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

* 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