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