NCAA Men's Basketball 1999-2000
Top 25 Measures and Ranking

For current rankings, see Jeff Sagarin's rating index

Linear
Rank
04-04
Rank
02-29
Rank
1998-9
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 877Michigan State (32-7 NCAA:6-0 646/847/517) 22
2 2 12 865Cincinnati (29-4 NCAA:1-1 617/881/598) 76
3 4 1 858Duke (29-5 NCAA:2-1 607/848/677) 11
4 1 9 856Stanford (26-4 NCAA:1-1 588/858/649) 33
5 8 111 844Iowa State (30-5 NCAA:3-1 581/841/551) 67
6 3 16 822Arizona (27-7 NCAA:1-1 624/933/501) 44
7 15 21 820Florida (29-8 NCAA:5-1 614/858/710) 1311
8 5 47 814Oklahoma State (26-7 NCAA:3-1 599/881/527) 1415
9 11 106 805LSU (28-6 NCAA:2-1 558/806/581) 109
10 19 53 804Oklahoma (27-7 NCAA:1-1 588/891/700) 1213
11 8 41 803Syracuse (26-6 NCAA:2-1 581/781/565) 1614
11 10 6 803Ohio State (23-7 NCAA:1-1 593/865/599) 88
13 6 20 801Temple (27-6 NCAA:1-1 612/942/649) 55
14 17 39 795Tulsa (31-5 NCAA:3-1 528/865/464) 1819
15 13 66 792Texas (24-9 NCAA:1-1 648/877/727) 1518
16 14 15 788Indiana (20-9 NCAA:0-1 660/878/579) 2217
17 12 23 787Tennessee (25-7 NCAA:2-1 608/897/545) 1110
18 18 10 786St. John's (25-8 NCAA:1-1 618/935/485) 912
19 16 7 781Kentucky (22-10 NCAA:1-1 673/800/581) 1920
20 21 18 775Purdue (23-10 NCAA:3-1 641/848/569) 2524
21 24 2 774Connecticut (25-10 NCAA:1-1 623/858/599) 2021
22 24 44 771Illinois (22-10 NCAA:1-1 639/761/698) 2123
23 34 11 770Wisconsin (22-14 NCAA:4-1 698/852/607)   
24 23 29 764Kansas (23-10 NCAA:1-1 645/727/757)   
25 22 5 754Maryland (25-10 NCAA:1-1 623/935/637) 1716
Current Home Court Advantage = 77 BCUs

WeekendHome
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

  1. For Men's College Basketball: Top 300 teams rankings
  2. For College Football predictions & Top 25 rankings
  3. 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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