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