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Sooners Ready to Beat Texas, in California?

by: Josh McCuistion10 hours agoJosh_Scoop

Since the arrival of Stacey Ford the Sooners presence in Southern California has been renewed and, in some ways, beyond even the golden era for the Sooners in the state. But even with pledges from Demare Dezeurn and Jaxsen Stokes it seems that Oklahoma football isn’t done in the Golden State.

Nowhere is that more clear than in their work pursuing Tustin, Calif. linebacker target Taven Epps. The former Texas commitment has taken two trips to Norman this season, watching Oklahoma’s win over LSU as well as their mid-season defeat to Ole Miss.

And in those visits Epps has raved about his time on each visit, to the point many feel Oklahoma is in a strong position.

““[The visit] was good, I had a great time,” Epps said following his Oct. 25 trip.

“Coach [Brent] Venables, Jim Nagy, coach [Nate] Dreiling, coach Stacey Ford. I got to talk to pretty much all of the staff.”

And it’s that ‘every man on deck’ approach that has us putting in a prediction for the Sooners to land Epps, the Navy All-American.

Highlights of On3’s Recruiting Prediction Machine:

  • Combines data and expert predictions on an AI platform.
  • V1 release December 2021 with V2 scheduled in Q2 of 2022
  • Algorithm is built to learn from past results; therefore, accuracy will continue to improve over time.
    • For example, it has the capabilities to learn trends like a coach’s success (or lack thereof) with a specific high school or region, the importance of an unofficial visit, and how valuable social media sentiment really is.
  • On3 is removing the manually controlled  “school interest” section with the RPM prediction.

Expertise and data used for the RPM algorithm:

  • Insight and inputted predictions from industry experts:
    • On3 National Recruiting Insiders
    • On3 Fan Site Recruiting Insiders
    • Industry experts with proven track records (Outside of On3 staff)
  • Visits (by the player and by coaches):
    • Official Visits 
    • Unofficial Visits
    • Coaches Visits
  • Sentiment, analyzed by machine-learning:
    • Social sentiment from the athlete
    • Media sentiment
  • Data of previous related outcomes:
    • Geographic Data
    • Coaching Staff historical data
    • State historical data
    • High School historical data

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