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