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

* 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