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Carey’s Point on the Analytics

Spacemon25

Sooner starter
Gold Member
Sep 19, 2017
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Tulsa, Oklahoma
This was a fantastic point @Carey Murdock and I’ve always waited to see if someone else brought it up first.

I believe this is something to watch for as back in 2015 (coincidentally the year LR was hired) OU started using what I believe to be APEX GPS tracking equipment on their players. I believe they were using other things occasionally before then but this is when you started seeing players with the vests on more often. In the beginning it was just relegated to guys coming off injury or stars watching their workload but now it’s basically on almost every scholarship athlete to some extent.

When Grinch began discussing height, speed, and other physical attributes, as well as identifying key stats that play into whether you win or lose football games (turnovers being the obvious one) when he arrived here, I had a hunch that this was one of the reasons Riley liked him as a DC. It’s pretty obvious they both like themselves some analytics.

As for the player rotations, and this may be me correlating things I shouldn’t but here it goes…. I don’t believe it’s a coincidence that the OU Science and Health team also works pretty close with the Thunder who LOVE player tracking and care a lot about finding those optimal ranges for player minutes, mph, caloric expenditure, Heart Rate Variability and other metrics. This data was the exact stuff keeping Westbrook from playing more in the playoffs. While I don’t deal in the research side of this stuff, I’m extremely aware of how this stuff is viewed in those circles and to many, the science is the science and you don’t argue with it.

The fact Grinch wants tall rangey DB’s with speed of course isn’t rocket science but the very specific heights do stem from data.

This data looks at the football field like a gigantic chart and the science says guys with specific attributes of speed/height are going to maximize the amount of space which can be covered and while that may seem obvious, some take it deeper in terms of looking at catch radius, probability for errors and also looking at the analytics to find out who absolutely SHOULD NOT play.

I say all these things while being an analytics fan myself. I train high school softball players as the predominant athlete I work with and it’s typically only athletes with certain swing speed, acceleration, base speed, hip speed, and rotational Rate of Force development that end up getting scholarships. Therefore, the data says if you can get that player to that point, in theory, you can make them become a scholarship level athlete.

Hope you guys enjoy my two cents on this and maybe connect some own dots for yourself as you all are certainly more in the know than I.

Thanks, Scoop!
 
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