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