For all your fancy-pants statistical needs.

Praise for The Basketball Distribution:

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

Indiana great. Gonzaga good. Cal mediocre.

This is my original method for rating teams after which my blog is named, although I've never done league-wide rankings.The normal distribution states that each team's chance of winning is dependent on their strength (efficiency or point differential) and the consistency of that strength (the standard deviation of efficiency differential).

I adjust standard deviation for competition by simply using Actual minus Expected point margin.*

Let's look at the ten teams who gain the most in terms of win% when we adjust for consistency.

teamwin% diffpyth rnk (adj rnk)adj win%
1Gonzaga8.7%34 (20)93.5%
2Murray St.8.7%50 (31)89.1%
3Temple8.6%36 (21)92.7%
4Creighton8.5%55 (33)88.2%
5Indiana8.1%14 (5)98.2%
6Miami FL7.7%63 (55)83.9%
7Lehigh7.6%88 (67)77.9%
8Tulsa7.3%95 (75)74.6%
9Connecticut7.2%23 (12)95.4%
10Long Beach St.7.0%47 (35)87.8%

Gonzaga's actual versus expected point margin only has a standard deviation of 6.1 - this is significantly low.

Next, the ten teams in Pomeroy's top-100++ who drop the most.
(++The list was mostly bottom-feeders anyways, so we'll just look at teams we care more about).

win% diffpyth rnk (adj rnk)adj win%
1California-2.7%20 (44)86.4%
2Purdue-2.7%15 (40)87.0%
3Virginia-0.4%24 (36)87.8%
4Florida St.-0.1%32 (48)85.8%
5Baylor0.7%13 (25)90.9%
6West Virginia1.2%33 (46)86.3%
7St. Louis1.2%22 (30)89.6%
8Missouri1.2%8 (17)94.0%
9Marquette1.5%6 (13)94.7%
10New Mexico1.8%37 (50)85.3%

Cal plays very strongly on average, but has an adjusted standard deviation of 15.7. Quite inconsistent.

And here are the top 25 overall:

teamadj win%pythdiff
2Ohio St.0.99070.96470.0259
4North Carolina0.98850.95190.0366
9Michigan St.0.97400.91550.0586
14Brigham Young0.94300.89640.0467
18Wichita St.0.93590.87940.0565
23Nevada Las Vegas0.91240.87420.0382
Yes, yes. The Hoosiers are doing quite well....

*- While explaining individual parts of a team or player in "tempo-free" language is beneficial, describing a team by "efficiency margin" rather than "point margin" is not particularly beneficial. In the NBA, point margin usually predicts win-percentage only slightly worse than efficiency margin (although last year, point margin did better). However, for numerous statistical and tactical reasons, pace is important to consider, in my opinion, when rating teams. It is much easier for a team to put away opponents if they are more efficient at a much quicker pace; statistically this makes sense: the more samples we have, the better chance the final output is near our expected output. Furthermore, I don't have access to game-by-game efficiency margin, so the only way I can measure standard deviations/variance/consistency is by measuring Pomeroy-Ratings's expected point margin versus the actual game point margin.

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