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🔗 suspect.gg/76561198834544981
LVL 15

Mavrix

WATCH
76561198834544981|FACEIT MARS301|TR|8.3 yrs old
Steam↗FACEIT↗Leetify↗
0
SUSPECT INDEX
WATCH
SIGNALS ONLY
590MS
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 accuracy30.4%
Share of shots that landed a hit, with aimed weapons, across analysed rounds.higher than 85% of analysed players
Headshot share16.3%
Of the hits that landed, how many struck the head. High alone is not proof.higher than 68% of analysed players
Damage-to-kill590ms
Average time from first landing damage to the kill (lower = closer to an instant kill).faster than 11% of analysed players
Crosshair pre-aim6.8°
How far the crosshair sat off the enemy at first contact (lower = already aimed before seeing them).tighter than 49% 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
Playtime3,372h
Account age8.3y
VAC bans0
Game bans0
No Premier rating available
FACEIT level 3
ELO 842
K/D0.93
HS %48%
No Leetify data
FACEIT · PERFORMANCE BY MAP
Premier is a single rating · shown above
nuke
3m
Win rate67%
K/D0.84
HS%46%
inferno
9m
Win rate44%
K/D0.92
HS%46%
mirage
18m
Win rate56%
K/D0.89
HS%51%
train
2m
Win rate50%
K/D1.37
HS%52%
overpass
3m
Win rate67%
K/D1.15
HS%50%
cache
1m
Win rate0%
K/D1.00
HS%75%
ancient
11m
Win rate45%
K/D0.83
HS%59%
dust2
13m
Win rate38%
K/D1.15
HS%54%
STEAM INVENTORY
ACTIVITY · LAST 120 DAYS
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MATCH HISTORY
Leetify history · demos enrich rosters & signals
W
MIRAGE
K / D12/11
ADR88
HS%58%
RATING1.33
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Analysed
ROSTER01TomJum02FReeZ03ReZ104Mavrix05Straati2k06TheUrbanSkunk07Syrusas08Rattlesnake Jake09Noxen10Skalpelis
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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â—† ALLEGEDSignals are model-flagged from demo analysis and public data: triage, not a verdict. Every finding is appealable.