Aerials

Aerials — CS2 Stats

NL76561198013570171[U:1:53304443]Steam profile ↗✓ No bans

689Tracked matches45%Win rate2020Tracked since
CSDB Rating4.5 DevelopingSupport
FaceitLevel 6Top 54.0% of ranked FACEIT players
WingmanMaster Guardian Elite
Ladder ranks via Leetify

Rating over time

Premier CS Rating: 12,474 -1,180 25 Dec – 27 Jan · 8 days played
12,47413,710peak 13,71025 Dec27 Jan
13,812Peak Premier in tracked matches
55Days played since 2024-10-24

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

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CSDB.GGAerialsFACEITLevel 6STANDINGTop 54.0% of rankedcsdb.gg/stats

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Performance scores

Aim54
Positioning38
Utility49

0–100 skill scores via Leetify.

Recent form

COLD37–52–11Last 10037%Win rateLLWWLLLLLW

Last 10 vs previous 10: 0pp win rate · −0.02 avg rating · −2.2pp headshot accuracy · +56ms 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

  • 2–3 · 40% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 19%
  • Avg reaction 659ms

Last 10

  • 3–7 · 30% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 18%
  • Avg reaction 637ms

Last 20

  • 6–14 · 30% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 19%
  • Avg reaction 609ms

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 (+2.3 against its own average).

Aim5.4
Utility4.9
Positioning3.8
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

NiKo

Plays most like NiKo 85% 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

T-side openings. Opening success drops from 40% on CT to 21% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 650ms 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

Aim5.4
Positioning3.8
Utility4.9
Mechanics6.1
Opening Duels1.2
Win Impact3.3

Composite 4.5/10 (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-0.02−0.00
first ⅓ avg -0.01 → last ⅓ avg -0.02
Reaction time642ms+18ms
first ⅓ avg 624ms → last ⅓ avg 642ms
Headshot accuracy19.1%+4.7%
first ⅓ avg 14.3% → last ⅓ avg 19.1%

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

0.11Best match rating · 13–6 · train, 16 Nov →
44%Best headshot accuracy · 8–13 · agency, 10 May →
422msFastest reaction time · 10–13 · inferno, 1 Dec →
13–1Biggest win · basalt, 3 May →

Across the last 100 tracked matches.

Highlights

3Longest win streak
L2Current streak
2–9In matches decided by ≤2 rounds
3Overtime games

Map breakdown

dust2Best map · 63% over 8cacheWeakest map · 14% over 7
MapGradePlayedRecordWin rateAvg rating
infernoB157–847%-0.02
mirageC145–936%-0.03
anubisD124–833%-0.03
nukeD92–722%-0.03
trainD93–633%-0.02
dust2A85–363%-0.03
ancientC83–538%0.00
cacheD71–614%-0.01
vertigoC52–340%0.00
basaltA53–260%0.02
overpass—22–0100%0.02
office—20–20%0.01
poseidon—10–10%-0.03
debris—10–10%-0.09
agency—10–10%-0.05
edin—10–10%-0.05

Across the last 100 tracked matches.

Faceit stats

Combat

616Matches
51%Win rate
0.97Avg K/D
72.5ADR
37%Headshot %

Clutches & streaks

0%1v1 clutch win
0%1v2 clutch win
7Longest win streak

Recent Faceit resultsLLWWL

MapMatchesWin rateAvg K/DAvg kills
Anubis333%0.6712.3
Ancient250%1.0218.5
Mirage10%0.7112.0

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.

18.0%Headshot accuracy
30.6%Accuracy (enemy spotted)
37.0%Spray accuracy
77.5%Counter-strafing
10.4°Preaim
650msReaction time
21.1%T opening success
39.8%CT opening success
0.55Enemies flashed / flash
4.6%Flash assists
9.14HE damage / grenade
8.55Flashes / 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 21.0837% — below the 40% mark we flag

Spend your practice time on Cache

Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.

14% win rate across 7 tracked games — your weakest map with enough games to be worth reading into.

Cache callouts & strategy →

Recent matches

MapScoreRatingHS%Date
cache10–13-0.0120%28 Jul →
inferno11–130.0119%25 Jul →
nuke13–10-0.0119%24 Jul →
anubis13–9-0.0115%24 Jul →
cache9–13-0.0524%23 Jul →
inferno11–13-0.0120%23 Jul →
anubis5–13-0.0612%21 Jul →
mirage5–13-0.0322%20 Jul →
anubis8–13-0.0614%20 Jul →
dust213–7-0.0414%20 Jul →
inferno13–2-0.0219%19 Jul →
inferno6–13-0.098%19 Jul →
inferno9–130.0022%17 Jul →
cache2–13-0.0122%17 Jul →
poseidon6–9-0.0327%16 Jul →
vertigo7–90.0619%16 Jul →
debris5–9-0.0933%16 Jul →
inferno13–60.0324%15 Jul →
cache11–130.0615%13 Jul →
anubis13–5-0.039%12 Jul →
mirage2–13-0.0118%12 Jul →
cache5–13-0.0225%12 Jul →
inferno16–13-0.089%11 Jul →
cache13–11-0.0020%11 Jul →
cache11–13-0.0717%17 Jun →
overpass13–40.0412%30 May →
agency8–13-0.0544%10 May →
nuke13–100.0120%5 May →
dust213–10-0.014%4 May →
mirage6–130.0124%4 May →
basalt12–12-0.1014%3 May →
basalt13–10.0828%3 May →
inferno13–40.0519%27 Mar →
basalt10–13-0.037%19 Mar →
ancient2–13-0.0916%19 Mar →
mirage13–90.0417%9 Mar →
inferno13–9-0.0121%14 Feb →
mirage13–60.0214%14 Feb →
mirage4–13-0.0717%27 Jan →
inferno13–5-0.0014%25 Jan →
mirage9–13-0.0513%9 Jan →
train12–12-0.0314%4 Jan →
anubis13–30.0214%4 Jan →
anubis10–13-0.0713%4 Jan →
inferno15–15-0.0413%3 Jan →
ancient13–40.0519%3 Jan →
ancient13–40.0617%2 Jan →
mirage13–16-0.082%1 Jan →
anubis9–13-0.0810%1 Jan →
ancient11–13-0.0413%1 Jan →
nuke7–13-0.0616%29 Dec →
mirage13–110.0620%29 Dec →
anubis11–13-0.0511%29 Dec →
nuke10–13-0.0411%28 Dec →
mirage0–13-0.1012%28 Dec →
dust213–3-0.019%28 Dec →
ancient4–13-0.0516%27 Dec →
overpass13–10-0.0122%25 Dec →
mirage13–10-0.0024%25 Dec →
ancient4–00.1117%25 Dec →
vertigo4–13-0.0518%25 Dec →
mirage11–13-0.0312%25 Dec →
mirage5–13-0.027%22 Dec →
ancient9–130.009%20 Dec →
inferno8–130.0115%19 Dec →
ancient6–13-0.0311%19 Dec →
anubis8–13-0.0519%13 Dec →
nuke5–13-0.0619%7 Dec →
anubis5–13-0.0317%3 Dec →
inferno10–13-0.0415%1 Dec →
nuke7–13-0.0515%1 Dec →
office12–12-0.0410%30 Nov →
dust213–90.0618%30 Nov →
vertigo13–60.048%30 Nov →
anubis12–12-0.0212%28 Nov →
train11–13-0.0010%27 Nov →
train4–13-0.0014%25 Nov →
office12–120.0617%25 Nov →
dust212–12-0.0312%24 Nov →
inferno13–40.0311%24 Nov →
vertigo3–13-0.0419%23 Nov →
anubis13–100.0818%21 Nov →
basalt13–100.0722%20 Nov →
train13–8-0.086%20 Nov →
edin9–13-0.0511%17 Nov →
train12–12-0.0618%17 Nov →
train10–13-0.1111%17 Nov →
basalt13–80.0712%16 Nov →
vertigo13–7-0.0113%16 Nov →
train13–60.1112%16 Nov →
inferno6–13-0.0913%16 Nov →
dust213–9-0.0117%16 Nov →
train13–100.039%16 Nov →
train12–12-0.0112%15 Nov →
dust22–13-0.0814%30 Oct →
nuke8–130.0313%30 Oct →
nuke12–12-0.0333%26 Oct →
dust210–13-0.0913%26 Oct →
mirage13–10-0.0821%26 Oct →
nuke12–12-0.059%24 Oct →

Match data via Leetify.

Recent teammates

Milestones

500 Matches
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

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