ᴀᴀᴠEシ

ᴀᴀᴠEシ — CS2 Stats

FI76561198010971773[U:1:50706045]Steam profile ↗✓ No bans

2,007Tracked matches52%Win rate2020Tracked since
CSDB Rating5.0 DevelopingSupport
FaceitLevel 6Top 54.0% of ranked FACEIT players
Ladder ranks via Leetify

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CSDB.GGᴀᴀᴠEシFACEITLevel 6STANDINGTop 54.0% of rankedcsdb.gg/stats

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Performance scores

Aim45
Positioning52
Utility67

0–100 skill scores via Leetify.

Recent form

COLD52–42–6Last 10052%Win rateTLWWTLLLWL

Last 10 vs previous 10: −20pp win rate · −0.03 avg rating · −1.9pp headshot accuracy · +33ms 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

  • 2–1–2 · 40% win rate
  • Avg rating 0.02
  • Avg headshot accuracy 14%
  • Avg reaction 605ms

Last 10

  • 3–5–2 · 30% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 13%
  • Avg reaction 616ms

Last 20

  • 8–10–2 · 40% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 14%
  • Avg reaction 600ms

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 (+3.3 against its own average).

Aim4.5
Utility6.7
Positioning5.2
Opening Duels1.8
Clutch0.0

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 67% 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

CT openings. Opening success drops from 50% on T to 34% on CT — first contacts on the defending side are being lost.

Reaction time. 603ms 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

Aim4.5
Positioning5.2
Utility6.7
Mechanics4.8
Opening Duels3.0
Win Impact5.6

Composite 5.0/10 (Developing), 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-0.00−0.01
first ⅓ avg 0.01 → last ⅓ avg -0.00
Reaction time618ms+2ms
first ⅓ avg 616ms → last ⅓ avg 618ms
Headshot accuracy14.9%+0.2%
first ⅓ avg 14.7% → last ⅓ avg 14.9%

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 · 13–3 · nuke, 3 Oct →
33%Best headshot accuracy · 1–13 · cache, 25 Oct →
469msFastest reaction time · 13–10 · inferno, 2 Nov →
13–1Biggest win · nuke, 4 Nov →

Across the last 100 tracked matches.

Highlights

7Longest win streak
10–6In matches decided by ≤2 rounds
1Overtime games

Map breakdown

ancientBest map · 67% over 9infernoWeakest map · 33% over 15
MapGradePlayedRecordWin rateAvg rating
nukeB2111–1052%0.03
infernoD155–1033%-0.02
anubisB147–750%0.00
dust2A106–460%-0.00
cacheA106–460%0.00
ancientS96–367%-0.01
vertigoS96–367%0.00
mirageA74–357%-0.01
overpass—41–325%-0.06
train—10–10%-0.03

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (33% over 15). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

415Matches
51%Win rate
1.05Avg K/D
83.5ADR
37%Headshot %

Clutches & streaks

18%1v1 clutch win
25%1v2 clutch win
11Longest win streak

Recent Faceit resultsWLLWL

MapMatchesWin rateAvg K/DAvg kills
Mirage2157%1.3917.5
Anubis1233%1.2715.4
Ancient1182%1.3517.6
Nuke1050%1.2816.3
Inferno956%1.3914.1
Vertigo560%1.6915.4
Overpass425%1.0115.8
Dust2475%1.2715.0

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.

14.6%Headshot accuracy
33.1%Accuracy (enemy spotted)
32.8%Spray accuracy
71.5%Counter-strafing
11.8°Preaim
603msReaction time
49.5%T opening success
34.1%CT opening success
0.59Enemies flashed / flash
8.6%Flash assists
11.12HE damage / grenade
11.81Flashes / 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 14.6017% — below the 15% mark we flag

    Aim Training →
  2. Advanced Mechanics

    Losing the first CT duel repeatedly usually means holding angles that favour the peeker.

    CT opening duels 34.1039% — 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.

33% win rate across 15 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
nuke12–12-0.015%4 Nov →
ancient10–13-0.0119%4 Nov →
vertigo13–9-0.0112%4 Nov →
nuke13–10.0715%4 Nov →
mirage12–120.0519%4 Nov →
inferno1–13-0.075%4 Nov →
mirage3–13-0.0214%3 Nov →
anubis1–13-0.126%3 Nov →
dust213–100.0417%3 Nov →
dust211–13-0.0116%3 Nov →
dust213–3-0.0515%2 Nov →
anubis10–13-0.035%2 Nov →
nuke13–9-0.0410%2 Nov →
inferno13–100.0417%2 Nov →
ancient8–13-0.0119%2 Nov →
nuke13–50.0816%29 Oct →
cache13–70.1013%29 Oct →
overpass6–13-0.0224%28 Oct →
nuke10–130.0819%28 Oct →
nuke7–130.049%27 Oct →
anubis13–110.0115%27 Oct →
ancient13–9-0.034%27 Oct →
overpass6–13-0.0416%27 Oct →
nuke12–12-0.007%27 Oct →
mirage13–5-0.0212%27 Oct →
nuke13–110.0315%26 Oct →
mirage13–90.0224%26 Oct →
inferno6–13-0.0524%26 Oct →
anubis13–100.0418%25 Oct →
cache1–13-0.0533%25 Oct →
vertigo11–130.0318%22 Oct →
cache13–80.0210%22 Oct →
overpass5–6-0.1024%22 Oct →
cache1–13-0.0819%16 Oct →
anubis6–13-0.0213%16 Oct →
inferno12–12-0.0514%15 Oct →
nuke13–30.1217%15 Oct →
anubis11–13-0.0112%15 Oct →
ancient13–3-0.0011%15 Oct →
train5–13-0.035%13 Oct →
inferno8–130.018%13 Oct →
anubis9–13-0.0219%11 Oct →
inferno8–13-0.029%11 Oct →
dust21–13-0.079%11 Oct →
ancient12–20.068%9 Oct →
nuke13–3-0.0314%8 Oct →
anubis10–13-0.0210%8 Oct →
ancient8–30.019%7 Oct →
mirage9–30.0110%6 Oct →
inferno13–7-0.0627%6 Oct →
dust213–10-0.0219%6 Oct →
anubis13–7-0.0530%5 Oct →
nuke6–130.0113%5 Oct →
cache9–13-0.026%5 Oct →
vertigo13–11-0.0115%5 Oct →
nuke13–11-0.0312%5 Oct →
nuke13–30.1415%3 Oct →
vertigo1–13-0.066%3 Oct →
ancient0–13-0.059%3 Oct →
nuke6–130.0223%2 Oct →
inferno6–13-0.0611%2 Oct →
dust213–100.097%2 Oct →
vertigo13–6-0.0014%2 Oct →
inferno10–13-0.067%1 Oct →
nuke8–130.0115%1 Oct →
inferno4–13-0.075%1 Oct →
mirage7–13-0.0219%30 Sept →
inferno13–70.0317%30 Sept →
cache13–40.0216%30 Sept →
vertigo13–20.037%30 Sept →
nuke11–00.1026%30 Sept →
ancient13–8-0.0311%30 Sept →
nuke5–12-0.0013%29 Sept →
dust23–13-0.0317%29 Sept →
dust213–100.0211%23 Sept →
cache13–6-0.0111%23 Sept →
nuke13–90.0314%23 Sept →
vertigo7–13-0.0114%23 Sept →
cache3–13-0.0314%22 Sept →
anubis13–100.069%22 Sept →
dust24–13-0.1130%22 Sept →
inferno12–12-0.047%21 Sept →
inferno13–4-0.0027%18 Sept →
anubis11–130.1011%17 Sept →
nuke11–13-0.0026%17 Sept →
inferno12–120.0416%17 Sept →
anubis5–10.0414%17 Sept →
overpass2–13-0.0713%16 Sept →
mirage13–8-0.096%16 Sept →
vertigo13–40.0311%16 Sept →
cache13–100.0125%16 Sept →
anubis13–90.0714%16 Sept →
nuke16–140.0414%16 Sept →
dust213–40.1218%16 Sept →
ancient13–11-0.0112%16 Sept →
nuke11–13-0.0314%15 Sept →
inferno13–110.034%15 Sept →
anubis13–11-0.0318%15 Sept →
vertigo13–110.0110%14 Sept →
cache13–70.0715%14 Sept →

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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