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

* 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