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

* 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