Cactus

Cactus — CS2 Stats

AU76561198131853063[U:1:171587335]Steam profile ↗✓ No bans

264Tracked matches66%Win rate2023Tracked since
581Hours in CS
CSDB Rating6.1 SolidAll-Rounder
Premier CS Rating11,136Blue band · top ~48.9% of ranked players (population est.)
WingmanGold Nova Master
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 10 May 2025 (535 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: 11,136 +2,442 24 Mar – 10 May · 9 days played
8,69411,136peak 11,13624 Mar10 May
11,136Peak Premier in tracked matches
42Days played since 2023-08-05

Premier CS Rating

  • At peak — 11,136
  • +1,929 over 30 days · improving
  • Next: Purple band at 15,000 — 3,864 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.

How this compares with the same rank

Median values for Blue band among CSDB-tracked players (n=21,222), 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 playerBlue band medianPurple band medianvs Purple band
Headshot rate31.5%42.0%44.4%12.8% short
Shot accuracy16.7%9.6%11.9%above
Kill/death ratio1.230.981.03above
Match win rate31.6%43.8%45.2%13.6% short

This profile matches the typical Purple band player on 2 of 4 comparable metrics.

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

Share this profile

CSDB.GGCactusPREMIER11,136 · Blue bandcsdb.gg/stats

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Performance scores

Aim56
Positioning56
Utility63

0–100 skill scores via Leetify.

Recent form

STEADY57–36–7Last 10057%Win rateLWLWLLLWWW

Last 10 vs previous 10: 0pp win rate · −0.02 avg rating · −0.7pp headshot accuracy · +108ms 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

  • 2–3 · 40% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 11%
  • Avg reaction 716ms

Last 10

  • 5–5 · 50% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 10%
  • Avg reaction 771ms

Last 20

  • 10–8–2 · 50% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 11%
  • Avg reaction 717ms

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.

Aim5.6
Utility6.3
Positioning5.6
Opening Duels5.1

Excellent counter-strafingStrong T-side openerLimited 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

ZywOo

Plays most like ZywOo 73% playstyle similarity

Most alike: opening-duel success, utility contribution.

Where you differ: lower positioning profile; 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. 675ms 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

Aim5.6
Positioning5.6
Utility6.3
Mechanics8.9
Opening Duels3.4
Win Impact10.0

Composite 6.1/10 (Solid), a weighted mean of the bars with a small opposition adjustment (×0.95 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.00−0.04
first ⅓ avg 0.04 → last ⅓ avg -0.00
Reaction time691ms+50ms
first ⅓ avg 641ms → last ⅓ avg 691ms
Headshot accuracy10.4%−1.2%
first ⅓ avg 11.6% → last ⅓ avg 10.4%

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

0.30Best match rating · 9–0 · inferno, 11 Mar →
26%Best headshot accuracy · 9–6 · inferno, 21 Apr →
422msFastest reaction time · 8–13 · inferno, 24 Mar →
13–0Biggest win · inferno, 10 May →

Across the last 100 tracked matches.

Highlights

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

Map breakdown

dust2Best map · 73% over 11infernoWeakest map · 50% over 34
MapGradePlayedRecordWin rateAvg rating
infernoB3417–1750%-0.00
mirageA2012–860%0.03
nukeB189–950%0.02
dust2S118–373%0.04
agency—31–233%-0.01
train—32–167%-0.00
palais—32–167%0.12
vertigo—33–0100%0.03
jura—22–0100%-0.03
brewery—10–10%-0.07
anubis—11–0100%0.01
overpass—10–10%0.09

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (50% over 34). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

50,433Lifetime kills
1.23K/D · Top 10% of Blue band
2,553Matches
31.6%Match win rate
31.5%Headshot %
16.7%Shot accuracy · Top 25% of Blue band
2,770MVPs
581Hours (in match)
603Bombs planted
118Bombs defused

Most-used weapons

Lifetime map wins

1,253dust2
1,094inferno
701vertigo
693nuke
428train
143lake
81safehouse
76cbble

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

Skill profile

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

9.9%Headshot accuracy
36.4%Accuracy (enemy spotted)
39.9%Spray accuracy
89.9%Counter-strafing
12.3°Preaim
675msReaction time
44.6%T opening success
42.2%CT opening success
0.60Enemies flashed / flash
4.3%Flash assists
11.73HE damage / grenade
3.45Flashes / 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

    Your crosshair sits further from where enemies appear than it needs to. Crosshair placement is the cheapest accuracy you can buy.

    Preaim 12.2782° — above the 12° mark we flag

    Aim Training →
  2. 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 9.9337% — below the 15% mark we flag

    Aim Training →
  3. Grenades & Utility

    You are buying and holding utility rather than using it. Unthrown flashes are wasted money every round.

    Flashes per match 3.4491 — below the 4 mark we flag

    Grenade Lineups →

Recent matches

MapScoreRatingHS%Date
inferno4–9-0.0221%29 Jun →
inferno9–70.2016%25 Jun →
inferno0–9-0.220%25 Jun →
inferno9–40.036%24 Jun →
inferno6–9-0.1410%23 Jun →
agency8–130.0211%11 Jun →
agency2–13-0.0215%11 Jun →
jura13–4-0.103%4 Jun →
jura13–30.0318%4 Jun →
mirage13–90.024%10 May →
inferno13–0-0.045%10 May →
agency13–10-0.036%10 May →
nuke4–9-0.026%10 May →
brewery3–9-0.0711%10 May →
inferno6–9-0.0519%27 Apr →
train13–70.037%27 Apr →
inferno8–8-0.0612%21 Apr →
inferno9–60.0726%21 Apr →
inferno12–120.118%21 Apr →
mirage13–00.0613%21 Apr →
mirage10–4-0.0210%17 Apr →
mirage13–110.064%13 Apr →
nuke9–70.069%12 Apr →
mirage3–13-0.047%12 Apr →
mirage13–40.0315%12 Apr →
dust213–10-0.0119%12 Apr →
train13–70.0110%12 Apr →
mirage13–70.039%11 Apr →
mirage14–160.0212%10 Apr →
mirage6–13-0.058%7 Apr →
dust28–13-0.0410%3 Apr →
anubis13–70.017%3 Apr →
mirage13–100.0411%31 Mar →
dust213–11-0.0210%24 Mar →
mirage13–70.0410%24 Mar →
inferno8–13-0.067%24 Mar →
dust213–3-0.0116%24 Mar →
nuke4–9-0.155%20 Mar →
inferno9–30.108%20 Mar →
nuke9–13-0.0112%18 Mar →
palais8–80.0416%17 Mar →
mirage13–30.0912%17 Mar →
mirage14–160.0013%17 Mar →
nuke9–30.115%17 Mar →
inferno5–9-0.153%17 Mar →
inferno9–7-0.058%17 Mar →
dust26–130.0111%13 Mar →
train0–7-0.0513%13 Mar →
mirage13–10.1012%12 Mar →
dust213–40.0612%11 Mar →
inferno9–00.3018%11 Mar →
inferno7–9-0.0114%10 Mar →
dust213–100.1216%10 Mar →
palais9–50.1315%10 Mar →
inferno4–9-0.1111%9 Mar →
nuke13–20.0611%9 Mar →
inferno9–7-0.088%9 Mar →
inferno5–9-0.037%9 Mar →
inferno9–7-0.060%6 Mar →
inferno9–70.114%6 Mar →
inferno9–40.0515%4 Mar →
inferno9–50.0514%4 Mar →
inferno9–3-0.095%4 Mar →
inferno9–40.095%3 Mar →
inferno9–6-0.049%3 Mar →
inferno6–9-0.036%1 Mar →
inferno6–9-0.0013%1 Mar →
nuke9–60.053%1 Mar →
mirage10–130.0220%1 Mar →
dust215–150.1114%1 Mar →
mirage2–130.0012%1 Mar →
nuke9–60.0317%1 Mar →
nuke9–60.039%27 Feb →
vertigo9–7-0.073%26 Feb →
nuke8–80.076%25 Feb →
nuke4–9-0.005%24 Feb →
overpass7–90.0913%24 Feb →
nuke5–9-0.0416%24 Feb →
nuke9–00.0222%24 Feb →
nuke9–10.2012%24 Feb →
vertigo9–20.106%24 Feb →
nuke9–60.052%24 Feb →
palais9–10.2021%24 Feb →
nuke4–9-0.104%24 Feb →
inferno8–8-0.059%23 Feb →
inferno4–9-0.097%23 Feb →
inferno9–60.0414%19 Feb →
vertigo9–60.0617%19 Feb →
nuke8–80.0712%19 Feb →
mirage13–60.0815%19 Feb →
dust216–120.0417%18 Feb →
mirage10–130.0311%13 Feb →
dust213–20.1711%11 Feb →
inferno4–13-0.0819%11 Feb →
nuke6–9-0.0211%10 Feb →
dust213–30.0211%10 Feb →
inferno6–90.0610%3 Feb →
inferno9–40.104%3 Feb →
mirage9–50.0218%5 Aug →
mirage7–90.0314%5 Aug →

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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