rellüM

rellüM — CS2 Stats

TV76561198064346123[U:1:104080395]Steam profile ↗✓ No bans

1,453Tracked matches52%Win rate2020Tracked since
6,802Hours in CS2Hrs last 2 wks
CSDB Rating6.8 SolidSupport
WingmanThe Global Elite
Ladder ranks via Leetify

Performance scores

Aim85
Positioning72
Utility73

0–100 skill scores via Leetify.

Recent form

STEADY54–42–4Last 10054%Win rateLWLWWLLLWW

Last 10 vs previous 10: +10pp win rate · −0.02 avg rating · +1.8pp headshot accuracy · −41ms 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 21%
  • Avg reaction 431ms

Last 10

  • 5–5 · 50% win rate
  • Avg rating 0.03
  • Avg headshot accuracy 24%
  • Avg reaction 446ms

Last 20

  • 9–11 · 45% win rate
  • Avg rating 0.04
  • Avg headshot accuracy 23%
  • Avg reaction 466ms

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 (+1.4 against its own average).

Aim8.5
Utility7.3
Positioning7.2
Opening Duels2.8
Clutch6.2

Sharp aimer

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

iM

Plays most like iM 84% playstyle similarity

Most alike: positioning profile, opening-duel success.

Similarity of playstyle shape across shared dimensions — it says how you play, not that you play at their level. Full comparison →

CSDB Rating breakdown

Aim8.5
Positioning7.2
Utility7.3
Mechanics5.2
Opening Duels3.7
Win Impact5.6

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 rating0.04−0.04
first ⅓ avg 0.08 → last ⅓ avg 0.04
Reaction time466ms−72ms
first ⅓ avg 538ms → last ⅓ avg 466ms
Headshot accuracy22.6%+1.2%
first ⅓ avg 21.4% → 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

0.27Best match rating · 13–0 · anubis, 19 Sept →
48%Best headshot accuracy · 5–13 · dust2, 11 Aug →
336msFastest reaction time · 13–0 · inferno, 13 Oct →
13–0Biggest win · inferno, 13 Oct →

Across the last 100 tracked matches.

Highlights

7Longest win streak
10–7In matches decided by ≤2 rounds
12Overtime games

Map breakdown

infernoBest map · 65% over 26ancientWeakest map · 36% over 14
MapGradePlayedRecordWin rateAvg rating
infernoS2617–965%0.07
mirageA2113–862%0.05
ancientC145–936%0.06
anubisA138–562%0.08
nukeC94–544%0.07
dust2C73–443%0.05
cacheA53–260%0.07
overpass—41–325%0.05
vertigo—10–10%0.09

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (36% over 14). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Lifetime stats

207,520Lifetime kills
1.56K/D
7,106Matches
57.8%Match win rate
48.5%Headshot %
17.2%Shot accuracy
27,157MVPs
3,134Hours (in match)
10,251Bombs planted
2,842Bombs defused

Most-used weapons

AK-4766,554
AWP19,296
P2506,934
MP94,229
Knife3,729
FAMAS3,664

Lifetime map wins

18,569inferno
10,296dust2
3,189nuke
2,509train
1,156vertigo
823cbble
271office
176lake

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

Faceit stats

Combat

663Matches
57%Win rate
1.39Avg K/D
82.6ADR
46%Headshot %

Clutches & streaks

51%1v1 clutch win
22%1v2 clutch win
8Longest win streak

Recent Faceit resultsWWWLW

MapMatchesWin rateAvg K/DAvg kills
Ancient5756%1.2316.0
Anubis4156%1.1014.7
Inferno3161%1.4017.0
Vertigo2157%1.3019.7
Mirage1867%1.2814.4
Overpass1136%0.9814.0
Nuke1164%1.3717.6
Dust2425%1.1915.5

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.

22.7%Headshot accuracy
35.2%Accuracy (enemy spotted)
41.2%Spray accuracy
73.4%Counter-strafing
9.0°Preaim
466msReaction time
46.8%T opening success
43.0%CT opening success
0.56Enemies flashed / flash
5.9%Flash assists
12.17HE damage / grenade
14.89Flashes / 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.

36% win rate across 14 tracked games — your weakest map with enough games to be worth reading into.

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

MapScoreRatingHS%Date
inferno13–160.0416%8 Nov →
mirage13–60.0427%2 Nov →
ancient11–130.0432%13 Oct →
inferno13–0-0.020%13 Oct →
inferno13–40.0930%13 Oct →
inferno11–130.0429%12 Oct →
ancient2–13-0.0229%12 Oct →
mirage10–130.0727%12 Oct →
nuke13–100.0532%30 Sept →
mirage13–5-0.0020%28 Sept →
nuke9–60.2422%25 Sept →
mirage5–130.0124%25 Sept →
mirage1–13-0.0519%19 Sept →
inferno16–130.0121%19 Sept →
ancient13–1-0.029%19 Sept →
ancient4–13-0.0212%19 Sept →
anubis13–00.2734%19 Sept →
inferno3–130.0227%13 Sept →
inferno8–130.0824%13 Sept →
inferno10–130.0331%13 Sept →
ancient13–100.1025%13 Sept →
nuke6–130.0625%8 Sept →
anubis13–70.0830%8 Sept →
anubis2–13-0.0623%8 Sept →
mirage13–30.0115%8 Sept →
mirage13–40.0921%7 Sept →
inferno16–120.0820%7 Sept →
ancient13–160.0422%7 Sept →
ancient15–15-0.0314%6 Sept →
inferno13–11-0.0215%6 Sept →
cache13–60.1126%1 Sept →
mirage13–90.0020%30 Aug →
mirage13–60.0326%30 Aug →
cache13–110.0114%29 Aug →
inferno13–80.0821%28 Aug →
anubis13–110.0723%28 Aug →
mirage8–130.0121%22 Aug →
inferno13–80.0321%22 Aug →
nuke8–130.0124%22 Aug →
mirage16–140.0724%22 Aug →
ancient13–110.0922%22 Aug →
anubis13–50.0222%14 Aug →
inferno13–50.0819%14 Aug →
cache5–130.0623%14 Aug →
inferno13–70.1836%14 Aug →
anubis13–90.1127%11 Aug →
inferno12–160.0225%11 Aug →
dust25–130.0248%11 Aug →
mirage13–80.1121%10 Aug →
inferno13–60.2118%10 Aug →
mirage13–110.0424%8 Aug →
anubis13–100.0314%8 Aug →
dust211–130.0321%8 Aug →
inferno13–80.0819%8 Aug →
mirage13–60.0319%4 Aug →
dust213–90.0818%4 Aug →
inferno13–110.0420%4 Aug →
anubis8–130.0822%3 Aug →
ancient13–100.1728%3 Aug →
ancient11–130.0626%3 Aug →
mirage13–40.0918%31 Jul →
inferno13–100.1121%31 Jul →
cache5–130.0626%31 Jul →
inferno13–00.0817%31 Jul →
ancient8–130.1123%31 Jul →
mirage13–40.0436%4 Jul →
dust213–100.1526%4 Jul →
dust210–13-0.0120%4 Jul →
dust213–110.1012%27 Jun →
cache13–40.0912%27 Jun →
vertigo3–130.0921%27 Jun →
inferno12–120.1023%27 Jun →
dust212–160.0126%24 Jun →
mirage10–130.0821%8 Jun →
ancient2–130.0330%8 Jun →
nuke15–150.1025%7 Jun →
anubis16–140.1415%4 Jun →
nuke13–90.0321%4 Jun →
nuke5–130.0821%3 Jun →
mirage5–130.1216%3 Jun →
inferno11–130.0314%3 Jun →
ancient13–90.2117%3 Jun →
overpass15–150.0621%1 Jun →
inferno13–110.2020%31 May →
overpass5–130.0122%31 May →
mirage7–130.0715%31 May →
anubis10–130.0721%31 May →
ancient9–130.0629%31 May →
inferno7–40.1622%31 May →
mirage11–130.0821%30 May →
nuke13–60.0321%28 May →
overpass13–50.1726%28 May →
inferno8–130.0822%28 May →
nuke14–160.0630%27 May →
anubis13–20.0926%27 May →
inferno13–90.0720%27 May →
anubis10–130.0421%27 May →
anubis10–130.0924%27 May →
mirage13–90.0527%21 May →
overpass8–13-0.0323%21 May →

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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