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

* 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