Computer Rating System Prediction Results for College Football (NCAA IA)

2021 Season Totals

Through 2022-01-11
Rank System Pct. Correct Against Spread Absolute Error Bias Mean Square Error games suw sul atsw atsl
1Line (Midweek)0.7116912.64290.4740246.300770548222
2Catherwood Ratings0.770270.5000013.74320.6351276.443148114347070
3Line (updated)0.718180.5000012.62470.4247245.711770553217165165
4Least Squares w/ HFA0.665840.5151515.65820.6268372.986401267134204192
5Brent Craig0.708430.5194213.07990.5354263.541415294121214198
6Linear Regression0.690770.4974713.19280.4825267.460401277124197199
7Bihl System0.693240.4939213.07530.2528258.480414287127203208
8Logistic Regression0.678300.4722215.4058-2.1711369.469401272129187209
9Computer Adjusted Line0.712990.4767412.65910.4734246.306770549221205225
10Roundtable0.692310.5019813.62660.7186293.881533369164253251
11FEI Projections0.682770.5041213.03170.3315257.875621424197306301
12Line (opening)0.707790.5000012.75260.2760251.696770545225337337
13Dunkel Index0.676300.4978013.50551.3240279.919692468224340343
14Laffaye RWP0.701410.5035913.86640.2000304.175710498212351346
15Pigskin Index0.683120.5021113.19470.9822262.218770526244357354
16Talisman Red0.689940.4908113.34320.1340271.022716494222347360
17Keeper0.710350.5153113.42451.2305283.635763542221387364
18DP Dwiggins0.691910.4958913.92950.3133304.002766530236362368
19ARGH Power Ratings0.716880.5000013.4558-0.1104281.555770552218369369
20Stat Fox0.687010.4903813.41172.1963274.644770529241357371
21Pi-Rate Ratings0.698700.5053113.20491.0277264.988770538232381373
22Howell0.708330.4938913.5013-0.1457282.844768544224364373
23Stephen Kerns0.688310.5072513.90701.0216301.969770530240385374
24Dave Congrove0.682700.5052813.72530.9936288.525769525244383375
25Pi-Ratings Mean0.709090.4939813.13661.0153261.875770546224369378
26Super List0.712990.4993415.29311.5619355.876770549221378379
27PI-Rate Bias0.710390.4953813.20861.1340264.793770547223375382
28Loudsound.org0.653950.4796214.7039-2.9618340.061760497263353383
29Beck Elo0.696100.4947413.45830.7619284.370770536234376384
30Moore Power Ratings0.692210.4881613.42320.6346279.246770533237371389
31Massey Consensus0.728570.4874813.59831.2433283.608770561209370389
32Laz Index0.714290.4868113.16090.6398263.527770550220369389
33TeamRankings.com0.703900.4827113.11300.7473263.278770542228363389
34System Average0.706490.4861712.96460.5539255.949770544226369390
35Born Power Index0.684420.4855313.54101.2933278.308770527243369391
36Donchess Inference0.716880.4738313.28990.2138267.461770552218353392
37Cleanup Hitter0.668830.4731214.32280.8333319.305770515255352392
38Massey Ratings0.690910.4828913.4370-0.0081276.138770532238367393
39Billingsley0.711690.4822113.88260.1543304.154770548222366393
40Edward Kambour0.709090.4815813.28060.6883265.528770546224366394
41PerformanZ Ratings0.709090.4795814.02440.5273308.273770546224364395
42System Median0.701300.4741012.95980.5655255.160770540230357396
43Sagarin Recent0.692210.4789513.56770.4317292.251770533237364396
44Payne W/L0.712990.4762514.3145-0.7173318.431770549221361397
45Versus Sports Simulator0.701430.4676413.32600.6634271.270767538229354403
46ESPN FPI0.698700.4677213.02181.1573260.627770538232355404
47Sagarin Golden Mean0.681820.4671113.51860.3441283.960770525245355405
48Payne Power Ratings0.703900.4671113.6009-0.1149286.661770542228355405
49Daniel Curry Index0.689610.4671113.87910.1010298.036770531239355405
50Payne Predict0.690910.4631613.86720.3339301.146770532238352408
51Dokter Entropy0.716510.4597113.14050.5041262.208769551218348409
52David Harville0.703900.4591013.14670.2825262.413770542228348410
53Sagarin Points0.692210.4585013.35170.2749270.378770533237348411
54Sagarin Ratings0.690910.4473713.25410.2903266.691770532238340420
* This system does not make predictions.  I make predictions for this
  system by translating it to a new scale that allows for making predictions.



Retrodictive records are found by taking the ratings from the current week
and applying them to the entire season to date.

The ideal system would be one that has the highest correct game decisions,
has the smallest mean error(deviation from the actual game result), and has
a bias of zero.

Mean Error = average[abs(prediction-actual)]

      Bias = agerage(prediction - actual)

      Std. = Standard Deviation of individual game biases