Velz

Velz — CS2 Stats

76561198075571550[U:1:115305822]Steam profile ↗✓ No bans

3,256Tracked matches53%Win rate2020Tracked since
CSDB Rating5.5 SolidSupport
Premier CS Rating23,327Top 25% of 213,399 CSDB-tracked playersPink band · top ~4.8% of ranked players (population est.)
CSDB Leaderboard#40129 of 260004 tracked
FaceitLevel 6 · 1,515 ELOTop 54.0% of ranked FACEIT players
WingmanLegendary Eagle
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 29 Oct 2026 (7 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: 23,327 +4,001 2 Sept – 29 Oct · 9 days played
19,08823,327peak 23,3272 Sept29 Oct
Faceit ELO: 1,515 +0 2 Sept – 20 Sept · 2 observations
1,5151,515peak 1,5152 Sept20 Sept
23,431Peak Premier in tracked matches
1,515Highest Faceit ELO seen on CSDB
43Days played since 2026-08-20

Faceit ELO

  • At peak — 1,515
  • Next: Level 8 at 1,531 — 16 to go
  • Reached: Level 7 · Level 6 · Level 5 · Level 4
  • Level 7 first seen 2026-09-02
  • Level 6 first seen 2026-09-02

Premier CS Rating

  • At peak — 23,327
  • +2,060 over 30 days · improving
  • Next: Red band at 25,000 — 1,673 to go
  • Reached: Pink band · Purple band · Blue band · Light Blue band
  • Purple band first seen 2026-09-02
  • Blue band first seen 2026-09-02

Premier comes from this profile’s tracked match history, so it reaches back as far as those matches do. Faceit ELO is CSDB’s own observation — no feed exposes ELO per match, so that line only has the days the profile was viewed and cannot be backfilled.

Share this profile

CSDB.GGVelzFACEITLevel 6 · 1,515 ELOSTANDINGTop 54.0% of rankedPREMIER23,327 · Pink bandPEAK ELO1,515csdb.gg/stats

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Performance scores

Aim66
Positioning41
Utility63

0–100 skill scores via Leetify.

Recent form

STEADY53–47Last 10053%Win rateLWWWWLLLLL

Last 10 vs previous 10: −10pp win rate · −0.01 avg rating · −1.1pp headshot accuracy · +20ms reaction

Win rate −10pp 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

  • 4–1 · 80% win rate
  • Avg rating -0.00
  • Avg headshot accuracy 12%
  • Avg reaction 567ms

Last 10

  • 4–6 · 40% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 14%
  • Avg reaction 574ms

Last 20

  • 9–11 · 45% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 15%
  • Avg reaction 564ms

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 (+3.1 against its own average).

Aim6.6
Utility6.3
Positioning4.1
Opening Duels0.0
Clutch2.6

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

ZywOo

Plays most like ZywOo 74% 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. 580ms 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

Aim6.6
Positioning4.1
Utility6.3
Mechanics7.1
Opening Duels0.0
Win Impact6.1

Composite 5.5/10 (Solid), a weighted mean of the bars with a small opposition adjustment (×1.02 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.01−0.00
first ⅓ avg -0.00 → last ⅓ avg -0.01
Reaction time570ms−31ms
first ⅓ avg 601ms → last ⅓ avg 570ms
Headshot accuracy14.8%+0.3%
first ⅓ avg 14.5% → last ⅓ avg 14.8%

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 · 13–3 · anubis, 14 Sept →
33%Best headshot accuracy · 11–13 · inferno, 31 Oct →
406msFastest reaction time · 5–13 · dust2, 22 Oct →
13–1Biggest win · nuke, 29 Oct →

Across the last 100 tracked matches.

Highlights

6Longest win streak
9–10In matches decided by ≤2 rounds
8Overtime games

Map breakdown

nukeBest map · 68% over 22infernoWeakest map · 17% over 6
MapGradePlayedRecordWin rateAvg rating
ancientA2515–1060%0.00
anubisA2212–1055%-0.00
nukeS2215–768%-0.00
cacheC198–1142%-0.01
infernoD61–517%0.00
dust2—31–233%0.02
mirage—21–150%-0.03
vertigo—10–10%-0.04

Across the last 100 tracked matches.

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

Faceit stats

Combat

414Matches
51%Win rate
0.97Avg K/D
68.5ADR
39%Headshot %

Clutches & streaks

33%1v1 clutch win
18%1v2 clutch win
11Longest win streak

Recent Faceit resultsLWLLW

MapMatchesWin rateAvg K/DAvg kills
Nuke10958%1.0313.6
Ancient10147%0.9613.8
Anubis8460%1.0614.6
Cache3540%0.7811.0
Inferno157%0.7912.5
Overpass1338%0.8813.1
Vertigo1242%1.0713.9
Mirage729%0.9214.1

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.

14.7%Headshot accuracy
33.2%Accuracy (enemy spotted)
35.3%Spray accuracy
82.0%Counter-strafing
9.5°Preaim
580msReaction time
21.1%T opening success
29.4%CT opening success
0.52Enemies flashed / flash
5.3%Flash assists
9.45HE damage / grenade
10.49Flashes / 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 14.6706% — below the 15% mark we flag

    Aim Training →
  2. 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 21.1268% — 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.

17% win rate across 6 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
anubis11–13-0.096%3 Nov →
anubis13–70.0413%2 Nov →
cache13–70.019%2 Nov →
anubis13–110.0114%1 Nov →
ancient13–80.0117%1 Nov →
nuke9–13-0.0611%1 Nov →
inferno11–130.0333%31 Oct →
ancient7–13-0.067%31 Oct →
anubis14–160.0011%30 Oct →
dust26–130.0022%29 Oct →
nuke13–10.1016%29 Oct →
inferno6–13-0.0112%28 Oct →
cache13–11-0.0614%28 Oct →
cache10–13-0.0419%23 Oct →
ancient13–7-0.0314%23 Oct →
nuke13–40.0219%22 Oct →
nuke13–20.0317%22 Oct →
dust25–130.0219%22 Oct →
ancient11–13-0.0116%21 Oct →
mirage6–13-0.048%20 Oct →
inferno13–80.0410%20 Oct →
mirage16–13-0.0213%19 Oct →
nuke13–10-0.0821%4 Oct →
ancient8–130.0212%4 Oct →
cache13–7-0.0118%4 Oct →
ancient13–6-0.0513%4 Oct →
ancient13–100.0116%3 Oct →
nuke13–30.0326%3 Oct →
anubis13–16-0.015%2 Oct →
ancient8–13-0.0118%2 Oct →
anubis13–40.0312%2 Oct →
anubis13–110.0418%30 Sept →
ancient13–6-0.0512%30 Sept →
anubis13–16-0.0615%29 Sept →
ancient13–50.0414%29 Sept →
anubis10–13-0.0414%29 Sept →
nuke13–60.066%29 Sept →
anubis9–13-0.0614%29 Sept →
cache7–13-0.0721%28 Sept →
cache13–6-0.0216%28 Sept →
nuke9–13-0.0210%24 Sept →
nuke13–11-0.0422%24 Sept →
ancient13–10.0610%24 Sept →
anubis13–110.018%23 Sept →
cache11–130.0122%23 Sept →
anubis13–4-0.0213%23 Sept →
nuke13–9-0.024%20 Sept →
nuke10–13-0.0212%19 Sept →
ancient7–13-0.0312%19 Sept →
cache3–13-0.0217%17 Sept →
ancient9–130.019%17 Sept →
nuke11–130.0319%16 Sept →
anubis13–70.0227%16 Sept →
cache6–13-0.007%16 Sept →
cache10–130.0314%15 Sept →
inferno11–130.0120%15 Sept →
ancient5–130.0211%14 Sept →
anubis9–130.0316%14 Sept →
ancient13–100.006%14 Sept →
anubis13–30.1210%14 Sept →
ancient13–90.028%13 Sept →
nuke13–80.038%13 Sept →
nuke13–7-0.005%9 Sept →
anubis8–130.0115%9 Sept →
ancient13–90.0125%8 Sept →
inferno3–13-0.0312%8 Sept →
nuke13–5-0.0013%8 Sept →
anubis13–70.048%7 Sept →
cache7–13-0.0115%7 Sept →
ancient13–110.0111%7 Sept →
inferno11–13-0.0222%6 Sept →
dust213–20.0229%5 Sept →
nuke13–6-0.0212%5 Sept →
ancient13–40.0316%4 Sept →
nuke13–9-0.0413%2 Sept →
anubis22–19-0.0514%2 Sept →
cache13–40.0211%1 Sept →
nuke10–13-0.0411%1 Sept →
cache13–30.0210%1 Sept →
cache13–40.0518%1 Sept →
nuke2–13-0.047%1 Sept →
anubis11–13-0.0514%31 Aug →
ancient8–13-0.0018%31 Aug →
vertigo10–13-0.042%31 Aug →
anubis13–9-0.0312%30 Aug →
cache9–13-0.0114%30 Aug →
ancient10–13-0.0311%29 Aug →
anubis6–13-0.0213%29 Aug →
cache8–130.0024%29 Aug →
nuke13–8-0.0220%27 Aug →
cache5–130.0320%27 Aug →
cache13–16-0.0419%26 Aug →
ancient13–80.0225%26 Aug →
cache13–5-0.0014%26 Aug →
ancient11–13-0.0112%25 Aug →
nuke5–130.0311%25 Aug →
nuke13–11-0.0113%25 Aug →
ancient16–140.0717%25 Aug →
anubis16–140.0713%24 Aug →
ancient13–10-0.027%20 Aug →

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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