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