Petal

Petal — CS2 Stats

AU76561198060709712[U:1:100443984]Steam profile ↗✓ No bans

3,846Tracked matches62%Win rate2023Tracked since
CSDB Rating5.1 DevelopingPositional Player
Premier CS Rating15,000Purple band · top ~28.4% of ranked players (population est.)
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 23 Sep 2026 (22 days ago). Ranks are recorded once per day this page is viewed.

−775Premier CS Rating · 15,775 → 15,000
+127Tracked matches · now 3,846
+19.2ppWin rate · 42.3% → 61.5%

Rating over time

Premier CS Rating: 15,775 +0 15 Sept – 23 Sept · 2 observations
15,77515,775peak 15,77515 Sept23 Sept
15,775Peak Premier on CSDB
16Days played since 2026-09-15

Premier CS Rating

  • 775 below peak (15,775)
  • Next: Pink band at 20,000 — 5,000 to go
  • Reached: Purple band · Blue band · Light Blue band
  • Purple band first seen 2026-09-15
  • Blue band first seen 2026-09-15

Premier is CSDB’s own observation — it builds from the day a profile is first viewed and cannot be backfilled.

Share this profile

CSDB.GGPetalPREMIER15,000 · Purple bandcsdb.gg/stats

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Performance scores

Aim43
Positioning53
Utility44

0–100 skill scores via Leetify.

Recent form

HOT53–36–11Last 10053%Win rateWWWWWLWWWW

Last 10 vs previous 10: +70pp win rate · +0.00 avg rating · +0.8pp headshot accuracy · −42ms reaction

Win rate up 70pp 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

  • 5–0 · 100% win rate
  • Avg rating 0.03
  • Avg headshot accuracy 14%
  • Avg reaction 633ms

Last 10

  • 9–1 · 90% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 14%
  • Avg reaction 649ms

Last 20

  • 11–6–3 · 55% win rate
  • Avg rating 0.00
  • Avg headshot accuracy 14%
  • Avg reaction 669ms

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: Positional Player — Positioning stands above the rest of this profile (+1.2 against its own average).

Aim4.3
Utility4.4
Positioning5.3
Opening Duels2.6

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

donk

Plays most like donk 73% 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. 659ms 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

Aim4.3
Positioning5.3
Utility4.4
Mechanics6.4
Opening Duels3.0
Win Impact8.8

Composite 5.1/10 (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 rating0.00−0.02
first ⅓ avg 0.02 → last ⅓ avg 0.00
Reaction time664ms+24ms
first ⅓ avg 639ms → last ⅓ avg 664ms
Headshot accuracy13.7%−3.4%
first ⅓ avg 17.2% → last ⅓ avg 13.7%

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

0.17Best match rating · 13–4 · overpass, 3 Oct →
28%Best headshot accuracy · 13–10 · inferno, 3 Oct →
492msFastest reaction time · 13–9 · ancient, 4 Oct →
13–0Biggest win · dust2, 1 Oct →

Across the last 100 tracked matches.

Highlights

6Longest win streak
W5Current streak
6–0In matches decided by ≤2 rounds

Map breakdown

vertigoBest map · 80% over 5anubisWeakest map · 33% over 6
MapGradePlayedRecordWin rateAvg rating
dust2B2210–1245%0.01
mirageB136–746%-0.01
nukeA127–558%0.02
infernoA116–555%0.01
ancientA95–456%0.02
trainA74–357%-0.01
anubisD62–433%0.01
officeB63–350%-0.01
cacheA53–260%0.00
vertigoS54–180%-0.01
overpass—43–175%0.06

Across the last 100 tracked matches.

Anubis is currently your weakest sufficiently-sampled map (33% over 6). Start with the 6 essential Anubis 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.

13.3%Headshot accuracy
33.0%Accuracy (enemy spotted)
33.4%Spray accuracy
78.6%Counter-strafing
12.0°Preaim
659msReaction time
40.2%T opening success
43.7%CT opening success
0.56Enemies flashed / flash
10.9%Flash assists
6.95HE damage / grenade
7.10Flashes / 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 13.2587% — below the 15% mark we flag

    Aim Training →
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.

33% win rate across 6 tracked games — your weakest map with enough games to be worth reading into.

Anubis callouts & strategy →Anubis grenade lineups →

Recent matches

MapScoreRatingHS%Date
cache13–20.0114%13 Oct →
dust210–30.1612%13 Oct →
inferno13–110.0415%13 Oct →
mirage13–8-0.056%13 Oct →
nuke13–9-0.0322%12 Oct →
cache7–13-0.0810%12 Oct →
nuke13–7-0.029%12 Oct →
ancient13–70.0124%12 Oct →
mirage13–10.0018%12 Oct →
overpass13–100.0214%12 Oct →
inferno3–13-0.0913%12 Oct →
dust212–12-0.0411%12 Oct →
dust29–130.0110%11 Oct →
train12–12-0.0313%11 Oct →
ancient12–120.0412%11 Oct →
dust25–13-0.0615%10 Oct →
dust26–70.018%10 Oct →
inferno10–130.0715%10 Oct →
nuke13–30.1416%10 Oct →
overpass3–13-0.0323%10 Oct →
vertigo9–130.0111%10 Oct →
anubis10–13-0.008%10 Oct →
mirage13–80.0118%10 Oct →
dust22–130.018%10 Oct →
mirage12–12-0.019%9 Oct →
dust213–40.0723%9 Oct →
mirage5–130.0412%9 Oct →
inferno13–7-0.0614%9 Oct →
vertigo13–8-0.055%9 Oct →
ancient13–8-0.0013%9 Oct →
mirage13–40.0118%9 Oct →
dust213–50.0020%9 Oct →
anubis10–13-0.0613%8 Oct →
ancient13–60.0418%8 Oct →
mirage7–13-0.0513%8 Oct →
inferno10–13-0.0411%8 Oct →
train13–10-0.0317%8 Oct →
dust22–13-0.0715%8 Oct →
nuke7–13-0.048%8 Oct →
train13–20.0417%7 Oct →
mirage13–80.0311%7 Oct →
dust213–90.0112%7 Oct →
inferno13–70.065%7 Oct →
train7–13-0.0710%7 Oct →
cache12–12-0.0113%6 Oct →
mirage7–13-0.0314%6 Oct →
ancient13–110.0915%6 Oct →
nuke5–13-0.0717%5 Oct →
inferno13–10.026%5 Oct →
dust22–13-0.0710%5 Oct →
anubis13–90.0713%5 Oct →
dust23–130.0515%5 Oct →
cache13–70.0926%5 Oct →
office0–13-0.1026%5 Oct →
mirage12–12-0.0312%5 Oct →
nuke13–100.0223%4 Oct →
office13–7-0.0417%4 Oct →
anubis10–130.0315%4 Oct →
inferno13–110.088%4 Oct →
dust212–120.0216%4 Oct →
ancient13–90.0323%4 Oct →
anubis6–13-0.0226%3 Oct →
dust210–130.0823%3 Oct →
overpass13–40.1726%3 Oct →
vertigo13–60.0113%3 Oct →
mirage13–40.037%3 Oct →
inferno13–100.0128%3 Oct →
dust213–11-0.0314%3 Oct →
ancient6–13-0.0222%3 Oct →
nuke9–130.0911%3 Oct →
office13–70.0427%3 Oct →
nuke10–13-0.0115%2 Oct →
dust24–13-0.0319%2 Oct →
train13–110.0710%2 Oct →
office13–10.0823%2 Oct →
vertigo13–60.0511%2 Oct →
dust25–130.0412%2 Oct →
nuke13–70.0816%2 Oct →
overpass13–20.1026%2 Oct →
cache13–60.0121%2 Oct →
inferno5–13-0.035%2 Oct →
ancient4–130.0118%2 Oct →
mirage2–13-0.068%2 Oct →
nuke13–100.0425%1 Oct →
dust213–00.0217%1 Oct →
ancient12–120.0111%1 Oct →
dust213–90.0121%1 Oct →
dust213–90.0825%30 Sept →
dust213–90.0414%30 Sept →
nuke13–60.0518%30 Sept →
anubis13–50.0323%30 Sept →
vertigo13–9-0.056%30 Sept →
inferno12–120.0114%30 Sept →
mirage12–120.0418%30 Sept →
train12–12-0.058%30 Sept →
office8–13-0.0223%30 Sept →
dust27–130.0213%29 Sept →
train13–9-0.0126%28 Sept →
office5–13-0.0027%28 Sept →
nuke10–13-0.0024%28 Sept →

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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