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

* 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