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90match
a33sports.com
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1 co-citations2 shared tech1 shared categorysoccer
90match
fccolumbia.com
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1 co-citations2 shared tech1 shared categorysoccer
80match
afs-official.com
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1 co-citations3 shared tech0 shared categoryamerican-football
80match
getdunn.com
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1 co-citations3 shared tech0 shared categoryfishing-sports
75match
hitjersey.com
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75match
gullionyouthfc.com
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75match
bai-football.com
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1 co-citations2 shared tech1 shared categorysoccer
75match
balconesfc.com
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75match
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1 co-citations2 shared tech1 shared categorysoccer
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cdgolstar.com
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1 co-citations2 shared tech1 shared categorysoccer
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cdunionmalacitano.com
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1 co-citations2 shared tech1 shared categorysoccer
75match
elitesoccerlab.com
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1 co-citations2 shared tech1 shared categorysoccer
75match
fcelitephoenix.com
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75match
fewsoccer.com
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1 co-citations2 shared tech1 shared categorysoccer
75match
georgianaorlandella.com
Georgiana Orlandella
1 co-citations2 shared tech1 shared categorysoccer
70match
jamswearables.com
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1 co-citations2 shared tech0 shared categorysports-equipment
70match
hammerheadofficial.com
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1 co-citations2 shared tech0 shared categoryfishing-sports
70match
adrenaline27.com
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1 co-citations2 shared tech0 shared categorybaseball

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.