ipasamad

ipasamad — CS2 Stats

FI76561197963554648[U:1:3288920]Steam profile ↗✓ No bans

1,618Tracked matches50%Win rate2020Tracked since
CSDB Rating4.9 DevelopingSupport
Premier CS Rating24,480Top 25% of 204,316 CSDB-tracked playersPink band · top ~3.6% of ranked players (population est.)
CSDB Leaderboard#26976 of 251390 tracked
FaceitLevel 8 · 1,942 ELOTop 31.2% of ranked FACEIT players
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 24 Sep 2026 (19 days ago). Ranks are recorded once per day this page is viewed.

0Premier CS Rating · 24,480 → 24,480
0Faceit ELO · 1,942 → 1,942
+2Tracked matches · now 1,618
0.0ppWin rate · 50.0% → 50.0%

Rating over time

Premier CS Rating: 24,480 +2,201 9 Jan – 24 Sept · 13 days played
16,52124,480peak 24,4809 Jan24 Sept
Faceit ELO: 1,942 +0 31 Aug – 24 Sept · 6 observations
1,9421,942peak 1,94231 Aug24 Sept
24,600Peak Premier in tracked matches
1,942Highest Faceit ELO seen on CSDB
32Days played since 2025-12-07

Faceit ELO

  • At peak — 1,942
  • 0 over 30 days · steady
  • Next: Level 10 at 2,001 — 59 to go
  • Reached: Level 9 · Level 8 · Level 7 · Level 6
  • Level 9 first seen 2026-08-31
  • Level 8 first seen 2026-08-31

Premier CS Rating

  • At peak — 24,480
  • Next: Red band at 25,000 — 520 to go
  • Reached: Pink band · Purple band · Blue band · Light Blue band
  • Pink band first seen 2026-09-05
  • Purple band first seen 2026-09-05

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

Share this profile

CSDB.GGipasamadFACEITLevel 8 · 1,942 ELOSTANDINGTop 31.2% of rankedPREMIER24,480 · Pink band30 DAYS0 ELOPEAK ELO1,942csdb.gg/stats

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Performance scores

Aim51
Positioning51
Utility65

0–100 skill scores via Leetify.

Recent form

HOT44–54–2Last 10044%Win rateWLLWWWWWLW

Last 10 vs previous 10: +20pp win rate · −0.00 avg rating · −2.9pp headshot accuracy · −125ms reaction

Win rate up 20pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp). Average match rating moved the other way (−0.00), so results shifted more than performance did.

Last 5 · 10 · 20 matches

Last 5

  • 3–2 · 60% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 19%
  • Avg reaction 516ms

Last 10

  • 7–3 · 70% win rate
  • Avg rating -0.00
  • Avg headshot accuracy 19%
  • Avg reaction 494ms

Last 20

  • 12–8 · 60% win rate
  • Avg rating -0.00
  • Avg headshot accuracy 20%
  • Avg reaction 556ms

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).

Aim5.1
Utility6.5
Positioning5.1
Opening Duels0.7
Clutch2.4

Effective flashes

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

Twistzz

Plays most like Twistzz 69% 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. 589ms 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

Aim5.1
Positioning5.1
Utility6.5
Mechanics4.6
Opening Duels1.3
Win Impact5.0

Composite 4.9/10 (Developing), 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 rating-0.01+0.00
first ⅓ avg -0.01 → last ⅓ avg -0.01
Reaction time592ms+26ms
first ⅓ avg 566ms → last ⅓ avg 592ms
Headshot accuracy20.2%+1.9%
first ⅓ avg 18.3% → last ⅓ avg 20.2%

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

0.09Best match rating · 13–4 · dust2, 16 Jul →
41%Best headshot accuracy · 13–2 · ancient, 16 Jan →
375msFastest reaction time · 13–6 · mirage, 4 Sept →
13–1Biggest win · nuke, 20 Dec →

Across the last 100 tracked matches.

Highlights

5Longest win streak
10–6In matches decided by ≤2 rounds
7Overtime games

Map breakdown

mirageBest map · 57% over 14infernoWeakest map · 38% over 13
MapGradePlayedRecordWin rateAvg rating
nukeB2411–1346%-0.00
dust2B189–950%-0.01
mirageA148–657%-0.00
infernoC135–838%0.01
ancientC125–742%-0.02
anubisC94–544%0.00
cache—42–250%-0.05
overpass—20–20%-0.00
train—20–20%-0.04
vertigo—10–10%-0.01
cache_b—10–10%-0.05

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (38% over 13). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

2,001Matches
50%Win rate
1.06Avg K/D
73.5ADR
44%Headshot %

Clutches & streaks

32%1v1 clutch win
22%1v2 clutch win
10Longest win streak

Recent Faceit resultsWLLWL

MapMatchesWin rateAvg K/DAvg kills
Nuke5650%0.9514.7
Mirage4947%0.9414.8
Anubis4436%0.9514.4
Ancient4264%1.0214.8
Inferno4068%1.0416.2
Dust22236%0.9615.7
Overpass367%1.1115.3
Vertigo250%0.7110.5

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.

20.4%Headshot accuracy
30.9%Accuracy (enemy spotted)
28.1%Spray accuracy
70.5%Counter-strafing
10.4°Preaim
589msReaction time
26.2%T opening success
40.1%CT opening success
0.72Enemies flashed / flash
6.0%Flash assists
8.39HE damage / grenade
11.60Flashes / 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 26.2257% — 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.

38% win rate across 13 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
nuke13–11-0.0210%18 Sept →
mirage17–19-0.0124%18 Sept →
mirage10–13-0.0520%4 Sept →
inferno13–70.0213%4 Sept →
mirage13–6-0.0127%4 Sept →
nuke13–10-0.0021%4 Sept →
anubis13–9-0.0214%4 Sept →
inferno22–200.0527%28 Aug →
mirage6–13-0.0113%28 Aug →
dust213–70.0118%28 Aug →
inferno11–13-0.0229%14 Aug →
cache11–13-0.0618%2 Aug →
mirage13–80.0024%25 Jul →
nuke8–130.0124%25 Jul →
dust213–60.0726%25 Jul →
anubis8–13-0.0313%25 Jul →
mirage13–8-0.0123%16 Jul →
dust213–40.0921%16 Jul →
ancient9–13-0.0621%16 Jul →
cache13–11-0.0116%16 Jul →
dust23–10-0.0217%14 Jun →
mirage6–13-0.0617%30 May →
cache7–13-0.1125%30 May →
cache13–11-0.0119%29 May →
nuke6–130.0215%29 May →
ancient1–13-0.079%15 May →
anubis0–13-0.0127%15 May →
nuke13–50.0323%15 May →
dust213–8-0.0221%15 May →
inferno8–130.0324%13 May →
dust213–100.0020%13 May →
mirage13–100.0428%13 May →
anubis13–11-0.0319%13 May →
nuke8–13-0.0324%13 May →
mirage9–13-0.0111%13 May →
dust211–13-0.0414%3 May →
nuke13–9-0.0618%27 Apr →
anubis10–130.0421%27 Apr →
ancient4–13-0.0330%27 Apr →
nuke6–13-0.0426%24 Apr →
inferno8–13-0.0829%17 Apr →
ancient13–70.026%17 Apr →
nuke13–9-0.0113%10 Apr →
ancient13–70.0117%10 Apr →
anubis2–130.028%10 Apr →
dust21–13-0.0123%10 Apr →
nuke7–13-0.0410%10 Apr →
mirage16–140.0023%8 Apr →
nuke13–70.0010%8 Apr →
dust29–13-0.0425%8 Apr →
nuke13–80.0512%13 Mar →
inferno7–13-0.0120%13 Mar →
anubis3–13-0.0619%27 Feb →
vertigo7–13-0.0129%27 Feb →
dust213–10-0.0119%27 Feb →
ancient8–13-0.0024%13 Feb →
nuke11–13-0.0023%13 Feb →
inferno9–130.0315%13 Feb →
nuke13–70.0312%13 Feb →
mirage7–130.0516%6 Feb →
nuke8–130.0116%6 Feb →
inferno12–16-0.0122%6 Feb →
dust213–5-0.0322%6 Feb →
anubis13–30.0614%6 Feb →
ancient13–5-0.0429%31 Jan →
dust25–13-0.0924%31 Jan →
mirage13–110.0124%31 Jan →
anubis13–70.0416%31 Jan →
nuke2–13-0.0515%31 Jan →
ancient13–10-0.0322%31 Jan →
nuke3–13-0.0218%31 Jan →
ancient8–130.010%24 Jan →
ancient13–16-0.020%24 Jan →
nuke7–130.020%24 Jan →
inferno8–80.0721%24 Jan →
dust23–13-0.0721%16 Jan →
nuke13–90.0518%16 Jan →
ancient13–2-0.0141%16 Jan →
mirage13–9-0.0219%16 Jan →
nuke15–150.0320%16 Jan →
dust29–13-0.0622%9 Jan →
overpass9–130.0117%9 Jan →
mirage13–50.0318%9 Jan →
nuke13–60.0327%9 Jan →
train10–13-0.0612%21 Dec →
nuke7–13-0.0622%21 Dec →
cache_b11–13-0.0521%21 Dec →
inferno16–130.0129%21 Dec →
dust29–13-0.0129%20 Dec →
nuke13–1-0.0119%20 Dec →
ancient1–13-0.080%20 Dec →
overpass10–13-0.0128%20 Dec →
dust213–11-0.0120%18 Dec →
inferno9–13-0.0122%18 Dec →
train10–13-0.0218%18 Dec →
dust213–110.0222%11 Dec →
inferno13–11-0.0219%11 Dec →
dust210–13-0.0314%8 Dec →
inferno13–20.0124%8 Dec →
nuke7–13-0.0410%7 Dec →

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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