Nahki

Nahki — CS2 Stats

FI76561197995523152[U:1:35257424]Steam profile ↗✓ No bans

945Tracked matches50%Win rate2020Tracked since
3,289Hours in CS28Hrs last 2 wks
CSDB Rating5.4 DevelopingSupport
Premier CS Rating17,412Purple band · top ~18.8% of ranked players (population est.)
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 2 Nov 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: 17,412 -166 11 Sept – 2 Nov · 9 days played
16,46917,578peak 17,57811 Sept2 Nov
17,578Peak Premier in tracked matches
28Days played since 2026-07-16

Premier CS Rating

  • 166 below peak (17,578)
  • +479 over 30 days · improving
  • Next: Pink band at 20,000 — 2,588 to go
  • Reached: Purple band · Blue band · Light Blue band

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

How this compares with the same rank

Median values for Purple band among CSDB-tracked players (n=37,767), from Valve's own lifetime stats. Aim, positioning and utility scores are deliberately not benchmarked here — those are a third-party provider's derived metrics, which CSDB does not store.

MetricThis playerPurple band medianPink band medianvs Pink band
Headshot rate39.5%44.4%46.9%7.5% short
Shot accuracy10.7%11.9%13.0%2.3% short
Kill/death ratio0.971.031.080.11 short
Match win rate48.8%45.2%46.7%above

This profile matches the typical Pink band player on 1 of 4 comparable metrics.

Widest gap: Shot accuracy. That is the metric furthest from the Pink band median in relative terms — not necessarily the one holding a rank back, which no statistic here can establish.

Share this profile

CSDB.GGNahkiPREMIER17,412 · Purple bandcsdb.gg/stats

The image is a snapshot; the link keeps updating. Nothing here is published anywhere — it is generated in your browser when you click.

Performance scores

Aim68
Positioning47
Utility58

0–100 skill scores via Leetify.

Recent form

HOT43–45–12Last 10043%Win rateTWWLWWLWWW

Last 10 vs previous 10: +50pp win rate · +0.00 avg rating · −1.7pp headshot accuracy · +16ms reaction

Win rate up 50pp 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

  • 3–1–1 · 60% win rate
  • Avg rating 0.03
  • Avg headshot accuracy 14%
  • Avg reaction 560ms

Last 10

  • 7–2–1 · 70% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 14%
  • Avg reaction 560ms

Last 20

  • 9–9–2 · 45% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 15%
  • Avg reaction 552ms

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 (+1.7 against its own average).

Aim6.8
Utility5.8
Positioning4.7
Opening Duels0.6
Clutch5.2

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 79% playstyle similarity

Most alike: utility contribution, positioning profile.

Where you differ: lower aim profile; lower opening-duel success.

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. 550ms 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
Positioning4.7
Utility5.8
Mechanics6.0
Opening Duels1.8
Win Impact5.0

Composite 5.4/10 (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 rating0.01+0.02
first ⅓ avg -0.01 → last ⅓ avg 0.01
Reaction time538ms−37ms
first ⅓ avg 574ms → last ⅓ avg 538ms
Headshot accuracy14.2%−3.4%
first ⅓ avg 17.6% → 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

0.12Best match rating · 2–0 · ancient, 31 Jul →
50%Best headshot accuracy · 2–0 · ancient, 31 Jul →
336msFastest reaction time · 5–1 · dust2, 16 Oct →
13–0Biggest win · dust2, 2 Nov →

Across the last 100 tracked matches.

Highlights

4Longest win streak
9–7In matches decided by ≤2 rounds
5Overtime games

Map breakdown

trainBest map · 67% over 6anubisWeakest map · 17% over 12
MapGradePlayedRecordWin rateAvg rating
dust2C219–1243%-0.00
infernoA159–660%0.02
anubisD122–1017%-0.02
nukeD113–827%-0.01
cacheD102–820%0.02
ancientA95–456%0.02
mirageA85–363%0.01
trainS64–267%0.01
vertigo—41–325%-0.01
overpass—43–175%0.02

Across the last 100 tracked matches.

Anubis is currently your weakest sufficiently-sampled map (17% over 12). Start with the 6 essential Anubis lineups, review the callouts, then spin up a practice server.

Lifetime stats

77,823Lifetime kills
0.97K/D
4,044Matches
48.8%Match win rate · Top 25% of Purple band
39.5%Headshot %
10.7%Shot accuracy
9,614MVPs
1,935Hours (in match)
6,020Bombs planted
1,033Bombs defused

Most-used weapons

AK-4722,765
AWP10,162
P2502,653
MAC-101,636
Tec-91,199
UMP-451,142

Lifetime map wins

8,357nuke
6,315inferno
4,426dust2
3,210train
1,228vertigo
544cbble
49dust
35office

Lifetime totals via Steam — visible because this profile's game details are public. Spans CS:GO and CS2.

Faceit stats

Combat

123Matches
40%Win rate
0.85Avg K/D
74.3ADR
41%Headshot %

Clutches & streaks

46%1v1 clutch win
8%1v2 clutch win
4Longest win streak

Recent Faceit resultsLLWLL

MapMatchesWin rateAvg K/DAvg kills
Inferno1145%1.0814.0
Dust2944%0.8411.6
Ancient838%0.7711.9
Nuke825%0.8214.4
Mirage617%0.9014.2
Anubis50%0.6610.0
Overpass1100%0.8713.0
Vertigo10%0.6511.0

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.

14.2%Headshot accuracy
34.8%Accuracy (enemy spotted)
37.5%Spray accuracy
77.1%Counter-strafing
9.5°Preaim
550msReaction time
41.2%T opening success
33.3%CT opening success
0.64Enemies flashed / flash
6.7%Flash assists
11.33HE damage / grenade
6.05Flashes / 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 14.2381% — 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 33.3278% — below the 40% mark we flag

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.

17% 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
dust215–15-0.0514%2 Nov →
dust213–110.0111%2 Nov →
dust213–00.1014%2 Nov →
cache7–13-0.0021%2 Nov →
cache13–20.1110%31 Oct →
inferno13–30.0420%30 Oct →
cache4–13-0.0315%30 Oct →
mirage13–40.0314%30 Oct →
inferno13–60.0013%30 Oct →
ancient13–10-0.0613%30 Oct →
train13–110.025%30 Oct →
cache10–130.0418%30 Oct →
dust212–120.0214%30 Oct →
inferno9–130.0026%24 Oct →
anubis7–13-0.0321%24 Oct →
cache5–13-0.0115%24 Oct →
ancient13–50.1018%24 Oct →
inferno11–13-0.0612%24 Oct →
ancient8–13-0.0214%24 Oct →
mirage8–130.0718%23 Oct →
vertigo12–120.0311%16 Oct →
inferno8–4-0.006%16 Oct →
nuke5–130.039%16 Oct →
dust25–1-0.0210%16 Oct →
dust28–13-0.0925%10 Oct →
anubis5–130.0621%9 Oct →
inferno8–13-0.038%9 Oct →
nuke13–50.0113%9 Oct →
overpass13–80.068%9 Oct →
cache12–12-0.0014%9 Oct →
train13–70.0410%9 Oct →
dust26–130.0416%9 Oct →
ancient12–120.1115%3 Oct →
dust28–130.0412%3 Oct →
mirage7–130.0619%3 Oct →
anubis15–150.008%2 Oct →
inferno13–10.0316%2 Oct →
dust210–13-0.0216%29 Sept →
inferno10–130.0317%25 Sept →
nuke7–13-0.0617%18 Sept →
ancient11–13-0.0712%18 Sept →
train13–80.0325%18 Sept →
ancient8–13-0.0418%12 Sept →
mirage7–10.0444%11 Sept →
vertigo9–130.0124%11 Sept →
anubis13–9-0.0125%11 Sept →
dust26–13-0.0115%11 Sept →
anubis7–130.0227%4 Sept →
dust213–90.0314%4 Sept →
nuke4–130.0017%4 Sept →
train9–130.0311%4 Sept →
overpass13–8-0.0126%4 Sept →
anubis13–3-0.0512%4 Sept →
dust211–30.0615%4 Sept →
nuke14–16-0.0318%29 Aug →
nuke5–130.0018%29 Aug →
anubis11–13-0.029%28 Aug →
mirage13–90.035%22 Aug →
anubis10–13-0.0213%22 Aug →
inferno13–110.0115%22 Aug →
overpass12–12-0.0615%21 Aug →
nuke11–13-0.0118%21 Aug →
inferno13–60.0510%21 Aug →
dust213–80.019%21 Aug →
dust213–11-0.0514%14 Aug →
mirage13–11-0.0116%14 Aug →
nuke15–15-0.0510%8 Aug →
anubis2–13-0.107%8 Aug →
dust213–100.0316%8 Aug →
dust213–110.0223%8 Aug →
ancient13–110.0017%8 Aug →
inferno13–50.0917%8 Aug →
mirage1–13-0.0616%8 Aug →
train2–13-0.066%7 Aug →
nuke13–100.0213%7 Aug →
overpass13–100.0917%7 Aug →
cache12–12-0.0119%7 Aug →
anubis3–13-0.0617%7 Aug →
ancient13–80.0213%7 Aug →
vertigo13–8-0.046%7 Aug →
inferno12–120.0314%7 Aug →
dust25–13-0.0424%7 Aug →
inferno13–110.0614%7 Aug →
dust22–13-0.1129%31 Jul →
nuke13–6-0.0716%31 Jul →
ancient2–00.1250%31 Jul →
inferno13–50.0219%31 Jul →
train13–70.0222%31 Jul →
vertigo12–12-0.0420%31 Jul →
cache13–30.0620%31 Jul →
anubis7–130.0216%31 Jul →
dust211–13-0.0225%31 Jul →
anubis5–13-0.0713%24 Jul →
mirage13–7-0.0915%24 Jul →
dust210–130.0021%24 Jul →
nuke12–120.0117%18 Jul →
inferno13–160.048%17 Jul →
cache4–130.0016%17 Jul →
cache2–13-0.0118%16 Jul →
dust211–13-0.0118%16 Jul →

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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