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<blockquote data-quote="85Escape" data-source="post: 906152" data-attributes="member: 3560"><p>Seems like the right way to include the score in these models would be to look at the time-weighted difference in the score throughout the game. Looking at the final score can be so deceptive since most coaches would rather be assured a win by 1 than take a chance on losing to win by 14. If the model looked at how 'tight' the game is across all 60 minutes, I think they would match better what we see with our eyes. Something like SUM<span style="font-size: 9px">0-60</span>(deltaScore/60). That way if a team jumps out to a big lead and trades points for clock it doesn't look closer than it is. (I'm not a data scientist never played one on TV and didn't sleep in a Holiday Inn Express last night.)</p></blockquote><p></p>
[QUOTE="85Escape, post: 906152, member: 3560"] Seems like the right way to include the score in these models would be to look at the time-weighted difference in the score throughout the game. Looking at the final score can be so deceptive since most coaches would rather be assured a win by 1 than take a chance on losing to win by 14. If the model looked at how 'tight' the game is across all 60 minutes, I think they would match better what we see with our eyes. Something like SUM[SIZE=1]0-60[/SIZE](deltaScore/60). That way if a team jumps out to a big lead and trades points for clock it doesn't look closer than it is. (I'm not a data scientist never played one on TV and didn't sleep in a Holiday Inn Express last night.) [/QUOTE]
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