For current rankings, see Jeff Sagarin's rating index
Rank based on measures
04-04-2000 |
Rank
02-29-2000 |
Rank
1998-9 |
Linear (Rasch) Measures
in BCUs
04-04 |
Men's College Basketball Team
(won-loss: opponent strength / best win / worst loss) thru 04-03 |
AP Rank
03-13 | ESPN Rank
03-12 |
|---|
| 1 |
7 |
3 |
877 | Michigan State (32-7 NCAA:6-0 646/847/517)
| 2 | 2 |
| 2 |
2 |
12 |
865 | Cincinnati (29-4 NCAA:1-1 617/881/598)
| 7 | 6 |
| 3 |
4 |
1 |
858 | Duke (29-5 NCAA:2-1 607/848/677)
| 1 | 1 |
| 4 |
1 |
9 |
856 | Stanford (26-4 NCAA:1-1 588/858/649)
| 3 | 3 |
| 5 |
8 |
111 |
844 | Iowa State (30-5 NCAA:3-1 581/841/551)
| 6 | 7 |
| 6 |
3 |
16 |
822 | Arizona (27-7 NCAA:1-1 624/933/501)
| 4 | 4 |
| 7 |
15 |
21 |
820 | Florida (29-8 NCAA:5-1 614/858/710)
| 13 | 11 |
| 8 |
5 |
47 |
814 | Oklahoma State (26-7 NCAA:3-1 599/881/527)
| 14 | 15 |
| 9 |
11 |
106 |
805 | LSU (28-6 NCAA:2-1 558/806/581)
| 10 | 9 |
| 10 |
19 |
53 |
804 | Oklahoma (27-7 NCAA:1-1 588/891/700)
| 12 | 13 |
| 11 |
8 |
41 |
803 | Syracuse (26-6 NCAA:2-1 581/781/565)
| 16 | 14 |
| 11 |
10 |
6 |
803 | Ohio State (23-7 NCAA:1-1 593/865/599)
| 8 | 8 |
| 13 |
6 |
20 |
801 | Temple (27-6 NCAA:1-1 612/942/649)
| 5 | 5 |
| 14 |
17 |
39 |
795 | Tulsa (31-5 NCAA:3-1 528/865/464)
| 18 | 19 |
| 15 |
13 |
66 |
792 | Texas (24-9 NCAA:1-1 648/877/727)
| 15 | 18 |
| 16 |
14 |
15 |
788 | Indiana (20-9 NCAA:0-1 660/878/579)
| 22 | 17 |
| 17 |
12 |
23 |
787 | Tennessee (25-7 NCAA:2-1 608/897/545)
| 11 | 10 |
| 18 |
18 |
10 |
786 | St. John's (25-8 NCAA:1-1 618/935/485)
| 9 | 12 |
| 19 |
16 |
7 |
781 | Kentucky (22-10 NCAA:1-1 673/800/581)
| 19 | 20 |
| 20 |
21 |
18 |
775 | Purdue (23-10 NCAA:3-1 641/848/569)
| 25 | 24 |
| 21 |
24 |
2 |
774 | Connecticut (25-10 NCAA:1-1 623/858/599)
| 20 | 21 |
| 22 |
24 |
44 |
771 | Illinois (22-10 NCAA:1-1 639/761/698)
| 21 | 23 |
| 23 |
34 |
11 |
770 | Wisconsin (22-14 NCAA:4-1 698/852/607)
| | |
| 24 |
23 |
29 |
764 | Kansas (23-10 NCAA:1-1 645/727/757)
| | |
| 25 |
22 |
5 |
754 | Maryland (25-10 NCAA:1-1 623/935/637)
| 17 | 16 |
| Current Home Court Advantage = 77 BCUs |
| Weekend | Home
games |
Home
wins |
Home
win % |
All
games |
Predictions from last Sunday's rank |
|---|
| Mike's wins% | Sagarin's Rate wins% | Sagarin's Pure wins% |
|---|
| Week 1 | 134 | 104 | 77 | 165 | 76 | | |
| Week 2 | 196 | 148 | 75 | 276 | 73 | | |
| Week 3 | 263 | 193 | 73 | 289 | 74 | | |
| Week 4 | 232 | 154 | 66 | 241 | 74 | | |
| Week 5 | 162 | 132 | 81 | 162 | 78 | | |
| Week 6 | 144 | 99 | 68 | 176 | 69 | | |
| Week 7 | 225 | 172 | 76 | 266 | 74 | | |
| Week 8 | 308 | 197 | 63 | 308 | 67 | 73 | 74 |
| Week 9 | 319 | 200 | 63 | 319 | 68 | 72 | 69 |
| Week 10 | 318 | 202 | 64 | 318 | 68 | 70 | 75 |
| Week 11 | 304 | 189 | 62 | 304 | 73 | 74 | 76 |
| Week 12 | 320 | 196 | 61 | 320 | 73 | 73 | 74 |
| Week 13 | 312 | 213 | 68 | 312 | 76 | 77 | 78 |
| Week 14 | 307 | 172 | 56 | 307 | 74 | 76 | 75 |
| Week 15 | 310 | 187 | 60 | 310 | 71 | 73 | 73 |
| Week 16 | 18 | 15 | 83 | 18 | 72 | 72 | 67 |
- For Men's College Basketball: Top 300 teams rankings
- For College Football predictions & Top 25 rankings
- For College Football all 200+ rankings
How to predict
Logit measures predict as follows:
(a) Note down the BCUs of the guest team.
(b) Note down the BCUs of the home team.
(c) Add the home court advantage BCUs to the home team.
(d) The team with more BCUs will win (guest or home+advantage)
(e) Teams in the complete ranking of Top 300 will win over unranked teams.
Notes:
- Home court advantage is worth extra BCUs (Basketball Competency Units).
These are added to the home team's BCUs.
- An advantage of 70 BCU's gives a team "2-to-1 on" odds of winning.
- Based on Rasch analysis of published results of games between Div. I teams at time of
computation.
- Intended solely as the demonstration of a measurement method.
- Game scores and rankings were provided by a variety of
Media and Internet sources, including ABC, NBC, CBS, ESPN, AP, Yahoo and the
websites of the schools themselves.
Linear Measures produced by Mike Linacre
The Health Care Finance Administration (HCFA) has recently approved
Rasch "paired comparison" methodology for the identification of misvalued
procedures, see Total Physician Work.
Leon L. Thurstone perceived the necessity that
measures be independent of sampling and testing specifics. Measures
based on paired comparisons meet this criterion. Bradley and Terry proposed
this model but from a descriptive, rather than measurement, perspective.
Consequently, they noticed, but did not capitalize on, the linearity of the
measures produced. Georg Rasch perceived the linearity of measures produced
from ordinal data by logit-linear models.
Since mathematical expression
of such models obscures their immediate utility and ease of application,
they are illustrated here by a simple paired comparison analysis of NCAA
game outcomes. Obvious advantages include:
(a) all teams are ranked, not just the favored few
(b) all teams are located on a linear measurement scale
(c) all teams are given equal attention
(d) the data are easy to obtain, even for non-experts
(e) rare, idiosyncratic results can be indentified, and, even if left
unremedied, produce minimal distortion.
(f) it is noticeable, in these data, that the conventional ranking services
pay too much attention to won-loss record - and even particular wins and losses
(the immediate details), and not enough to strength of schedule (the big picture).
Thurstone, L.L. The method of paired comparisons for social
values. Journal of Abnormal and Social Psychology, 1927b, 21,
384-400.
Bradley, R.A. and Terry, M E. Rank analysis of incomplete
block designs I: The method of paired comparisons. Biometrika,
1952, 39, 324-345.
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