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

* 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