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NC State QB who played against us last year, who was great, got hurt a few games after us and then during the BC game, their freshman backup QB got hurt and played through it, but it was serious enough where he missed the rest of the season. This year they have a transfer QB that is teaming up with the old offensive coordinator coordinator from Virginia, where he threw for 4,500 yards and scored 40 tds in a season. Comparing the team that lost to BC to the one they are bringing to the rent is not a great comparison.Kind of on/off topic but was just going through Massey game estimates for next Saturday and it also shows through 8/31 games. Saw UConn was 34 to 10 dog, thought last time looked was about 16 points. Someone (or lot of someones) is not buying Mora's "reload".
NC State lost to BCU last year so not like they just missed the playoffs. So lose 41-10 there and have almost no relative improvement UConn vs. NCS once adjust for home field. Must agree NCS stopped playing after 3 quarters up 38-3 last year.
Sure hope Massey is waaaaaay wrong, they act like I'm going to win the starting QB battle, 10 points would be quite disappointing. Not saying they can't score 34 points but last year scored over 30 points in only one FBS game, against UConn.
BC beat NC State in a game NC State played without their starting QB.Kind of on/off topic but was just going through Massey game estimates for next Saturday and it also shows through 8/31 games. Saw UConn was 34 to 10 dog, thought last time looked was about 16 points. Someone (or lot of someones) is not buying Mora's "reload".
NC State lost to BCU last year so not like they just missed the playoffs. So lose 41-10 there and have almost no relative improvement UConn vs. NCS once adjust for home field. Must agree NCS stopped playing after 3 quarters up 38-3 last year.
Sure hope Massey is waaaaaay wrong, they act like I'm going to win the starting QB battle, 10 points would be quite disappointing. Not saying they can't score 34 points but last year scored over 30 points in only one FBS game, against UConn.
well, we did beat BC without our starting QBBC beat NC State in a game NC State played without their starting QB.



BC beat NC State in a game NC State played without their starting QB.
I thought Massey was kind of crazy at +24, wonder what their "algorithm" is, it sure doesn't like UConn.Line started at 16. Now down to 14.5. People are betting UConn.
Pretty sure Massey leans heavy on historical data but I could be wrongI thought Massey was kind of crazy at +24, wonder what their "algorithm" is, it sure doesn't like UConn.
I thought Massey was kind of crazy at +24, wonder what their "algorithm" is, it sure doesn't like UConn.
And so will UConn.NC ST will have a new QB this year.
NC ST will have a new QB this year.
And last season's results.At the beginning of the year computer ranking are largle based on human guesses.
And so will UConn.
They have talent on D, some holes to fill on offense. But if they're buying bad press on us or living in the past I think we make them pay. My way too early prediction is we win by more than 7 points. Only key injuries would change that prediction.NC ST will have a new QB this year
Which were compiled by a team they may or may not resemble the 2023 squad.And last season's results.
Sure seems like it.Maybe
Exactly - and I think with the advent of the immediate eligibility transfer portal, last year’s results have less correlation to this year's anticipated results for more teams than in the past.Which were compiled by a team they may or may not resemble the 2023 squad.
Well thats just not true. A data model is derived from datasets and patterns in said data. The differences in models is what data they use and how they interpret it, meaning if lets say model A heavily weights projected wins on shoelace brand and model B on last 5 seasons win rate, you will likely get very different outputs.At the beginning of the year computer ranking are largle based on human guesses.
Well thats just not true. A data model is derived from datasets and patterns in said data. The differences in models is what data they use and how they interpret it, meaning if lets say model A heavily weights projected wins on shoelace brand and model B on last 5 seasons win rate, you will likely get very different outputs.
Or even 10 year historical data versus 5 years historical data will show major differences
Not even. With proper tools, you can create processes that figure out what data points are relevant or insignificant. Its not at all about football, its just data. The more the better.So, they have to make assumptions or guesses on how much to weight different factors?