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