gruft0r

gruft0r — CS2 Stats

76561198034015214[U:1:73749486]Steam profile ↗✓ No bans

1,192Tracked matches63%Win rate2020Tracked since
CSDB Rating5.8 SolidSupport
Ladder ranks via Leetify

Performance scores

Aim70
Positioning52
Utility53

0–100 skill scores via Leetify.

Recent form

STEADY50–50Last 10050%Win rateWWLLWLWWLW

Last 10 vs previous 10: −20pp win rate · −0.04 avg rating · −5.5pp headshot accuracy · −42ms 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

  • 3–2 · 60% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 18%
  • Avg reaction 609ms

Last 10

  • 6–4 · 60% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 18%
  • Avg reaction 624ms

Last 20

  • 14–6 · 70% win rate
  • Avg rating 0.00
  • Avg headshot accuracy 21%
  • Avg reaction 645ms

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 (+1.1 against its own average).

Aim7.0
Utility5.3
Positioning5.2
Opening Duels2.8
Clutch3.4

Strong 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 91% 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

T-side openings. Opening success drops from 52% on CT to 26% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 618ms 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

Aim7.0
Positioning5.2
Utility5.3
Mechanics6.4
Opening Duels2.8
Win Impact9.4

Composite 5.8/10 (Solid), 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-0.00−0.01
first ⅓ avg 0.00 → last ⅓ avg -0.00
Reaction time623ms+19ms
first ⅓ avg 604ms → last ⅓ avg 623ms
Headshot accuracy20.5%+3.1%
first ⅓ avg 17.4% → last ⅓ avg 20.5%

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

0.10Best match rating · 13–5 · nuke, 9 Aug →
34%Best headshot accuracy · 13–10 · nuke, 6 Jan →
391msFastest reaction time · 10–13 · nuke, 17 Mar →
13–3Biggest win · overpass, 19 Mar →

Across the last 100 tracked matches.

Highlights

7Longest win streak
W2Current streak
9–7In matches decided by ≤2 rounds
19Overtime games

Map breakdown

nukeBest map · 68% over 22ancientWeakest map · 0% over 5
MapGradePlayedRecordWin rateAvg rating
infernoA2816–1257%-0.01
dust2C2410–1442%-0.02
nukeS2215–768%0.01
overpassA106–460%-0.00
ancientD50–50%0.01
mirage—41–325%0.01
train—40–40%-0.03
anubis—21–150%-0.03
cache—11–0100%-0.00

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (0% over 5). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

961Matches
52%Win rate
1.02Avg K/D
72.0ADR
44%Headshot %

Clutches & streaks

37%1v1 clutch win
18%1v2 clutch win
7Longest win streak

Recent Faceit resultsWLWLL

MapMatchesWin rateAvg K/DAvg kills
Dust230654%1.0514.6
Inferno26155%1.0114.2
Nuke14656%1.0514.4
Ancient7639%0.8913.9
Overpass6665%0.9614.8
Mirage3119%0.8413.8
Train1619%0.9713.8
Vertigo838%0.9314.9

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.

20.4%Headshot accuracy
35.7%Accuracy (enemy spotted)
40.3%Spray accuracy
78.8%Counter-strafing
10.7°Preaim
618msReaction time
25.7%T opening success
52.2%CT opening success
0.58Enemies flashed / flash
4.3%Flash assists
10.72HE damage / grenade
7.11Flashes / 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. Advanced Mechanics

    You are losing most of the first duels you take on T side, which is usually a peeking and spacing problem, not aim.

    T opening duels 25.6782% — below the 40% mark we flag

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.

0% win rate across 5 tracked games — your weakest map with enough games to be worth reading into.

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

MapScoreRatingHS%Date
nuke13–50.1019%9 Aug →
cache19–16-0.0022%9 Aug →
ancient4–13-0.0414%4 Aug →
inferno3–13-0.1014%4 Aug →
nuke13–11-0.0023%3 Aug →
inferno4–13-0.0315%3 Aug →
dust228–25-0.0121%30 Jul →
nuke16–13-0.0321%29 Jul →
inferno3–13-0.0512%27 Jul →
inferno13–8-0.0318%26 Jul →
nuke22–20-0.0417%20 Jul →
mirage5–130.0632%11 Apr →
inferno13–8-0.0511%7 Apr →
overpass11–130.0232%6 Apr →
overpass13–30.1015%19 Mar →
overpass13–30.0626%19 Mar →
inferno13–80.0327%5 Mar →
nuke13–90.0632%1 Mar →
nuke16–140.0424%20 Feb →
overpass16–13-0.0317%20 Feb →
inferno13–60.0221%20 Feb →
overpass9–13-0.0114%19 Feb →
inferno13–110.0331%19 Feb →
ancient7–13-0.0421%18 Feb →
inferno6–13-0.0215%18 Feb →
dust27–13-0.0513%18 Feb →
inferno13–90.0423%2 Feb →
anubis13–7-0.0416%19 Jan →
anubis3–13-0.0132%12 Jan →
inferno22–19-0.0115%12 Jan →
dust21–13-0.089%8 Jan →
nuke13–100.0334%6 Jan →
nuke7–13-0.0122%4 Jan →
inferno13–110.0518%30 Dec →
overpass13–6-0.0610%21 Dec →
overpass10–130.0016%20 Dec →
dust210–13-0.088%3 Dec →
nuke10–13-0.0320%1 Nov →
inferno13–70.0011%9 Oct →
overpass16–13-0.0513%7 Oct →
dust29–13-0.0421%5 Oct →
nuke16–140.0118%4 Oct →
nuke11–130.0114%26 Sept →
overpass11–13-0.0313%25 Sept →
dust213–90.0111%25 Sept →
inferno4–13-0.0230%29 Aug →
dust213–60.078%10 Aug →
nuke16–13-0.0522%8 Aug →
dust28–13-0.0814%8 Aug →
overpass11–8-0.0014%29 Jul →
inferno13–6-0.0119%29 Jul →
nuke13–80.0615%27 Jun →
inferno13–70.0321%18 Jun →
inferno11–13-0.0323%16 Jun →
dust210–13-0.0416%10 Jun →
inferno16–13-0.0124%3 Jun →
dust210–13-0.0421%1 Jun →
nuke8–13-0.0319%30 May →
nuke13–70.0616%30 May →
nuke5–4-0.080%23 May →
mirage13–10-0.0130%12 May →
nuke10–13-0.0219%7 May →
inferno19–16-0.0016%1 May →
dust27–13-0.0712%8 Apr →
dust213–9-0.0112%7 Apr →
train9–13-0.0524%6 Apr →
inferno2–13-0.0020%6 Apr →
dust213–7-0.0015%6 Apr →
ancient4–130.0218%1 Apr →
dust28–13-0.0019%1 Apr →
inferno7–13-0.0217%1 Apr →
inferno11–13-0.0314%27 Mar →
mirage13–160.0515%27 Mar →
mirage6–13-0.0724%24 Mar →
dust213–50.0826%24 Mar →
ancient10–130.0615%24 Mar →
dust213–50.0817%21 Mar →
train5–13-0.066%21 Mar →
dust28–13-0.0218%18 Mar →
dust26–130.0012%17 Mar →
nuke10–13-0.0133%17 Mar →
inferno13–16-0.0415%16 Mar →
dust213–110.0221%15 Mar →
nuke19–15-0.0123%15 Mar →
nuke16–13-0.0116%15 Mar →
inferno13–50.0217%15 Mar →
dust27–130.0113%15 Mar →
inferno4–13-0.067%12 Mar →
nuke13–30.0915%11 Mar →
inferno19–170.0113%9 Mar →
dust213–11-0.0023%9 Mar →
ancient10–130.0416%24 Feb →
train3–13-0.0423%21 Feb →
inferno13–90.0424%19 Feb →
dust28–13-0.0420%19 Feb →
nuke19–16-0.0116%19 Feb →
train11–130.0317%12 Feb →
dust216–13-0.0113%10 Feb →
inferno1–130.0118%10 Feb →
dust27–13-0.0818%9 Feb →

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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