Baddi — CS2 Stats

76561198442787663[U:1:482521935]

6,559Tracked matches66%Win rate2020Tracked since

CSDB Rating7.0 StrongAggressive Rifler

FaceitLevel 10 · 2,269 ELOTop 12.4% of ranked FACEIT players

WingmanLegendary Eagle Master

Ladder ranks via Leetify

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Performance scores

Aim80

Positioning59

Utility88

0–100 skill scores via Leetify.

Recent form

STEADY56–43–1Last 10056%Win rateLWWWWLWTWL

Last 10 vs previous 10: +0pp win rate · +0.09 avg rating

Player DNA

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

Aim8.0

Aggression7.8

Utility8.8

Positioning5.9

Opening Duels3.3

Clutch3.8

Strong CT-side openerHigh utility contribution

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 90% playstyle similarity

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

Where you differ: lower aim profile; higher 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

Utility. Utility score of 88 — grenade impact well above the norm.

Areas to improve

T-side openings. Opening success drops from 56% on CT to 30% 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

Aim8.0

Positioning5.9

Utility8.8

Mechanics5.1

Opening Duels3.3

Win Impact10.0

Composite 7.0/10 (Strong), a weighted mean of the bars with a small opposition adjustment (×1.00 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating0.05+0.03

first ⅓ avg 0.01 → last ⅓ avg 0.05

Reaction time507ms−34ms

first ⅓ avg 541ms → last ⅓ avg 507ms

Headshot accuracy19.6%+2.6%

first ⅓ avg 17.1% → last ⅓ avg 19.6%

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

0.39Best rating — sanctum 9–0

13–3Biggest win — cache

4Longest win streak

10–7In matches decided by ≤2 rounds

14Overtime games

Map breakdown

anubisBest map · 73% over 11infernoWeakest map · 14% over 7

Map	Grade	Played	Record	Win rate	Avg rating
ancient	S	23	15–8	65%	0.03
nuke	S	17	11–6	65%	0.04
dust2	D	12	3–9	25%	0.01
overpass	A	12	7–5	58%	0.01
anubis	S	11	8–3	73%	-0.01
cache	A	10	6–4	60%	0.02
inferno	D	7	1–6	14%	-0.02
mirage	A	5	3–2	60%	0.00
train	—	1	1–0	100%	0.34
sanctum	—	1	1–0	100%	0.39
vertigo	—	1	0–1	0%	-0.02

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (14% over 7). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

8,670Matches

50%Win rate

1.10Avg K/D

81.9ADR

47%Headshot %

Clutches & streaks

39%1v1 clutch win

22%1v2 clutch win

12Longest win streak

Recent Faceit resultsLWLWW

Map	Matches	Win rate	Avg K/D	Avg kills
Ancient	702	53%	1.06	16.3
Nuke	511	57%	1.15	16.6
Anubis	392	52%	1.05	15.4
Dust2	330	42%	0.94	14.5
Vertigo	177	53%	1.10	16.1
Inferno	159	40%	0.96	15.3
Mirage	140	43%	0.97	15.2
Overpass	136	59%	1.14	16.4

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.

19.7%Headshot accuracy

37.3%Accuracy (enemy spotted)

40.9%Spray accuracy

72.9%Counter-strafing

9.9°Preaim

496msReaction time

30.2%T opening success

56.1%CT opening success

0.70Enemies flashed / flash

13.8%Flash assists

11.98HE damage / grenade

18.33Flashes / 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 30.2192% — 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.

14% win rate across 7 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
cache	7–13	0.10	25%	27 Aug →
mirage	13–5	0.11	25%	27 Aug →
ancient	13–11	-0.02	21%	23 Aug →
anubis	16–12	-0.00	21%	16 Aug →
cache	19–16	0.03	14%	15 Aug →
dust2	7–13	0.13	16%	11 Aug →
cache	13–3	0.16	21%	11 Aug →
inferno	12–12	0.08	18%	11 Aug →
train	13–7	0.34	22%	11 Aug →
nuke	11–13	0.09	19%	11 Aug →
ancient	12–16	0.01	12%	29 Jul →
anubis	13–7	0.03	19%	27 Jul →
ancient	8–13	0.04	24%	26 Jul →
ancient	14–16	0.02	20%	25 Jul →
ancient	13–3	0.08	20%	24 Jul →
cache	13–9	-0.07	18%	24 Jul →
ancient	13–9	0.04	21%	23 Jul →
ancient	13–6	-0.02	14%	14 Jul →
cache	9–13	-0.01	19%	14 Jul →
anubis	13–8	-0.02	22%	13 Jul →
ancient	13–4	0.07	25%	13 Jul →
nuke	10–13	0.04	18%	13 Jul →
anubis	13–11	0.04	19%	13 Jul →
ancient	13–7	0.07	18%	12 Jul →
nuke	14–16	-0.03	26%	6 Jul →
anubis	13–6	-0.04	26%	5 Jul →
inferno	3–13	-0.02	20%	3 Jul →
nuke	13–8	0.04	23%	1 Jul →
overpass	16–12	0.05	20%	30 Jun →
overpass	13–7	0.08	9%	30 Jun →
ancient	16–13	0.04	17%	30 Jun →
dust2	8–13	0.04	18%	29 Jun →
ancient	13–7	0.08	17%	29 Jun →
ancient	13–10	-0.02	10%	29 Jun →
nuke	6–13	0.01	28%	29 Jun →
dust2	12–16	-0.00	18%	18 Jun →
anubis	3–13	-0.05	18%	16 Jun →
overpass	13–4	0.11	21%	15 Jun →
anubis	13–8	-0.01	23%	31 May →
nuke	5–13	-0.03	12%	30 May →
ancient	13–7	0.05	24%	18 May →
cache	11–13	-0.01	14%	17 May →
nuke	13–9	-0.02	18%	17 May →
ancient	13–6	0.04	18%	17 May →
dust2	13–8	-0.03	22%	14 May →
nuke	10–13	-0.03	32%	13 May →
cache	16–13	0.01	22%	13 May →
mirage	10–13	-0.05	24%	13 May →
dust2	13–16	-0.01	21%	11 May →
anubis	13–9	-0.03	12%	10 May →
overpass	4–13	-0.03	26%	10 May →
nuke	13–5	0.10	13%	10 May →
cache	13–11	0.03	18%	10 May →
nuke	13–11	0.03	11%	8 May →
nuke	13–9	0.02	47%	8 May →
inferno	9–13	0.02	38%	3 May →
cache	9–13	-0.06	14%	1 May →
cache	13–11	-0.01	20%	29 Apr →
inferno	16–14	-0.07	10%	29 Apr →
overpass	7–13	-0.00	19%	22 Apr →
mirage	15–19	-0.03	7%	20 Apr →
overpass	19–16	-0.01	15%	19 Apr →
inferno	10–13	0.03	19%	19 Apr →
mirage	13–6	-0.02	11%	15 Apr →
ancient	13–10	0.02	8%	15 Apr →
ancient	13–5	0.09	20%	13 Apr →
dust2	9–13	0.02	19%	12 Apr →
inferno	8–13	-0.02	11%	12 Apr →
dust2	10–13	-0.05	12%	12 Apr →
overpass	10–13	-0.02	17%	12 Apr →
ancient	9–13	0.01	9%	11 Apr →
nuke	9–5	0.16	28%	11 Apr →
sanctum	9–0	0.39	13%	11 Apr →
vertigo	7–9	-0.02	23%	11 Apr →
inferno	3–9	-0.17	6%	11 Apr →
anubis	13–9	0.04	15%	11 Apr →
dust2	10–13	0.01	23%	11 Apr →
nuke	13–11	0.03	27%	10 Apr →
mirage	13–9	0.01	16%	10 Apr →
ancient	11–13	-0.04	8%	10 Apr →
ancient	13–7	0.05	13%	10 Apr →
ancient	11–13	0.00	15%	10 Apr →
anubis	8–13	-0.04	25%	10 Apr →
overpass	13–11	-0.03	11%	9 Apr →
nuke	13–8	0.00	31%	9 Apr →
dust2	10–13	0.02	13%	9 Apr →
overpass	13–11	0.01	13%	9 Apr →
ancient	16–14	0.01	20%	9 Apr →
overpass	9–13	-0.05	15%	9 Apr →
dust2	13–7	-0.01	25%	8 Apr →
nuke	13–9	0.06	13%	8 Apr →
overpass	13–7	-0.02	17%	8 Apr →
nuke	13–10	0.05	18%	8 Apr →
ancient	2–13	-0.01	17%	8 Apr →
dust2	1–5	-0.09	20%	7 Apr →
anubis	4–13	-0.07	22%	6 Apr →
overpass	7–13	0.06	20%	6 Apr →
nuke	13–7	0.08	20%	30 Mar →
ancient	9–13	0.03	19%	23 Mar →
dust2	13–8	0.09	8%	23 Mar →