I don’t think you can find a person who would call the Big 12 North anything but a three team race for the title, but exactly how close is that race?  I decided to try to figure it out.

Using the original PredictaTron theories about how betting lines translate to win/loss probabilities (see bottom of post for more details) I developed a model to forecast the outcome of the Big 12 North race.  To simplify the model I broke the North into two groups:

1. Missouri, Kansas and Nebraska (“The Haves”)
2. Iowa State, Kansas State and Colorado (“The Have Nots”)

Using these two groups I simplified things by assuming The Haves would dominate The Have Nots.  I also assumed that in order for a team to win the North they could go no worse than 2-1 against The Have Nots (while it is possible for a team to win the North while going 1-2 or even 0-3 against The Have Nots, it requires curvature of the space time continuum I am not capable of modeling with Excel). 

After some number crunching the model spits out the odds that each of The Haves wins the North while going 3-0 against The Have Nots or Going 2-1 against The Have Nots while the other two teams go 3-0 against The Have Nots.

I intentionally omitted scenarios where two of The Haves go 2-1, but you can probably induce that these scenarios make the lone 3-0 team a very heavy favorite.  I also omitted the scenarios where all three of The Haves go 2-1, but likewise you can probably induce that these scenarios closely track the scenario where all three teams go 3-0 (because of tiebreaking rules the exact probabilities in this scenario would depend on which of The Have Nots the teams lost to).

What does all this modeling tell us?  In a nutshell, this is an extremely tight race for the Big 12 North title (sure you probably knew that before I modeled it, but now you know for sure). 

Where does each team stand?

Missouri

Schedule – Missouri has a very favorable schedule because they don’t have to travel to Nebraska or Kansas and they only have one road game against a South division opponent (Oklahoma State). 

Key Games – Missouri’s season could be made in the first two weeks of conference play.  If they take care of business against Nebraska it sets up one of the biggest games of the season on October 17 at Oklahoma State.  If the Tigers can find a way to win that game and go to 2-0 in conference play, they improve their chances of winning the North to nearly 75% (they still have a 50% chance of winning the North with an unexpected loss to one of The Have Nots).  

Kansas

Schedule – Kansas is in the wicked two year cycle where they face Oklahoma, Texas and Texas Tech.  This is brutality is slightly mitigated by the fact that they don’t have to travel to Nebraska or Missouri. 

Key Games – Kansas’ season really hinges on the Halloween game at Texas Tech.  Kansas can ill afford to go 0-3 against the South.  If Kansas goes 0-3 against the South their chances of winning the North drop to 20% (compared to roughly 40% for Missouri and Nebraska).  On the other hand, picking up a win against Texas Tech should allow KU to control its own destiny when it faces Nebraska and Missouri. 

Nebraska

Schedule – While Nebraska’s schedule against the South screams 2-1, the Huskers have the bad luck of facing both Mizzou and Kansas on the road (just wait until next year).

Key Games – Nebraska’s season hinges on the October 8, 2009 game at Missouri.  Winning this game puts Nebraska at better than 50% to win the North and should allow Nebraska to lose to Kansas, go 2-1 against the South and still win the North.

Don’t believe me?  Don’t like my assumptions?  Check for yourself with the interactive PredictaNorth model below:

Same ground rules as usual:

 the model is pre-populated with preliminary predictions
 only the yellow cells can be edited
 please hit F9 on your keyboard to update the calculations

More on the Original PredictaTron Theory:

Everyone is familiar with Vegas point spreads and most learned college football fans have a decent gut feel about where a line is likely to settle in for a particular game.  Well, every game also has a money line, and it turns out it is relatively easy to translate the money line into a percentage chance that a team wins.  In fact, the analysis has been done multiple times.  From there, you can translate a point spread into a percentage chance of winning.  So, Euclid built into the Predictatron the historical Vegas “point spread to money line to percentage chance of winning” ratio.  You pick a line; the PredictaNorth spits out a chance of winning, based off historical Vegas ratios.