Aiquaha

Aiquaha — CS2 Stats

DE76561198036121382[U:1:75855654]Steam profile ↗✓ No bans

216Tracked matches25%Win rate2021Tracked since
270Hours in CS
CSDB Rating3.7 LearningPositional Player
Ladder ranks via Leetify

Performance scores

Aim49
Positioning41
Utility37

0–100 skill scores via Leetify.

Recent form

COLD32–61–7Last 10032%Win rateLWLLLLTLLL

Last 10 vs previous 10: −10pp win rate · −0.01 avg rating · +0.0pp headshot accuracy · +37ms reaction

Win rate −10pp 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

  • 1–4 · 20% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 14%
  • Avg reaction 624ms

Last 10

  • 1–8–1 · 10% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 14%
  • Avg reaction 606ms

Last 20

  • 3–15–2 · 15% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 14%
  • Avg reaction 588ms

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 (+1.3 against its own average).

Aim4.9
Utility3.7
Positioning4.1
Opening Duels0.7

Limited utility dependence

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 87% playstyle similarity

Most alike: utility contribution, opening-duel success.

Where you differ: lower positioning profile; 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. 608ms 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

Aim4.9
Positioning4.1
Utility3.7
Mechanics5.1
Opening Duels1.0
Win Impact0.0

Composite 3.7/10 (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-0.02+0.00
first ⅓ avg -0.03 → last ⅓ avg -0.02
Reaction time614ms−58ms
first ⅓ avg 672ms → last ⅓ avg 614ms
Headshot accuracy15.8%+3.8%
first ⅓ avg 12.1% → last ⅓ avg 15.8%

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

0.14Best match rating · 13–9 · office, 13 Aug →
57%Best headshot accuracy · 13–10 · office, 1 Apr →
422msFastest reaction time · 13–9 · office, 13 Aug →
13–0Biggest win · office, 1 Sept →

Across the last 100 tracked matches.

Highlights

3Longest win streak
5–2In matches decided by ≤2 rounds

Map breakdown

mirageBest map · 42% over 12infernoWeakest map · 25% over 12
MapGradePlayedRecordWin rateAvg rating
officeC3212–2038%-0.00
mirageC125–742%-0.02
infernoD123–925%-0.01
dust2D113–827%-0.03
ancientD82–625%-0.02
anubisC83–538%-0.03
cacheC52–340%-0.03
train—41–325%-0.05
nuke—40–40%-0.05
vertigo—20–20%-0.12
overpass—10–10%-0.03
jura—11–0100%-0.08

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (25% over 12). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

11,641Lifetime kills
1.00K/D
708Matches
43.9%Match win rate
34.6%Headshot %
13.0%Shot accuracy · Top 50% of tracked players
1,337MVPs
270Hours (in match)
597Bombs planted
120Bombs defused

Most-used weapons

Lifetime map wins

805dust2
751inferno
597nuke
525office
196cbble
162train
44vertigo
28italy

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.

16.1%Headshot accuracy
29.9%Accuracy (enemy spotted)
28.6%Spray accuracy
73.0%Counter-strafing
8.9°Preaim
608msReaction time
37.9%T opening success
26.2%CT opening success
0.55Enemies flashed / flash
4.9%Flash assists
5.53HE damage / grenade
3.95Flashes / 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

    You are buying and holding utility rather than using it. Unthrown flashes are wasted money every round.

    Flashes per match 3.9543 — below the 4 mark we flag

    Grenade Lineups →
  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 37.8508% — 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.

25% 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
train7–13-0.0522%21 Sept →
cache13–11-0.026%21 Sept →
overpass3–13-0.0315%10 Sept →
office7–13-0.0514%10 Sept →
train7–13-0.0612%9 Sept →
cache10–13-0.039%9 Sept →
office12–12-0.0130%9 Sept →
dust21–13-0.0618%8 Sept →
vertigo2–13-0.110%7 Sept →
ancient9–13-0.0619%6 Sept →
office0–13-0.137%6 Sept →
anubis10–13-0.008%5 Sept →
anubis1–13-0.1118%5 Sept →
office13–11-0.068%1 Sept →
office13–00.1418%1 Sept →
office12–12-0.0523%31 Aug →
office9–13-0.0217%31 Aug →
office6–13-0.067%24 Aug →
office3–13-0.0329%24 Aug →
office5–13-0.0210%13 Aug →
office13–90.1418%13 Aug →
office9–130.010%12 Aug →
office9–130.0019%12 Aug →
office13–00.0425%12 Aug →
office7–130.0215%12 Aug →
office11–13-0.0313%26 Jul →
office0–13-0.0622%26 Jul →
office13–90.0031%26 Jul →
mirage6–13-0.0114%13 Jun →
dust213–6-0.0430%18 May →
cache4–13-0.056%7 May →
cache13–7-0.0631%7 May →
cache8–130.0210%7 May →
office13–30.0615%22 Apr →
office5–13-0.0324%22 Apr →
mirage4–13-0.0918%22 Apr →
office13–10-0.0057%1 Apr →
inferno0–6-0.0910%1 Apr →
inferno2–13-0.0512%18 Mar →
inferno10–130.0248%18 Mar →
office13–90.0118%18 Mar →
dust211–130.0014%18 Mar →
office7–13-0.0221%18 Mar →
nuke9–13-0.037%12 Mar →
inferno5–130.003%12 Mar →
dust212–12-0.0219%12 Mar →
anubis13–10-0.069%4 Mar →
mirage8–13-0.025%26 Feb →
mirage9–130.0511%26 Feb →
nuke2–13-0.100%15 Feb →
dust212–12-0.009%15 Feb →
office4–13-0.050%14 Feb →
office8–130.010%14 Feb →
inferno5–130.017%5 Feb →
office2–13-0.018%5 Feb →
office13–100.018%3 Feb →
office13–20.1133%3 Feb →
nuke7–13-0.0012%17 Jan →
mirage13–3-0.0112%17 Jan →
mirage13–10.035%13 Jan →
office1–13-0.0622%13 Jan →
ancient3–13-0.0211%1 Jan →
dust213–9-0.0113%1 Jan →
office7–13-0.0223%6 Dec →
dust213–1-0.068%6 Dec →
mirage13–90.026%6 Dec →
anubis5–13-0.050%14 Nov →
vertigo6–13-0.129%23 Sept →
dust23–13-0.059%13 Sept →
ancient1–13-0.0913%13 Sept →
office13–5-0.027%12 Sept →
train13–8-0.028%12 Sept →
mirage13–9-0.072%12 Sept →
inferno9–13-0.0317%10 Sept →
mirage12–12-0.0413%27 Aug →
dust28–13-0.068%21 Aug →
inferno13–8-0.068%13 Aug →
inferno1–13-0.059%1 Aug →
office13–100.0515%30 Jul →
inferno13–10-0.029%29 Jul →
mirage13–4-0.0123%13 Jul →
ancient3–13-0.074%10 Jul →
mirage6–13-0.0433%10 Jul →
ancient13–11-0.0113%3 Jul →
ancient13–110.016%25 Jun →
anubis9–13-0.0117%25 Jun →
inferno13–40.0711%25 Jun →
dust29–13-0.0413%17 Jun →
inferno12–120.0616%11 Jun →
ancient6–130.0319%10 Jun →
anubis13–110.0210%10 Jun →
ancient8–130.0320%23 May →
train8–13-0.0519%22 May →
jura13–9-0.0816%20 May →
anubis13–6-0.0219%20 May →
nuke8–13-0.070%18 May →
dust212–120.0117%18 May →
inferno10–13-0.048%17 May →
anubis5–13-0.010%17 May →
mirage9–13-0.058%17 May →

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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