Squid

Squid — CS2 Stats

76561198088101660[U:1:127835932]Steam profile ↗✓ No bans

1,296Tracked matches54%Win rate2020Tracked since
3,274Hours in CS1Hrs last 2 wks
CSDB Rating6.8 SolidSupport
WingmanGold Nova Master
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 8 Sep 2026 (47 days ago). Ranks are recorded once per day this page is viewed.

+40Tracked matches · now 1,296
−25.5ppWin rate · 79.3% → 53.8%

Rating over time

39Days played since 2026-06-08

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

Performance scores

Aim86
Positioning61
Utility61

0–100 skill scores via Leetify.

Recent form

STEADY57–37–6Last 10057%Win rateTLLWLWLWWW

Last 10 vs previous 10: +20pp win rate · +0.06 avg rating · +0.9pp headshot accuracy · +76ms reaction

Win rate up 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 0.01
  • Avg headshot accuracy 22%
  • Avg reaction 623ms

Last 10

  • 5–4–1 · 50% win rate
  • Avg rating 0.07
  • Avg headshot accuracy 25%
  • Avg reaction 566ms

Last 20

  • 8–9–3 · 40% win rate
  • Avg rating 0.04
  • Avg headshot accuracy 25%
  • Avg reaction 528ms

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 against its own average).

Aim8.6
Utility6.1
Positioning6.1
Opening Duels3.2

Sharp aimerStrong CT-side opener

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

NiKo

Plays most like NiKo 95% playstyle similarity

Most alike: utility contribution, positioning profile.

Where you differ: 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

Strengths

Aim. Aim score of 86 — the mechanical foundation is a clear strength.

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

Aim8.6
Positioning6.1
Utility6.1
Mechanics6.2
Opening Duels4.9
Win Impact6.3

Composite 6.8/10 (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 rating0.04+0.01
first ⅓ avg 0.03 → last ⅓ avg 0.04
Reaction time520ms−42ms
first ⅓ avg 562ms → last ⅓ avg 520ms
Headshot accuracy23.4%+4.5%
first ⅓ avg 18.9% → last ⅓ avg 23.4%

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

0.32Best match rating · 5–1 · rush_001, 9 Oct →
50%Best headshot accuracy · 4–0 · rush_001, 30 Sept →
344msFastest reaction time · 13–7 · vertigo, 28 Aug →
13–0Biggest win · train, 11 Sept →

Across the last 100 tracked matches.

Highlights

8Longest win streak
6–4In matches decided by ≤2 rounds
3Overtime games

Map breakdown

trainBest map · 80% over 5ancientWeakest map · 33% over 9
MapGradePlayedRecordWin rateAvg rating
anubisB2111–1052%0.01
vertigoB137–654%0.04
nukeS139–469%0.02
cacheA117–464%0.05
ancientD93–633%0.01
infernoC83–538%0.01
rush_001S64–267%0.13
trainS54–180%0.05
officeA53–260%0.09
overpass—21–150%0.01
shelter—22–0100%0.06
italy—11–0100%0.04
dust2—10–10%0.04
alpine—11–0100%0.02
stronghold—10–10%0.13
mirage—11–0100%0.04

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (33% over 9). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Lifetime stats

166,179Lifetime kills
1.34K/D · Top 10% of tracked players
6,511Matches
41.9%Match win rate
44.5%Headshot % · Top 50% of tracked players
20.4%Shot accuracy · Top 5% of tracked players
15,598MVPs
2,594Hours (in match)
3,308Bombs planted
1,839Bombs defused

Most-used weapons

AK-4749,104
AWP33,023
SSG 084,873
P2503,078
SG 5532,599
UMP-452,376

Lifetime map wins

5,127inferno
5,037nuke
4,822vertigo
3,465dust2
3,000train
699cbble
354lake
277office

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.

21.9%Headshot accuracy
38.4%Accuracy (enemy spotted)
39.4%Spray accuracy
77.9%Counter-strafing
8.7°Preaim
509msReaction time
48.1%T opening success
51.5%CT opening success
0.51Enemies flashed / flash
12.9%Flash assists
11.63HE damage / grenade
9.75Flashes / match

Recommended for you

Spend your practice time on Ancient

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 9 tracked games — your weakest map with enough games to be worth reading into.

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

MapScoreRatingHS%Date
anubis15–150.0417%18 Oct →
inferno4–13-0.016%18 Oct →
vertigo5–130.0123%10 Oct →
cache13–20.0239%10 Oct →
rush_0014–8-0.0224%9 Oct →
rush_0015–10.3218%9 Oct →
rush_0015–80.1342%9 Oct →
nuke13–70.0231%9 Oct →
rush_0017–30.1240%9 Oct →
rush_0015–10.0710%8 Oct →
anubis13–9-0.0126%5 Oct →
ancient10–130.0418%5 Oct →
train13–100.0125%5 Oct →
rush_0014–00.1450%30 Sept →
inferno9–130.0317%27 Sept →
anubis7–13-0.0112%27 Sept →
vertigo12–120.0323%25 Sept →
anubis15–15-0.0029%25 Sept →
ancient11–13-0.0620%25 Sept →
anubis2–13-0.0622%25 Sept →
nuke13–90.0514%25 Sept →
anubis13–90.0119%24 Sept →
vertigo12–120.0224%24 Sept →
nuke13–50.0520%24 Sept →
cache13–40.1426%23 Sept →
nuke0–6-0.0450%23 Sept →
vertigo8–130.0119%21 Sept →
office13–20.1113%21 Sept →
nuke7–130.0517%21 Sept →
anubis13–110.0824%21 Sept →
ancient9–13-0.0113%17 Sept →
anubis13–100.0224%17 Sept →
italy13–60.0417%17 Sept →
inferno6–130.0234%17 Sept →
cache13–80.0825%15 Sept →
train13–00.1432%11 Sept →
nuke13–110.0022%11 Sept →
cache13–90.0529%11 Sept →
anubis13–100.0817%5 Sept →
office13–80.1618%5 Sept →
ancient13–9-0.054%5 Sept →
anubis13–00.1024%3 Sept →
inferno8–130.0613%3 Sept →
dust28–130.0418%2 Sept →
office13–10.1125%30 Aug →
nuke13–110.0028%30 Aug →
inferno13–80.0611%30 Aug →
nuke13–50.0318%30 Aug →
anubis13–8-0.0324%29 Aug →
anubis13–8-0.0422%29 Aug →
nuke11–130.0315%28 Aug →
vertigo13–70.0922%28 Aug →
cache13–2-0.0116%28 Aug →
vertigo13–60.0128%22 Aug →
overpass13–70.0210%22 Aug →
shelter13–8-0.0137%21 Aug →
cache13–70.0427%21 Aug →
ancient8–130.0128%14 Aug →
anubis13–110.0211%14 Aug →
inferno3–130.0213%14 Aug →
anubis13–80.0727%9 Aug →
ancient13–8-0.0013%7 Aug →
shelter13–80.1219%2 Aug →
nuke13–40.0010%2 Aug →
vertigo13–90.0317%2 Aug →
cache12–120.0930%30 Jul →
nuke13–20.0920%30 Jul →
vertigo1–130.0024%30 Jul →
office9–130.0620%26 Jul →
office11–13-0.0112%24 Jul →
vertigo13–50.1725%24 Jul →
nuke2–13-0.0319%24 Jul →
anubis11–130.0425%24 Jul →
anubis6–13-0.0514%16 Jul →
cache13–70.0225%16 Jul →
ancient8–130.0317%4 Jul →
inferno13–3-0.0312%4 Jul →
ancient13–160.0214%4 Jul →
anubis6–130.0111%3 Jul →
anubis5–130.0716%1 Jul →
cache3–130.0636%1 Jul →
vertigo8–130.0718%1 Jul →
train3–13-0.037%28 Jun →
anubis12–12-0.0721%26 Jun →
alpine13–40.0222%26 Jun →
overpass10–13-0.0120%26 Jun →
nuke13–110.0222%26 Jun →
anubis13–60.039%21 Jun →
ancient13–80.0720%21 Jun →
cache10–130.0211%19 Jun →
stronghold7–130.1322%19 Jun →
anubis7–130.0124%19 Jun →
mirage13–70.0421%19 Jun →
vertigo13–80.0315%19 Jun →
inferno13–7-0.0531%12 Jun →
train13–40.1021%12 Jun →
cache7–130.0224%12 Jun →
vertigo13–70.0224%8 Jun →
vertigo7–10.0522%8 Jun →
train2–00.010%8 Jun →

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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