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[Jeff P]

Thanks for posting that. (I really enjoyed watching the presentation.)

Ok. Sticking with your days since last raced example...

Suppose, hypothetically, that big data suggests optimal returns occur (could be thousands of parimutuel tickets cashed or thousands of checks for purse money earned) when race day occurs on the 28th day after the most recent start.

It should be obvious that thick data -- if put in the right context -- has the ability to completely overrule whatever observations might have been gleaned from big data.

Big data example: You have a database and can generate large sample stats for horses returning off a 180 day layoff since their most recent start.

Thick data example: You have insight into what transpired during the layoff.

What if you are able to make the thick data observation that a specific horse was turned out to a private farm for six months? And was given steroids and worked vigorously on the half mile track there?

And shows up in the paddock today carrying muscle mass and confidence he didn't have before?

When you are able to connect the dots in a thick data way you'd be crazy to just blindly go with your big data model.


-jp

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