Gunfri — CS2 Stats

US76561198142167996[U:1:181902268]Steam profile ↗✓ No bans

581Tracked matches50%Win rate2020Tracked since

2,033Hours in CS

CSDB Rating5.6 SolidHybrid Rifler

WingmanGold Nova Master

Ladder ranks via Leetify

Performance scores

Aim61

Positioning56

Utility44

0–100 skill scores via Leetify.

Recent form

COLD44–51–5Last 10044%Win rateWLLLLLWLLL

Last 10 vs previous 10: -50pp win rate · -0.02 avg rating

Player DNA

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

Aim6.1

Aggression7.7

Utility4.4

Positioning5.6

Opening Duels3.0

Limited utility dependence

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 88% playstyle similarity

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

Where you differ: lower utility contribution; 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

Reaction time. 654ms 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

Aim6.1

Positioning5.6

Utility4.4

Mechanics7.6

Opening Duels3.0

Win Impact5.0

Composite 5.6/10 (Solid), 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 rating0.00−0.02

first ⅓ avg 0.02 → last ⅓ avg 0.00

Reaction time665ms+7ms

first ⅓ avg 658ms → last ⅓ avg 665ms

Headshot accuracy13.4%−0.9%

first ⅓ avg 14.3% → last ⅓ avg 13.4%

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

0.13Best rating — overpass 13–4

13–2Biggest win — dust2

5Longest win streak

7–12In matches decided by ≤2 rounds

13Overtime games

Map breakdown

nukeBest map · 60% over 5infernoWeakest map · 22% over 9

Map	Grade	Played	Record	Win rate	Avg rating
mirage	B	19	9–10	47%	0.01
dust2	C	18	7–11	39%	-0.00
ancient	A	16	9–7	56%	0.01
cache	B	13	6–7	46%	0.01
anubis	C	13	5–8	38%	0.01
inferno	D	9	2–7	22%	0.01
overpass	B	6	3–3	50%	0.01
nuke	A	5	3–2	60%	0.01
train	—	1	0–1	0%	0.06

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (22% over 9). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

73,945Lifetime kills

1.24K/D · Top 25% of tracked players

4,271Matches

40.9%Match win rate

31.8%Headshot %

18.9%Shot accuracy · Top 10% of tracked players

7,133MVPs

2,033Hours (in match)

2,756Bombs planted

800Bombs defused

Most-used weapons

AK-4724,247

M4A4 / M4A1-S14,428

AWP4,565

P2000 / USP-S2,553

Glock-182,175

P902,120

Tec-91,904

MP71,831

Five-SeveN1,622

Galil AR1,467

Lifetime map wins

6,472dust2

3,031inferno

1,282nuke

887cbble

874train

597vertigo

220office

165italy

Lifetime totals via Steam — visible because this profile's game details are public. Spans CS:GO and CS2.

Faceit stats

Combat

64Matches

70%Win rate

1.51Avg K/D

—ADR

28%Headshot %

Clutches & streaks

—1v1 clutch win

—1v2 clutch win

7Longest win streak

Recent Faceit resultsLLLWW

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

39.6%Accuracy (enemy spotted)

37.8%Spray accuracy

84.0%Counter-strafing

12.1°Preaim

654msReaction time

40.2%T opening success

43.4%CT opening success

0.51Enemies flashed / flash

5.3%Flash assists

8.96HE damage / grenade

3.26Flashes / 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
Your crosshair sits further from where enemies appear than it needs to. Crosshair placement is the cheapest accuracy you can buy.
Preaim 12.1278° — above the 12° mark we flag
Aim Training →
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 13.3071% — below the 15% mark we flag
Aim Training →
Grenades & Utility
You are buying and holding utility rather than using it. Unthrown flashes are wasted money every round.
Flashes per match 3.2608 — below the 4 mark we flag
Grenade Lineups →

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.

22% win rate across 9 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
ancient	13–7	0.02	10%	13 Aug →
mirage	9–13	-0.02	19%	15 Jul →
cache	10–13	-0.02	10%	5 Jul →
cache	10–13	0.02	11%	1 Jul →
anubis	9–13	0.01	29%	1 Jul →
cache	6–13	-0.05	20%	1 Jul →
dust2	13–9	0.01	13%	29 Jun →
dust2	4–13	-0.04	12%	22 Jun →
cache	11–13	-0.02	10%	22 Jun →
mirage	11–13	-0.04	7%	22 Jun →
ancient	13–3	0.03	17%	16 Jun →
anubis	11–13	-0.05	5%	13 Jun →
nuke	13–4	0.05	20%	13 Jun →
dust2	11–13	0.00	26%	11 Jun →
mirage	13–6	-0.02	6%	11 Jun →
ancient	13–9	0.00	12%	11 Jun →
mirage	13–9	-0.00	9%	27 May →
cache	5–13	-0.00	7%	10 May →
cache	13–9	0.08	17%	10 May →
cache	13–7	-0.01	18%	9 May →
cache	13–3	0.08	13%	9 May →
cache	13–11	0.03	7%	4 May →
inferno	11–13	0.01	10%	4 May →
cache	13–4	0.04	15%	2 May →
cache	9–13	-0.00	12%	2 May →
cache	10–13	0.03	14%	30 Apr →
dust2	7–13	0.04	25%	30 Apr →
cache	13–7	-0.05	7%	30 Apr →
anubis	13–9	0.05	7%	25 Apr →
mirage	13–4	0.09	14%	25 Apr →
dust2	9–13	-0.01	18%	23 Apr →
nuke	6–13	-0.04	8%	21 Apr →
anubis	9–13	-0.07	14%	19 Apr →
ancient	4–13	-0.09	23%	19 Apr →
anubis	10–13	0.00	13%	19 Apr →
ancient	15–15	0.02	12%	19 Apr →
ancient	11–13	-0.03	17%	15 Apr →
inferno	7–13	-0.05	24%	11 Apr →
anubis	13–11	0.03	15%	11 Apr →
inferno	8–8	0.07	18%	11 Apr →
ancient	6–13	-0.03	11%	11 Apr →
ancient	13–9	0.06	9%	11 Apr →
inferno	7–13	-0.01	6%	11 Apr →
inferno	13–4	0.02	16%	11 Apr →
ancient	13–5	0.02	40%	11 Apr →
anubis	14–16	-0.03	5%	11 Apr →
ancient	11–13	0.09	16%	8 Apr →
dust2	13–11	0.00	14%	8 Apr →
mirage	6–13	0.04	6%	7 Apr →
anubis	13–3	0.06	4%	4 Apr →
ancient	11–13	0.03	12%	4 Apr →
anubis	14–16	0.01	11%	31 Mar →
dust2	8–13	-0.10	6%	30 Mar →
overpass	3–13	0.06	11%	30 Mar →
dust2	13–2	0.04	15%	30 Mar →
dust2	13–9	-0.01	14%	29 Mar →
mirage	13–11	-0.01	11%	29 Mar →
overpass	13–7	-0.07	7%	29 Mar →
nuke	7–13	-0.02	7%	28 Mar →
dust2	15–15	0.05	20%	28 Mar →
ancient	13–7	-0.01	9%	28 Mar →
mirage	2–13	-0.02	19%	28 Mar →
mirage	13–8	-0.00	9%	28 Mar →
mirage	13–11	-0.04	14%	28 Mar →
dust2	6–13	-0.06	11%	27 Mar →
anubis	7–13	-0.02	7%	27 Mar →
mirage	13–10	0.06	8%	27 Mar →
anubis	13–3	0.06	10%	27 Mar →
dust2	13–11	0.08	10%	27 Mar →
overpass	16–13	0.00	6%	27 Mar →
nuke	13–10	0.02	8%	26 Mar →
ancient	15–15	0.07	12%	26 Mar →
mirage	13–16	-0.01	12%	26 Mar →
dust2	16–13	-0.04	23%	26 Mar →
ancient	16–14	-0.05	16%	25 Mar →
anubis	13–7	0.05	11%	23 Mar →
overpass	7–13	0.00	13%	23 Mar →
inferno	11–13	0.00	15%	22 Mar →
ancient	13–3	0.01	14%	22 Mar →
overpass	13–4	0.13	20%	22 Mar →
mirage	13–16	-0.00	16%	22 Mar →
nuke	13–10	0.06	19%	21 Mar →
dust2	8–13	-0.05	14%	21 Mar →
anubis	11–13	0.06	14%	21 Mar →
dust2	9–13	0.02	8%	21 Mar →
mirage	3–13	0.04	27%	14 Mar →
mirage	16–13	0.06	12%	14 Mar →
inferno	13–9	0.07	10%	14 Mar →
mirage	13–5	0.05	11%	14 Mar →
mirage	9–13	-0.04	13%	10 Mar →
mirage	15–15	-0.00	15%	7 Mar →
dust2	8–13	-0.02	13%	7 Mar →
mirage	7–13	0.04	26%	17 Oct →
inferno	7–13	0.00	11%	17 Oct →
overpass	4–13	-0.06	7%	17 Oct →
dust2	9–13	0.02	27%	14 Oct →
train	4–13	0.06	8%	13 Oct →
dust2	13–9	0.06	24%	13 Oct →
inferno	13–16	0.01	10%	11 Oct →
ancient	7–3	0.00	17%	11 Oct →