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