Abdulah Klappstuhlah

Abdulah Klappstuhlah — CS2 Stats

DE76561198984022856[U:1:1023757128]Steam profile ↗✓ No bans

415Tracked matches55%Win rate2021Tracked since
669Hours in CS2Hrs last 2 wks
CSDB Rating6.2 SolidAll-Rounder
Ladder ranks via Leetify

Rating over time

Premier CS Rating: 17,273 +1,764 1 Jan – 30 Jun · 33 days played
14,51117,273peak 17,2731 Jan30 Jun
17,273Peak Premier in tracked matches
53Days played since 2026-01-01

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

Performance scores

Aim67
Positioning62
Utility69

0–100 skill scores via Leetify.

Recent form

STEADY49–47–4Last 10049%Win rateWLLLWWLLTW

Last 10 vs previous 10: −20pp win rate · −0.02 avg rating · +1.4pp headshot accuracy · −32ms 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

  • 2–3 · 40% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 14%
  • Avg reaction 508ms

Last 10

  • 4–5–1 · 40% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 15%
  • Avg reaction 514ms

Last 20

  • 10–9–1 · 50% win rate
  • Avg rating 0.03
  • Avg headshot accuracy 15%
  • Avg reaction 529ms

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: All-Rounder — No style dimension stands clear of the others in this profile.

Aim6.7
Utility6.9
Positioning6.2
Opening Duels5.3

Strong CT-side opener

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 81% 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 60% on CT to 42% on T — the same duels are being taken with worse setups on the attacking side.

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
Utility6.9
Mechanics4.8
Opening Duels5.2
Win Impact6.7

Composite 6.2/10 (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 rating0.02−0.00
first ⅓ avg 0.03 → last ⅓ avg 0.02
Reaction time547ms−55ms
first ⅓ avg 602ms → last ⅓ avg 547ms
Headshot accuracy14.2%−1.2%
first ⅓ avg 15.5% → last ⅓ avg 14.2%

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

0.18Best match rating · 13–3 · nuke, 18 Apr →
29%Best headshot accuracy · 8–13 · inferno, 16 Mar →
391msFastest reaction time · 5–13 · inferno, 19 Sept →
13–0Biggest win · nuke, 27 May →

Across the last 100 tracked matches.

Highlights

5Longest win streak
6–4In matches decided by ≤2 rounds
9Overtime games

Map breakdown

infernoBest map · 58% over 26anubisWeakest map · 42% over 12
MapGradePlayedRecordWin rateAvg rating
infernoA2615–1158%0.03
ancientB2110–1148%0.03
nukeB137–654%0.03
anubisC125–742%0.03
overpassA127–558%0.02
dust2C94–544%0.02
mirage—41–325%-0.02
train—20–20%-0.00
cache—10–10%0.00

Across the last 100 tracked matches.

Anubis is currently your weakest sufficiently-sampled map (42% over 12). Start with the 6 essential Anubis lineups, review the callouts, then spin up a practice server.

Lifetime stats

15,533Lifetime kills
1.01K/D
889Matches
44.0%Match win rate
37.7%Headshot %
9.2%Shot accuracy
2,053MVPs
373Hours (in match)
898Bombs planted
184Bombs defused

Most-used weapons

Lifetime map wins

2,194inferno
1,036nuke
838dust2
258vertigo
205train
51office
8safehouse
5ar_shoots

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.

14.4%Headshot accuracy
35.9%Accuracy (enemy spotted)
41.6%Spray accuracy
71.5%Counter-strafing
9.9°Preaim
548msReaction time
42.1%T opening success
59.8%CT opening success
0.57Enemies flashed / flash
12.8%Flash assists
7.06HE damage / grenade
10.41Flashes / 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 14.3738% — below the 15% mark we flag

    Aim Training →
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.

42% win rate across 12 tracked games — your weakest map with enough games to be worth reading into.

Anubis callouts & strategy →Anubis grenade lineups →

Recent matches

MapScoreRatingHS%Date
inferno13–100.0014%22 Oct →
cache10–130.0019%17 Oct →
nuke2–13-0.0410%19 Sept →
inferno5–130.0212%19 Sept →
dust213–70.0613%19 Sept →
anubis13–8-0.0419%1 Sept →
inferno5–130.0519%25 Aug →
dust25–13-0.0117%25 Aug →
inferno15–150.0415%9 Aug →
inferno13–110.0616%9 Aug →
inferno11–130.0926%26 Jul →
anubis9–13-0.0112%9 Jul →
anubis13–80.059%30 Jun →
ancient11–130.0112%30 Jun →
inferno16–120.0320%25 Jun →
nuke13–100.1011%25 Jun →
anubis13–60.0614%13 Jun →
overpass13–60.0115%13 Jun →
inferno13–40.0413%13 Jun →
nuke10–13-0.009%13 Jun →
ancient13–60.0415%12 Jun →
mirage7–13-0.046%12 Jun →
ancient13–70.077%12 Jun →
dust210–130.0218%11 Jun →
inferno8–130.0019%11 Jun →
overpass13–40.0518%9 Jun →
nuke13–00.0217%27 May →
ancient13–10-0.038%27 May →
anubis10–130.0515%25 May →
ancient13–9-0.0414%16 May →
inferno13–70.0315%16 May →
overpass13–90.0411%13 May →
ancient10–130.0915%13 May →
mirage8–13-0.051%13 May →
ancient15–15-0.0211%2 May →
inferno13–8-0.0119%27 Apr →
overpass13–90.088%27 Apr →
overpass8–13-0.0114%24 Apr →
dust22–13-0.0628%24 Apr →
nuke13–30.1815%18 Apr →
inferno8–13-0.067%18 Apr →
ancient16–120.0813%18 Apr →
inferno13–60.0918%18 Apr →
anubis8–13-0.0226%18 Apr →
anubis6–130.0627%13 Apr →
mirage13–90.0423%13 Apr →
anubis13–110.0114%13 Apr →
nuke10–13-0.0219%9 Apr →
overpass2–13-0.0618%5 Apr →
dust213–110.0113%4 Apr →
inferno13–50.0819%4 Apr →
ancient7–130.0825%28 Mar →
inferno11–13-0.0315%26 Mar →
nuke9–13-0.018%26 Mar →
dust213–60.0420%26 Mar →
overpass15–150.0618%25 Mar →
nuke3–13-0.0128%24 Mar →
ancient13–100.0811%23 Mar →
ancient7–13-0.0112%22 Mar →
inferno13–40.0720%19 Mar →
anubis9–13-0.0218%19 Mar →
nuke16–130.0521%18 Mar →
anubis7–13-0.0119%16 Mar →
ancient13–3-0.0112%16 Mar →
inferno8–13-0.0129%16 Mar →
ancient7–13-0.049%15 Mar →
anubis13–80.1116%6 Mar →
anubis10–130.0617%25 Feb →
nuke13–50.0417%25 Feb →
inferno13–90.0627%23 Feb →
inferno13–50.0321%23 Feb →
overpass10–130.0113%22 Feb →
mirage6–13-0.0413%21 Feb →
dust213–70.0216%20 Feb →
inferno2–13-0.0715%13 Feb →
inferno7–130.0222%10 Feb →
ancient9–130.0123%8 Feb →
ancient6–10.0814%8 Feb →
inferno16–130.0713%8 Feb →
overpass13–20.0613%29 Jan →
ancient8–13-0.0310%29 Jan →
nuke13–10-0.035%29 Jan →
inferno9–130.0111%27 Jan →
inferno13–70.0315%19 Jan →
train4–13-0.0813%19 Jan →
overpass13–110.0919%19 Jan →
nuke10–130.0614%19 Jan →
ancient5–130.0711%19 Jan →
nuke13–40.0422%19 Jan →
inferno13–110.0721%18 Jan →
train10–130.0713%18 Jan →
ancient16–120.0614%18 Jan →
ancient11–130.0915%18 Jan →
overpass13–11-0.048%13 Jan →
inferno13–50.057%13 Jan →
dust215–150.0214%10 Jan →
overpass7–13-0.0115%10 Jan →
ancient7–13-0.0221%5 Jan →
dust28–130.0519%5 Jan →
ancient13–80.0715%1 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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