BlackPuffle

BlackPuffle — CS2 Stats

76561198120854734[U:1:160589006]Steam profile ↗✓ No bans

1,852Tracked matches57%Win rate2022Tracked since
3,413Hours in CS36Hrs last 2 wks
CSDB Rating5.9 SolidSupport
Premier CS Rating17,852Purple band · top ~17% of ranked players (population est.)
WingmanMaster Guardian II
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 31 Oct 2026 (2 days ago). Ranks are recorded once per day this page is viewed.

+352Premier CS Rating · 17,500 → 17,852

Rating over time

Premier CS Rating: 17,852 +1,691 30 Aug – 2 Nov · 27 days played
14,98417,852peak 17,85230 Aug2 Nov
17,852Peak Premier in tracked matches
31Days played since 2026-08-30

Premier CS Rating

  • At peak — 17,852
  • +2,868 over 30 days · improving
  • Next: Pink band at 20,000 — 2,148 to go
  • Reached: 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.

How this compares with the same rank

Median values for Purple band among CSDB-tracked players (n=37,767), 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 playerPurple band medianPink band medianvs Pink band
Headshot rate43.1%44.4%46.9%3.8% short
Shot accuracy9.4%11.9%13.0%3.6% short
Kill/death ratio0.941.031.080.14 short
Match win rate42.1%45.2%46.7%4.6% short

This profile sits below the typical Pink band player on every metric we can compare.

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

Share this profile

CSDB.GGBlackPufflePREMIER17,852 · Purple bandcsdb.gg/stats

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Performance scores

Aim76
Positioning43
Utility49

0–100 skill scores via Leetify.

Recent form

STEADY51–44–5Last 10051%Win rateWLLWWLWWLL

Last 10 vs previous 10: −10pp win rate · −0.01 avg rating · +2.0pp headshot accuracy · −30ms 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

  • 3–2 · 60% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 12%
  • Avg reaction 519ms

Last 10

  • 5–5 · 50% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 14%
  • Avg reaction 533ms

Last 20

  • 11–7–2 · 55% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 13%
  • Avg reaction 548ms

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 (+1.7 against its own average).

Aim7.6
Utility4.9
Positioning4.3
Opening Duels0.5
Clutch3.2

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

ropz

Plays most like ropz 89% 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

Positioning. Positioning trails aim by 33 points — deaths here waste a strong aim profile.

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

Aim7.6
Positioning4.3
Utility4.9
Mechanics6.7
Opening Duels2.9
Win Impact7.4

Composite 5.9/10 (Solid), 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-0.02−0.01
first ⅓ avg -0.01 → last ⅓ avg -0.02
Reaction time542ms−31ms
first ⅓ avg 573ms → last ⅓ avg 542ms
Headshot accuracy13.7%−1.5%
first ⅓ avg 15.2% → 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

0.13Best match rating · 13–6 · inferno, 24 Oct →
33%Best headshot accuracy · 6–13 · train, 16 Oct →
406msFastest reaction time · 13–10 · dust2, 6 Sept →
13–0Biggest win · mirage, 14 Sept →

Across the last 100 tracked matches.

Highlights

5Longest win streak
9–10In matches decided by ≤2 rounds
4Overtime games

Map breakdown

mirageBest map · 60% over 30infernoWeakest map · 37% over 19
MapGradePlayedRecordWin rateAvg rating
mirageA3018–1260%0.00
infernoC197–1237%-0.01
dust2A169–756%-0.02
anubisC104–640%-0.04
ancientA106–460%-0.03
cacheC52–340%-0.02
train—31–233%-0.00
nuke—32–167%-0.04
vertigo—32–167%0.00
office—10–10%-0.10

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (37% over 19). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

63,643Lifetime kills
0.94K/D
3,148Matches
42.1%Match win rate
43.1%Headshot %
9.4%Shot accuracy
5,841MVPs
1,913Hours (in match)
1,659Bombs planted
750Bombs defused

Most-used weapons

AK-4721,878
AWP6,990
MP91,447
P2501,356
FAMAS1,149

Lifetime map wins

7,014dust2
5,179inferno
1,321vertigo
1,010nuke
394train
101office
82assault
82dust

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

Faceit stats

Combat

134Matches
48%Win rate
0.88Avg K/D
68.0ADR
46%Headshot %

Clutches & streaks

36%1v1 clutch win
6%1v2 clutch win
6Longest win streak

Recent Faceit resultsLLWWL

MapMatchesWin rateAvg K/DAvg kills
Mirage3941%0.8111.8
Inferno3158%0.9213.1
Anubis2035%0.9916.1
Ancient1560%0.8813.1
Vertigo922%0.528.0
Dust2475%1.0814.0
Overpass250%0.5614.0
Nuke1100%1.5012.0

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.

13.2%Headshot accuracy
36.5%Accuracy (enemy spotted)
38.0%Spray accuracy
80.4%Counter-strafing
8.0°Preaim
543msReaction time
45.7%T opening success
37.7%CT opening success
0.50Enemies flashed / flash
2.8%Flash assists
7.99HE damage / grenade
13.60Flashes / 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 13.1665% — below the 15% mark we flag

    Aim Training →
  2. Advanced Mechanics

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

    CT opening duels 37.6845% — 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.

37% win rate across 19 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
anubis13–7-0.0212%2 Nov →
inferno5–13-0.0415%1 Nov →
anubis11–13-0.0410%31 Oct →
mirage13–11-0.0113%31 Oct →
inferno13–9-0.108%31 Oct →
mirage11–130.0224%31 Oct →
anubis13–11-0.0115%29 Oct →
inferno13–7-0.0012%28 Oct →
anubis6–13-0.0510%27 Oct →
train8–13-0.0424%27 Oct →
dust213–11-0.0814%27 Oct →
inferno10–13-0.028%26 Oct →
anubis8–13-0.0713%25 Oct →
mirage13–7-0.0211%25 Oct →
anubis13–10-0.077%25 Oct →
inferno12–12-0.0613%25 Oct →
cache13–70.0019%25 Oct →
dust212–120.0211%24 Oct →
inferno13–60.1313%24 Oct →
dust213–60.0216%24 Oct →
nuke13–10-0.0410%24 Oct →
dust211–130.0111%24 Oct →
dust213–4-0.0815%24 Oct →
mirage13–40.047%20 Oct →
mirage6–13-0.0318%17 Oct →
cache9–13-0.069%17 Oct →
inferno11–130.058%17 Oct →
mirage13–9-0.066%16 Oct →
inferno2–13-0.0523%16 Oct →
mirage13–80.0426%16 Oct →
inferno13–11-0.0013%16 Oct →
nuke4–13-0.0920%16 Oct →
anubis11–13-0.0320%16 Oct →
train6–13-0.0233%16 Oct →
mirage10–13-0.0213%14 Oct →
mirage13–20.1021%11 Oct →
mirage13–110.0127%11 Oct →
mirage13–7-0.0516%11 Oct →
mirage6–13-0.0223%11 Oct →
inferno13–40.0223%11 Oct →
ancient7–13-0.063%11 Oct →
mirage13–40.0421%11 Oct →
cache13–60.0323%11 Oct →
mirage13–70.0525%10 Oct →
mirage12–12-0.0716%10 Oct →
mirage13–30.0827%10 Oct →
dust213–7-0.0223%10 Oct →
mirage6–13-0.0212%10 Oct →
inferno8–130.0313%10 Oct →
mirage13–30.0112%10 Oct →
vertigo13–100.0624%5 Oct →
inferno8–13-0.0220%5 Oct →
mirage7–13-0.0422%5 Oct →
mirage7–13-0.0433%5 Oct →
ancient13–3-0.0119%4 Oct →
anubis13–9-0.0319%4 Oct →
ancient13–11-0.0722%4 Oct →
dust213–70.0217%4 Oct →
vertigo12–12-0.0016%3 Oct →
mirage6–13-0.0424%3 Oct →
dust213–80.0422%3 Oct →
dust211–13-0.0611%3 Oct →
anubis5–13-0.087%3 Oct →
dust28–13-0.0518%3 Oct →
ancient13–110.0117%3 Oct →
mirage5–13-0.0418%30 Sept →
dust213–8-0.0211%30 Sept →
cache3–13-0.048%30 Sept →
mirage11–130.0718%30 Sept →
inferno13–5-0.0020%29 Sept →
ancient15–15-0.019%27 Sept →
inferno9–13-0.0423%27 Sept →
cache13–16-0.0315%27 Sept →
nuke13–110.0021%27 Sept →
mirage13–40.0211%27 Sept →
vertigo13–6-0.060%26 Sept →
inferno9–13-0.0514%26 Sept →
mirage9–13-0.098%26 Sept →
ancient11–13-0.0113%26 Sept →
dust213–3-0.0117%26 Sept →
inferno8–13-0.0429%19 Sept →
office9–13-0.1019%19 Sept →
dust27–13-0.0510%19 Sept →
mirage13–70.0412%19 Sept →
ancient13–11-0.0210%19 Sept →
mirage13–40.0230%18 Sept →
ancient4–13-0.1015%15 Sept →
inferno13–5-0.0126%15 Sept →
anubis13–160.0213%15 Sept →
ancient13–9-0.019%14 Sept →
mirage13–00.0218%14 Sept →
dust213–16-0.029%11 Sept →
mirage13–30.0623%11 Sept →
ancient13–3-0.016%11 Sept →
mirage13–20.0216%6 Sept →
train13–50.0520%6 Sept →
dust213–10-0.0111%6 Sept →
dust29–13-0.0515%5 Sept →
inferno11–130.0116%4 Sept →
inferno11–130.0421%30 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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