I'll do a very general description of my methodology, with the caveat that this process is super un-scientific and probably just as likely to be flawed as any prediction done off hand by scanning the rosters. Don't worry -- I honestly didn't spend very much time on these crappy predictions.. i've taken a few classes in Excel/VBA so I was able to get this done during a really boring class today lol

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I copied and pasted the team rosters from the league page using Excel's data import tool, and I subsequently used Excel formulas to rate the teams in various areas such as Assists, Def Rating, Off Rebounding prowress, turnover ratings, etc. This was generally done for most categories using things like the advanced stats or usage tiers in conjunction with the minutes played and the like. The category rating numbers themselves of these ratings don't really tell you much for the most part, but the relative amounts compared to other teams do.

Using those category ratings, i ranked each team from 1-24 in each of the categories.

These rankings from 1-24 in each category were then put into an arbritary formula that I created that is likely completely flawed based on my own intuition to try to guess how good each team is. The formula I used is:

2.5 * Usg tiered points

7 * efg

2* (created Turnover rating)

5*O reb

1.5*FT rating

3*Ast %

6*Def Reb

9*Def

The lowest scores based on the composite scores from the above formula were ranked first.

Note: Each stat was scaled to a per minute amount, meaning that drafting above or below 19680 minutes shouldn't affect the data too much. However, this means that I tended to overrate teams with low minutes (who will encounter fatigue I don't take into account) and underrate teams with too many minutes who can cut the inefficient minutes out

Also, the ranking of teams from 1 to 24 is dumb, not scientific, and probably not as predictive as i'd like it to be. And it probably overrates my team, since I'm generally drafting with the same ideas in mind that the formula reflects.

Anyways.... the final rankings (in order)

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Rank	Teams	Usg*Min	RankUsg*Min	eFG%Min	RankeFG%Min	OReb%Min	RankOReb%Min	DReb%Min	RankDReb%Min	Ast%Min	RankAst%Min	USG PtsMin	RankUSG PtsMin	Def RtgMin	RankDef RtgMin	FT Rating	RankFT Rating	Turnt	RankTurnt	Comp Score (High is bad)	Comp Rank
1	benjihall	18.80561	23	51.56625	8	6.166191	2	17.53434	1	9.45388	24	2.147901	21	57.30006	4	338.4362	18	2.576141	11	281.5	1
2	krishnaman	19.40837	20	53.40109	2	5.831687	6	14.33861	17	10.80148	16	2.45205	14	60.59405	1	363.783	17	2.68871	15	293.5	2
3	eleibowitz	20.78462	9	51.76907	7	5.242338	11	16.14405	3	12.61184	10	2.538542	12	51.37246	11	375.6275	15	2.788849	18	339.5	3
4	klinke	18.03017	24	52.36327	4	5.349606	8	14.82005	11	12.49735	11	1.908621	24	51.4331	10	289.456	22	2.636387	12	374	4
5	uofa	19.03579	21	52.38729	3	6.095679	3	15.74136	5	12.89648	7	2.239375	19	45.03996	22	314.7599	20	2.575721	10	382.5	5
6	jkaye24	18.87025	22	51.82453	6	5.297731	10	15.76319	4	10.6567	18	2.055752	23	50.25461	14	336.5679	19	2.543263	6	394	6
7	robusk	21.1888	6	51.31859	13	4.740184	18	15.0151	10	13.63684	3	2.832467	8	51.51319	9	488.7314	5	2.798862	19	396.5	7
8	miller97	20.32636	12	52.04886	5	5.111192	12	13.39859	22	10.56703	20	2.426107	15	57.03002	6	436.4471	11	2.406333	2	399	8
9	natenoy	21.75079	3	49.2931	21	4.575811	20	15.06225	9	13.43965	5	3.328436	3	59.23649	3	573.4476	3	3.058295	23	401	9
10	98average	20.91895	7	51.33897	12	4.601545	19	13.46765	21	13.42517	6	2.998889	5	57.08468	5	476.2143	7	2.698789	16	423	10
11	dh555	19.50592	19	51.39333	11	5.699118	7	15.4903	7	12.81231	9	2.209154	20	48.77174	17	370.6905	16	2.573659	9	426	11
12	tarheel1991	20.49544	10	50.80386	16	4.762546	17	14.35487	16	13.62767	4	2.861185	7	55.26885	7	592.1081	1	2.870512	22	431	12
13	thomcat	20.8256	8	50.84115	15	5.844969	5	15.4715	8	11.26359	14	2.732804	10	49.99858	15	412.4586	13	2.711993	17	433.5	13
14	bigmac945	20.27508	13	49.63188	20	5.933723	4	16.33965	2	13.97204	2	2.532693	13	47.27115	21	280.6067	23	2.540851	5	444	14
15	bigkochman	19.64653	18	50.99492	14	6.894038	1	14.77701	12	10.31022	23	2.123168	22	51.26551	12	238.5321	24	2.559054	7	457	15
16	benhoidal	20.46556	11	53.78411	1	4.195788	23	15.51229	6	11.75313	13	2.354065	16	48.01862	19	312.7814	21	2.820926	20	479.5	16
17	Jgreene	21.41714	4	47.46501	24	4.944795	15	13.53587	20	10.43416	21	3.108508	4	60.33668	2	464.8368	8	2.654236	13	492	17
18	bkbillups	20.05304	16	50.27441	18	5.314651	9	14.16504	18	15.04075	1	2.733047	9	48.88717	16	435.5292	12	2.67061	14	494.5	18
19	shawnnj	20.08863	15	50.80327	17	4.973751	14	14.71009	14	10.39171	22	2.330276	17	50.59397	13	409.6952	14	2.483884	3	525.5	19
20	hoopsrus	21.21315	5	49.95524	19	4.803774	16	14.73927	13	11.13669	15	2.919899	6	48.13512	18	507.4782	4	2.503711	4	527	20
21	edsortails	20.15945	14	51.42687	9	5.09869	13	13.06775	23	10.67665	17	2.61193	11	44.74492	23	442.8852	10	2.317743	1	568.5	21
22	rjk2781	22.68104	2	48.45412	22	2.935603	24	11.62181	24	12.86063	8	3.418242	2	53.9793	8	487.3782	6	2.841754	21	570	22
23	smokey57	19.70735		51.41465	10	4.250684	22	14.05503	19	10.60197	19	2.289055	18	48.01192	20	447.1319	9	2.568173	8	605.5	23
24	Yankeejoe5	23.68335	1	47.68166	23	4.505611	21	14.50056	15	12.40238	12	4.060515	1	43.58993	24	579.54	2	3.074578	24	661.5	24

More detailed spreadsheet found here (complete with kinda complicated calculations for every team. if you are a pro with excel, then you might wanna look at them maybe):

https://drive.google.com/file/d/0B6GU2xzrSEuRdjE3Q011dWRkWXc/edit?usp=sharing
(download into excel)

Wow cool Krish.