HiFi — CS2 Stats

CA76561198772832442[U:1:812566714]Steam profile ↗✓ No bans

560Tracked matches47%Win rate2025Tracked since

CSDB Rating6.3 SolidAggressive Rifler

Premier CS Rating12,716Blue band · top ~40.5% of ranked players (population est.)

CSDB Leaderboard#10634 of 15239 tracked

Ladder ranks via Leetify

Rating over time

12,716Peak Premier on CSDB

1Days observed since 2026-08-28

Premier CS Rating

At peak — 12,716
Next: Purple band at 15,000 — 2,284 to go
Reached: Blue band · Light Blue band
Blue band first seen 2026-08-28
Light Blue band first seen 2026-08-28

CSDB's own observations — this history builds from the day a profile is first viewed and cannot be backfilled.

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Performance scores

Aim86

Positioning63

Utility53

0–100 skill scores via Leetify.

Recent form

STEADY48–47–5Last 10048%Win rateWLLLWLLLWW

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

Player DNA

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

Aim8.6

Aggression8.4

Utility5.3

Positioning6.3

Opening Duels4.5

Clutch9.4

Sharp aimerReliable in 1v1s

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 93% playstyle similarity

Most alike: positioning profile, opening-duel success.

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

Aim. Aim score of 86 — the mechanical foundation is a clear strength.

Areas to improve

Preaim. Mechanical aim is strong but the crosshair sits 10.4° off target when enemies appear — placement, not flicking, is the bigger win available.

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

Reaction time. 583ms 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

Aim8.6

Positioning6.3

Utility5.3

Mechanics8.0

Opening Duels4.5

Win Impact3.9

Composite 6.3/10 (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 0.01 → last ⅓ avg -0.00

Reaction time587ms−32ms

first ⅓ avg 619ms → last ⅓ avg 587ms

Headshot accuracy15.3%+3.1%

first ⅓ avg 12.2% → last ⅓ avg 15.3%

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 — cache 13–4

13–1Biggest win — train

6Longest win streak

9–10In matches decided by ≤2 rounds

5Overtime games

Map breakdown

cacheBest map · 80% over 5infernoWeakest map · 33% over 9

Map	Grade	Played	Record	Win rate	Avg rating
ancient	C	20	7–13	35%	0.02
nuke	A	16	9–7	56%	0.00
anubis	C	14	6–8	43%	0.00
dust2	A	14	9–5	64%	0.00
mirage	C	12	5–7	42%	0.03
inferno	D	9	3–6	33%	0.01
train	B	8	4–4	50%	0.01
cache	S	5	4–1	80%	0.01
overpass	—	2	1–1	50%	0.03

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (33% over 9). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

3Matches

67%Win rate

0.76Avg K/D

82.6ADR

45%Headshot %

Clutches & streaks

67%1v1 clutch win

0%1v2 clutch win

2Longest win streak

Recent Faceit resultsLLWWL

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.

14.9%Headshot accuracy

44.3%Accuracy (enemy spotted)

46.1%Spray accuracy

86.0%Counter-strafing

10.4°Preaim

583msReaction time

51.5%T opening success

44.2%CT opening success

0.53Enemies flashed / flash

10.5%Flash assists

7.68HE damage / grenade

7.87Flashes / 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 14.8857% — below the 15% mark we flag
Aim Training →

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.

33% 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
cache	13–4	0.13	19%	26 Aug →
cache	0–7	-0.13	0%	26 Aug →
anubis	13–16	0.08	13%	26 Aug →
mirage	14–16	0.01	19%	26 Aug →
overpass	13–5	0.06	10%	25 Aug →
inferno	3–13	-0.07	16%	25 Aug →
ancient	11–13	-0.02	8%	25 Aug →
mirage	8–13	-0.02	16%	25 Aug →
anubis	13–11	-0.05	9%	25 Aug →
dust2	13–7	-0.03	14%	25 Aug →
dust2	13–6	0.04	25%	25 Aug →
anubis	7–13	0.05	20%	25 Aug →
nuke	13–4	-0.02	12%	24 Aug →
overpass	6–13	-0.00	5%	24 Aug →
ancient	13–2	0.02	10%	24 Aug →
ancient	10–13	0.01	12%	24 Aug →
dust2	13–10	0.04	16%	24 Aug →
ancient	11–13	-0.02	10%	24 Aug →
mirage	9–13	0.00	31%	24 Aug →
dust2	13–6	0.06	21%	24 Aug →
nuke	13–7	-0.03	16%	23 Aug →
ancient	10–13	-0.03	16%	23 Aug →
nuke	11–13	-0.00	8%	23 Aug →
dust2	13–5	0.00	11%	22 Aug →
ancient	13–4	0.08	13%	20 Aug →
ancient	11–13	0.01	13%	19 Aug →
inferno	2–13	-0.02	24%	19 Aug →
ancient	13–11	-0.01	19%	18 Aug →
inferno	6–13	-0.04	16%	18 Aug →
nuke	13–9	-0.04	19%	18 Aug →
dust2	2–13	-0.07	12%	18 Aug →
train	13–4	0.02	21%	18 Aug →
train	13–4	-0.01	33%	18 Aug →
cache	13–4	0.03	15%	15 Aug →
dust2	13–11	-0.00	12%	15 Aug →
train	12–12	0.02	23%	15 Aug →
train	13–1	0.10	6%	15 Aug →
train	6–13	-0.02	20%	15 Aug →
train	12–12	-0.06	11%	15 Aug →
dust2	13–11	-0.02	15%	15 Aug →
dust2	12–12	0.05	19%	15 Aug →
train	4–13	0.08	12%	15 Aug →
anubis	7–13	-0.01	12%	27 Jul →
ancient	13–11	0.02	17%	27 Jul →
cache	13–6	0.01	22%	27 Jul →
inferno	13–4	0.03	8%	27 Jul →
mirage	13–3	0.06	14%	27 Jul →
dust2	13–5	-0.01	6%	27 Jul →
ancient	11–13	-0.03	10%	27 Jul →
dust2	13–5	0.03	16%	27 Jul →
ancient	11–13	0.01	17%	26 Jul →
inferno	13–11	0.04	16%	25 Jul →
train	13–6	-0.04	19%	25 Jul →
anubis	5–13	-0.08	6%	25 Jul →
mirage	13–7	0.06	15%	25 Jul →
dust2	10–13	-0.03	24%	25 Jul →
nuke	5–13	0.08	11%	25 Jul →
ancient	13–8	0.05	15%	25 Jul →
mirage	13–7	0.08	21%	25 Jul →
ancient	6–13	0.04	9%	25 Jul →
nuke	13–3	-0.03	18%	23 Jul →
anubis	5–13	0.01	13%	23 Jul →
nuke	13–11	0.04	14%	23 Jul →
anubis	13–11	0.06	13%	23 Jul →
nuke	5–13	-0.03	4%	22 Jul →
anubis	5–13	0.02	9%	22 Jul →
inferno	13–8	0.04	9%	22 Jul →
nuke	13–6	-0.00	9%	22 Jul →
anubis	8–13	-0.03	9%	22 Jul →
anubis	13–10	-0.01	14%	22 Jul →
mirage	8–13	-0.02	9%	21 Jul →
ancient	10–13	0.09	17%	21 Jul →
mirage	13–16	0.08	17%	21 Jul →
nuke	10–13	0.02	13%	21 Jul →
ancient	13–7	0.02	8%	21 Jul →
ancient	8–13	0.04	12%	20 Jul →
anubis	13–6	-0.05	5%	20 Jul →
nuke	7–13	-0.05	26%	19 Jul →
nuke	8–13	0.02	8%	19 Jul →
mirage	13–9	-0.02	2%	19 Jul →
anubis	10–13	0.01	9%	19 Jul →
ancient	12–12	-0.03	7%	18 Jul →
mirage	12–12	0.04	10%	18 Jul →
mirage	9–13	0.04	5%	17 Jul →
ancient	13–11	0.03	10%	17 Jul →
anubis	13–6	0.05	17%	17 Jul →
anubis	13–10	-0.03	10%	17 Jul →
nuke	16–12	0.08	16%	17 Jul →
cache	13–8	0.01	4%	17 Jul →
mirage	13–7	0.03	9%	15 Jul →
inferno	11–13	0.05	14%	15 Jul →
dust2	11–13	-0.01	9%	15 Jul →
nuke	13–10	0.01	22%	15 Jul →
inferno	7–13	0.02	20%	15 Jul →
nuke	13–10	0.04	29%	13 Jul →
ancient	8–13	0.05	13%	13 Jul →
ancient	9–13	0.02	14%	13 Jul →
dust2	12–16	-0.02	9%	12 Jul →
nuke	11–13	-0.02	20%	12 Jul →
inferno	6–13	0.05	10%	12 Jul →