For all your fancy-pants statistical needs.

Praise for The Basketball Distribution:

"...confusing." - CBS
"...quite the pun master." - ESPN

The One-Seeds

I told my friend Stephen that Kentucky will not be a 1-seed come tournament time.

That was a pretty dumb thing to say

I picked the top few teams that I thought might make #1 seeds, and did some analysis from their stats from

Anyways, here's my #1 seed bracketology:

Time Left on Shot Clock

By doing some simple multiplication and division of stats from, we can estimate the mean/median/expected number of seconds left on the shot clock when a team's possession will end.

I expect a high standard deviation of this number for most teams, but it is interesting to look at.

Here's the results (internet explorer might be required, hopefully not)

Adjusted Player Offensive Ratings

I adjusted Ken Pomeroy's 100 most efficient college players (with a minimum of 40% minutes played) for opponents' quality of defense.

The results are here.

(EDIT: the Usage% represents how much a teams' possessions a player ends up 'using' via shots/turnovers/etc. players under 20% are below average in usage. I will soon adjust only those who are above the 20% mark)

Texas v. UNC

Ken Pomeroy's Stats predict Carolina to lose to Texas by 20 points. Here's my basic info you need to know on these 2 teams:

1) Texas' point margin vs. predicted has a standard deviation of about 8.07 points
2) North Carolina's point margin vs. predicted has a standard deviation of about 9.86

this gives us an average of 8.97 for both teams
which means that there is, according to the normal distribution:

-a 68.2% chance that Carolina's final margin is between {-11 and -29}
-a 95.4% chance that Carolina's final margin is between {-2 and -38}

Simply by using standard deviations, Carolina has a 1.29% chance of winning, less than Ken Pomeroy's estimation (using the Log5 method) of 5%

Nathan's Statistical Rankings

Here is a link to my statistical rating of college basketball teams, according to my best possible model given the stats I currently have (which is similar in nature to the LRMC model, and similar in appearance to Sagarin ratings).

Hopefully in January I will have a model adjusted including diminishing returns, consistency, and 'game point margin' which accurately reflects the 'real score' of a game, rather than one that was altered in the last 30 seconds to a game-insignificant-degree. (To do this, we will use Bill James' "time statistically over" stat from

UNC's terrible 2nd halves

Carolina is beating their opponents by .36 points per possession in the first half of their games.
But in the second half, they average -.04 points per possession.

Not good!


About Me

I wish my heart were as often large as my hands.