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Models can process huge amounts of statistics, while human analysis can account for things like injuries, coaching decisions, motivation, and unusual circumstances. Do you think the best approach is combining both, or have you found one consistently more useful? The page discusses computer analytics alongside these more contextual factors.
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I know this thread is a bit old, but I wanted to jump in. I think the real edge comes from using models for the broad picture and human insight for the weird stuff like weather or a star player's mood. Do you folks ever test a model pick only to overrule it with your gut? That tension is where the real fun lives.