Computer Rating System Prediction Results for College Football (NCAA IA)

2014 Retrodiction Results

Through
Rank System Pct. Correct Against Spread Absolute Error Bias Mean Square Error games suw sul atsw atsl
1Ashby AccuRatings0.773680.0000010.93951.7062193.15776058817200
2Beck Elo0.802630.0000011.14330.3585199.18576061015000
3Born Power Index0.790790.0000010.98841.4090192.65976060115900
4Covers.com0.778950.0000011.82710.0870224.36976059216800
5CPA Rankings0.792110.0000010.53700.7913176.04076060215800
6CPA Retro0.793420.0000011.74210.7469229.05176060315700
7Edward Kambour0.780260.0000010.67161.1649182.11976059316700
8Laz Index0.778950.0000010.85800.6309189.53876059216800
9Least Squares0.800000.0000010.45060.8218173.85476060815200
10Least Squares w/ HFA0.826320.000009.28850.0512135.15176062813200
11Logistic Regression0.834210.0000015.3849-1.7076639.42376063412600
12Massey Concensus Rank0.814470.0000011.02120.9871197.71776061914100
13NutShell Combo0.790790.0000011.55420.4336223.36376060115900
14Nutshell Girl0.771050.0000011.80990.6992237.48476058617400
15NutShell Sports0.789470.0000011.96730.1682231.62576060016000
16Payne Power Ratings0.815790.0000011.0928-0.2774195.87676062014000
17PerformanZ Ratings0.789470.0000011.28910.8602198.88476060016000
18Pigskin Index0.765790.0000011.16970.8485199.92576058217800
19Sagarin0.809210.0000010.55210.5754179.99576061514500
20Sagarin Elo0.832890.0000011.38970.7004208.56276063312700
21Sagarin Predictive0.789470.0000010.62230.5138179.15276060016000
22Sonny Moore0.786840.0000011.16380.9848196.18676059816200
23Stat Fox0.760530.0000011.12371.9051192.92876057818200
24Stortrends0.766120.0000011.50450.2327208.12166751115600
25SuperList0.756580.0000012.54720.7683256.46976057518500
26System Average0.806580.0000010.70800.7210184.02076061314700
27system Median0.802630.0000010.64450.7746182.14276061015000
* 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