Thanks for the data aaeismacgychel. Do you know if this is built into the KenPom model?
As a mere mortal, I'll approximate this as a score difference of: 11pts + 1pt/min remaining
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Illinois 69, Ohio State 60 Postgame
As a mere mortal, I'll approximate this as a score difference of: 11pts + 1pt/min remaining
Thanks for the data aaeismacgychel. Do you know if this is built into the KenPom model?
As a mere mortal, I'll approximate this as a score difference of: 11pts + 1pt/min remaining
For the overall rankings that you see, it isn't for the most part. If you win by more than 25pts, there are limiters in place where each successive margin of victory point has diminishing returns (i.e. beating a team by 60 is about the same as beating a team by 40), but that's really about it. For his predictive algorithm though (Fanmatch), it is a very complex formula based on statistical correlation models. The last time I had the opportunity to test the Fanmatch system was before he performed the study I shared so I can't say for certain whether he has started to include this in his predictive models, however, based on previous time working with him, I would be fairly surprised if he hasn't updated his predictive model to account for these additional garbage time metrics. His predictive model is ever-evolving, and though not his end goal as he's in it for the stats, if he can get it to the point where it can outperform Vegas...