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73match
laquintaysa.com
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6 co-citations10 shared tech2 shared categorybaseball
71match
keizerlittleleague.com
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5 co-citations10 shared tech2 shared categorybaseball
70match
ivcyouthathletics.com
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4 co-citations10 shared tech2 shared categorysoftball
69match
applevalleylittleleague.com
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4 co-citations10 shared tech2 shared categorysoftball
68match
kennedyparkbaseball.com
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4 co-citations10 shared tech2 shared categorybaseball
68match
gfybso.com
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4 co-citations10 shared tech2 shared categorybaseball
68match
centralll.com
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5 co-citations10 shared tech2 shared categorybaseball
66match
greentreeathletics.com
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4 co-citations10 shared tech2 shared categorysoftball
62match
elkridgetennis.com
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4 co-citations8 shared tech2 shared categorybaseball
62match
elkridgebaseball.com
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4 co-citations8 shared tech2 shared categorybaseball
62match
elkridgecanes.com
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4 co-citations8 shared tech2 shared categorybaseball
62match
elkridgehurricanes.com
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4 co-citations8 shared tech2 shared categorybaseball
62match
elkridgesoccer.com
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4 co-citations8 shared tech2 shared categorybaseball
62match
elkridgesoftball.com
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4 co-citations8 shared tech2 shared categorybaseball
62match
fcgso.com
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5 co-citations10 shared tech1 shared categorysoftball
61match
iwvyb.com
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6 co-citations10 shared tech1 shared categorybaseball
61match
greenwoodbaseball.com
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6 co-citations10 shared tech1 shared categorybaseball
60match
ebsatrenton.com
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5 co-citations10 shared tech1 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.