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
1Keeper0.710350.5153113.42451.2305283.635763542221387364
2Stephen Kerns0.688310.5072513.90701.0216301.969770530240385374
3Dave Congrove0.682700.5052813.72530.9936288.525769525244383375
4Pi-Rate Ratings0.698700.5053113.20491.0277264.988770538232381373
5Super List0.712990.4993415.29311.5619355.876770549221378379
6Beck Elo0.696100.4947413.45830.7619284.370770536234376384
7PI-Rate Bias0.710390.4953813.20861.1340264.793770547223375382
8Moore Power Ratings0.692210.4881613.42320.6346279.246770533237371389
9Massey Consensus0.728570.4874813.59831.2433283.608770561209370389
10Born Power Index0.684420.4855313.54101.2933278.308770527243369391
11Pi-Ratings Mean0.709090.4939813.13661.0153261.875770546224369378
12ARGH Power Ratings0.716880.5000013.4558-0.1104281.555770552218369369
13Laz Index0.714290.4868113.16090.6398263.527770550220369389
14System Average0.706490.4861712.96460.5539255.949770544226369390
15Massey Ratings0.690910.4828913.4370-0.0081276.138770532238367393
16Billingsley0.711690.4822113.88260.1543304.154770548222366393
17Edward Kambour0.709090.4815813.28060.6883265.528770546224366394
18Howell0.708330.4938913.5013-0.1457282.844768544224364373
19Sagarin Recent0.692210.4789513.56770.4317292.251770533237364396
20PerformanZ Ratings0.709090.4795814.02440.5273308.273770546224364395
21TeamRankings.com0.703900.4827113.11300.7473263.278770542228363389
22DP Dwiggins0.691910.4958913.92950.3133304.002766530236362368
23Payne W/L0.712990.4762514.3145-0.7173318.431770549221361397
24Pigskin Index0.683120.5021113.19470.9822262.218770526244357354
25System Median0.701300.4741012.95980.5655255.160770540230357396
26Stat Fox0.687010.4903813.41172.1963274.644770529241357371
27Sagarin Golden Mean0.681820.4671113.51860.3441283.960770525245355405
28Daniel Curry Index0.689610.4671113.87910.1010298.036770531239355405
29ESPN FPI0.698700.4677213.02181.1573260.627770538232355404
30Payne Power Ratings0.703900.4671113.6009-0.1149286.661770542228355405
31Versus Sports Simulator0.701430.4676413.32600.6634271.270767538229354403
32Loudsound.org0.653950.4796214.7039-2.9618340.061760497263353383
33Donchess Inference0.716880.4738313.28990.2138267.461770552218353392
34Cleanup Hitter0.668830.4731214.32280.8333319.305770515255352392
35Payne Predict0.690910.4631613.86720.3339301.146770532238352408
36Laffaye RWP0.701410.5035913.86640.2000304.175710498212351346
37Sagarin Points0.692210.4585013.35170.2749270.378770533237348411
38Dokter Entropy0.716510.4597113.14050.5041262.208769551218348409
39David Harville0.703900.4591013.14670.2825262.413770542228348410
40Talisman Red0.689940.4908113.34320.1340271.022716494222347360
41Dunkel Index0.676300.4978013.50551.3240279.919692468224340343
42Sagarin Ratings0.690910.4473713.25410.2903266.691770532238340420
43Line (opening)0.707790.5000012.75260.2760251.696770545225337337
44FEI Projections0.682770.5041213.03170.3315257.875621424197306301
45Roundtable0.692310.5019813.62660.7186293.881533369164253251
46Brent Craig0.708430.5194213.07990.5354263.541415294121214198
47Computer Adjusted Line0.712990.4767412.65910.4734246.306770549221205225
48Least Squares w/ HFA0.665840.5151515.65820.6268372.986401267134204192
49Bihl System0.693240.4939213.07530.2528258.480414287127203208
50Linear Regression0.690770.4974713.19280.4825267.460401277124197199
51Logistic Regression0.678300.4722215.4058-2.1711369.469401272129187209
52Line (updated)0.718180.5000012.62470.4247245.711770553217165165
53Catherwood Ratings0.770270.5000013.74320.6351276.443148114347070
54Line (Midweek)0.7116912.64290.4740246.300770548222
* 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