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68match
keizerlittleleague.com
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4 co-citations10 shared tech2 shared categorybaseball
68match
jasperyouthbaseball.com
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3 co-citations10 shared tech2 shared categorybaseball
68match
holbrooklittleleague.com
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4 co-citations10 shared tech2 shared categorybaseball
68match
ivcyouthathletics.com
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3 co-citations10 shared tech2 shared categorysoftball
67match
applevalleylittleleague.com
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3 co-citations10 shared tech2 shared categorysoftball
67match
kennedyparkbaseball.com
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3 co-citations10 shared tech2 shared categorybaseball
67match
centralll.com
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4 co-citations10 shared tech2 shared categorybaseball
66match
couloak.com
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5 co-citations10 shared tech2 shared categorybaseball
64match
arapahoelittleleague.com
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3 co-citations10 shared tech2 shared categorybaseball
64match
greentreeathletics.com
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3 co-citations10 shared tech2 shared categorysoftball
59match
greenwoodbaseball.com
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5 co-citations10 shared tech1 shared categorybaseball
59match
fcgso.com
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4 co-citations10 shared tech1 shared categorysoftball
59match
hobgoodparkbaseball.com
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4 co-citations10 shared tech1 shared categorybaseball
58match
coralspringssoftball.com
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4 co-citations10 shared tech1 shared categorysoftball
58match
kentonlittleleague.com
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4 co-citations10 shared tech1 shared categorybaseball
57match
iwvyb.com
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3 co-citations10 shared tech1 shared categorybaseball
55match
ivbaseball.com
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4 co-citations10 shared tech1 shared categorybaseball
55match
ivctravelbaseball.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.