Computer Rating System Prediction Results for College Football (NCAA IA)

2021 Second Half Totals

Through 2022-01-11
Rank System Pct. Correct Against Spread Absolute Error Bias Mean Square Error games suw sul atsw atsl
1Massey Consensus0.728180.4708913.42661.3417277.534401292109186209
2PerformanZ Ratings0.728180.4848513.38211.3665279.104401292109192204
3Billingsley0.720700.4621213.64960.6766292.054401289112183213
4Payne W/L0.715710.4695413.8109-0.1546292.611401287114185209
5Howell0.716790.4816813.12400.4676262.054399286113184198
6Line (updated)0.713220.4625912.51000.6072243.5514012861156879
7ARGH Power Ratings0.710720.4843813.15210.5686266.275401285116186198
8Payne Predict0.708230.4873713.08650.5846265.448401284117193203
9Super List0.705740.5088615.17641.4997351.861401283118201194
10Keeper0.708540.5102013.28821.3486273.753398282116200192
11Payne Power Ratings0.703240.4671713.23470.0998269.784401282119185211
12Computer Adjusted Line0.700750.4600012.54990.6621244.08940128112092108
13Donchess Inference0.698250.4832913.10480.7736262.332401280121188201
14DP Dwiggins0.701010.4960213.06281.3693266.616398279119187190
15Laz Index0.695760.4949512.96510.8169258.561401279122196200
16Line (opening)0.693270.5202312.64710.5424251.068401278123180166
17Edward Kambour0.693270.4722212.95810.6587255.632401278123187209
18Beck Elo0.693270.5126313.10211.1038265.591401278123203193
19Stephen Kerns0.693270.4987313.23120.9541263.526401278123197198
20Linear Regression0.690770.4974713.19280.4825267.460401277124197199
21David Harville0.690770.4658212.93050.4803255.005401277124184211
22ESPN FPI0.690770.4708912.86201.3393258.710401277124186209
23TeamRankings.com0.690770.5217412.85360.8357256.795401277124204187
24Line (Midweek)0.6907712.54990.6471244.872401277124
25Versus Sports Simulator0.692500.4708913.13560.7023264.443400277123186209
26Dokter Entropy0.692500.4656512.96331.0217262.196400277123183210
27System Average0.690770.5000012.76630.8347250.233401277124198198
28Sagarin Points0.688280.4531613.00820.4445257.926401276125179216
29FEI Projections0.687500.4987312.92320.3387255.325400275125196197
30PI-Rate Bias0.685790.4974713.09611.0840264.370401275126197199
31Pi-Ratings Mean0.685790.4845413.10751.0182262.925401275126188200
32Moore Power Ratings0.685790.5000013.25161.1222272.744401275126198198
33Sagarin Ratings0.683290.4368712.90940.4542253.158401274127173223
34Sagarin Recent0.683290.4848512.99430.5106260.414401274127192204
35Daniel Curry Index0.683290.4722213.24980.6096268.991401274127187209
36System Median0.678300.4923912.80620.7864251.569401272129194200
37Logistic Regression0.678300.4722215.4058-2.1711369.469401272129187209
38Sagarin Golden Mean0.675810.4621212.99450.4566256.537401271130183213
39Pi-Rate Ratings0.675810.5012713.09530.9686264.247401271130197196
40Talisman Red0.678390.4809213.10380.3416265.505398270128189204
41Pigskin Index0.670820.4904613.04480.9206261.144401269132180187
42Massey Ratings0.670820.4697013.18500.4600263.324401269132186210
43Born Power Index0.670820.4949513.18591.3675268.773401269132196200
44Loudsound.org0.683670.4760613.6862-1.9923281.273392268124179197
45Dunkel Index0.668330.4924213.11881.2242262.208401268133195201
46Stat Fox0.665840.4973713.15222.3942268.566401267134189191
47Least Squares w/ HFA0.665840.5151515.65820.6268372.986401267134204192
48Dave Congrove0.662500.5000013.43861.1211277.764400265135197197
49Cleanup Hitter0.653370.4687514.36040.9542316.503401262139180204
50Brent Craig0.714290.5235512.93150.7749258.543364260104189172
51Bihl System0.702480.4972212.93010.2500254.820363255108179181
52Laffaye RWP0.692750.5000013.58301.0170286.990345239106170170
53Roundtable0.706070.5120312.72840.4792250.23631322192149142
* 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