23%	3 Apr →
inferno	13–5	0.00	13%	3 Apr →
inferno	9–1	0.03	16%	3 Apr →
inferno	7–9	-0.10	21%	3 Apr →
inferno	13–4	0.03	22%	3 Apr →
overpass	4–9	-0.06	18%	3 Apr →
inferno	8–8	-0.00	0%	30 Mar →
nuke	5–13	0.03	19%	30 Mar →
overpass	13–10	0.01	9%	30 Mar →
inferno	13–10	-0.02	13%	30 Mar →
nuke	8–13	-0.01	8%	30 Mar →
inferno	9–4	0.18	37%	30 Mar →
overpass	9–5	-0.00	8%	30 Mar →
alpine	13–7	0.06	14%	28 Mar →
train	13–6	0.07	20%	28 Mar →
inferno	13–10	-0.07	57%	28 Mar →
overpass	9–3	0.04	41%	28 Mar →
inferno	9–2	0.23	40%	28 Mar →
inferno	9–3	0.17	45%	23 Mar →