Week 9 Picks and Prediction Model (PM) 3.0

This week I will start with picks, and then describe the prediction model I used to generate the picks below. I'm also getting a big head so I thought someone might be interested in my own picks--and that way we can see if I'm smarter than my own computer. The prediction model (PM 3.0 this week) and I will go head to head on 5 games a week, picking winners and against the spread, and then I will also post PM 3.0's picks for the rest of D 1-A (aka FBS).

If you are interested in spreads, covers.com is the place to go. I have included the handicap for the home team in parentheses.

Game 1. Ohio State @ Penn State (+4)
I don't think this game will be as close as it looks like it should be. Sure, its in Happy Valley, and, sure, Ohio State and Penn State statistically look very similar--except in one very important area, the win/loss record. Watch Morelli to crack like Woodson at SC and OSU will win this walking away.

Me:
To Win: Ohio State
Against the Spread: Ohio State

PM 3.0:
To Win: Ohio State
Against the Spread: Ohio State

Game 2. West Virginia @ Rutgers (+6.5)
Again, the better team is on the road. Pat White will be healthy (or as healthy as he ever is) and West Virginia will be flying around the field again. It is important in this game to consider match ups. South Florida beat WV (at home) because they had the speed on defense to contain Slaton and White. Rutgers beat South Florida (at home) because that speed didn't translate well when Rice was slamming it down their throats. Rutgers, so far, has been a flat, uninspiring team with the exception of one Thursday night. West Virginia will break it open in the second half and score to many points for Rice to keep up.

Me:
To Win: West Virginia
Against the Spread: West Virginia

PM 3.0:
To Win: Rutgers
Against the Spread: Rutgers

Game 3. South Florida @ Connecticut (+4.5)
I have included this game only because I can. Who would have predicted at the beginning of the year that this game would pit two ranked, one-loss teams against each other with Big East title hopes alive? But seriously, I can't get myself to believe that UConn has a good team--when has Connecticut ever produced a good athlete? And I'm not the only one to think this.1 South Florida is definitely the better team, but cold weather and inexperience may slow them down. They still win easily.

Me:
To Win: South Florida
Against the Spread: South Florida

PM 3.0:
To Win: South Florida
Against the Spread: South Florida

Game 4. USC @ Oregon (-3)
I was worried that Mark Sanchez off the bench might give SC the spark they needed to be a good football team again. Fortunately, he's not everything he was supposed to be. It looks like Booty's finger will be well enough and he will lead his team to another mediocre performance. A note on USC--their big victories are against Nebraska (cupcake) and Notre Dame (wedding cake). They lost to Stanford (cheese puff) and almost lost to Arizona (lost little child). Oregon's beat down of Michigan was impressive, but that was a Michigan team that is still recovering from the week 1 train wreck. Both teams are talented, but with PAC-10 talent - either could win by 30 or flake out and lose to my high school team. I take USC, because they have more raw talent to start with.

Me:
To Win: USC
Against the Spread: USC

PM 3.0:
To Win: USC
Against the Spread: USC

Game 5a. Boston College @ Virginia Tech (-3)
See Game 4. Two teams that have not been all that impressive, but, to their credit, they have been winning a lot of games. I'm taking Virginia Tech to knock off the first top 10 team this weekend on Thursday, but it will be close.

Me:
To Win: Virginia Tech
Against the Spread: Boston College

PM 3.0:
To Win: Boston College
Against the Spread: Boston College

Game 5b. Kansas @ Texas A&M (+2.5)
I had to include this game for a number of reasons. First, this might be Kansas's only weekend in the top 10, so we must take a moment to recognize it. Second, I would like to note that Kansas is actually very good and undefeated for a reason (the same reason that BC is undefeated but without the same level of respect). After Saturday Kansas will have two cupcakes (Iowa State and Nebraska) and a road game in Stillwater before the final match up against Missouri. Finally, I have included this game so I can point out that, while they are getting no love from the national media, the Aggies have only lost twice and they are tied for first in the South. The outcome of the game depends on the Aggie passing game. If Kansas can put 8 in the box all night, they win and cover the spread; if not, and A&M burns the secondary a time or two at Kyle Field, it could be very interesting. One last quick note on Kansas--they have covered the last five weeks.

Me:
To Win: I abstain
Against the Spread: I abstain

PM 3.0:
To Win: Kansas
Against the Spread: Kansas

The Rest: Click image to see a legible version
It includes the probability for each team of winning and beating the spread and the yards. Obviously, if a team has a better than 50% chance of winning then they are "favored".




















PM 3.0

Prediction Model 3.0 is my first model to account for match ups. The method I have chosen to do this is too simple, but I'm building on trial and error for now. The basic idea is that teams have relatively consistent run to pass ratios. I use time of possession and plays per second to estimate how many plays a team will have in a game (adjusted for how long their opponent will have the ball) and then estimate the number of run and pass plays each team will run. Using their average yards per run play, completion percentage, and average yards per completion, adjusting for the other teams defensive strengths, I can get a figure on the number of total yards a team should have. I then use the basic rating system I used in PM 2.11 and give a bonus to the team that will generate more yards.

In these circumstances, the only real variables that I have to decide on are the adjustments I will be using. I have decided to use a k of 3/sqrt(1+t) where t is the week in which the game took place. The figure, therefore, should stabilize as the season progresses, which I believe mirrors reality.

The adjustment of the rating is Rating + 10*(team yards/opponent yards). I chose ten rather at random, but it really means that in the most extreme cases a team may have 5 to 10 points added to their estimated margin of victory.

The problem with a prediction model that adjusts for match ups is that it cannot be used to rank teams. In a rating system it is necessary that if A>B>C then A>C, but if we take match ups into account then if A>B>C it is still possible that C>A if A matches up poorly against C and well against B. This means that it can't be used for ranking teams, but only for predicting the winner if two teams play. I have thought up a method of getting around that, but programing it will take some time.