Neck

Neck — CS2 Stats

76561198076833736[U:1:116568008]Steam profile ↗✓ No bans

602Tracked matches39%Win rate2022Tracked since
CSDB Rating4.1 DevelopingSupport
FaceitLevel 4Top 81.0% of ranked FACEIT players
Ladder ranks via Leetify

Rating over time

Premier CS Rating: 11,659 -1,270 3 May – 12 Jul · 8 days played
11,48213,756peak 13,7563 May12 Jul
13,756Peak Premier in tracked matches
35Days played since 2025-05-03

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

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CSDB.GGNeckFACEITLevel 4STANDINGTop 81.0% of rankedcsdb.gg/stats

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Performance scores

Aim53
Positioning35
Utility52

0–100 skill scores via Leetify.

Recent form

STEADY44–48–8Last 10044%Win rateLWWWLLWLWL

Last 10 vs previous 10: +40pp win rate · +0.02 avg rating · +0.0pp headshot accuracy · −47ms reaction

Win rate up 40pp 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

  • 3–2 · 60% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 20%
  • Avg reaction 555ms

Last 10

  • 5–5 · 50% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 18%
  • Avg reaction 549ms

Last 20

  • 6–14 · 30% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 18%
  • Avg reaction 573ms

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 against its own average).

Aim5.3
Utility5.2
Positioning3.5
Opening Duels0.0
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

Twistzz

Plays most like Twistzz 77% 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. 592ms 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

Aim5.3
Positioning3.5
Utility5.2
Mechanics5.6
Opening Duels0.0
Win Impact1.4

Composite 4.1/10 (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-0.04−0.00
first ⅓ avg -0.04 → last ⅓ avg -0.04
Reaction time596ms−43ms
first ⅓ avg 639ms → last ⅓ avg 596ms
Headshot accuracy17.9%+2.9%
first ⅓ avg 15.0% → last ⅓ avg 17.9%

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

0.08Best match rating · 13–5 · train, 11 Jul →
38%Best headshot accuracy · 13–5 · nuke, 11 Jul →
414msFastest reaction time · 11–13 · inferno, 18 Oct →
13–1Biggest win · dust2, 4 Jun →

Across the last 100 tracked matches.

Highlights

5Longest win streak
10–9In matches decided by ≤2 rounds
4Overtime games

Map breakdown

trainBest map · 50% over 6infernoWeakest map · 38% over 24
MapGradePlayedRecordWin rateAvg rating
dust2B2612–1446%-0.04
infernoC249–1538%-0.05
anubisC218–1338%-0.04
nukeC73–443%-0.03
trainB63–350%-0.01
mirage—42–250%-0.05
cache—31–233%-0.04
ancient—32–167%-0.06
grail—22–0100%-0.01
alpine—10–10%-0.02
overpass—11–0100%-0.04
jura—11–0100%-0.01
vertigo—10–10%-0.03

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (38% over 24). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

2Matches
50%Win rate
0.62Avg K/D
58.5ADR
61%Headshot %

Clutches & streaks

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

Recent Faceit resultsLLLLW

MapMatchesWin rateAvg K/DAvg kills
Ancient1100%0.7311.0
Dust210%0.509.0

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.

17.5%Headshot accuracy
28.8%Accuracy (enemy spotted)
31.9%Spray accuracy
75.1%Counter-strafing
9.5°Preaim
592msReaction time
22.3%T opening success
24.1%CT opening success
0.59Enemies flashed / flash
6.1%Flash assists
9.09HE damage / grenade
5.90Flashes / 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 22.3235% — 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.

38% win rate across 24 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
anubis11–13-0.0313%2 Nov →
dust213–60.0229%2 Nov →
anubis13–50.0118%28 Oct →
mirage13–11-0.0824%28 Oct →
inferno11–13-0.0815%18 Oct →
anubis10–13-0.0717%18 Oct →
anubis13–4-0.0715%15 Oct →
inferno12–16-0.0516%30 Sept →
inferno13–11-0.046%27 Sept →
inferno1–13-0.0624%24 Sept →
nuke7–13-0.0610%24 Sept →
dust25–13-0.0529%24 Sept →
anubis8–13-0.0719%20 May →
inferno6–13-0.1014%20 May →
dust24–13-0.0629%20 May →
dust26–13-0.0729%20 May →
inferno3–13-0.1111%13 May →
dust26–13-0.0611%13 May →
nuke13–7-0.0119%13 May →
cache6–13-0.075%13 May →
cache13–6-0.0123%6 May →
cache1–13-0.0532%6 May →
anubis15–15-0.0221%27 Apr →
dust213–11-0.087%27 Apr →
anubis13–11-0.056%13 Apr →
dust213–90.0318%13 Apr →
anubis11–13-0.0227%2 Apr →
dust211–13-0.0417%2 Apr →
inferno13–11-0.0318%2 Apr →
dust212–12-0.0615%25 Mar →
inferno7–13-0.0213%25 Mar →
inferno12–120.0121%23 Mar →
dust213–10-0.0218%23 Mar →
anubis6–13-0.0219%23 Mar →
dust28–13-0.0521%16 Mar →
inferno11–13-0.0819%16 Mar →
anubis2–13-0.0625%16 Mar →
inferno13–10-0.0923%4 Feb →
alpine12–12-0.0229%4 Feb →
anubis13–9-0.0316%4 Feb →
overpass13–6-0.047%16 Jul →
dust213–7-0.0126%16 Jul →
inferno13–9-0.0114%16 Jul →
inferno13–7-0.0212%16 Jul →
mirage11–13-0.0313%12 Jul →
train13–60.0512%12 Jul →
train13–50.080%11 Jul →
train9–13-0.030%11 Jul →
nuke13–50.0338%11 Jul →
anubis8–130.0110%11 Jul →
dust23–13-0.099%11 Jul →
inferno13–40.0618%11 Jul →
inferno12–12-0.0611%1 Jul →
mirage7–13-0.076%27 Jun →
dust213–70.0211%27 Jun →
anubis10–13-0.0517%27 Jun →
anubis8–130.008%21 Jun →
inferno13–5-0.0510%21 Jun →
dust212–12-0.0619%21 Jun →
dust27–13-0.0228%19 Jun →
anubis13–6-0.089%19 Jun →
dust212–12-0.073%19 Jun →
inferno13–6-0.0213%19 Jun →
ancient7–13-0.0420%4 Jun →
anubis13–4-0.114%4 Jun →
dust213–1-0.0213%4 Jun →
jura13–2-0.0111%4 Jun →
inferno2–13-0.109%1 Jun →
vertigo12–12-0.0316%1 Jun →
anubis13–50.0315%1 Jun →
train8–13-0.0518%1 Jun →
dust213–5-0.0011%1 Jun →
train13–2-0.039%30 May →
anubis5–13-0.0517%30 May →
dust213–4-0.0414%30 May →
anubis13–8-0.0614%20 May →
nuke11–13-0.0320%15 May →
train4–13-0.088%15 May →
inferno3–13-0.1031%15 May →
dust213–11-0.0415%15 May →
inferno13–1-0.057%15 May →
nuke14–16-0.0211%14 May →
dust27–13-0.0420%14 May →
inferno11–13-0.0118%14 May →
mirage13–11-0.037%11 May →
ancient13–11-0.1018%11 May →
nuke13–9-0.0936%11 May →
inferno9–13-0.0623%11 May →
anubis9–13-0.0515%11 May →
nuke3–13-0.069%9 May →
grail13–5-0.0412%9 May →
grail13–70.026%9 May →
dust26–13-0.0519%7 May →
anubis7–13-0.0122%7 May →
dust216–14-0.0617%6 May →
dust26–13-0.019%6 May →
ancient13–7-0.0311%6 May →
dust213–11-0.0723%6 May →
inferno2–13-0.039%3 May →
inferno7–13-0.068%3 May →

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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