N4kkuZ

N4kkuZ — CS2 Stats

76561199301825585[U:1:1341559857]Steam profile ↗✓ No bans

307Tracked matches56%Win rate2025Tracked since
CSDB Rating6.0 SolidAll-Rounder
Premier CS Rating9,705Light Blue band · top ~56.3% of ranked players (population est.)
WingmanGold Nova II
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 10 Oct 2026 (3 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: 9,705 +3,069 6 Jan – 10 Oct · 18 days played
6,63611,533peak 11,5336 Jan10 Oct
11,533Peak Premier in tracked matches
61Days played since 2026-01-06

Premier CS Rating

  • 1,828 below peak (11,533)
  • -880 over 30 days · declining
  • Next: Blue band at 10,000 — 295 to go
  • Reached: Blue band · Light Blue band

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

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CSDB.GGN4kkuZPREMIER9,705 · Light Blue bandcsdb.gg/stats

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Performance scores

Aim68
Positioning75
Utility63

0–100 skill scores via Leetify.

Recent form

STEADY55–39–6Last 10055%Win rateWWLLLLWWLL

Last 10 vs previous 10: −30pp win rate · −0.02 avg rating · +3.3pp headshot accuracy · +21ms reaction

Win rate down 30pp 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 0.00
  • Avg headshot accuracy 21%
  • Avg reaction 583ms

Last 10

  • 4–6 · 40% win rate
  • Avg rating 0.02
  • Avg headshot accuracy 20%
  • Avg reaction 560ms

Last 20

  • 11–9 · 55% win rate
  • Avg rating 0.03
  • Avg headshot accuracy 18%
  • Avg reaction 550ms

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.

Aim6.8
Utility6.3
Positioning7.5
Opening Duels6.3

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

jL

Plays most like jL 78% 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

Strengths

CT openings. 62% CT opening-duel success — winning the first fight on the defending side is rare and valuable.

Areas to improve

T-side openings. Opening success drops from 62% on CT to 44% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 562ms 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

Aim6.8
Positioning7.5
Utility6.3
Mechanics6.1
Opening Duels5.8
Win Impact6.9

Composite 6.0/10 (Solid), 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 rating0.03+0.00
first ⅓ avg 0.03 → last ⅓ avg 0.03
Reaction time565ms−14ms
first ⅓ avg 580ms → last ⅓ avg 565ms
Headshot accuracy19.2%+5.3%
first ⅓ avg 13.9% → last ⅓ avg 19.2%

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

0.27Best match rating · 13–5 · anubis, 20 Jun →
40%Best headshot accuracy · 5–1 · mirage, 23 Jun →
406msFastest reaction time · 13–9 · anubis, 10 Oct →
13–0Biggest win · anubis, 29 May →

Across the last 100 tracked matches.

Highlights

4Longest win streak
W2Current streak
8–8In matches decided by ≤2 rounds
7Overtime games

Map breakdown

infernoBest map · 77% over 13ancientWeakest map · 29% over 7
MapGradePlayedRecordWin rateAvg rating
anubisS2315–865%0.06
dust2C156–940%0.03
cacheA138–562%0.04
infernoS1310–377%0.05
mirageC94–544%0.03
ancientD72–529%0.03
nukeB63–350%0.00
overpassS64–267%0.03
office—42–250%0.04
train—41–325%-0.03

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (29% over 7). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Skill profile

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

19.4%Headshot accuracy
34.0%Accuracy (enemy spotted)
36.6%Spray accuracy
77.6%Counter-strafing
10.5°Preaim
562msReaction time
44.4%T opening success
62.0%CT opening success
0.57Enemies flashed / flash
9.2%Flash assists
9.78HE damage / grenade
12.69Flashes / match

Recommended for you

Spend your practice time on Ancient

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

29% win rate across 7 tracked games — your weakest map with enough games to be worth reading into.

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

MapScoreRatingHS%Date
anubis13–90.0310%10 Oct →
anubis13–90.0623%13 Sept →
cache5–13-0.0625%12 Sept →
dust23–130.0426%9 Sept →
anubis10–13-0.0520%5 Sept →
office10–13-0.0211%5 Sept →
nuke13–60.0614%5 Sept →
anubis13–30.0931%30 Aug →
ancient11–130.0214%30 Aug →
ancient9–130.0222%28 Aug →
cache16–140.0919%28 Aug →
ancient5–130.0112%27 Aug →
inferno13–4-0.0018%27 Aug →
anubis13–70.0412%21 Aug →
cache13–100.0717%2 Aug →
dust211–130.0815%2 Aug →
cache13–60.0219%1 Aug →
inferno8–130.1111%1 Aug →
dust27–6-0.0815%1 Aug →
inferno9–70.0325%1 Aug →
anubis11–130.0122%31 Jul →
cache13–70.2122%31 Jul →
inferno5–130.0616%31 Jul →
dust212–120.0021%31 Jul →
cache13–5-0.0529%31 Jul →
anubis15–15-0.0513%30 Jul →
ancient12–12-0.0233%30 Jul →
inferno13–90.1231%30 Jul →
anubis13–30.1622%30 Jul →
cache13–110.0323%30 Jul →
cache6–130.0720%22 Jul →
nuke6–13-0.0312%3 Jul →
cache7–13-0.0310%24 Jun →
dust213–60.0111%24 Jun →
mirage5–10.0840%23 Jun →
inferno7–90.0514%22 Jun →
dust213–50.1118%22 Jun →
anubis9–130.0516%21 Jun →
ancient6–130.0717%21 Jun →
anubis13–50.2721%20 Jun →
inferno13–70.0539%20 Jun →
anubis13–60.0530%19 Jun →
train9–13-0.0210%18 Jun →
dust212–120.0935%16 Jun →
inferno13–40.115%30 May →
overpass16–14-0.005%30 May →
anubis13–0-0.004%29 May →
anubis13–60.088%26 May →
dust28–13-0.0221%26 May →
mirage2–130.0323%26 May →
mirage15–150.1324%25 May →
anubis13–50.0412%24 May →
dust215–15-0.0416%24 May →
dust213–60.0411%23 May →
mirage13–00.0320%23 May →
office13–80.085%16 May →
office8–13-0.0010%15 May →
office13–70.1019%14 May →
dust211–130.0115%13 May →
anubis6–130.0924%13 May →
cache13–80.0914%12 May →
cache8–130.0118%12 May →
cache7–13-0.0217%9 May →
cache13–10.0433%29 Apr →
dust24–130.0210%26 Apr →
train13–10-0.0226%25 Apr →
train7–13-0.0315%25 Apr →
ancient13–110.0618%19 Apr →
nuke13–50.0316%18 Apr →
anubis8–130.0616%18 Apr →
overpass11–130.0511%18 Apr →
nuke13–50.0420%18 Apr →
dust213–10-0.0016%20 Mar →
dust26–130.0512%20 Mar →
inferno13–70.0123%7 Mar →
inferno13–60.022%21 Feb →
dust213–60.0811%13 Feb →
anubis7–13-0.0213%8 Feb →
mirage13–110.0213%7 Feb →
inferno16–120.019%7 Feb →
mirage9–130.0118%6 Feb →
anubis14–160.049%5 Feb →
nuke6–13-0.0117%3 Feb →
inferno9–60.0815%1 Feb →
anubis13–20.1611%1 Feb →
overpass13–110.0416%31 Jan →
nuke7–13-0.078%31 Jan →
mirage6–130.0315%31 Jan →
inferno13–70.0515%30 Jan →
anubis13–50.1212%29 Jan →
mirage11–13-0.028%27 Jan →
anubis13–100.0422%26 Jan →
mirage13–90.0016%26 Jan →
anubis13–80.0420%19 Jan →
overpass7–13-0.0015%17 Jan →
train8–13-0.0514%17 Jan →
overpass13–70.0716%16 Jan →
anubis13–7-0.0018%15 Jan →
ancient13–50.036%12 Jan →
overpass13–40.024%6 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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