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MinV and MVP College Football Rankings |
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Performance
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Below is an assessment of the ranking systems posted on Kenneth Massey's ranking compilation page for this season. Using a simple form of Data Envelopment Analysis and software from Holger Scheel, each system is given an efficiency score representing how close its combined retrodictive and predictive performance is to the efficient frontier. Retrodictive performance is measured by the number of past head-to-head results that were correctly matched by the weekly ranking (the first number in the "Retrodictive" column below), while predictive performance is measured by the number of games in the following week that were correctly predicted (the first number in the "Predictive" column below). If one were to plot each system's performance, with retrodictive performance on one axis and predictive performance on the other, the efficient frontier would be formed by the most extreme systems in all possible directions. The efficient frontier approach is an arguably fair way to compare ranking systems regardless of whether they focus on retrodictive or predictive performance (or some combination thereof). The approach determines the implicit weight that a system places on each objective, and uses these weights (shown in parenthese below) to determine the appropriate comparison point on the efficient frontier. Each system is thus evaluated only against peers with a similar combination of objectives. Any system with 100% efficiency is on the efficient frontier, meaning that if the weights noted for that system are applied, no other system surpasses it. If a system has an efficiency less than 100%, then even if the weights noted for the system are applied, there is at least one other system (or a linear combination of other systems) whose performance is better. Columns can be sorted from high to low by clicking on the column header. Results shown are for games on or before December 2, 2017 using rankings posted through the games of the respective preceding week. Note that the system abbreviations are the same as those used by Massey, who denotes the MinV ranking as "CMV" and the MVP ranking as "MVP." |
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Full Season Last Week
Rnk System Efficiency Retrodictive Predictive Efficiency Retrodictive Predictive
1 ABC 81.12% 3713 (1.00) 331 (.000) 96.78% 593 (.690) 13 (.310)
2 ACU 88.81% 4065 (1.00) 460 (.000) 95.65% 583 (.690) 13 (.310)
3 AND 54.95% 2515 (1.00) 178 (.000) 97.00% 595 (.690) 13 (.310)
4 ARG 98.38% 4046 (.090) 533 (.910) 92.34% 574 (.700) 12 (.300)
5 ASH 80.53% 3686 (1.00) 342 (.000) 95.76% 584 (.690) 13 (.310)
6 ATC 39.17% 1793 (1.00) 166 (.000) 88.56% 581 (.740) 10 (.260)
7 BAS 89.73% 3863 (.090) 484 (.910) 87.20% 569 (.740) 10 (.260)
8 BBT 95.54% 3976 (.090) 517 (.910) 92.00% 571 (.700) 12 (.300)
9 BCM 65.94% 3018 (1.00) 302 (.000) 92.68% 577 (.700) 12 (.300)
10 BDF 97.16% 3867 (.080) 528 (.920) 84.89% 562 (1.00) 9 (.000)
11 BIH 86.02% 3937 (1.00) 367 (.000) 97.00% 595 (.690) 13 (.310)
12 BIL 98.38% 4125 (.090) 532 (.910) 93.81% 587 (.710) 12 (.290)
13 BMC 75.01% 3117 (.090) 406 (.910) 93.13% 581 (.710) 12 (.290)
14 BOW 70.59% 3231 (1.00) 295 (.000) 96.10% 587 (.690) 13 (.310)
15 BRN 99.37% 3952 (.080) 540 (.920) 89.71% 571 (.720) 11 (.280)
16 BSS 86.34% 3952 (1.00) 402 (.000) 95.54% 582 (.690) 13 (.310)
17 BWE 98.59% 3983 (.080) 535 (.920) 89.94% 573 (.720) 11 (.280)
18 CGV 96.85% 3958 (.090) 525 (.910) 93.50% 564 (.680) 13 (.320)
19 CI 32.82% 1502 (1.00) 163 (.000) . . (. ) . (. )
20 CMV 100.0% 4577 (.950) 536 (.050) 100.0% 662 (1.00) 11 (.000)
21 COF 80.14% 3668 (1.00) 341 (.000) 95.31% 580 (.690) 13 (.310)
22 COL 88.44% 4048 (1.00) 431 (.000) 96.21% 588 (.690) 13 (.310)
23 CSL 87.00% 3982 (1.00) 424 (.000) 95.99% 586 (.690) 13 (.310)
24 CTW 96.03% 3971 (.090) 520 (.910) 88.33% 579 (.740) 10 (.260)
25 Con 98.88% 4122 (.090) 535 (.910) 95.54% 582 (.690) 13 (.310)
26 DCI 97.62% 4005 (.090) 529 (.910) 86.71% 574 (1.00) 9 (.000)
27 DES 97.62% 4006 (.090) 529 (.910) 87.88% 575 (.740) 10 (.260)
28 DEZ 96.25% 3996 (.090) 521 (.910) 90.17% 575 (.720) 11 (.280)
29 DII 98.79% 4079 (.090) 535 (.910) 98.50% 588 (.670) 14 (.330)
30 DOK 100.0% 3932 (.000) 544 (1.00) 90.39% 577 (.720) 11 (.280)
31 DOL 88.60% 4055 (1.00) 399 (.000) 96.44% 590 (.690) 13 (.310)
32 DUN 51.93% 2164 (.090) 281 (.910) . . (. ) . (. )
33 DWI 97.38% 4050 (.090) 527 (.910) 93.25% 582 (.710) 12 (.290)
34 ENG 95.30% 4023 (.090) 515 (.910) 93.25% 582 (.710) 12 (.290)
35 EZ 81.21% 3717 (1.00) 327 (.000) 97.00% 595 (.690) 13 (.310)
36 FAS 84.12% 3850 (1.00) 375 (.000) 94.97% 577 (.690) 13 (.310)
37 FEI 85.46% 3754 (.090) 460 (.910) 90.87% 561 (.700) 12 (.300)
38 FMG 85.16% 3898 (1.00) 373 (.000) 96.33% 589 (.690) 13 (.310)
39 FPI 99.53% 3947 (.080) 541 (.920) 89.71% 571 (.720) 11 (.280)
40 GBE 68.71% 3145 (1.00) 254 (.000) 95.65% 583 (.690) 13 (.310)
41 GLD 74.90% 3428 (1.00) 290 (.000) 95.08% 578 (.690) 13 (.310)
42 GRR 59.71% 2733 (1.00) 218 (.000) 100.0% 581 (.000) 15 (1.00)
43 HAT 94.84% 4128 (.090) 511 (.910) 95.54% 582 (.690) 13 (.310)
44 HEN 79.97% 3660 (1.00) 336 (.000) 95.42% 581 (.690) 13 (.310)
45 HKB 84.66% 3875 (1.00) 374 (.000) 95.76% 584 (.690) 13 (.310)
46 HNL 78.41% 3589 (1.00) 339 (.000) 94.86% 576 (.690) 13 (.310)
47 HOW 96.18% 4041 (.090) 520 (.910) 94.97% 577 (.690) 13 (.310)
48 HUF 34.91% 1598 (1.00) 98 (.000) 92.57% 576 (.700) 12 (.300)
49 ISR 80.84% 3700 (1.00) 328 (.000) 96.67% 592 (.690) 13 (.310)
50 JNK 85.14% 3897 (1.00) 364 (.000) 96.10% 587 (.690) 13 (.310)
51 JTR 86.24% 3947 (1.00) 411 (.000) 94.86% 576 (.690) 13 (.310)
52 JWN 52.33% 2395 (1.00) 180 (.000) 95.90% 565 (.670) 14 (.330)
53 KAM 98.14% 3930 (.080) 533 (.920) 92.34% 574 (.700) 12 (.300)
54 KEE 75.11% 3438 (1.00) 303 (.000) 95.54% 582 (.690) 13 (.310)
55 KEL 95.71% 4057 (.090) 517 (.910) 95.08% 578 (.690) 13 (.310)
56 KEN 79.05% 3618 (1.00) 329 (.000) 94.41% 572 (.680) 13 (.320)
57 KH 44.19% 2010 (.100) 237 (.900) . . (. ) . (. )
58 KLK 77.10% 3529 (1.00) 342 (.000) 92.23% 573 (.700) 12 (.300)
59 KNT 69.22% 3168 (1.00) 260 (.000) 96.33% 589 (.690) 13 (.310)
60 KPK 97.30% 4011 (.090) 527 (.910) 95.31% 580 (.690) 13 (.310)
61 KRA 36.12% 1653 (1.00) 97 (.000) 97.00% 595 (.690) 13 (.310)
62 LAB 67.90% 3108 (1.00) 256 (.000) 92.23% 573 (.700) 12 (.300)
63 LAZ 97.63% 4088 (.090) 528 (.910) 94.52% 573 (.690) 13 (.310)
64 LMC 75.09% 3437 (1.00) 341 (.000) 91.78% 569 (.700) 12 (.300)
65 LOG 96.33% 3954 (.090) 522 (.910) 86.86% 575 (1.00) 9 (.000)
66 LSD 88.55% 4053 (1.00) 459 (.000) 95.54% 582 (.690) 13 (.310)
67 MAR 95.41% 3997 (.090) 516 (.910) 94.18% 570 (.680) 13 (.320)
68 MAS 97.80% 4089 (.090) 529 (.910) 94.15% 590 (.710) 12 (.290)
69 MCK 80.10% 3666 (1.00) 294 (.000) 94.71% 627 (1.00) 10 (.000)
70 MDS 74.46% 3408 (1.00) 289 (.000) 95.20% 579 (.690) 13 (.310)
71 MGN 79.13% 3622 (1.00) 333 (.000) 91.89% 570 (.700) 12 (.300)
72 MGS 94.51% 4132 (.090) 509 (.910) 93.13% 581 (.710) 12 (.290)
73 MJS 74.94% 3430 (1.00) 285 (.000) 95.42% 581 (.690) 13 (.310)
74 MOR 96.97% 3933 (.080) 526 (.920) 85.35% 565 (1.00) 8 (.000)
75 MRK 80.45% 3682 (1.00) 329 (.000) 95.99% 586 (.690) 13 (.310)
76 MTN 61.85% 2831 (1.00) 222 (.000) 95.08% 578 (.690) 13 (.310)
77 MvG 80.40% 3680 (1.00) 327 (.000) 95.76% 584 (.690) 13 (.310)
78 NGS 50.27% 2301 (1.00) 231 (.000) 91.10% 563 (.700) 12 (.300)
79 NUT 93.28% 3866 (.090) 505 (.910) 92.46% 575 (.700) 12 (.300)
80 OSC 89.60% 3557 (.080) 487 (.920) 95.31% 580 (.690) 13 (.310)
81 PAY 97.83% 4102 (.090) 529 (.910) 94.74% 575 (.690) 13 (.310)
82 PCP 84.51% 3868 (1.00) 375 (.000) 91.98% 591 (.730) 11 (.270)
83 PFZ 88.85% 3600 (.080) 482 (.920) 88.25% 558 (.710) 11 (.290)
84 PGH 95.83% 4198 (.090) 516 (.910) 96.67% 592 (.690) 13 (.310)
85 PIG 99.17% 4019 (.080) 538 (.920) 87.88% 575 (.740) 10 (.260)
86 PIR 98.35% 4109 (.090) 532 (.910) 95.87% 585 (.690) 13 (.310)
87 PIT 68.39% 3130 (1.00) 365 (.000) . . (. ) . (. )
88 PPP 92.72% 4082 (.090) 499 (.910) 95.99% 586 (.690) 13 (.310)
89 RBA 33.58% 1537 (1.00) 97 (.000) 87.91% 555 (.710) 11 (.290)
90 REW 69.46% 3179 (1.00) 255 (.000) 91.30% 585 (.720) 11 (.280)
91 RME 84.33% 3860 (1.00) 363 (.000) 93.36% 583 (.710) 12 (.290)
92 RSL 80.05% 3664 (1.00) 374 (.000) 92.12% 572 (.700) 12 (.300)
93 RT 83.79% 3835 (1.00) 381 (.000) 92.91% 579 (.700) 12 (.300)
94 RTB 93.31% 4271 (1.00) 359 (.000) 99.43% 657 (.750) 11 (.250)
95 RTH 69.83% 3196 (1.00) 257 (.000) 96.55% 591 (.690) 13 (.310)
96 RTP 95.34% 4125 (.090) 514 (.910) 92.31% 594 (.730) 11 (.270)
97 RUD 98.62% 4160 (.090) 533 (.910) 96.21% 588 (.690) 13 (.310)
98 RWP 95.35% 4050 (.090) 515 (.910) 95.65% 583 (.690) 13 (.310)
99 S&P 80.99% 3707 (1.00) 434 (.000) 89.97% 553 (.690) 12 (.310)
100 SAG 97.95% 4001 (.090) 531 (.910) 88.44% 580 (.740) 10 (.260)
101 SEL 81.47% 3729 (1.00) 337 (.000) 96.21% 588 (.690) 13 (.310)
102 SFX 77.58% 3551 (1.00) 374 (.000) 88.13% 557 (.710) 11 (.290)
103 SGR 78.50% 3593 (1.00) 335 (.000) 93.25% 582 (.710) 12 (.290)
104 SMS 99.67% 4018 (.080) 541 (.920) 95.08% 578 (.690) 13 (.310)
105 SOR 82.57% 3583 (.090) 445 (.910) 97.12% 596 (.690) 13 (.310)
106 SP 29.56% 1308 (.090) 159 (.910) . . (. ) . (. )
107 STH 12.54% 574 (1.00) 14 (.000) 96.92% 574 (.670) 14 (.330)
108 TFG 34.08% 1560 (1.00) 103 (.000) 90.42% 557 (.700) 12 (.300)
109 TPR 92.57% 3849 (.090) 501 (.910) 89.15% 566 (.720) 11 (.280)
110 TRP 72.56% 3321 (1.00) 303 (.000) 90.28% 576 (.720) 11 (.280)
111 VRN 75.66% 3463 (1.00) 291 (.000) 96.44% 590 (.690) 13 (.310)
112 WEL 84.68% 3876 (1.00) 361 (.000) 95.65% 583 (.690) 13 (.310)
113 WIL 86.32% 3951 (1.00) 363 (.000) 97.23% 597 (.690) 13 (.310)
114 WLK 82.81% 3790 (1.00) 379 (.000) 95.54% 582 (.690) 13 (.310)
115 WOB 35.83% 1640 (1.00) 95 (.000) 96.78% 593 (.690) 13 (.310)
116 WOL 69.87% 3198 (1.00) 258 (.000) 96.67% 592 (.690) 13 (.310)
117 WWP 45.12% 2065 (1.00) 139 (.000) 95.31% 580 (.690) 13 (.310)
118 YAG 82.35% 3059 (.000) 448 (1.00) 88.47% 560 (.720) 11 (.280)
119 YCM 74.87% 3427 (1.00) 327 (.000) 94.18% 570 (.680) 13 (.320)
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B. Jay Coleman, Ph.D. Richard deR. Kip Professor of Operations Management & Quantitative Methods Department of Management | Coggin College of Business | University of North Florida | Jacksonville, FL 32224 jcoleman@unf.edu Disclaimer |
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