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🔗 suspect.gg/76561199075758491
LVL 14

zxc

WATCH
76561199075758491|FACEIT XxentaiX|6.1 yrs old
SteamFACEITLeetify
0
SUSPECT INDEX
WATCH
SIGNALS ONLY
642MS
TIME TO DAMAGENormal
%
WIN RATErecent

We parsed the demo and flagged the signals below. Render the evidence to see them on video.

SIGNAL BREAKDOWN
vs 1,846 analysed players
Aim accuracy22.3%
Share of shots that landed a hit, with aimed weapons, across analysed rounds.higher than 46% of analysed players
Headshot share8.1%
Of the hits that landed, how many struck the head. High alone is not proof.higher than 17% of analysed players
Damage-to-kill642ms
Average time from first landing damage to the kill (lower = closer to an instant kill).faster than 7% of analysed players
Crosshair pre-aim3.1°
How far the crosshair sat off the enemy at first contact (lower = already aimed before seeing them).tighter than 97% of analysed players
PROOFsignals flagged · clip not rendered
AWAITING RENDER
No clip rendered yet

We've parsed the demo and flagged the signals on the left. Render the evidence to see the flagged rounds on video.

free · rate-limited
MULTI-SOURCE STATS
Playtime
Account age6.1y
VAC bans0
Game bans0
No Premier rating available
FACEIT level 4
ELO 1,025
K/D1.01
HS %39%
No Leetify data
FACEIT · PERFORMANCE BY MAP
Premier is a single rating · shown above
mirage
60m
Win rate47%
K/D0.97
HS%41%
anubis
29m
Win rate45%
K/D1.03
HS%37%
nuke
10m
Win rate50%
K/D0.86
HS%42%
inferno
13m
Win rate54%
K/D1.18
HS%38%
ancient
24m
Win rate50%
K/D1.10
HS%43%
vertigo
6m
Win rate50%
K/D1.07
HS%42%
train
6m
Win rate67%
K/D1.09
HS%34%
dust2
28m
Win rate68%
K/D1.24
HS%50%
STEAM INVENTORY
ACTIVITY · LAST 120 DAYS
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MATCH HISTORY
Leetify history · demos enrich rosters & signals
W
ANCIENT
K / D11/18
ADR79
HS%46%
RATING0.72
Watch
Analysed
ROSTER01𝔹𝕝oo𝕕𝕪ℂ𝕒𝕥02zxc03SkraFas04Syrusas05daringdo06iZi07TomJum08BliauBinas09|YB|NIKRES10Autistic dumpster fire
WEAPON BREAKDOWN
Per-weapon kills are collected as we analyse this player's demos — this section fills in on the next analysis pass.
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◆ ALLEGEDSignals are model-flagged from demo analysis and public data — triage, not a verdict. Every finding is appealable.