the just released schedules were a flat-out statement that the B10 doesn't believe SOS will matter in playoff selection
Desperately grasping at something, anything, that would stop me from gagging after yesterday's loss had me ask myself: "If this fractured team is miraculously capable of piecing things back together the rest of the season, will it make up for last night's embarrasing loss?"
We all know the schedule:
- MSU at home
- Purdue on the road
- IU at home
After yesterday, I hate everything, and I'm sure some of you may even object to me asking the question, but I had to ask because I sure as hell don't know.
4 wins from another B1G Title.
Now let's bang...
...posbang, that is.
Tim Hardaway Jr. is #68 in this preseason ranking:
Personally, I'd take him over William Buford of OSU (in the top twenty there), who I see as less versatile as Hardaway. Anyway, it's probably asking too much for a preseason ranking to be anything more than a discussion topic.
The California Institute of Technology (my alma mater!) broke a 310 game conference losing streak, beating Occidental College (president Obama's alma mater!) 46-45 on a last second free throw. The Caltech Beavers play in the very competitive SCIAC (Southern California Intercollegiate Atheletic Conference), in NCAA DIvision III.
The last time the Beavers won a conference game was Jan. 23, 1985 - almost two years before I was born. People are gonna get hammered tonight in Pasadena.
To follow up on the previous KenPom charts and graphs, I decided to pick my NCAA tourney bracket based on Ken's predictions and see how accurate he is. The way I used the data is as follows: I assumed that M = (AdjOffence - AdjDefense)* (AdjTempo)/100 gives an average margin of victory for the dataset. Then, M1 - M2 = margin of victory difference between the two team playing. To apply to the Michigan/Ohio State games gives
Michigan = (107.0 - 92.7) * (62.7/100) = 8.99
Ohio State = (118.9 - 89.8) * (65.8 / 100) = 19.1
Which predicts a 10.1 point margin of victory for OSU, pretty close to the actual KenPom prediction.
I'll save you all the eye chart of the data table. If you're interested, it's here:
The relevant data:
|team||adjusted tempo||adjusted offense||adjusted defense||difference||rd1|
|Texas El Paso||69.45||107.47143||88.3081||13.30893269|
|Nevada Las Vegas||67.3486||109.46974||90.69505||12.64449087|
|San Diego St.||64.6273||110.97383||92.09351||12.20184105|
Calculating the probable winners in this fashion gave a win/loss of 24/8. And, four of those that are wrong were predicted to be 3 point games, and ended up +/- 3. Here's the corresponding chart.
I calculated the total average margin of error (absolute value) for all games at 7.44, and margin of error in games correct at 7.07, and margin of error in games wrong at 10.6.
I next calculated the distribution of error. Since I used absolute value in the previous calculation, I ended up with half a bell-curve distribution. Data:
What's interesting is that this is a better prediction than just using KenPom as a relative rating. By picking solely based on the higher ranked team, the record is 23/9.
If you can draw any conclusion from all this, it is that Ken is pretty accurate, except when he's not. I didn't expect to be 100%, because I don't think any system out there will predict Georgetown, or Kansas or Villanova to lose, based on the numbers. But, by this point in the season, the system is remarkably accurate in predicting probable outcomes. It has some margin for error in predicted close games, but I don't think there's any system that would be able to predict close games, either. They just come down to the luck of the draw.