Computer Rating System Prediction Results for College Football (NCAA IA)

2022 Second Half Totals

Through 2023-01-10
Rank System Pct. Correct Against Spread Absolute Error Bias Mean Square Error games suw sul atsw atsl
1ARGH Power Ratings0.665840.4814812.77310.8953272.858401267134182196
2Beck Elo0.675810.4758312.78651.6138278.086401271130187206
3Bihl System0.688020.4747212.79961.0667268.819359247112169187
4Billingsley0.673320.5113412.81531.1215277.332401270131203194
5Born Power Index0.678300.4886612.77831.4112275.438401272129194203
6Brent Craig0.697220.4845912.74961.7930271.689360251109173184
7Catherwood Ratings0.670820.4772112.95512.3965283.654401269132178195
8Cleanup Hitter0.620950.4883713.59600.9279298.045401249152189198
9Computer Adjusted Line0.688280.4978012.17460.9401246.707401276125113114
10Daniel Curry Index0.683420.5152312.83510.8798277.612398272126203191
11Dave Congrove0.678300.5075812.63511.2525273.307401272129201195
12David Harville0.678300.5189912.37320.7194258.638401272129205190
13Dokter Entropy0.688280.5025312.30291.3881253.666401276125199197
14Donchess Inference0.680800.5216312.38881.2311253.483401273128205188
15DP Dwiggins0.675000.5118112.8200-0.3600273.570400270130195186
16Dunkel Index0.686870.5127612.80341.4600279.992396272124201191
17Edward Kambour0.683290.5113412.40240.9204257.449401274127203194
18ESPN FPI0.685790.5188912.34181.5017256.291401275126206191
19FEI Projections0.685790.5101512.5154-1.2731260.921401275126201193
20Howell0.680800.4867013.08731.2844287.375401273128183193
21Keeper0.687500.5164612.82922.1349272.116400275125204191
22Laffaye RWP0.678300.4962212.84301.2225277.720401272129197200
23Laz Index0.679200.5304612.52391.1060258.874399271128209185
24Least Squares w/ HFA0.662250.5351214.37191.5603327.602302200102160139
25Line (Midweek)0.6882812.16580.8865246.572401276125
26Line (opening)0.680800.5150612.18580.9214248.344401273128171161
27Line (updated)0.690770.4879512.16960.9352245.6374012771248185
28Linear Regression0.684810.4956512.85461.3205278.097349239110171174
29Logistic Regression0.647560.5362314.2849-0.8567362.924349226123185160
30Loudsound.org0.688570.5365912.7886-1.6800271.466350241109176152
31Massey Consensus0.675810.4987413.02191.7170287.824401271130198199
32Massey Ratings0.680800.5440812.43260.5710259.567401273128216181
33Moore Power Ratings0.670820.4760712.89680.7591277.022401269132189208
34Payne Power Ratings0.673320.4937012.81270.8145277.013401270131196201
35Payne Predict0.673320.4747512.94101.0327283.334401270131188208
36Payne W/L0.650870.5025313.15450.6977291.661401261140199197
37PerformanZ Ratings0.665840.5214113.69891.9048305.681401267134207190
38PI-Rate Bias0.693270.4860812.46671.2967262.545401278123192203
39Pi-Rate Ratings0.703240.4936112.39471.2003261.324401282119193198
40Pi-Ratings Mean0.708230.4910012.30651.2143257.356401284117191198
41Pigskin Index0.688280.5092812.58601.1550268.728401276125192185
42Roundtable0.699660.4982512.72700.8498267.25029320588142143
43Sagarin Golden Mean0.678300.4987412.37470.5489257.745401272129198199
44Sagarin Points0.665840.5188912.45930.3785255.950401267134206191
45Sagarin Ratings0.690770.5138512.35370.3741254.033401277124204193
46Sagarin Recent0.678300.5214112.44300.3165257.696401272129207190
47Stat Fox0.675810.4960612.77322.4193275.892401271130189192
48Stephen Kerns0.685790.5304612.63031.3528267.524401275126209185
49System Average0.678300.4949512.32191.0654255.928401272129196200
50System Median0.683290.5089112.28631.0033254.703401274127200193
51Talisman Red0.675810.5314912.65890.6449269.413401271130211186
52TeamRankings.com0.690770.4809212.40250.7067258.742401277124189204
53Versus Sports Simulator0.673320.4911812.71741.0186270.507401270131195202
54Waywardtrends0.668330.5479812.45541.4761263.560401268133217179

* 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