A Sooners 5-Star Prediction
As predictions rolled in from around the On3 network for a number of top national names, I leaned into one I’ve considered for a little while now. That is Denton (Texas) Ryan offensive tackle Ty Haywood. The Sooners have been heavy contenders for Haywood for quite some time now and with On3’s RPM day it was time to put in the pick for Oklahoma and the nation’s No. 12 overall player in the On3 Industry rankings.
The prediction goes in over the likes of Texas, Texas A&M, Georgia, Oregon, Alabama, LSU, and national champion Michigan. The thinking is largely based on not only Haywood’s relationship with the Sooners and his impressions of their NFL production along the offensive line but also how much his parents seem to embrace his potential situation in Norman.
A confidence or 50% was put into the RPM-day pick for the Sooners. To date my success rate has been 86.9-percent with On3 predictions. In the 2025 class I’ve also got Sooners predictions in for Jonah Williams, CJ Nickson, Tory Blaylock, Max Granville, Cobey Sellers, Nate Roberts, and Trystan Haynes.
Others See Sooners Trending for Haywood
“One of the top offensive tackles in 2025 has a number of programs in the hunt, but none have made a stronger impression than the Oklahoma Sooners.
That’s why I’ve entered an RPM with 65% confidence for Haywood to pick OU,” said Justin Wells of Inside Texas.
On3 Recruiting Prediction Machine (RPM)
With the Sooners considerable move in the RPM for Haywood over the past few months it’s worth a look at what exactly the RPM data portrays. Aside from my own prediction for the Sooners, and that of Justin Wells, there is considerable data that is culled into the RPM’s 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
