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