Pickle

Pickle — CS2 Stats

CA76561198137970318[U:1:177704590]Steam profile ↗✓ No bans

723Tracked matches39%Win rate2022Tracked since
1,286Hours in CS24Hrs last 2 wks
CSDB Rating4.7 DevelopingPositional Player
Premier CS Rating14,394Blue band · top ~31.6% of ranked players (population est.)
WingmanMaster Guardian II
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 16 Oct 2026 (yesterday). 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: 14,394 +594 15 Aug – 16 Oct · 34 days played
13,80017,375peak 17,37515 Aug16 Oct
17,375Peak Premier in tracked matches
45Days played since 2026-07-11

Premier CS Rating

  • 2,981 below peak (17,375)
  • -605 over 30 days · declining
  • Next: Purple band at 15,000 — 606 to go
  • Reached: Purple band · 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 rate66.9%42.0%44.3%above
Shot accuracy5.3%9.6%11.9%6.6% short
Kill/death ratio1.770.981.03above
Match win rate49.7%43.8%45.2%above

This profile matches the typical Purple band player on 3 of 4 comparable metrics.

Widest gap: Shot accuracy. 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.GGPicklePREMIER14,394 · Blue bandcsdb.gg/stats

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Performance scores

Aim58
Positioning49
Utility41

0–100 skill scores via Leetify.

Recent form

COLD42–49–9Last 10042%Win rateLWLTLLTLLL

Last 10 vs previous 10: −20pp win rate · −0.01 avg rating · −3.9pp headshot accuracy · −62ms reaction

Win rate down 20pp 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

  • 1–3–1 · 20% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 13%
  • Avg reaction 591ms

Last 10

  • 1–7–2 · 10% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 16%
  • Avg reaction 596ms

Last 20

  • 4–14–2 · 20% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 18%
  • Avg reaction 628ms

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 (+2 against its own average).

Aim5.8
Utility4.1
Positioning4.9
Opening Duels2.5
Clutch0.0

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

donk

Plays most like donk 89% 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

CT openings. Opening success drops from 54% on T to 39% on CT — first contacts on the defending side are being lost.

Reaction time. 629ms 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

Aim5.8
Positioning4.9
Utility4.1
Mechanics7.2
Opening Duels4.0
Win Impact1.4

Composite 4.7/10 (Developing), 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-0.02−0.01
first ⅓ avg -0.02 → last ⅓ avg -0.02
Reaction time631ms+26ms
first ⅓ avg 605ms → last ⅓ avg 631ms
Headshot accuracy17.1%−4.3%
first ⅓ avg 21.3% → last ⅓ avg 17.1%

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

0.09Best match rating · 13–1 · mirage, 16 Sept →
100%Best headshot accuracy · 4–6 · dust2, 11 Jul →
422msFastest reaction time · 13–7 · dust2, 17 Sept →
13–1Biggest win · mirage, 16 Sept →

Across the last 100 tracked matches.

Highlights

4Longest win streak
10–9In matches decided by ≤2 rounds
13Overtime games

Map breakdown

cacheBest map · 50% over 14anubisWeakest map · 29% over 7
MapGradePlayedRecordWin rateAvg rating
dust2B2210–1245%-0.02
mirageC166–1038%-0.01
nukeC156–940%-0.02
cacheB147–750%-0.01
infernoB147–750%-0.03
ancientC114–736%-0.03
anubisD72–529%-0.04
train—10–10%-0.04

Across the last 100 tracked matches.

Anubis is currently your weakest sufficiently-sampled map (29% over 7). Start with the 6 essential Anubis lineups, review the callouts, then spin up a practice server.

Lifetime stats

40,354Lifetime kills
1.77K/D · Top 5% of Blue band
1,234Matches
49.7%Match win rate · Top 25% of Blue band
66.9%Headshot % · Top 5% of Blue band
5.3%Shot accuracy
3,185MVPs
702Hours (in match)
588Bombs planted
290Bombs defused

Most-used weapons

Lifetime map wins

2,362dust2
2,010inferno
1,310nuke
564train
372vertigo
178office
123aztec
117italy

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

Faceit stats

Combat

4Matches
25%Win rate
1.14Avg K/D
107.2ADR
40%Headshot %

Clutches & streaks

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

Recent Faceit resultsLLWLL

MapMatchesWin rateAvg K/DAvg kills
Anubis20%1.1822.0
Ancient10%0.6710.0
Dust21100%1.5030.0

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.

17.7%Headshot accuracy
31.6%Accuracy (enemy spotted)
35.2%Spray accuracy
82.6%Counter-strafing
11.2°Preaim
629msReaction time
53.7%T opening success
38.5%CT opening success
0.50Enemies flashed / flash
2.0%Flash assists
12.08HE damage / grenade
1.61Flashes / 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. Grenades & Utility

    Most of your flashes are blinding nobody. A handful of reliable pop-flash lineups fixes this faster than anything else.

    Enemies flashed per flash 0.4954 — below the 0.5 mark we flag

    Grenade Lineups →
  2. Advanced Mechanics

    Losing the first CT duel repeatedly usually means holding angles that favour the peeker.

    CT opening duels 38.5269% — below the 40% mark we flag

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.

29% win rate across 7 tracked games — your weakest map with enough games to be worth reading into.

Anubis callouts & strategy →Anubis grenade lineups →

Recent matches

MapScoreRatingHS%Date
nuke4–13-0.097%16 Oct →
nuke13–110.0313%16 Oct →
dust29–13-0.095%16 Oct →
cache15–15-0.0526%14 Oct →
cache7–13-0.0414%12 Oct →
inferno9–13-0.0415%12 Oct →
mirage12–12-0.068%12 Oct →
inferno9–13-0.0041%11 Oct →
nuke9–13-0.0613%11 Oct →
mirage14–16-0.0222%11 Oct →
mirage2–13-0.0112%9 Oct →
inferno6–13-0.0240%9 Oct →
anubis5–130.0028%8 Oct →
cache13–40.0023%8 Oct →
cache8–13-0.0618%6 Oct →
anubis3–13-0.100%6 Oct →
mirage13–11-0.0228%6 Oct →
mirage6–13-0.1015%5 Oct →
mirage9–13-0.0120%30 Sept →
ancient16–14-0.0419%29 Sept →
cache13–60.0216%28 Sept →
inferno8–80.0420%27 Sept →
anubis13–9-0.0010%27 Sept →
anubis13–50.0418%27 Sept →
cache13–90.0210%27 Sept →
dust27–13-0.0416%25 Sept →
cache13–60.0519%25 Sept →
inferno13–9-0.0617%20 Sept →
mirage14–16-0.0114%19 Sept →
dust213–100.0117%17 Sept →
ancient11–130.0120%17 Sept →
dust213–7-0.0611%17 Sept →
ancient13–11-0.078%16 Sept →
mirage13–10.0919%16 Sept →
dust213–6-0.0418%16 Sept →
inferno8–13-0.0515%13 Sept →
mirage15–15-0.0423%13 Sept →
cache10–13-0.0924%13 Sept →
train12–12-0.0435%12 Sept →
mirage13–100.0527%11 Sept →
ancient1–13-0.1111%11 Sept →
nuke5–2-0.0118%11 Sept →
inferno10–13-0.0311%11 Sept →
mirage10–13-0.0121%10 Sept →
dust213–11-0.0526%9 Sept →
ancient10–13-0.0319%8 Sept →
dust210–13-0.0129%8 Sept →
nuke1–13-0.0431%8 Sept →
ancient13–7-0.0414%4 Sept →
mirage6–13-0.0421%4 Sept →
dust211–13-0.0518%4 Sept →
mirage3–13-0.0312%3 Sept →
ancient10–13-0.0113%2 Sept →
nuke13–4-0.0215%1 Sept →
dust213–9-0.0024%1 Sept →
cache13–6-0.0218%31 Aug →
dust26–13-0.0729%30 Aug →
nuke15–150.0216%30 Aug →
ancient5–13-0.0610%28 Aug →
nuke13–3-0.0612%28 Aug →
dust213–50.0814%28 Aug →
inferno13–100.008%28 Aug →
ancient14–160.0419%26 Aug →
dust213–10.038%26 Aug →
inferno13–11-0.0516%26 Aug →
inferno13–8-0.0424%24 Aug →
nuke11–13-0.0314%24 Aug →
cache13–10.0519%22 Aug →
dust24–13-0.0221%22 Aug →
inferno13–50.0420%22 Aug →
anubis15–15-0.0111%22 Aug →
dust213–11-0.0224%19 Aug →
dust26–13-0.0415%19 Aug →
inferno13–9-0.0625%19 Aug →
nuke14–160.049%15 Aug →
dust29–13-0.0219%15 Aug →
cache2–13-0.0625%15 Aug →
nuke5–4-0.0233%15 Aug →
dust213–90.0520%15 Aug →
dust215–150.0417%15 Aug →
mirage13–90.0334%15 Aug →
nuke13–110.0824%14 Aug →
inferno3–13-0.0926%14 Aug →
dust211–130.0113%11 Aug →
nuke10–13-0.0020%10 Aug →
ancient16–14-0.0222%7 Aug →
nuke5–13-0.0619%26 Jul →
ancient4–110.0114%25 Jul →
mirage16–120.0228%25 Jul →
dust213–9-0.0131%25 Jul →
cache13–80.0212%25 Jul →
nuke15–15-0.0615%24 Jul →
dust24–13-0.0911%24 Jul →
cache6–13-0.0513%19 Jul →
anubis3–13-0.125%19 Jul →
cache5–13-0.017%17 Jul →
anubis7–13-0.0724%17 Jul →
mirage13–9-0.0311%17 Jul →
inferno13–4-0.0116%11 Jul →
dust24–6-0.06100%11 Jul →

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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