Zipfel_3_Klatscher

Zipfel_3_Klatscher — CS2 Stats

76561198749212401[U:1:788946673]Steam profile ↗✓ No bans

183Tracked matches32%Win rate2026Tracked since
80Hours in CS
CSDB Rating3.8 LearningPositional Player
Premier CS Rating7,232Light Blue band · top ~68.1% of ranked players (population est.)
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 2 Oct 2026 (4 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: 7,232 +38 12 Apr – 2 Oct · 42 days played
6,8099,445peak 9,44512 Apr2 Oct
9,549Peak Premier in tracked matches
47Days played since 2026-04-12

Premier CS Rating

  • 2,213 below peak (9,445)
  • -416 over 30 days · declining
  • -1,288 over 90 days
  • Next: Blue band at 10,000 — 2,768 to go
  • Reached: Light Blue band
  • Light Blue band first seen 2026-09-10

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

Compare periods

Last 30 days vs previous 30

MetricLast 30Previous 30Change
Matches16——
Win rate25%——
K/D0.76——
Headshot %32%——

Measured 9 Sep 2026 → 6 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 Light Blue band among CSDB-tracked players (n=8,820), 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 playerLight Blue band medianBlue band medianvs Blue band
Headshot rate35.5%39.9%42.0%6.5% short
Shot accuracy0.0%7.0%9.6%9.6% short
Kill/death ratio0.890.920.980.09 short
Match win rate33.6%42.3%43.8%10.2% short

This profile sits below the typical Blue band player on every metric we can compare.

Widest gap: Shot accuracy. That is the metric furthest from the Blue band median in relative terms — not necessarily the one holding a rank back, which no statistic here can establish.

Share this profile

CSDB.GGZipfel_3_KlatscherPREMIER7,232 · Light Blue bandcsdb.gg/stats

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Performance scores

Aim58
Positioning49
Utility26

0–100 skill scores via Leetify.

Recent form

COLD41–53–6Last 10041%Win rateTLLWLTLLWL

Last 10 vs previous 10: 0pp win rate · −0.00 avg rating · −0.6pp headshot accuracy · +29ms reaction

Win rate 0pp 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

  • 1–3–1 · 20% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 21%
  • Avg reaction 548ms

Last 10

  • 2–6–2 · 20% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 19%
  • Avg reaction 594ms

Last 20

  • 4–14–2 · 20% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 19%
  • Avg reaction 579ms

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 (+1.5 against its own average).

Aim5.8
Utility2.6
Positioning4.9
Opening Duels2.7

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 92% playstyle similarity

Most alike: positioning profile, utility contribution.

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

Utility. Utility contribution runs far behind the mechanical game — the cheapest rating gain on this profile is thrown, not aimed.

Reaction time. 582ms 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

Aim5.8
Positioning4.9
Utility2.6
Mechanics5.0
Opening Duels4.1
Win Impact0.0

Composite 3.8/10 (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-0.02−0.03
first ⅓ avg 0.01 → last ⅓ avg -0.02
Reaction time578ms−60ms
first ⅓ avg 638ms → last ⅓ avg 578ms
Headshot accuracy16.8%+1.0%
first ⅓ avg 15.8% → last ⅓ avg 16.8%

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

0.18Best match rating · 13–6 · inferno, 4 Aug →
50%Best headshot accuracy · 13–2 · nuke, 17 Jul →
406msFastest reaction time · 6–13 · inferno, 8 Sept →
13–2Biggest win · nuke, 17 Jul →

Across the last 100 tracked matches.

Highlights

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

Map breakdown

mirageBest map · 62% over 13infernoWeakest map · 17% over 12
MapGradePlayedRecordWin rateAvg rating
dust2D258–1732%0.00
nukeC198–1142%-0.01
anubisB189–950%-0.01
mirageA138–562%-0.00
infernoD122–1017%-0.00
ancientB84–450%-0.01
cache—41–325%-0.01
vertigo—11–0100%0.03

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (17% over 12). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

4,004Lifetime kills
0.89K/D
265Matches
33.6%Match win rate
35.5%Headshot %
0.0%Shot accuracy
400MVPs
80Hours (in match)
151Bombs planted
42Bombs defused

Most-used weapons

Lifetime map wins

397dust2
300nuke
218inferno
49vertigo
15ar_shoots
10italy
4train
1ar_baggage

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

Skill profile

Aggregate performance across tracked matches — stats via Leetify. Percentile context against other CSDB-tracked players arrives as our own benchmark data accumulates.

17.1%Headshot accuracy
32.0%Accuracy (enemy spotted)
30.2%Spray accuracy
72.6%Counter-strafing
8.7°Preaim
582msReaction time
48.5%T opening success
44.2%CT opening success
0.32Enemies flashed / flash
5.0%Flash assists
4.62HE damage / grenade
5.07Flashes / 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. Grenades & Utility

    Most of your flashes are blinding nobody. A handful of reliable pop-flash lineups fixes this faster than anything else.

    Enemies flashed per flash 0.3165 — below the 0.5 mark we flag

    Grenade Lineups →
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.

17% win rate across 12 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
inferno15–15-0.067%2 Oct →
ancient9–13-0.0724%2 Oct →
mirage9–130.0033%24 Sept →
dust213–110.0125%24 Sept →
anubis5–13-0.0214%23 Sept →
mirage15–150.0117%23 Sept →
inferno4–13-0.0018%23 Sept →
dust28–13-0.1129%23 Sept →
nuke13–11-0.0113%23 Sept →
mirage11–13-0.066%22 Sept →
dust213–90.0220%21 Sept →
nuke7–13-0.0222%21 Sept →
mirage13–80.084%17 Sept →
inferno14–16-0.0925%16 Sept →
dust26–130.0210%16 Sept →
inferno4–13-0.0420%9 Sept →
inferno6–13-0.032%8 Sept →
anubis7–13-0.0836%7 Sept →
anubis3–13-0.1025%2 Sept →
inferno2–13-0.0527%25 Aug →
nuke13–10-0.0123%24 Aug →
ancient4–13-0.0810%24 Aug →
mirage13–80.0015%23 Aug →
ancient13–60.0812%21 Aug →
anubis3–13-0.0217%20 Aug →
dust27–13-0.0011%18 Aug →
dust22–13-0.0114%18 Aug →
anubis8–13-0.0317%15 Aug →
anubis13–7-0.045%14 Aug →
ancient13–110.0416%14 Aug →
dust22–13-0.0425%14 Aug →
anubis5–13-0.017%14 Aug →
mirage13–90.064%13 Aug →
anubis8–13-0.086%12 Aug →
inferno11–13-0.0416%11 Aug →
dust20–13-0.0910%11 Aug →
anubis13–6-0.0613%10 Aug →
mirage13–6-0.028%10 Aug →
dust26–13-0.0817%10 Aug →
dust213–7-0.0122%10 Aug →
mirage13–10-0.014%9 Aug →
nuke10–13-0.0111%9 Aug →
dust25–130.0518%7 Aug →
nuke15–15-0.0412%7 Aug →
anubis13–70.016%7 Aug →
nuke2–80.0242%6 Aug →
cache11–13-0.049%6 Aug →
nuke10–13-0.017%6 Aug →
mirage5–13-0.0017%5 Aug →
inferno8–80.104%5 Aug →
mirage13–70.0617%5 Aug →
cache7–00.0843%5 Aug →
cache4–130.0026%5 Aug →
dust213–11-0.0111%5 Aug →
dust29–13-0.0213%5 Aug →
mirage13–8-0.0113%4 Aug →
dust28–130.0311%4 Aug →
nuke4–13-0.039%4 Aug →
inferno13–60.1816%4 Aug →
dust213–70.017%4 Aug →
inferno13–80.0213%4 Aug →
nuke13–5-0.0124%4 Aug →
dust211–130.1018%4 Aug →
anubis12–4-0.0117%4 Aug →
dust26–130.027%3 Aug →
cache9–13-0.078%3 Aug →
inferno10–13-0.059%3 Aug →
nuke4–13-0.0412%3 Aug →
anubis13–70.0217%3 Aug →
dust213–160.0319%29 Jul →
ancient6–13-0.0517%29 Jul →
nuke13–8-0.0118%29 Jul →
dust29–13-0.0213%23 Jul →
anubis2–13-0.0522%23 Jul →
nuke1–13-0.1312%19 Jul →
anubis13–40.0514%17 Jul →
ancient13–100.0223%17 Jul →
nuke13–20.0950%17 Jul →
dust23–13-0.0818%17 Jul →
nuke12–12-0.0047%6 Jul →
ancient13–11-0.068%6 Jul →
anubis14–160.036%4 Jun →
anubis16–130.0917%31 May →
mirage13–6-0.0621%31 May →
dust215–150.0012%24 May →
anubis13–30.097%22 May →
nuke13–100.139%18 May →
dust213–50.1110%18 May →
dust216–120.1011%15 May →
anubis13–80.0719%5 May →
nuke13–7-0.0515%4 May →
vertigo13–30.0314%27 Apr →
inferno6–130.0311%24 Apr →
mirage2–13-0.1011%21 Apr →
dust213–90.069%18 Apr →
nuke16–140.0111%18 Apr →
ancient6–130.0310%18 Apr →
nuke4–13-0.069%18 Apr →
nuke5–9-0.0014%18 Apr →
dust27–13-0.0816%12 Apr →

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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