Gunnedah 8 December 2018

Explanation of Inputs for Neural Analysis:
CP - Career performance assessment based on weight/class algorithmsCF - Current form measured by class/weight algorithmsTIM - Revolutionary time assessment (adjusted algorithm)JA - Jockey ability algorithmTA - Trainer ability algorithmJT - Jockey/trainer combination algorithmWT - Wet track performance algorithmCrs - Course suitability algorithmD - Distance suitability algorithm$ - Prizemoney earned algorithmBP - Barrier position (course & distance) algorithmDLR - days since last run algorithm

The user sets the level of importance from the Preference Scale (1 to 5 or No) for each algorithm and the output weightings are listed within seconds.

The Neural factor analysis will quickly identify for you just who are the main chances in each race, the highest to the lowest based on the factor weightings.

Explanation of Inputs for Neural Analysis:
CP - Career performance assessment based on weight/class algorithmsCF - Current form measured by class/weight algorithmsTIM - Revolutionary time assessment (adjusted algorithm)JA - Jockey ability algorithmTA - Trainer ability algorithmJT - Jockey/trainer combination algorithmWT - Wet track performance algorithmCrs - Course suitability algorithmD - Distance suitability algorithm$ - Prizemoney earned algorithmBP - Barrier position (course & distance) algorithmDLR - days since last run algorithm

The user sets the level of importance from the Preference Scale (1 to 5 or No) for each algorithm and the output weightings are listed within seconds.

The Neural factor analysis will quickly identify for you just who are the main chances in each race, the highest to the lowest based on the factor weightings.

Explanation of Inputs for Neural Analysis:
CP - Career performance assessment based on weight/class algorithmsCF - Current form measured by class/weight algorithmsTIM - Revolutionary time assessment (adjusted algorithm)JA - Jockey ability algorithmTA - Trainer ability algorithmJT - Jockey/trainer combination algorithmWT - Wet track performance algorithmCrs - Course suitability algorithmD - Distance suitability algorithm$ - Prizemoney earned algorithmBP - Barrier position (course & distance) algorithmDLR - days since last run algorithm

The user sets the level of importance from the Preference Scale (1 to 5 or No) for each algorithm and the output weightings are listed within seconds.

The Neural factor analysis will quickly identify for you just who are the main chances in each race, the highest to the lowest based on the factor weightings.

Explanation of Inputs for Neural Analysis:
CP - Career performance assessment based on weight/class algorithmsCF - Current form measured by class/weight algorithmsTIM - Revolutionary time assessment (adjusted algorithm)JA - Jockey ability algorithmTA - Trainer ability algorithmJT - Jockey/trainer combination algorithmWT - Wet track performance algorithmCrs - Course suitability algorithmD - Distance suitability algorithm$ - Prizemoney earned algorithmBP - Barrier position (course & distance) algorithmDLR - days since last run algorithm

The user sets the level of importance from the Preference Scale (1 to 5 or No) for each algorithm and the output weightings are listed within seconds.

The Neural factor analysis will quickly identify for you just who are the main chances in each race, the highest to the lowest based on the factor weightings.

Explanation of Inputs for Neural Analysis:
CP - Career performance assessment based on weight/class algorithmsCF - Current form measured by class/weight algorithmsTIM - Revolutionary time assessment (adjusted algorithm)JA - Jockey ability algorithmTA - Trainer ability algorithmJT - Jockey/trainer combination algorithmWT - Wet track performance algorithmCrs - Course suitability algorithmD - Distance suitability algorithm$ - Prizemoney earned algorithmBP - Barrier position (course & distance) algorithmDLR - days since last run algorithm

The user sets the level of importance from the Preference Scale (1 to 5 or No) for each algorithm and the output weightings are listed within seconds.

The Neural factor analysis will quickly identify for you just who are the main chances in each race, the highest to the lowest based on the factor weightings.

Explanation of Inputs for Neural Analysis:
CP - Career performance assessment based on weight/class algorithmsCF - Current form measured by class/weight algorithmsTIM - Revolutionary time assessment (adjusted algorithm)JA - Jockey ability algorithmTA - Trainer ability algorithmJT - Jockey/trainer combination algorithmWT - Wet track performance algorithmCrs - Course suitability algorithmD - Distance suitability algorithm$ - Prizemoney earned algorithmBP - Barrier position (course & distance) algorithmDLR - days since last run algorithm

The user sets the level of importance from the Preference Scale (1 to 5 or No) for each algorithm and the output weightings are listed within seconds.

The Neural factor analysis will quickly identify for you just who are the main chances in each race, the highest to the lowest based on the factor weightings.

Explanation of Inputs for Neural Analysis:
CP - Career performance assessment based on weight/class algorithmsCF - Current form measured by class/weight algorithmsTIM - Revolutionary time assessment (adjusted algorithm)JA - Jockey ability algorithmTA - Trainer ability algorithmJT - Jockey/trainer combination algorithmWT - Wet track performance algorithmCrs - Course suitability algorithmD - Distance suitability algorithm$ - Prizemoney earned algorithmBP - Barrier position (course & distance) algorithmDLR - days since last run algorithm

The user sets the level of importance from the Preference Scale (1 to 5 or No) for each algorithm and the output weightings are listed within seconds.

The Neural factor analysis will quickly identify for you just who are the main chances in each race, the highest to the lowest based on the factor weightings.