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