m4Ssa

m4Ssa — CS2 Stats

DE76561197984815908[U:1:24550180]Steam profile ↗✓ No bans

1,977Tracked matches78%Win rate2020Tracked since
10,451Hours in CS4Hrs last 2 wks
CSDB Rating6.5 SolidSupport
FaceitLevel 9Top 20.7% of ranked FACEIT players
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 2 Sep 2026 (30 days ago). Ranks are recorded once per day this page is viewed.

+10Tracked matches · now 1,977
0.0ppWin rate · 77.8% → 77.8%

Rating over time

54Days played since 2025-01-25

Premier is CSDB’s own observation — it builds from the day a profile is first viewed and cannot be backfilled.

Compare periods

Last 30 days vs previous 30

MetricLast 30Previous 30Change
Matches16——
Win rate63%——
K/D0.83——
Headshot %59%——

Measured 2 Sep 2026 → 2 Oct 2026; no observation near 60 days ago, so there is no previous window yet.

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 9 among CSDB-tracked players (n=14,336), 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 9 medianLevel 10 medianvs Level 10
Headshot rate63.7%48.2%50.9%above
Shot accuracy17.4%13.3%13.8%above
Kill/death ratio0.901.071.090.19 short
Match win rate46.7%46.9%48.4%1.7% short

This profile matches the typical Level 10 player on 2 of 4 comparable metrics.

Widest gap: Kill/death ratio. That is the metric furthest from the Level 10 median in relative terms — not necessarily the one holding a rank back, which no statistic here can establish.

Share this profile

CSDB.GGm4SsaFACEITLevel 9STANDINGTop 20.7% of rankedcsdb.gg/stats

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Performance scores

Aim67
Positioning62
Utility62

0–100 skill scores via Leetify.

Recent form

STEADY60–34–6Last 10060%Win rateWLWWWWTWLL

Last 10 vs previous 10: −10pp win rate · +0.00 avg rating · +1.0pp headshot accuracy · −25ms reaction

Win rate −10pp across the last 10 against the 10 before — within the normal variation of a 10-match window (±20pp), so no real shift yet.

Last 5 · 10 · 20 matches

Last 5

  • 4–1 · 80% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 23%
  • Avg reaction 526ms

Last 10

  • 6–3–1 · 60% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 24%
  • Avg reaction 539ms

Last 20

  • 13–5–2 · 65% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 24%
  • Avg reaction 552ms

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 (+0.9 against its own average).

Aim6.7
Utility6.2
Positioning6.2
Opening Duels4.9
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

jL

Plays most like jL 83% playstyle similarity

Most alike: utility contribution, opening-duel success.

Where you differ: 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

T-side openings. Opening success drops from 53% on CT to 34% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 573ms 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

Aim6.7
Positioning6.2
Utility6.2
Mechanics6.6
Opening Duels3.5
Win Impact10.0

Composite 6.5/10 (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 rating0.01+0.01
first ⅓ avg -0.00 → last ⅓ avg 0.01
Reaction time575ms+12ms
first ⅓ avg 564ms → last ⅓ avg 575ms
Headshot accuracy23.3%−0.6%
first ⅓ avg 24.0% → last ⅓ avg 23.3%

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

0.15Best match rating · 13–2 · ancient, 21 Jul →
41%Best headshot accuracy · 13–2 · dust2, 24 Apr →
344msFastest reaction time · 8–13 · mirage, 26 Jan →
13–0Biggest win · nuke, 12 Mar →

Across the last 100 tracked matches.

Highlights

9Longest win streak
10–2In matches decided by ≤2 rounds
13Overtime games

Map breakdown

trainBest map · 80% over 5anubisWeakest map · 30% over 10
MapGradePlayedRecordWin rateAvg rating
dust2A3220–1263%0.00
mirageA169–756%-0.01
nukeA138–562%-0.00
anubisD103–730%-0.04
ancientS96–367%0.02
cacheA85–363%0.02
trainS54–180%0.03
inferno—33–0100%0.03
overpass—31–233%-0.01
vertigo—11–0100%0.06

Across the last 100 tracked matches.

Anubis is currently your weakest sufficiently-sampled map (30% over 10). Start with the 6 essential Anubis lineups, review the callouts, then spin up a practice server.

Lifetime stats

293,327Lifetime kills
0.90K/D
8,484Matches
46.7%Match win rate
63.7%Headshot % · Top 5% of Level 9 players
17.4%Shot accuracy · Top 25% of Level 9 players
18,728MVPs
6,836Hours (in match)
9,145Bombs planted
2,904Bombs defused

Most-used weapons

AK-47133,983
AWP11,413
P2505,540
FAMAS4,514
MP93,225

Lifetime map wins

16,192dust2
14,477inferno
5,341nuke
2,924vertigo
1,185train
326cbble
33ar_shoots
30militia

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

Faceit stats

Combat

1,492Matches
52%Win rate
1.12Avg K/D
74.6ADR
54%Headshot %

Clutches & streaks

39%1v1 clutch win
20%1v2 clutch win
10Longest win streak

Recent Faceit resultsLLLWW

MapMatchesWin rateAvg K/DAvg kills
Mirage15749%0.9713.9
Dust211954%1.0315.2
Ancient9652%1.2016.1
Anubis8547%1.0215.1
Nuke4547%1.1415.2
Inferno2741%0.9713.8
Overpass2045%1.0113.8
Train1362%1.1217.2

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
31.3%Accuracy (enemy spotted)
34.0%Spray accuracy
79.6%Counter-strafing
10.5°Preaim
573msReaction time
34.3%T opening success
53.4%CT opening success
0.65Enemies flashed / flash
9.4%Flash assists
6.69HE damage / grenade
14.13Flashes / 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 34.3398% — below the 40% mark we flag

Spend your practice time on Anubis

Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.

30% win rate across 10 tracked games — your weakest map with enough games to be worth reading into.

Anubis callouts & strategy →Anubis grenade lineups →

Recent matches

MapScoreRatingHS%Date
mirage13–40.1027%7 Sept →
dust29–130.0327%7 Sept →
dust216–14-0.0510%4 Sept →
dust219–17-0.0718%2 Sept →
cache13–70.0630%12 Aug →
cache13–90.0222%10 Aug →
nuke15–15-0.0224%9 Aug →
cache13–7-0.0123%9 Aug →
nuke10–130.0133%9 Aug →
cache13–160.0227%9 Aug →
mirage16–12-0.0225%9 Aug →
ancient13–60.0629%9 Aug →
ancient1–13-0.0818%22 Jul →
dust213–20.0513%20 Jul →
cache12–12-0.0211%20 Jul →
cache9–13-0.0221%26 May →
cache13–10.0725%26 May →
cache13–3-0.0029%25 May →
inferno13–70.0223%12 Mar →
nuke13–00.0236%12 Mar →
dust213–10-0.0514%29 Nov →
nuke13–4-0.0534%29 Nov →
mirage13–2-0.0518%29 Nov →
train13–6-0.017%29 Nov →
dust213–70.0126%12 Nov →
ancient5–13-0.0326%8 Nov →
train12–120.0327%8 Nov →
train13–70.0720%4 Nov →
nuke13–5-0.0119%5 Aug →
dust213–70.0221%5 Aug →
train16–140.0521%21 Jul →
ancient13–20.1531%21 Jul →
dust213–11-0.0135%17 Jul →
dust211–130.0218%17 Jul →
dust213–30.0522%17 Jul →
overpass8–13-0.0615%16 Jul →
dust215–15-0.0122%16 Jul →
dust29–13-0.0027%19 May →
dust219–160.0822%16 May →
mirage13–20.0714%14 May →
ancient19–170.0427%13 May →
inferno13–00.0418%9 May →
train13–20.0116%9 May →
anubis7–13-0.0839%9 May →
anubis9–13-0.0715%2 May →
mirage12–12-0.0424%27 Apr →
dust213–2-0.0141%24 Apr →
ancient6–13-0.0424%24 Apr →
dust25–13-0.0517%23 Apr →
inferno13–80.0238%22 Apr →
mirage13–5-0.054%22 Apr →
dust213–40.0922%22 Apr →
anubis5–13-0.0323%22 Apr →
overpass4–130.0118%17 Apr →
dust212–120.0224%17 Apr →
dust213–90.0028%12 Apr →
nuke13–7-0.0324%7 Apr →
anubis13–90.0219%31 Mar →
anubis2–13-0.110%30 Mar →
dust212–16-0.0216%30 Mar →
mirage13–10-0.0218%30 Mar →
overpass13–60.0111%27 Mar →
vertigo13–40.0619%27 Mar →
dust24–13-0.0319%27 Mar →
mirage7–13-0.0610%24 Mar →
nuke0–13-0.0433%23 Mar →
mirage2–13-0.0416%23 Mar →
mirage8–130.0731%19 Mar →
anubis10–13-0.0321%19 Mar →
mirage8–13-0.0521%17 Mar →
mirage13–50.0024%13 Mar →
ancient13–40.0716%13 Mar →
anubis11–13-0.0313%13 Mar →
nuke6–130.0521%13 Mar →
dust213–11-0.0222%10 Mar →
anubis13–11-0.0225%9 Mar →
dust29–130.0231%8 Mar →
nuke13–50.0619%8 Mar →
dust213–30.0319%7 Mar →
dust222–180.0313%6 Mar →
dust213–40.0125%5 Mar →
nuke13–10.0232%5 Mar →
dust216–120.0134%5 Mar →
mirage19–220.0327%3 Mar →
dust213–8-0.0031%17 Feb →
mirage13–8-0.0524%17 Feb →
anubis13–2-0.0034%17 Feb →
dust213–100.0120%17 Feb →
ancient13–100.0133%11 Feb →
nuke9–13-0.0327%31 Jan →
dust25–130.0532%28 Jan →
ancient13–11-0.0518%27 Jan →
mirage13–6-0.0232%27 Jan →
dust26–13-0.0629%27 Jan →
dust26–13-0.0822%26 Jan →
dust213–7-0.0219%26 Jan →
nuke13–11-0.0730%26 Jan →
mirage8–13-0.0515%26 Jan →
anubis3–13-0.0313%26 Jan →
nuke13–110.0219%25 Jan →

Match data via Leetify.

Recent teammates

Milestones

1,000 Matches
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

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