Useless

Useless — CS2 Stats

NO76561198020912201[U:1:60646473]Steam profile ↗✓ No bans

3,368Tracked matches31%Win rate2023Tracked since
3,145Hours in CS9Hrs last 2 wks
CSDB Rating5.9 SolidAll-Rounder
Premier CS Rating18,539Purple band · top ~14.3% of ranked players (population est.)
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 2 Nov 2026 (3 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: 18,539 +1,593 29 Aug – 2 Nov · 27 days played
15,61418,552peak 18,55229 Aug2 Nov
18,552Peak Premier in tracked matches
39Days played since 2026-08-29

Premier CS Rating

  • 13 below peak (18,552)
  • -13 over 30 days · steady
  • Next: Pink band at 20,000 — 1,461 to go
  • Reached: Purple band · Blue band · Light Blue band
  • Purple band first seen 2026-09-16
  • Blue band first seen 2026-09-16

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 rate61.9%44.4%46.9%above
Shot accuracy3.8%11.9%13.0%9.2% short
Kill/death ratio1.411.031.08above
Match win rate48.7%45.2%46.7%above

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

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.GGUselessPREMIER18,539 · Purple bandcsdb.gg/stats

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Performance scores

Aim73
Positioning59
Utility49

0–100 skill scores via Leetify.

Recent form

STEADY39–53–8Last 10039%Win rateWLWTWLLLWL

Last 10 vs previous 10: 0pp win rate · −0.02 avg rating · −0.2pp headshot accuracy · +28ms 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

  • 3–1–1 · 60% win rate
  • Avg rating 0.02
  • Avg headshot accuracy 26%
  • Avg reaction 611ms

Last 10

  • 4–5–1 · 40% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 24%
  • Avg reaction 630ms

Last 20

  • 8–11–1 · 40% win rate
  • Avg rating 0.02
  • Avg headshot accuracy 24%
  • Avg reaction 616ms

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.

Aim7.3
Utility4.9
Positioning5.9
Opening Duels5.7

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

device

Plays most like device 92% playstyle similarity

Most alike: positioning profile, aim profile.

Where you differ: lower opening-duel success; lower utility contribution.

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

Strengths

T openings. 70% T opening-duel success — entries that actually open the round.

Areas to improve

Reaction time. 601ms 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

Aim7.3
Positioning5.9
Utility4.9
Mechanics6.5
Opening Duels8.3
Win Impact0.0

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 rating0.01−0.00
first ⅓ avg 0.02 → last ⅓ avg 0.01
Reaction time605ms−30ms
first ⅓ avg 635ms → last ⅓ avg 605ms
Headshot accuracy22.6%+0.5%
first ⅓ avg 22.1% → last ⅓ avg 22.6%

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

0.24Best match rating · 13–3 · nuke, 2 Nov →
44%Best headshot accuracy · 1–13 · mirage, 2 Oct →
461msFastest reaction time · 13–1 · cache, 22 Sept →
13–1Biggest win · cache, 22 Sept →

Across the last 100 tracked matches.

Highlights

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

Map breakdown

anubisBest map · 57% over 7infernoWeakest map · 22% over 9
MapGradePlayedRecordWin rateAvg rating
nukeC219–1243%0.02
dust2C114–736%0.00
mirageC114–736%0.01
vertigoA116–555%0.03
ancientD93–633%-0.01
cacheD93–633%0.03
infernoD92–722%0.01
trainC83–538%0.05
anubisA74–357%0.04
overpass—31–233%-0.07
office—10–10%-0.06

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (22% over 9). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

92,997Lifetime kills
1.41K/D · Top 10% of Purple band
5,068Matches
48.7%Match win rate · Top 25% of Purple band
61.9%Headshot % · Top 5% of Purple band
3.8%Shot accuracy
16,412MVPs
1,703Hours (in match)
2,427Bombs planted
1,423Bombs defused

Most-used weapons

AK-4734,817
AWP4,387
SSG 081,340
MP91,040
P90803

Lifetime map wins

11,324dust2
4,834nuke
4,371inferno
3,200vertigo
1,711train
285office
113lake
100ar_shoots

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.

22.5%Headshot accuracy
33.4%Accuracy (enemy spotted)
30.1%Spray accuracy
79.1%Counter-strafing
7.5°Preaim
601msReaction time
70.5%T opening success
56.2%CT opening success
0.54Enemies flashed / flash
5.9%Flash assists
6.87HE damage / grenade
11.27Flashes / match

Recommended for you

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.

22% win rate across 9 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
nuke13–30.2444%2 Nov →
dust21–13-0.0415%2 Nov →
ancient13–6-0.0217%1 Nov →
nuke12–12-0.0421%1 Nov →
mirage13–9-0.0431%1 Nov →
nuke1–13-0.0517%1 Nov →
dust29–130.0225%1 Nov →
dust23–13-0.0122%1 Nov →
vertigo13–80.0327%1 Nov →
cache7–130.0219%27 Oct →
anubis13–90.0219%26 Oct →
nuke13–110.1025%26 Oct →
anubis9–13-0.0116%19 Oct →
ancient3–13-0.0716%18 Oct →
anubis13–110.1322%18 Oct →
cache5–130.0227%18 Oct →
mirage8–20.1429%18 Oct →
dust24–13-0.0630%16 Oct →
nuke6–130.0335%16 Oct →
anubis10–130.0520%15 Oct →
train12–120.0717%14 Oct →
ancient7–130.0215%14 Oct →
vertigo12–120.0418%8 Oct →
dust28–130.0130%8 Oct →
cache7–13-0.0621%8 Oct →
nuke11–13-0.0328%7 Oct →
train12–120.0820%7 Oct →
ancient9–130.0124%6 Oct →
nuke1–13-0.115%6 Oct →
dust22–13-0.0418%6 Oct →
nuke16–13-0.0023%5 Oct →
nuke10–13-0.0227%5 Oct →
train13–70.0521%5 Oct →
mirage1–13-0.0120%5 Oct →
inferno7–130.0229%4 Oct →
inferno12–120.0526%4 Oct →
anubis13–30.0636%3 Oct →
dust213–100.0227%3 Oct →
ancient10–13-0.0321%3 Oct →
mirage13–3-0.0112%3 Oct →
vertigo6–13-0.0328%2 Oct →
office10–13-0.0621%2 Oct →
anubis13–80.0029%2 Oct →
inferno4–13-0.0710%2 Oct →
mirage1–13-0.0044%2 Oct →
cache6–20.0240%2 Oct →
inferno6–90.0429%2 Oct →
mirage11–13-0.0118%2 Oct →
nuke7–130.0625%2 Oct →
ancient3–13-0.0325%2 Oct →
nuke13–50.1331%2 Oct →
vertigo6–130.0729%1 Oct →
cache4–130.0224%1 Oct →
vertigo13–70.0727%1 Oct →
inferno13–20.1131%30 Sept →
cache12–120.0529%29 Sept →
train4–13-0.0126%29 Sept →
vertigo13–100.0123%29 Sept →
inferno8–13-0.0414%29 Sept →
vertigo10–130.0422%29 Sept →
nuke12–120.0136%28 Sept →
overpass2–13-0.0419%28 Sept →
nuke13–110.0632%25 Sept →
cache13–90.0319%24 Sept →
nuke16–14-0.0124%24 Sept →
mirage11–130.0826%24 Sept →
nuke6–13-0.0426%23 Sept →
dust23–13-0.0513%23 Sept →
inferno13–110.0133%23 Sept →
nuke13–7-0.0227%23 Sept →
dust213–40.1030%23 Sept →
train10–130.0022%23 Sept →
inferno8–13-0.0317%22 Sept →
cache13–10.0716%22 Sept →
dust213–11-0.0915%20 Sept →
nuke15–150.0425%20 Sept →
ancient16–14-0.0423%20 Sept →
mirage16–12-0.0015%19 Sept →
vertigo13–11-0.0225%17 Sept →
nuke13–30.0422%17 Sept →
mirage10–130.0329%16 Sept →
overpass4–6-0.0918%16 Sept →
vertigo13–100.0820%15 Sept →
mirage8–130.0217%15 Sept →
nuke1–13-0.0533%14 Sept →
cache7–130.0929%14 Sept →
dust213–50.1620%13 Sept →
ancient13–100.0512%12 Sept →
vertigo9–13-0.0224%6 Sept →
train13–60.1529%6 Sept →
overpass3–13-0.0632%5 Sept →
train8–130.0528%5 Sept →
ancient11–130.0116%4 Sept →
nuke5–130.0421%4 Sept →
train13–40.0115%1 Sept →
anubis10–130.0020%31 Aug →
vertigo13–110.0223%31 Aug →
nuke13–20.0835%31 Aug →
mirage10–13-0.0416%30 Aug →
inferno5–130.0012%29 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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