Virginia vs North Carolina odds, row: 2021 college football pick, week 3 predictions from a computer model
Things heat up in the ACC this weekend when the Virginia Cavaliers and North Carolina Tar Heels face off at 7:30 p.m. ET on Saturday at Kenan Memorial Stadium. North Carolina shrugged off their Week 1 issues to respond with a monster win over Georgia State last weekend. Virginia is looking to follow Virginia Tech and Pitt in the Coastal Division with their third victory. UNC must also regain ground in the Coastal after their season opener loss to the Hokies. The Cavaliers aren’t going to make it easy, as they average over 550 yards on offense in each of their first two games.
The Tar Heels are favored by eight points in the latest North Carolina vs Virginia odds from Caesars Sportsbook, and the undercut is set at 66.5. Before locking in Virginia’s picks against North Carolina, you’ll want to see the model’s college football predictions at SportsLine.
The SportsLine projection model simulates every FBS college football game 10,000 times. Over the past five plus years, the proprietary computer model has generated an astonishing profit of over $ 3,500 per 100 players from $ 100 on its top-rated college football picks versus the spread. He also enters Week 3 of the 2021 season on a 67-50 run on all of the top-rated college football picks. Anyone who has followed him has seen huge returns.
Now the model has set its sights on North Carolina vs. Virginia and just revealed his choices and predictions. You can head over to SportsLine now to see the model’s UNC vs Virginia picks.. Here are several college football odds and the betting lines for North Carolina vs Virginia:
- Gap between North Carolina and Virginia: North Carolina -8
- North Carolina vs Virginia over-under: 66.5 points
- North Carolina vs Virginia silver line: North Carolina -320, Virginia +250
- VIR: 4-0 overall In the last four meetings with UNC; 4-0 ATS
- UNC: 8-1 overall as a home favorite since 2019; 5-3-1 ATS
Featured Game | North Carolina Tar Heels vs. Virginia Cavaliers
What you need to know about North Carolina
North Carolina bounced back from a disappointing Week 1 and expressed frustration over Georgia State last weekend. The Heels hurt the Panthers at home at 59-17. The contest was almost over by the end of the third quarter, when UNC had established a 45-10 advantage.
Quarterback Sam Howell had a dynamite game for UNC, scoring three touchdowns and 352 yards on 29 attempts as well as rushing for two touchdowns and 104 yards. Near the top of the highlight reel was Howell’s 57-yard TD bomb against WR Antoine Green in the first quarter. Howell will need to be at the top of his game again this week, as its offensive line has already allowed eight sacks in its first two games, while Virginia has only allowed opponents to complete 55% of their passes.
What you need to know about Virginie
Virginia really established herself as one of the ACC’s best offensives last week, scoring 557 yards against Illinois last Saturday. Quarterback Brennan Armstrong had a stellar game for Virginia as he had five touchdowns and 405 yards on 36 attempts, in addition to 31 rushing yards.
While the Cavs have had a host of players taking transfers in the backfield, offense is all about passing. Keytaon Thompson and Billy Kemp each have ten receptions, and second student in red shirt Dontayvion Wicks leads all UVA wide receivers with 163 yards so far this season. Defensively, Virginia is tied for 17th in the country in passing yards, and held opposing passers to just 5.1 yards per attempt.
How to choose between North Carolina and Virginia
The model has simulated North Carolina against Virginia 10,000 times and the results are available. We can tell you that the model looks at total points and it also generated a point distribution choice that hits in over 50% of the simulations. . You can only see the model choice on SportsLine.
So who wins North Carolina over Virginia? And which side of the spread is a must-have? Visit SportsLine now to see which side of the spread you need to jump on, all from the computer model that crushed his college football picks, and find out.