M4TH

M4TH — CS2 Stats

DK76561198851755948[U:1:891490220]Steam profile ↗✓ No bans

2,822Tracked matches50%Win rate2020Tracked since
7,793Hours in CS20Hrs last 2 wks
CSDB Rating6.1 SolidSupport
FaceitLevel 10 · 2,703 ELOTop 12.4% of ranked FACEIT players
WingmanDistinguished Master Guardian
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 23 Sep 2026 (33 days ago). Ranks are recorded once per day this page is viewed.

0Faceit ELO · 2,703 → 2,703
+20Tracked matches · now 2,822
+3.3ppWin rate · 46.7% → 50.0%

Rating over time

2,703Highest Faceit ELO seen on CSDB
69Days played since 2025-06-15

Faceit ELO

  • At peak — 2,703
  • Next: 3,000 ELO at 3,000 — 297 to go
  • Reached: 2,500 ELO · Level 10 · Level 9 · Level 8
  • 2,500 ELO first seen 2026-09-23
  • Level 10 first seen 2026-09-23

Premier is CSDB’s own observation — it builds from the day a profile is first viewed and cannot be backfilled. Faceit ELO is CSDB’s own observation — no feed exposes ELO per match, so that line only has the days the profile was viewed and cannot be backfilled.

Compare periods

Last 30 days vs previous 30

MetricLast 30Previous 30Change
Matches4062−22
Win rate35%47%−12pp
K/D1.311.45−0.14
Headshot %51%67%−15pp

Measured 23 Sep 2026 → 26 Oct 2026; previous 30 Aug 2026 → 23 Sep 2026.

Differences between Valve's lifetime totals on the days CSDB observed this profile — every mode Valve counts, not only ranked. A window appears only when an observation sits within a few days of each end.

How this compares with the same rank

Median values for Level 10 among CSDB-tracked players (n=30,986), 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.

MetricThis playerLevel 10 median
Headshot rate49.4%50.9%
Shot accuracy11.8%13.7%
Kill/death ratio1.011.09
Match win rate46.3%48.4%

Share this profile

CSDB.GGM4THFACEITLevel 10 · 2,703 ELOSTANDINGTop 12.4% of rankedPEAK ELO2,703csdb.gg/stats

The image is a snapshot; the link keeps updating. Nothing here is published anywhere — it is generated in your browser when you click.

Performance scores

Aim82
Positioning54
Utility61

0–100 skill scores via Leetify.

Recent form

HOT56–43–1Last 10056%Win rateWWLWWLWLWW

Last 10 vs previous 10: +40pp win rate · +0.01 avg rating · −1.4pp headshot accuracy · −70ms reaction

Win rate up 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

  • 4–1 · 80% win rate
  • Avg rating 0.02
  • Avg headshot accuracy 23%
  • Avg reaction 466ms

Last 10

  • 7–3 · 70% win rate
  • Avg rating 0.02
  • Avg headshot accuracy 23%
  • Avg reaction 485ms

Last 20

  • 10–10 · 50% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 24%
  • Avg reaction 520ms

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 (+1.8 against its own average).

Aim8.2
Utility6.1
Positioning5.4
Opening Duels2.1
Clutch3.8

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

ZywOo

Plays most like ZywOo 92% playstyle similarity

Most alike: positioning profile, utility contribution.

Where you differ: lower aim profile; lower opening-duel success.

Similarity of playstyle shape across shared dimensions — it says how you play, not that you play at their level. Full comparison →

CSDB Rating breakdown

Aim8.2
Positioning5.4
Utility6.1
Mechanics5.7
Opening Duels2.0
Win Impact5.0

Composite 6.1/10 (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 rating0.01+0.01
first ⅓ avg -0.00 → last ⅓ avg 0.01
Reaction time549ms−6ms
first ⅓ avg 555ms → last ⅓ avg 549ms
Headshot accuracy22.9%+2.3%
first ⅓ avg 20.5% → last ⅓ avg 22.9%

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

0.27Best match rating · 13–10 · train, 12 Aug →
42%Best headshot accuracy · 13–6 · train, 11 Dec →
375msFastest reaction time · 11–13 · ancient, 17 Aug →
13–1Biggest win · nuke, 14 Nov →

Across the last 100 tracked matches.

Highlights

5Longest win streak
W2Current streak
12–12In matches decided by ≤2 rounds
12Overtime games

Map breakdown

nukeBest map · 69% over 16infernoWeakest map · 33% over 9
MapGradePlayedRecordWin rateAvg rating
ancientB2413–1154%-0.00
nukeS1611–569%0.02
overpassA159–660%0.01
trainA148–657%0.02
mirageA117–464%0.02
infernoD93–633%0.00
dust2—31–233%-0.01
anubis—21–150%0.01
vertigo—21–150%0.02
cache—11–0100%-0.04
tuscan_d—11–0100%0.03
cache_b—10–10%0.01
aztec_rbcs—10–10%-0.01

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.

Lifetime stats

158,670Lifetime kills
1.01K/D
6,812Matches
46.3%Match win rate
49.4%Headshot %
11.8%Shot accuracy
22,386MVPs
3,243Hours (in match)
6,718Bombs planted
4,904Bombs defused

Most-used weapons

AK-4761,636
AWP18,425
SG 5535,921
Knife3,967
AUG2,945

Lifetime map wins

10,879inferno
10,013vertigo
6,853nuke
5,101dust2
3,749train
863cbble
199lake
180office

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

Faceit stats

Combat

2,370Matches
52%Win rate
1.02Avg K/D
77.1ADR
48%Headshot %

Clutches & streaks

39%1v1 clutch win
22%1v2 clutch win
12Longest win streak

Recent Faceit resultsLLLLW

MapMatchesWin rateAvg K/DAvg kills
Ancient37251%1.0715.6
Nuke24459%1.0515.3
Anubis19344%0.9914.8
Mirage18848%1.0114.2
Overpass14859%1.0414.9
Inferno14848%1.0015.0
Vertigo13660%1.1216.7
Train8364%1.2015.9

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.

22.5%Headshot accuracy
35.9%Accuracy (enemy spotted)
38.4%Spray accuracy
75.8%Counter-strafing
8.2°Preaim
538msReaction time
38.8%T opening success
37.3%CT opening success
0.60Enemies flashed / flash
5.0%Flash assists
8.16HE damage / grenade
8.84Flashes / 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.

  1. 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 38.7916% — 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.

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

MapScoreRatingHS%Date
mirage13–90.0625%5 Oct →
ancient13–40.1020%24 Sept →
ancient7–13-0.0516%11 Sept →
cache13–11-0.0428%1 Sept →
ancient13–20.0225%1 Sept →
nuke14–16-0.0217%31 Aug →
ancient13–10-0.0529%31 Aug →
inferno16–190.0423%29 Aug →
ancient13–50.0424%27 Aug →
nuke13–110.1024%20 Aug →
nuke9–130.0324%18 Aug →
ancient11–13-0.0621%17 Aug →
mirage13–90.0923%7 Aug →
nuke13–3-0.0031%1 Jul →
inferno11–130.0018%27 Jun →
dust214–160.0223%18 Jun →
nuke9–13-0.0131%4 Jun →
inferno11–13-0.0031%3 Jun →
overpass7–13-0.0121%3 May →
inferno13–40.0121%3 May →
nuke19–220.0122%3 May →
dust29–13-0.0218%3 May →
ancient13–5-0.0022%28 Apr →
overpass11–13-0.0428%17 Apr →
nuke13–40.0017%6 Apr →
overpass13–100.0017%30 Mar →
nuke8–80.0231%10 Mar →
nuke13–40.0529%3 Mar →
overpass11–130.0315%26 Feb →
ancient10–13-0.0016%25 Feb →
ancient13–50.0122%23 Feb →
mirage13–90.0720%9 Feb →
anubis13–16-0.0124%9 Feb →
nuke13–8-0.0516%7 Feb →
mirage11–13-0.0115%7 Feb →
nuke13–110.0225%5 Feb →
anubis13–90.0323%5 Feb →
train8–130.0123%8 Jan →
ancient16–14-0.0423%5 Jan →
inferno13–60.0617%3 Jan →
overpass13–90.0232%28 Dec →
tuscan_d13–110.0326%28 Dec →
vertigo17–190.0216%28 Dec →
ancient13–5-0.0214%22 Dec →
overpass13–60.0019%18 Dec →
overpass13–80.0518%17 Dec →
ancient6–13-0.0232%13 Dec →
cache_b10–130.0133%13 Dec →
nuke13–80.0523%11 Dec →
train13–60.0642%11 Dec →
vertigo13–110.0318%11 Dec →
ancient13–90.0418%20 Nov →
nuke13–10.0521%14 Nov →
inferno8–130.0128%14 Nov →
dust213–1-0.040%10 Nov →
overpass4–13-0.0624%31 Oct →
overpass13–70.008%31 Oct →
overpass13–70.0510%31 Oct →
mirage16–13-0.0118%27 Oct →
overpass8–13-0.0314%21 Oct →
ancient3–13-0.0310%19 Oct →
ancient5–13-0.0126%16 Oct →
train13–100.0531%13 Oct →
ancient13–110.0517%13 Oct →
mirage13–6-0.0129%12 Oct →
train13–70.0319%7 Oct →
nuke13–90.0830%7 Oct →
ancient10–130.0117%4 Oct →
mirage4–13-0.0718%1 Oct →
mirage8–13-0.009%28 Sept →
ancient11–130.0123%27 Sept →
ancient16–140.0434%26 Sept →
inferno13–70.0116%25 Sept →
ancient6–130.0514%25 Sept →
train13–110.0223%3 Sept →
nuke13–6-0.0422%3 Sept →
train13–110.0114%28 Aug →
nuke13–11-0.0524%28 Aug →
overpass13–7-0.0117%28 Aug →
aztec_rbcs7–13-0.0131%28 Aug →
train11–130.0111%25 Aug →
ancient13–10-0.0920%25 Aug →
train13–80.0214%25 Aug →
train6–13-0.0511%16 Aug →
overpass13–70.0528%16 Aug →
train13–5-0.0718%16 Aug →
overpass9–13-0.037%16 Aug →
inferno11–13-0.0712%14 Aug →
train13–100.2733%12 Aug →
overpass13–80.0829%12 Aug →
mirage19–170.0123%3 Aug →
inferno7–13-0.0341%14 Jul →
ancient9–130.0416%12 Jul →
ancient13–70.0021%3 Jul →
train4–13-0.0721%3 Jul →
mirage16–190.0321%3 Jul →
ancient10–13-0.0822%2 Jul →
train5–13-0.0612%24 Jun →
train12–16-0.0126%16 Jun →
mirage13–100.0528%15 Jun →

Match data via Leetify.

Recent teammates

Milestones

Faceit Level 102.5K ELO1,000 Matches
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

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