Fab

Fab — CS2 Stats

KP76561198006812489[U:1:46546761]Steam profile ↗✓ No bans

132Tracked matches43%Win rate2020Tracked since
1,038Hours in CS
CSDB Rating3.9 LearningPositional Player
Premier CS Rating5,148Light Blue band · top ~78.1% of ranked players (population est.)
FaceitLevel 1Top 100.0% of ranked FACEIT players
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 7 Feb 2026 (258 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: 5,148 +35 4 Feb – 7 Feb · 36 days played
4,9997,948peak 7,9484 Feb7 Feb
7,948Peak Premier in tracked matches
53Days played since 2025-01-29

Premier CS Rating

  • 2,800 below peak (7,948)
  • Next: Blue band at 10,000 — 4,852 to go
  • Reached: 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 1 among CSDB-tracked players (n=2,310), 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 1 medianLevel 2 medianvs Level 2
Headshot rate41.8%40.9%41.3%meets
Shot accuracy14.2%14.2%12.7%above
Kill/death ratio0.930.950.950.02 short
Match win rate39.6%42.1%42.6%2.9% short

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

Widest gap: Match win rate. That is the metric furthest from the Level 2 median in relative terms — not necessarily the one holding a rank back, which no statistic here can establish.

Share this profile

CSDB.GGFabFACEITLevel 1STANDINGTop 100.0% of rankedPREMIER5,148 · Light Blue bandcsdb.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

Aim42
Positioning50
Utility30

0–100 skill scores via Leetify.

Recent form

COLD50–45–5Last 10050%Win rateLLLWLLLLWL

Last 10 vs previous 10: −60pp win rate · −0.01 avg rating · +2.5pp headshot accuracy · +57ms reaction

Win rate down 60pp 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

  • 1–4 · 20% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 19%
  • Avg reaction 762ms

Last 10

  • 2–8 · 20% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 14%
  • Avg reaction 719ms

Last 20

  • 10–10 · 50% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 13%
  • Avg reaction 690ms

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: Positional Player — Positioning stands above the rest of this profile (+1.8 against its own average).

Aim4.2
Utility3.0
Positioning5.0
Opening Duels4.7
Clutch0.0

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

s1mple

Plays most like s1mple 79% 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

Reaction time. 660ms 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

Aim4.2
Positioning5.0
Utility3.0
Mechanics6.7
Opening Duels3.9
Win Impact2.8

Composite 3.9/10 (Learning), a weighted mean of the bars with a small opposition adjustment (×0.90 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 time663ms−10ms
first ⅓ avg 673ms → last ⅓ avg 663ms
Headshot accuracy14.0%−2.5%
first ⅓ avg 16.5% → last ⅓ avg 14.0%

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 · 9–0 · palais, 30 Apr →
75%Best headshot accuracy · 3–2 · train, 1 Feb →
430msFastest reaction time · 6–2 · ancient, 25 May →
11–0Biggest win · nuke, 3 Feb →

Across the last 100 tracked matches.

Highlights

7Longest win streak
L3Current streak
12–4In matches decided by ≤2 rounds
8Overtime games

Map breakdown

trainBest map · 67% over 9nukeWeakest map · 31% over 13
MapGradePlayedRecordWin rateAvg rating
anubisA1710–759%-0.00
infernoA159–660%-0.02
dust2C156–940%-0.01
nukeD134–931%-0.01
mirageA127–558%-0.02
ancientC114–736%-0.02
trainS96–367%0.02
vertigo—42–250%-0.02
grail—21–150%-0.07
palais—11–0100%0.27
overpass—10–10%-0.08

Across the last 100 tracked matches.

Nuke is currently your weakest sufficiently-sampled map (31% over 13). Start with the 6 essential Nuke lineups, review the callouts, then spin up a practice server.

Lifetime stats

46,863Lifetime kills
0.93K/D
2,515Matches
39.6%Match win rate
41.8%Headshot % · Top 50% of Level 1 players
14.2%Shot accuracy · Top 50% of Level 1 players
4,605MVPs
1,038Hours (in match)
1,544Bombs planted
386Bombs defused

Most-used weapons

Lifetime map wins

7,519dust2
2,463inferno
864nuke
289vertigo
275train
220cbble
55lake
32office

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

Faceit stats

Combat

63Matches
54%Win rate
1.00Avg K/D
59.2ADR
39%Headshot %

Clutches & streaks

0%1v1 clutch win
0%1v2 clutch win
7Longest win streak

Recent Faceit resultsWWLWL

MapMatchesWin rateAvg K/DAvg kills
Dust2333%0.7012.3
Inferno1100%0.7311.0
Ancient1100%0.9415.0

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.

14.1%Headshot accuracy
32.1%Accuracy (enemy spotted)
33.1%Spray accuracy
80.1%Counter-strafing
12.4°Preaim
660msReaction time
39.1%T opening success
51.8%CT opening success
0.52Enemies flashed / flash
0.9%Flash assists
3.92HE damage / grenade
6.04Flashes / 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

    Your crosshair sits further from where enemies appear than it needs to. Crosshair placement is the cheapest accuracy you can buy.

    Preaim 12.3907° — above the 12° mark we flag

    Aim Training →
  2. 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 14.0642% — below the 15% mark we flag

    Aim Training →
Spend your practice time on Nuke

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

31% win rate across 13 tracked games — your weakest map with enough games to be worth reading into.

Nuke callouts & strategy →Nuke grenade lineups →

Recent matches

MapScoreRatingHS%Date
nuke3–13-0.0517%7 Feb →
mirage1–13-0.0620%7 Feb →
inferno9–130.0225%6 Feb →
dust213–50.0313%5 Feb →
nuke12–16-0.0220%5 Feb →
ancient7–13-0.0610%5 Feb →
inferno4–13-0.067%5 Feb →
ancient10–13-0.048%5 Feb →
ancient13–90.0212%5 Feb →
dust211–13-0.0611%5 Feb →
mirage0–2-0.020%5 Feb →
dust213–7-0.0116%4 Feb →
anubis9–4-0.0210%4 Feb →
nuke4–13-0.0110%4 Feb →
ancient13–7-0.0119%4 Feb →
inferno13–10-0.025%3 Feb →
mirage13–9-0.0217%2 Feb →
inferno13–11-0.039%2 Feb →
mirage13–11-0.0310%30 Jan →
mirage13–40.0320%23 Jan →
train12–5-0.016%27 Jul →
inferno2–13-0.0310%27 Jul →
nuke5–13-0.0215%18 Jul →
mirage13–110.0811%2 Jul →
train7–130.0319%30 Jun →
dust29–130.0023%30 Jun →
anubis13–110.0215%29 Jun →
anubis9–13-0.0122%29 Jun →
anubis5–13-0.0819%23 Jun →
mirage4–13-0.1013%23 Jun →
dust22–130.0322%23 Jun →
ancient10–130.0116%20 Jun →
inferno13–30.0218%19 Jun →
nuke2–130.0314%18 Jun →
anubis13–70.0723%17 Jun →
mirage4–13-0.086%9 Jun →
inferno13–6-0.055%5 Jun →
inferno13–10-0.0217%2 Jun →
anubis13–4-0.048%2 Jun →
train13–70.0619%2 Jun →
dust213–11-0.0111%1 Jun →
inferno13–10-0.0214%30 May →
anubis13–100.0620%30 May →
grail1–7-0.090%29 May →
train12–120.0414%29 May →
nuke15–15-0.039%29 May →
inferno13–6-0.0314%26 May →
mirage13–50.0114%25 May →
anubis13–100.0210%25 May →
nuke13–50.036%25 May →
ancient6–2-0.070%25 May →
inferno10–13-0.0216%19 May →
inferno8–130.0419%16 May →
nuke13–60.0522%10 May →
grail13–5-0.046%9 May →
dust28–13-0.0116%3 May →
dust23–13-0.1115%1 May →
palais9–00.2750%30 Apr →
nuke7–9-0.0522%30 Apr →
overpass6–13-0.0817%27 Apr →
inferno13–8-0.049%21 Apr →
vertigo13–110.0315%20 Apr →
train13–110.0217%20 Apr →
ancient7–13-0.065%5 Apr →
anubis15–150.0318%5 Apr →
dust27–13-0.0629%5 Apr →
dust25–130.0918%4 Apr →
nuke13–11-0.0218%4 Apr →
anubis9–130.0312%4 Apr →
anubis4–00.027%4 Apr →
vertigo2–13-0.0323%4 Apr →
dust215–15-0.0521%3 Apr →
anubis16–14-0.039%3 Apr →
anubis13–9-0.066%3 Apr →
anubis8–13-0.0117%2 Apr →
mirage16–12-0.0210%2 Apr →
inferno8–13-0.0114%13 Feb →
vertigo13–50.0321%12 Feb →
dust213–4-0.0532%12 Feb →
vertigo2–13-0.120%11 Feb →
dust29–20.0317%11 Feb →
anubis4–13-0.0216%8 Feb →
ancient9–13-0.0016%7 Feb →
nuke14–16-0.059%6 Feb →
mirage0–13-0.057%5 Feb →
ancient13–8-0.0412%5 Feb →
train8–130.0419%5 Feb →
dust213–70.0010%5 Feb →
mirage13–70.0412%4 Feb →
train13–60.0227%4 Feb →
anubis13–110.0315%3 Feb →
nuke11–00.0410%3 Feb →
ancient10–13-0.0014%2 Feb →
train3–2-0.0275%1 Feb →
nuke9–13-0.0613%31 Jan →
ancient9–13-0.0113%29 Jan →
dust215–150.0325%29 Jan →
anubis6–13-0.055%29 Jan →
train5–10.0014%29 Jan →
inferno13–11-0.0529%29 Jan →

Match data via Leetify.

Recent teammates

Milestones

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

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