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

2022 Season Totals

Through 2023-01-10
Rank System Pct. Correct Against Spread Absolute Error Bias Mean Square Error games suw sul atsw atsl
1Laffaye RWP0.682830.4803913.5330-0.0155291.424722493229343371
2Moore Power Ratings0.680000.4817213.1106-0.0252276.591775527248369397
3Sagarin Recent0.699740.4915312.6528-0.0329257.407776543233377390
4Sagarin Golden Mean0.680410.5065112.5151-0.0336253.531776528248389379
5Massey Ratings0.697160.5319412.4786-0.0418251.724776541235408359
6ARGH Power Ratings0.675260.4789113.0938-0.0512276.669776524252352383
7David Harville0.694590.5091412.49660.0855253.125776539237390376
8Sagarin Ratings0.707470.5065112.4622-0.1380249.689776549227389379
9Sagarin Points0.689430.5039212.5489-0.1454253.307776535241386380
10Billingsley0.664520.5026113.43790.1611292.406775515260385381
11Payne Power Ratings0.688140.4947912.86810.2151267.004776534242380388
12Payne W/L0.658510.4895613.5289-0.2410295.208776511265375391
13Howell0.676550.4768413.41240.2463294.056776525251350384
14Talisman Red0.692010.5234412.92520.2529271.807776537239402366
15TeamRankings.com0.703610.4789512.39570.2530249.400776546230364396
16Laz Index0.688630.5189512.84010.4397263.631774533241397368
17Donchess Inference0.701030.5218512.45160.4972250.995776544232394361
18System Median0.693300.5085912.36080.5109246.750776538238385372
19System Average0.688140.4993512.43380.5424248.770776534242383384
20Roundtable0.683400.5029813.04440.5502279.380518354164253250
21Cleanup Hitter0.648200.4699614.00450.5626310.485776503273352397
22Line (Midweek)0.7203612.05990.5664234.538776559217
23Edward Kambour0.688630.4856412.66170.5814259.395774533241372394
24Computer Adjusted Line0.713920.4690312.09210.5844234.884776554222212240
25Line (updated)0.715210.4928012.06510.6012233.847776555221171176
26Line (opening)0.706190.5077912.08050.6617236.294776548228326316
27Bihl System0.673170.4815712.86940.7020271.105410276134196211
28Stephen Kerns0.690720.4842913.89670.7545312.164776536240370394
29Daniel Curry Index0.685640.5006513.16720.7610280.958773530243383382
30Versus Sports Simulator0.697280.4758212.73390.7699262.842773539234364401
31Payne Predict0.684280.4732013.15130.7929278.935776531245362403
32Waywardtrends0.684280.5110812.77760.8167264.981776531245392375
33Beck Elo0.677840.4803713.13710.8316277.478776526250367397
34Logistic Regression0.647560.5362314.2849-0.8567362.924349226123185160
35DP Dwiggins0.692900.4865613.2723-0.9342283.183775537238362382
36Pi-Ratings Mean0.711340.4920812.36400.9370246.959776552224373385
37Dave Congrove0.684280.5071712.93430.9805267.934776531245389378
38PerformanZ Ratings0.666240.4856814.00260.9886310.134776517259373395
39ESPN FPI0.703510.5052612.27330.9943244.335769541228384376
40Dokter Entropy0.703610.4908612.27911.0174243.062776546230376390
41Pigskin Index0.702320.4897112.56571.0377255.484776545231357372
42Pi-Rate Ratings0.711340.4921112.43841.0485251.072776552224374386
43PI-Rate Bias0.702320.4902012.46051.0799251.311776545231375390
44Brent Craig0.703300.4549212.67821.2032256.102728512216328393
45Dunkel Index0.696480.4963113.07131.2361279.665682475207336341
46Born Power Index0.694590.4791713.03841.2843276.156776539237368400
47Linear Regression0.684810.4956512.85461.3205278.097349239110171174
48Massey Consensus0.681700.4700513.04111.4341279.200776529247361407
49Least Squares w/ HFA0.662250.5351214.37191.5603327.602302200102160139
50Keeper0.695480.5248013.15671.6739279.270775539236402364
51FEI Projections0.675720.4943312.6622-1.6793261.959626423203305312
52Catherwood Ratings0.677840.4614313.26681.9858281.530776526250341398
53Stat Fox0.690100.4925212.74492.0602262.238768530238362373
54Loudsound.org0.664350.5167913.9540-3.3022309.256718477241354331
* 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