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