Vega

Vega — CS2 Stats

76561197962512331[U:1:2246603]Steam profile ↗✓ No bans

270Tracked matches24%Win rate2023Tracked since
CSDB Rating4.0 DevelopingSupport
Ladder ranks via Leetify

Performance scores

Aim47
Positioning41
Utility52

0–100 skill scores via Leetify.

Recent form

COLD26–67–7Last 10026%Win rateLLWLLWLLLL

Last 10 vs previous 10: 0pp win rate · −0.01 avg rating · −3.7pp headshot accuracy · +23ms 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

  • 1–4 · 20% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 20%
  • Avg reaction 641ms

Last 10

  • 2–8 · 20% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 17%
  • Avg reaction 620ms

Last 20

  • 4–16 · 20% win rate
  • Avg rating -0.05
  • 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 (+1.9 against its own average).

Aim4.7
Utility5.2
Positioning4.1
Opening Duels0.6

Limited utility dependence

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

b1t

Plays most like b1t 76% 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

Reaction time. 608ms 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

Aim4.7
Positioning4.1
Utility5.2
Mechanics6.0
Opening Duels0.8
Win Impact0.0

Composite 4.0/10 (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 time615ms−111ms
first ⅓ avg 725ms → last ⅓ avg 615ms
Headshot accuracy18.8%−0.9%
first ⅓ avg 19.7% → last ⅓ avg 18.8%

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

0.07Best match rating · 13–10 · cache, 25 Aug →
48%Best headshot accuracy · 9–13 · train, 13 Sept →
453msFastest reaction time · 2–13 · overpass, 12 May →
12–0Biggest win · inferno, 11 Aug →

Across the last 100 tracked matches.

Highlights

3Longest win streak
L2Current streak
5–8In matches decided by ≤2 rounds

Map breakdown

trainBest map · 57% over 7mirageWeakest map · 8% over 12
MapGradePlayedRecordWin rateAvg rating
dust2D338–2524%-0.04
infernoC177–1041%-0.04
mirageD121–118%-0.04
ancientD81–713%-0.03
trainA74–357%-0.03
cacheD72–529%-0.01
vertigoD51–420%-0.06
nukeD51–420%-0.05
anubis—31–233%-0.07
overpass—20–20%-0.06
boulder—10–10%-0.03

Across the last 100 tracked matches.

Mirage is currently your weakest sufficiently-sampled map (8% over 12). Start with the 5 essential Mirage 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.

18.7%Headshot accuracy
27.7%Accuracy (enemy spotted)
28.8%Spray accuracy
77.0%Counter-strafing
10.6°Preaim
608msReaction time
29.1%T opening success
36.1%CT opening success
0.65Enemies flashed / flash
5.7%Flash assists
11.41HE damage / grenade
4.43Flashes / 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 29.0652% — below the 40% mark we flag

Spend your practice time on Mirage

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

8% win rate across 12 tracked games — your weakest map with enough games to be worth reading into.

Mirage callouts & strategy →Mirage grenade lineups →

Recent matches

MapScoreRatingHS%Date
mirage11–13-0.0922%5 Nov →
mirage7–13-0.0535%1 Nov →
inferno13–5-0.0010%1 Nov →
mirage4–13-0.0417%25 Oct →
inferno10–13-0.0417%25 Oct →
inferno13–90.0215%24 Oct →
dust25–13-0.0522%12 Oct →
inferno7–13-0.0612%5 Oct →
inferno5–13-0.1011%5 Oct →
dust26–13-0.0612%5 Oct →
inferno6–13-0.0521%14 Sept →
vertigo7–13-0.0523%13 Sept →
train9–130.0048%13 Sept →
dust25–13-0.0719%12 Sept →
mirage13–11-0.0323%30 Aug →
dust29–13-0.0823%28 Aug →
dust29–13-0.0611%28 Aug →
dust213–90.0311%28 Aug →
dust27–13-0.0320%27 Aug →
cache7–13-0.0911%26 Aug →
cache13–100.0721%25 Aug →
inferno12–12-0.0414%24 Aug →
dust29–13-0.0517%24 Aug →
nuke11–13-0.0223%15 Aug →
inferno13–10-0.0715%15 Aug →
cache9–13-0.0126%15 Aug →
nuke7–13-0.117%11 Aug →
inferno12–0-0.010%11 Aug →
dust26–13-0.0324%10 Aug →
dust27–13-0.0320%10 Aug →
dust24–13-0.0525%9 Aug →
inferno12–12-0.0624%9 Aug →
mirage8–13-0.0424%9 Aug →
cache10–13-0.0320%9 Aug →
dust25–13-0.0625%9 Aug →
ancient13–50.0418%3 Aug →
mirage10–130.0123%2 Aug →
dust27–13-0.0615%2 Aug →
anubis2–13-0.0913%2 Aug →
dust211–130.0021%2 Aug →
mirage3–13-0.0321%1 Aug →
nuke7–20.0324%31 Jul →
inferno3–13-0.0714%31 Jul →
inferno13–70.0410%27 Jul →
dust213–11-0.0420%27 Jul →
vertigo2–13-0.1110%26 Jul →
inferno13–9-0.0715%26 Jul →
ancient6–13-0.0311%26 Jul →
dust211–13-0.0714%26 Jul →
mirage5–13-0.0717%25 Jul →
dust213–7-0.0414%25 Jul →
anubis5–13-0.057%25 Jul →
mirage8–130.009%24 Jul →
vertigo4–13-0.0419%24 Jul →
ancient2–13-0.0012%24 Jul →
anubis13–7-0.068%22 Jul →
dust23–13-0.0610%18 Jul →
dust25–13-0.095%18 Jul →
dust213–100.0017%18 Jul →
dust25–13-0.069%18 Jul →
mirage1–13-0.075%18 Jul →
nuke12–12-0.0527%15 Jul →
dust211–13-0.0916%15 Jul →
ancient4–13-0.0123%15 Jul →
train12–12-0.0512%14 Jul →
inferno9–13-0.0712%14 Jul →
cache4–13-0.0311%13 Jul →
ancient3–13-0.1011%13 Jul →
boulder6–13-0.0322%13 Jul →
vertigo13–11-0.0213%12 Jul →
ancient12–12-0.0729%12 Jul →
dust212–120.0114%12 Jul →
ancient3–13-0.0619%12 Jul →
dust29–13-0.0130%12 Jul →
train13–8-0.0125%5 Jul →
inferno2–13-0.0713%5 Jul →
dust213–11-0.0010%4 Jul →
nuke11–13-0.1025%22 Jun →
mirage5–13-0.0713%16 Jun →
cache13–40.0119%16 Jun →
overpass6–13-0.0621%10 Jun →
dust213–4-0.0513%7 Jun →
ancient10–13-0.0130%7 Jun →
train13–9-0.0114%7 Jun →
dust26–13-0.0912%26 May →
cache11–13-0.0216%19 May →
dust28–13-0.0916%19 May →
inferno12–120.0215%16 May →
overpass2–13-0.0613%12 May →
train13–8-0.0419%3 May →
dust213–50.0618%2 May →
train11–13-0.0724%27 Apr →
dust213–60.0037%27 Apr →
train13–11-0.0227%26 Apr →
inferno13–3-0.0043%26 Apr →
dust26–13-0.1022%12 Apr →
dust28–13-0.028%12 Apr →
mirage0–13-0.0324%12 Apr →
vertigo8–13-0.0618%28 Mar →
dust24–13-0.0620%25 Dec →

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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