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.

	team	win% diff	pyth rnk (adj rnk)	adj win%
1	Gonzaga	8.7%	34 (20)	93.5%
2	Murray St.	8.7%	50 (31)	89.1%
3	Temple	8.6%	36 (21)	92.7%
4	Creighton	8.5%	55 (33)	88.2%
5	Indiana	8.1%	14 (5)	98.2%
6	Miami FL	7.7%	63 (55)	83.9%
7	Lehigh	7.6%	88 (67)	77.9%
8	Tulsa	7.3%	95 (75)	74.6%
9	Connecticut	7.2%	23 (12)	95.4%
10	Long 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% diff	pyth rnk (adj rnk)	adj win%
1	California	-2.7%	20 (44)	86.4%
2	Purdue	-2.7%	15 (40)	87.0%
3	Virginia	-0.4%	24 (36)	87.8%
4	Florida St.	-0.1%	32 (48)	85.8%
5	Baylor	0.7%	13 (25)	90.9%
6	West Virginia	1.2%	33 (46)	86.3%
7	St. Louis	1.2%	22 (30)	89.6%
8	Missouri	1.2%	8 (17)	94.0%
9	Marquette	1.5%	6 (13)	94.7%
10	New Mexico	1.8%	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:

	team	adj win%	pyth	diff
1	Kentucky	0.9913	0.9686	0.0227
2	Ohio St.	0.9907	0.9647	0.0259
3	Syracuse	0.9890	0.9511	0.0379
4	North Carolina	0.9885	0.9519	0.0366
5	Indiana	0.9817	0.9011	0.0806
6	Kansas	0.9813	0.9310	0.0503
7	Wisconsin	0.9812	0.9733	0.0080
8	Duke	0.9789	0.9264	0.0525
9	Michigan St.	0.9740	0.9155	0.0586
10	Florida	0.9710	0.9158	0.0552
11	Louisville	0.9690	0.9237	0.0452
12	Connecticut	0.9539	0.8816	0.0723
13	Marquette	0.9471	0.9321	0.0149
14	Brigham Young	0.9430	0.8964	0.0467
15	Xavier	0.9406	0.8957	0.0448
16	Georgetown	0.9400	0.8933	0.0467
17	Missouri	0.9397	0.9272	0.0124
18	Wichita St.	0.9359	0.8794	0.0565
19	Stanford	0.9355	0.8967	0.0388
20	Gonzaga	0.9353	0.8485	0.0868
21	Temple	0.9269	0.8408	0.0861
22	Texas	0.9234	0.8812	0.0422
23	Nevada Las Vegas	0.9124	0.8742	0.0382
24	Alabama	0.9119	0.8851	0.0268
25	Baylor	0.9086	0.9012	0.0074

 
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.