Pudsy — CS2 Stats

GB76561198246534754[U:1:286269026]Steam profile ↗✓ No bans

913Tracked matches63%Win rate2025Tracked since

CSDB Rating6.6 SolidAggressive Rifler

Premier CS Rating22,422Top 50% of 819 CSDB-tracked playersPink band · top ~5.8% of ranked players (population est.)

CSDB Leaderboard#3921 of 12578 tracked

WingmanGold Nova II

Ladder ranks via Leetify

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Performance scores

Aim80

Positioning52

Utility42

0–100 skill scores via Leetify.

Recent form

STEADY54–40–6Last 10054%Win rateLLWTTWLWLW

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.

Aim8.0

Aggression7.3

Utility4.2

Positioning5.2

Opening Duels3.0

Clutch4.2

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 ropz 89% playstyle similarity

Most alike: opening-fight frequency, utility contribution.

Where you differ: lower opening-duel success; 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

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

Reaction time. 572ms 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

Aim8.0

Positioning5.2

Utility4.2

Mechanics7.8

Opening Duels3.0

Win Impact9.3

Composite 6.6/10 (Solid), 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 rating-0.01+0.01

first ⅓ avg -0.02 → last ⅓ avg -0.01

Reaction time573ms−18ms

first ⅓ avg 590ms → last ⅓ avg 573ms

Headshot accuracy18.6%+1.3%

first ⅓ avg 17.4% → last ⅓ avg 18.6%

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

0.16Best rating — cache 9–13

13–0Biggest win — dust2

7Longest win streak

L2Current streak

8–2In matches decided by ≤2 rounds

5Overtime games

Map breakdown

overpassBest map · 75% over 8infernoWeakest map · 43% over 14

Map	Grade	Played	Record	Win rate	Avg rating
ancient	B	23	12–11	52%	-0.01
nuke	A	17	10–7	59%	0.01
inferno	C	14	6–8	43%	-0.02
cache	A	14	9–5	64%	-0.00
anubis	A	10	6–4	60%	-0.00
dust2	B	10	5–5	50%	-0.01
overpass	S	8	6–2	75%	-0.03
mirage	—	3	0–3	0%	-0.07
vertigo	—	1	0–1	0%	-0.07

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (43% over 14). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

87Matches

62%Win rate

1.04Avg K/D

75.5ADR

42%Headshot %

Clutches & streaks

41%1v1 clutch win

25%1v2 clutch win

10Longest win streak

Recent Faceit resultsWLLLW

Map	Matches	Win rate	Avg K/D	Avg kills
Inferno	23	65%	0.99	13.7
Mirage	19	53%	0.89	12.8
Overpass	15	67%	1.01	13.4
Ancient	15	67%	1.20	15.9
Nuke	10	50%	1.27	13.4
Dust2	4	75%	1.08	19.5
Anubis	1	100%	0.60	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.

18.8%Headshot accuracy

36.2%Accuracy (enemy spotted)

42.0%Spray accuracy

85.2%Counter-strafing

9.8°Preaim

572msReaction time

35.2%T opening success

49.1%CT opening success

0.52Enemies flashed / flash

4.5%Flash assists

6.17HE damage / grenade

6.40Flashes / 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 35.1518% — 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.

43% win rate across 14 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
inferno	7–13	-0.03	25%	23 Jul →
ancient	5–13	-0.10	20%	23 Jul →
nuke	13–3	-0.05	40%	23 Jul →
ancient	15–15	0.03	28%	23 Jul →
ancient	15–15	-0.01	22%	22 Jul →
nuke	13–8	0.04	24%	22 Jul →
inferno	4–13	-0.01	26%	22 Jul →
ancient	13–7	-0.01	20%	19 Jul →
cache	11–13	-0.07	15%	19 Jul →
nuke	13–8	-0.01	14%	19 Jul →
cache	12–12	-0.00	18%	16 Jul →
ancient	13–11	0.03	19%	16 Jul →
anubis	8–13	-0.01	10%	14 Jul →
ancient	13–8	0.03	13%	14 Jul →
anubis	13–9	0.09	12%	14 Jul →
cache	13–11	-0.00	11%	14 Jul →
ancient	13–5	0.08	32%	13 Jul →
anubis	13–9	-0.04	12%	13 Jul →
inferno	13–8	-0.01	21%	13 Jul →
nuke	13–2	0.05	12%	13 Jul →
mirage	7–13	-0.03	17%	11 Jul →
dust2	8–13	0.00	13%	11 Jul →
cache	13–6	-0.05	27%	11 Jul →
dust2	6–13	-0.03	16%	10 Jul →
ancient	13–4	0.06	19%	10 Jul →
dust2	10–13	-0.01	14%	10 Jul →
inferno	13–8	-0.02	25%	10 Jul →
vertigo	0–13	-0.07	6%	10 Jul →
dust2	13–5	0.01	16%	10 Jul →
ancient	13–10	-0.05	18%	10 Jul →
inferno	5–13	-0.07	18%	10 Jul →
dust2	13–3	0.01	19%	9 Jul →
ancient	6–13	-0.02	14%	9 Jul →
mirage	6–13	-0.08	23%	9 Jul →
ancient	10–13	-0.03	19%	9 Jul →
cache	13–11	-0.01	17%	9 Jul →
inferno	13–11	0.02	12%	5 Jul →
inferno	7–13	-0.00	24%	24 Jun →
anubis	13–10	0.02	19%	24 Jun →
ancient	13–4	0.04	14%	23 Jun →
anubis	9–13	-0.02	13%	23 Jun →
overpass	8–13	-0.01	14%	23 Jun →
ancient	7–13	-0.02	21%	23 Jun →
ancient	13–6	-0.04	16%	23 Jun →
overpass	13–9	-0.00	14%	23 Jun →
cache	13–9	-0.05	13%	24 May →
ancient	10–13	0.02	10%	24 May →
dust2	13–7	-0.09	29%	24 May →
nuke	13–8	0.02	31%	24 May →
nuke	13–4	0.03	15%	17 May →
nuke	13–1	-0.01	16%	11 May →
nuke	13–6	0.02	10%	11 May →
anubis	13–8	0.00	15%	11 May →
ancient	9–13	-0.00	18%	11 May →
overpass	13–11	-0.02	16%	6 May →
ancient	13–10	-0.04	16%	6 May →
inferno	7–13	-0.09	9%	6 May →
cache	8–13	0.01	9%	3 May →
cache	9–13	0.16	15%	3 May →
cache	8–1	-0.04	8%	3 May →
cache	10–13	-0.02	21%	3 May →
nuke	15–15	0.07	22%	3 May →
ancient	6–13	0.03	25%	3 May →
anubis	13–11	-0.00	25%	3 May →
inferno	13–8	-0.04	19%	3 May →
inferno	8–13	0.00	14%	30 Apr →
overpass	5–13	-0.08	20%	30 Apr →
cache	13–8	0.04	28%	30 Apr →
ancient	13–7	-0.02	25%	29 Apr →
nuke	13–4	0.03	17%	29 Apr →
anubis	13–5	-0.02	24%	29 Apr →
cache	13–10	-0.01	21%	29 Apr →
cache	13–9	-0.00	18%	29 Apr →
cache	13–9	-0.01	17%	29 Apr →
nuke	11–13	-0.04	17%	28 Apr →
nuke	8–13	-0.02	16%	28 Apr →
dust2	7–13	-0.06	14%	16 Apr →
overpass	13–8	0.01	19%	16 Apr →
nuke	8–13	-0.04	21%	16 Apr →
nuke	13–2	0.08	19%	16 Apr →
nuke	3–13	-0.04	21%	16 Apr →
dust2	13–7	0.05	21%	16 Apr →
inferno	9–13	-0.03	19%	23 Mar →
overpass	13–7	-0.04	9%	23 Mar →
mirage	4–13	-0.10	7%	23 Mar →
ancient	13–10	-0.05	4%	14 Mar →
overpass	13–11	-0.04	13%	14 Mar →
inferno	13–3	-0.01	17%	12 Mar →
ancient	13–4	-0.02	22%	12 Mar →
ancient	10–13	-0.11	18%	12 Mar →
dust2	13–0	0.02	6%	3 Mar →
nuke	2–13	-0.08	15%	3 Mar →
anubis	15–15	-0.04	15%	21 Feb →
dust2	8–13	-0.01	27%	21 Feb →
overpass	3–1	-0.09	25%	21 Feb →
nuke	10–13	0.04	7%	5 Feb →
anubis	9–13	-0.03	17%	5 Feb →
inferno	7–13	-0.03	23%	1 Feb →
ancient	12–12	0.07	16%	1 Feb →
inferno	16–13	0.01	15%	29 Jan →