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