Paugafan

Paugafan — CS2 Stats

SE76561197963029421[U:1:2763693]Steam profile ↗✓ No bans

1,088Tracked matches50%Win rate2021Tracked since
CSDB Rating3.6 LearningSupport
FaceitLevel 3Top 90.6% of ranked FACEIT players
Ladder ranks via Leetify

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CSDB.GGPaugafanFACEITLevel 3STANDINGTop 90.6% of rankedcsdb.gg/stats

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Performance scores

Aim24
Positioning40
Utility58

0–100 skill scores via Leetify.

Recent form

STEADY44–56Last 10044%Win rateWLLWWLLLLW

Last 10 vs previous 10: −20pp win rate · −0.01 avg rating · −5.4pp headshot accuracy · −15ms reaction

Win rate down 20pp 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

  • 3–2 · 60% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 13%
  • Avg reaction 609ms

Last 10

  • 4–6 · 40% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 13%
  • Avg reaction 659ms

Last 20

  • 10–10 · 50% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 16%
  • Avg reaction 666ms

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 (+2.8 against its own average).

Aim2.4
Utility5.8
Positioning4.0
Opening Duels0.3
Clutch2.0

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

iM

Plays most like iM 60% 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. 675ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 69% of shots are taken properly stopped — moving-shot inaccuracy is quietly taxing every duel.

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

Aim2.4
Positioning4.0
Utility5.8
Mechanics4.2
Opening Duels0.7
Win Impact5.0

Composite 3.6/10 (Learning), 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 rating-0.04−0.02
first ⅓ avg -0.02 → last ⅓ avg -0.04
Reaction time675ms+1ms
first ⅓ avg 674ms → last ⅓ avg 675ms
Headshot accuracy15.0%−3.6%
first ⅓ avg 18.7% → last ⅓ avg 15.0%

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

0.07Best match rating · 11–13 · inferno, 2 Jul →
33%Best headshot accuracy · 9–13 · nuke, 1 May →
398msFastest reaction time · 1–13 · inferno, 26 Oct →
13–2Biggest win · overpass, 27 Oct →

Across the last 100 tracked matches.

Highlights

4Longest win streak
10–12In matches decided by ≤2 rounds
14Overtime games

Map breakdown

dust2Best map · 63% over 19infernoWeakest map · 32% over 19
MapGradePlayedRecordWin rateAvg rating
mirageC229–1341%-0.03
dust2A1912–763%-0.03
infernoD196–1332%-0.05
nukeC145–936%-0.03
anubisD62–433%-0.02
cacheB63–350%-0.01
overpassC52–340%-0.02
ancientC52–340%-0.04
vertigo—32–167%-0.04
cache_b—11–0100%0.01

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (32% over 19). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

122Matches
48%Win rate
0.83Avg K/D
60.2ADR
39%Headshot %

Clutches & streaks

30%1v1 clutch win
11%1v2 clutch win
5Longest win streak

Recent Faceit resultsWWWWW

MapMatchesWin rateAvg K/DAvg kills
Nuke2357%0.9912.5
Mirage2339%0.699.3
Dust21662%0.9810.6
Anubis1520%0.629.9
Inferno1553%0.9713.0
Ancient1338%0.8311.0
Overpass4100%0.7314.0
Vertigo450%0.6310.8

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
26.1%Accuracy (enemy spotted)
26.9%Spray accuracy
69.0%Counter-strafing
11.4°Preaim
675msReaction time
35.7%T opening success
29.8%CT opening success
0.68Enemies flashed / flash
2.5%Flash assists
10.08HE damage / grenade
9.83Flashes / 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. 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 14.8822% — below the 15% mark we flag

    Aim Training →
  2. 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 35.7254% — 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.

32% win rate across 19 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
anubis13–5-0.114%1 Nov →
nuke3–13-0.0310%1 Nov →
dust215–19-0.0411%1 Nov →
anubis16–120.0224%1 Nov →
cache13–9-0.0219%31 Oct →
anubis14–16-0.0517%31 Oct →
mirage5–13-0.0911%31 Oct →
overpass9–13-0.0018%31 Oct →
cache9–130.046%31 Oct →
dust213–9-0.0813%31 Oct →
inferno11–13-0.0125%31 Oct →
nuke13–7-0.0522%31 Oct →
mirage13–4-0.0313%31 Oct →
nuke16–12-0.0210%31 Oct →
mirage13–10-0.0214%28 Oct →
inferno6–13-0.0523%28 Oct →
inferno5–13-0.0517%27 Oct →
overpass13–2-0.0113%27 Oct →
mirage8–13-0.0324%27 Oct →
inferno13–6-0.0326%26 Oct →
mirage13–9-0.0817%26 Oct →
dust213–11-0.0317%26 Oct →
inferno1–13-0.109%26 Oct →
mirage10–13-0.0617%20 Oct →
ancient13–3-0.0716%20 Oct →
dust213–8-0.050%20 Oct →
mirage13–9-0.063%20 Oct →
inferno11–13-0.0711%19 Oct →
mirage7–13-0.0721%19 Oct →
nuke8–13-0.0316%22 Jul →
cache13–100.0118%20 Jul →
mirage10–13-0.0218%20 Jul →
dust213–110.0219%20 Jul →
mirage7–13-0.0821%20 Jul →
cache13–80.0413%18 Jul →
dust25–13-0.0725%18 Jul →
inferno11–130.0723%2 Jul →
vertigo13–60.0122%2 Jul →
ancient9–13-0.0513%2 Jul →
mirage10–130.0116%2 Jul →
dust29–13-0.0417%2 Jul →
nuke13–9-0.0015%2 Jul →
mirage14–16-0.0212%14 Jun →
inferno14–16-0.0413%14 Jun →
dust213–70.0423%14 Jun →
inferno16–19-0.0822%13 Jun →
dust213–90.0112%13 Jun →
nuke9–13-0.0521%12 Jun →
vertigo13–8-0.0318%12 Jun →
anubis10–13-0.0132%12 Jun →
inferno7–13-0.0620%12 Jun →
ancient6–13-0.0416%29 May →
inferno2–13-0.1115%29 May →
dust210–13-0.0117%19 May →
mirage9–13-0.019%19 May →
dust211–13-0.0313%13 May →
dust213–11-0.019%13 May →
dust211–13-0.079%11 May →
cache10–13-0.0218%4 May →
mirage16–12-0.0225%4 May →
vertigo7–13-0.0924%4 May →
inferno3–13-0.068%2 May →
nuke9–13-0.089%2 May →
cache4–13-0.074%2 May →
mirage7–130.0223%1 May →
inferno13–7-0.098%1 May →
inferno13–7-0.0421%1 May →
inferno13–100.0418%1 May →
nuke9–13-0.0333%1 May →
nuke13–40.0217%24 Apr →
mirage22–180.0318%24 Apr →
anubis11–13-0.0120%24 Apr →
overpass19–170.0113%24 Apr →
inferno9–13-0.069%24 Apr →
dust213–11-0.0417%24 Apr →
mirage13–10-0.0316%24 Apr →
dust213–70.0311%22 Apr →
nuke10–13-0.0215%22 Apr →
anubis10–130.0230%21 Apr →
nuke6–13-0.0725%21 Apr →
inferno8–3-0.0120%21 Apr →
ancient9–13-0.029%14 Apr →
mirage16–12-0.0615%14 Apr →
dust213–11-0.0022%1 Apr →
mirage14–160.0026%28 Mar →
nuke12–16-0.0115%5 Mar →
inferno13–7-0.0316%5 Mar →
dust27–13-0.0322%19 Feb →
mirage6–13-0.0525%19 Feb →
nuke13–11-0.0022%16 Feb →
cache_b13–90.0130%16 Feb →
overpass11–13-0.0416%15 Feb →
dust213–11-0.0316%15 Feb →
mirage4–13-0.0528%15 Feb →
inferno8–13-0.0731%15 Feb →
ancient13–11-0.0314%14 Feb →
mirage13–4-0.0117%14 Feb →
nuke11–13-0.0410%14 Feb →
overpass13–16-0.0811%13 Feb →
dust213–11-0.0411%12 Feb →

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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