reggÆ;🐺 ❟❛❟ G❤ — CS2 Stats

CL76561198302563304[U:1:342297576]Steam profile ↗✓ No bans

2,611Tracked matches45%Win rate2021Tracked since

2,478Hours in CS

CSDB Rating5.2 DevelopingSupport

Premier CS Rating14,369Blue band · top ~31.7% of ranked players (population est.)

CSDB Leaderboard#156792 of 261516 tracked

FaceitLevel 5 · 923 ELOTop 67.6% of ranked FACEIT players

WingmanLegendary Eagle

Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 8 Nov 2026 (6 days ago). Ranks are recorded once per day this page is viewed.

No change since then. Play, then come back: the next observation lands here.

Rating over time

Premier CS Rating: 14,369 +1,851 30 Sept – 8 Nov · 29 days played

16,584Peak Premier in tracked matches

923Highest Faceit ELO seen on CSDB

34Days played since 2026-09-30

Faceit ELO

At peak — 923
Next: Level 5 at 1,051 — 128 to go
Reached: Level 4 · Level 3 · Level 2

Premier CS Rating

1,673 below peak (16,042)
+1,654 over 30 days · improving
Next: Purple band at 15,000 — 631 to go
Reached: Purple band · Blue band · Light Blue band

Premier comes from this profile’s tracked match history, so it reaches back as far as those matches do.

How this compares with the same rank

Median values for Level 5 among CSDB-tracked players (n=14,283), from Valve's own lifetime stats. Aim, positioning and utility scores are deliberately not benchmarked here — those are a third-party provider's derived metrics, which CSDB does not store.

Metric	This player	Level 5 median	Level 6 median	vs Level 6
Headshot rate	50.5%	43.8%	44.8%	above
Shot accuracy	11.5%	12.0%	12.4%	0.9% short
Kill/death ratio	1.02	1.02	1.03	meets
Match win rate	40.6%	44.7%	45.2%	4.6% short

This profile matches the typical Level 6 player on 2 of 4 comparable metrics.

Widest gap: Match win rate. That is the metric furthest from the Level 6 median in relative terms — not necessarily the one holding a rank back, which no statistic here can establish.

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Performance scores

Aim75

Positioning40

Utility57

0–100 skill scores via Leetify.

Recent form

COLD47–45–8Last 10047%Win rateLLWLWLTWLL

Last 10 vs previous 10: −40pp win rate · −0.01 avg rating · −2.5pp headshot accuracy · +31ms reaction

Win rate down 40pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp).

Last 5 · 10 · 20 matches

Last 5

2–3 · 40% win rate
Avg rating -0.00
Avg headshot accuracy 20%
Avg reaction 691ms

Last 10

3–6–1 · 30% win rate
Avg rating -0.02
Avg headshot accuracy 19%
Avg reaction 649ms

Last 20

10–9–1 · 50% win rate
Avg rating -0.01
Avg headshot accuracy 21%
Avg reaction 634ms

Newest first, from the last 100 tracked matches. Each block is its own sample — one result moves a 5-match win rate by 20 points.

Player DNA

Primary style: Support — Utility contribution stands above the rest of this profile (+2.3 against its own average).

Aim7.5

Utility5.7

Positioning4.0

Opening Duels0.1

Clutch4.0

Style profile from tracked-match aggregates — how this player plays, not how good they are. Classification rules are deterministic and documented in code.

What this cannot see yet: which weapons you use — so CSDB cannot identify an AWPer, and no style here implies a rifle or a sniper. It also cannot see how often you take opening duels, only how often you win them, nor where you hold, so roles that depend on those (entry, lurk, anchor) are deliberately absent rather than guessed. All of it needs round-by-round demo data, which is the next thing being built.

Your pro match

Plays most like ropz 81% playstyle similarity

Most alike: utility contribution, positioning profile.

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

Positioning. Positioning trails aim by 35 points — deaths here waste a strong aim profile.

Reaction time. 620ms 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

Aim7.5

Positioning4.0

Utility5.7

Mechanics7.0

Opening Duels1.6

Win Impact3.3

Composite 5.2/10 (Developing), 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 time621ms−11ms

first ⅓ avg 632ms → last ⅓ avg 621ms

Headshot accuracy19.3%+0.3%

first ⅓ avg 19.0% → last ⅓ avg 19.3%

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.

Personal bests

0.20Best match rating · 13–3 · anubis, 22 Oct →

39%Best headshot accuracy · 8–13 · cache, 17 Oct →

438msFastest reaction time · 7–13 · mirage, 11 Oct →

13–1Biggest win · dust2, 17 Oct →

Across the last 100 tracked matches.

Highlights

4Longest win streak

L2Current streak

10–8In matches decided by ≤2 rounds

15Overtime games

Map breakdown

nukeBest map · 60% over 5infernoWeakest map · 18% over 11

Map	Grade	Played	Record	Win rate	Avg rating
dust2	A	27	16–11	59%	-0.01
anubis	A	19	11–8	58%	0.01
cache	C	12	5–7	42%	-0.01
ancient	B	11	5–6	45%	-0.01
inferno	D	11	2–9	18%	-0.01
mirage	B	11	5–6	45%	-0.01
nuke	A	5	3–2	60%	-0.04
train	—	4	0–4	0%	-0.01

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (18% over 11). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

129,213Lifetime kills

1.02K/D · Top 50% of Level 5 players

6,294Matches

40.6%Match win rate

50.5%Headshot % · Top 25% of Level 5 players

11.5%Shot accuracy

11,923MVPs

2,478Hours (in match)

5,961Bombs planted

1,226Bombs defused

Most-used weapons

AK-4738,316

M4A4 / M4A1-S20,227

AWP14,490

Desert Eagle8,653

P2000 / USP-S7,588

P2506,817

Glock-185,420

FAMAS2,570

Galil AR2,488

XM10142,411

Lifetime map wins

25,372dust2

6,000inferno

3,972nuke

1,535vertigo

968train

356cbble

44safehouse

32office

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

Faceit stats

Combat

584Matches

48%Win rate

0.90Avg K/D

72.3ADR

49%Headshot %

Clutches & streaks

40%1v1 clutch win

23%1v2 clutch win

8Longest win streak

Recent Faceit resultsLLWLL

Map	Matches	Win rate	Avg K/D	Avg kills
Dust2	227	55%	0.92	13.6
Mirage	117	41%	0.85	13.4
Anubis	62	47%	0.85	13.5
Ancient	54	54%	0.96	14.4
Nuke	53	34%	0.81	14.0
Inferno	39	31%	0.98	15.1
Cache	17	65%	1.07	16.2
Overpass	9	56%	1.05	15.3

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.

19.9%Headshot accuracy

36.3%Accuracy (enemy spotted)

39.1%Spray accuracy

81.6%Counter-strafing

9.4°Preaim

620msReaction time

42.0%T opening success

30.7%CT opening success

0.61Enemies flashed / flash

3.3%Flash assists

6.32HE damage / grenade

14.58Flashes / 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
Losing the first CT duel repeatedly usually means holding angles that favour the peeker.
CT opening duels 30.698% — 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.

18% win rate across 11 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
dust2	1–13	-0.11	16%	8 Nov →
ancient	8–13	-0.02	18%	7 Nov →
cache	11–3	0.00	8%	7 Nov →
inferno	10–13	0.01	36%	7 Nov →
dust2	13–7	0.10	22%	7 Nov →
mirage	11–13	-0.00	25%	6 Nov →
inferno	15–15	-0.01	24%	4 Nov →
dust2	13–10	-0.01	17%	4 Nov →
anubis	6–13	-0.01	21%	3 Nov →
dust2	3–13	-0.11	7%	2 Nov →
dust2	13–10	0.03	35%	1 Nov →
anubis	13–8	0.00	11%	1 Nov →
nuke	9–13	-0.09	13%	1 Nov →
dust2	13–10	0.04	27%	1 Nov →
dust2	2–13	-0.09	27%	1 Nov →
dust2	16–14	0.01	21%	1 Nov →
nuke	13–11	0.03	25%	1 Nov →
mirage	13–8	0.01	14%	1 Nov →
inferno	7–13	-0.01	26%	31 Oct →
ancient	16–14	-0.01	19%	31 Oct →
anubis	10–13	-0.00	20%	29 Oct →
mirage	8–13	-0.06	11%	27 Oct →
anubis	13–7	-0.01	24%	27 Oct →
inferno	6–13	-0.00	13%	26 Oct →
dust2	9–13	-0.07	26%	26 Oct →
mirage	10–13	0.04	16%	26 Oct →
cache	12–16	-0.08	15%	25 Oct →
anubis	6–13	-0.03	11%	25 Oct →
ancient	13–4	0.00	6%	25 Oct →
ancient	13–8	0.04	28%	25 Oct →
inferno	13–4	0.05	25%	25 Oct →
dust2	11–13	-0.03	17%	25 Oct →
dust2	13–4	0.03	14%	25 Oct →
nuke	13–8	-0.02	17%	25 Oct →
dust2	13–6	-0.09	16%	24 Oct →
mirage	13–8	-0.02	34%	24 Oct →
inferno	14–16	-0.04	19%	24 Oct →
dust2	13–7	0.04	27%	24 Oct →
ancient	14–16	-0.01	26%	23 Oct →
cache	12–12	-0.01	28%	23 Oct →
anubis	13–3	0.20	17%	22 Oct →
anubis	13–2	0.17	36%	22 Oct →
cache	13–5	0.03	24%	22 Oct →
dust2	7–13	-0.05	15%	21 Oct →
anubis	15–15	-0.02	33%	20 Oct →
inferno	10–13	-0.01	22%	19 Oct →
anubis	13–2	-0.02	21%	19 Oct →
anubis	10–13	-0.03	25%	18 Oct →
mirage	15–15	-0.04	24%	18 Oct →
mirage	13–8	0.02	13%	18 Oct →
dust2	13–4	0.03	22%	18 Oct →
dust2	13–11	-0.04	28%	18 Oct →
ancient	16–14	0.01	14%	18 Oct →
anubis	11–13	-0.07	30%	18 Oct →
dust2	7–13	-0.01	23%	18 Oct →
cache	8–13	-0.02	39%	17 Oct →
ancient	13–4	0.02	21%	17 Oct →
mirage	16–14	0.02	23%	17 Oct →
ancient	9–13	0.05	30%	17 Oct →
anubis	13–7	-0.02	11%	17 Oct →
cache	15–15	-0.01	26%	17 Oct →
inferno	10–13	-0.02	31%	17 Oct →
dust2	13–1	0.10	36%	17 Oct →
ancient	9–13	-0.09	18%	16 Oct →
dust2	7–13	0.04	27%	15 Oct →
dust2	13–5	0.01	18%	13 Oct →
anubis	5–13	-0.08	7%	13 Oct →
dust2	13–9	-0.06	10%	13 Oct →
inferno	13–16	-0.04	12%	12 Oct →
anubis	13–11	-0.01	13%	12 Oct →
train	9–13	0.03	10%	12 Oct →
anubis	13–9	-0.03	17%	12 Oct →
cache	14–16	-0.00	20%	12 Oct →
anubis	13–10	0.00	9%	12 Oct →
dust2	15–15	0.01	24%	12 Oct →
mirage	7–13	-0.01	20%	11 Oct →
inferno	13–10	0.00	12%	10 Oct →
cache	13–10	0.04	16%	10 Oct →
dust2	2–13	-0.05	18%	10 Oct →
cache	10–13	-0.06	18%	10 Oct →
anubis	13–4	-0.02	14%	10 Oct →
cache	13–7	0.03	26%	10 Oct →
ancient	7–13	-0.07	9%	9 Oct →
train	12–12	-0.04	29%	9 Oct →
dust2	6–13	-0.04	22%	9 Oct →
cache	13–11	-0.02	24%	8 Oct →
train	12–12	-0.02	15%	8 Oct →
train	4–13	0.01	31%	8 Oct →
dust2	16–14	-0.02	18%	7 Oct →
dust2	13–7	0.05	31%	7 Oct →
inferno	11–13	-0.01	17%	5 Oct →
mirage	13–8	-0.05	18%	5 Oct →
nuke	11–13	-0.03	23%	3 Oct →
anubis	13–4	0.14	28%	2 Oct →
mirage	8–13	-0.07	23%	2 Oct →
dust2	13–4	0.12	22%	2 Oct →
cache	4–13	-0.05	10%	1 Oct →
anubis	8–13	-0.02	26%	1 Oct →
nuke	13–11	-0.08	22%	30 Sept →
ancient	10–13	-0.02	19%	30 Sept →