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