Sigmund Freud — CS2 Stats

SE76561197962366591[U:1:2100863]Steam profile ↗✓ No bans

1,853Tracked matches43%Win rate2020Tracked since

CSDB Rating3.6 Learning

Premier CS Rating14,519Blue band · top ~30.9% of ranked players (population est.)

CSDB Leaderboard#14406 of 22023 tracked

FaceitLevel 6Top 54.0% of ranked FACEIT players

Ladder ranks via Leetify

Share this profile

The image is a snapshot; the link keeps updating. Nothing here is published anywhere — it is generated in your browser when you click.

Performance scores

Aim41

Positioning31

Utility66

0–100 skill scores via Leetify.

Recent form

COLD42–58Last 10042%Win rateWWLLLLWLLL

Last 10 vs previous 10: -20pp win rate · +0.01 avg rating

Player DNA

Aim4.1

Aggression3.5

Utility6.6

Positioning3.1

Opening Duels0.0

Clutch0.0

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 76% playstyle similarity

Most alike: utility contribution, opening-fight frequency.

Where you differ: lower positioning profile; 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 35% on CT to 19% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 624ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 70% of shots are taken properly stopped — moving-shot inaccuracy is quietly taxing every duel.

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

Aim4.1

Positioning3.1

Utility6.6

Mechanics4.4

Opening Duels0.6

Win Impact2.8

Composite 3.6/10 (Learning), 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-0.01+0.00

first ⅓ avg -0.01 → last ⅓ avg -0.01

Reaction time640ms+23ms

first ⅓ avg 617ms → last ⅓ avg 640ms

Headshot accuracy12.9%−0.2%

first ⅓ avg 13.1% → last ⅓ avg 12.9%

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

0.09Best rating — nuke 10–0

13–1Biggest win — nuke

4Longest win streak

W2Current streak

5–10In matches decided by ≤2 rounds

8Overtime games

Map breakdown

ancientBest map · 67% over 12infernoWeakest map · 39% over 18

Map	Grade	Played	Record	Win rate	Avg rating
nuke	B	24	11–13	46%	-0.01
cache	B	20	9–11	45%	-0.00
inferno	C	18	7–11	39%	-0.02
anubis	B	15	7–8	47%	-0.01
ancient	S	12	8–4	67%	-0.00
overpass	—	4	0–4	0%	-0.03
vertigo	—	3	0–3	0%	-0.03
mirage	—	3	0–3	0%	-0.02
dust2	—	1	0–1	0%	-0.08

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (39% over 18). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

86Matches

55%Win rate

1.07Avg K/D

66.8ADR

32%Headshot %

Clutches & streaks

17%1v1 clutch win

8%1v2 clutch win

5Longest win streak

Recent Faceit resultsLWLLL

Map	Matches	Win rate	Avg K/D	Avg kills
Inferno	18	50%	1.26	13.2
Vertigo	8	62%	1.27	15.6
Anubis	7	86%	1.12	15.9
Overpass	7	43%	1.22	13.9
Nuke	5	80%	1.01	14.4
Ancient	5	0%	0.79	12.8
Mirage	3	67%	1.19	9.0

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.

13.3%Headshot accuracy

30.5%Accuracy (enemy spotted)

34.5%Spray accuracy

70.0%Counter-strafing

10.9°Preaim

624msReaction time

18.7%T opening success

34.6%CT opening success

0.54Enemies flashed / flash

1.6%Flash assists

9.58HE damage / grenade

17.02Flashes / 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.

Best CS2 Crosshair
Your shots are landing on bodies more often than heads — usually a crosshair-height and placement habit rather than raw aim.
Headshot accuracy 13.3223% — below the 15% mark we flag
Aim Training →
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 18.6965% — 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.

39% win rate across 18 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
anubis	13–7	0.06	19%	8 Jul →
cache	13–8	0.02	16%	7 Jul →
vertigo	6–13	-0.04	6%	7 Jul →
ancient	9–13	-0.00	20%	7 Jul →
anubis	6–13	-0.05	9%	7 Jul →
inferno	7–13	-0.03	8%	7 Jul →
cache	13–9	0.02	13%	7 Jul →
nuke	11–13	0.04	16%	7 Jul →
inferno	6–13	-0.06	8%	7 Jul →
overpass	17–19	0.01	11%	6 Jul →
inferno	10–13	-0.01	10%	6 Jul →
anubis	13–10	0.09	13%	6 Jul →
cache	11–13	-0.01	13%	6 Jul →
nuke	13–9	-0.02	17%	6 Jul →
inferno	16–13	0.00	31%	5 Jul →
nuke	13–6	0.01	10%	5 Jul →
anubis	3–13	-0.09	31%	5 Jul →
nuke	9–13	-0.07	10%	5 Jul →
cache	6–13	-0.03	21%	2 Jul →
ancient	13–8	-0.04	13%	2 Jul →
cache	8–13	-0.05	8%	30 Jun →
nuke	13–1	-0.03	4%	30 Jun →
inferno	9–13	-0.01	9%	30 Jun →
ancient	13–5	0.02	6%	25 Jun →
nuke	13–5	0.05	3%	25 Jun →
cache	13–8	0.04	14%	25 Jun →
inferno	3–13	-0.08	11%	25 Jun →
anubis	10–13	-0.06	11%	25 Jun →
inferno	6–13	-0.05	23%	25 Jun →
nuke	13–4	0.08	7%	24 Jun →
overpass	11–13	0.00	7%	24 Jun →
ancient	10–13	-0.00	18%	24 Jun →
cache	13–6	0.06	10%	24 Jun →
inferno	4–13	0.03	19%	24 Jun →
anubis	13–4	0.02	18%	23 Jun →
cache	7–13	-0.03	19%	23 Jun →
inferno	13–11	-0.09	12%	23 Jun →
nuke	5–13	-0.06	19%	23 Jun →
cache	13–8	0.03	19%	23 Jun →
overpass	7–13	-0.12	6%	23 Jun →
cache	11–13	-0.07	12%	22 Jun →
nuke	7–13	-0.03	20%	22 Jun →
anubis	11–13	0.02	9%	22 Jun →
dust2	5–13	-0.08	21%	22 Jun →
ancient	16–12	0.00	14%	21 Jun →
inferno	7–13	0.00	14%	21 Jun →
mirage	9–13	-0.03	18%	18 Jun →
ancient	13–10	-0.05	14%	18 Jun →
overpass	10–13	-0.01	26%	18 Jun →
cache	13–7	-0.02	13%	18 Jun →
nuke	13–5	0.01	15%	18 Jun →
cache	6–13	-0.08	9%	17 Jun →
anubis	19–17	0.04	21%	17 Jun →
nuke	13–10	0.02	11%	17 Jun →
inferno	13–7	-0.02	5%	17 Jun →
nuke	13–2	-0.04	17%	10 Jun →
cache	8–13	-0.03	23%	10 Jun →
nuke	9–13	-0.05	14%	4 Jun →
vertigo	9–13	-0.01	17%	4 Jun →
nuke	4–13	-0.01	19%	3 Jun →
anubis	10–13	-0.04	6%	3 Jun →
ancient	6–13	-0.04	16%	3 Jun →
vertigo	6–13	-0.05	18%	3 Jun →
cache	10–13	0.05	15%	2 Jun →
ancient	13–6	0.02	18%	2 Jun →
nuke	13–7	-0.03	9%	2 Jun →
inferno	16–13	-0.02	13%	1 Jun →
nuke	14–16	-0.04	7%	1 Jun →
anubis	13–8	-0.03	15%	31 May →
inferno	7–13	-0.01	28%	31 May →
nuke	13–4	0.02	4%	31 May →
cache	8–13	-0.06	17%	31 May →
anubis	14–16	-0.03	13%	31 May →
ancient	13–10	-0.02	5%	28 May →
anubis	13–8	-0.03	10%	28 May →
cache	11–13	-0.02	20%	28 May →
mirage	6–13	-0.00	5%	27 May →
cache	13–11	0.04	16%	27 May →
inferno	13–3	-0.00	15%	27 May →
nuke	10–0	0.09	13%	27 May →
nuke	17–19	0.02	16%	26 May →
inferno	13–9	0.03	20%	26 May →
nuke	7–13	-0.08	11%	26 May →
anubis	13–11	-0.01	14%	25 May →
inferno	13–11	0.01	24%	25 May →
nuke	7–13	-0.06	8%	25 May →
cache	13–2	0.03	13%	25 May →
mirage	8–13	-0.04	19%	24 May →
ancient	13–7	0.02	14%	24 May →
anubis	6–13	-0.06	2%	24 May →
ancient	13–5	0.07	10%	23 May →
nuke	8–13	-0.05	8%	23 May →
cache	8–13	0.01	14%	23 May →
ancient	10–13	-0.02	14%	22 May →
inferno	7–13	0.03	10%	22 May →
anubis	9–13	-0.02	12%	22 May →
nuke	10–13	-0.06	24%	22 May →
inferno	10–13	-0.12	4%	17 May →
cache	13–8	0.08	12%	17 May →
nuke	9–13	-0.01	13%	17 May →