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