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