Algorithmic relational odds nexus system

A process to model a game-like scenario and predict a game-like scenario outcome, the process comprising:

• constructing a predictive model population comprising a plurality of neural network models each configured to predict a game-like scenario outcome, wherein the game-like scenario outcome is determined as a function of the measured performances of at least a first participant and a second participant in each game-like scenario;
• evolving the predictive model population of neural network models by a genetic algorithm until the game-like scenario outcomes predicted by the predictive model population of neural network models satisfy a criterion determined as a function of the measured historical performances of at least the first participant and the second participant in each game-like scenario;
• and,
• providing a user with access to the evolved predictive model population of neural network models to predict a future game-like scenario outcome determined as a function of historical data input.

A process to model a game-like scenario and predict a game-like scenario outcome, the process comprising:

• constructing a predictive model population comprising a plurality of neural network models each configured to predict a game-like scenario outcome, wherein the game-like scenario comprises a sporting event, and wherein each of the plurality of neural network models is configured to predict the game-like scenario outcome determined as a function of a weight value governing the influence of a neural network input on a neural network output, and wherein each weight value is initialized to a value determined as a function of a category of statistical data associated with the game-like scenario, and wherein the game-like scenario outcome is determined as a function of the measured performances of at least a first participant and a second participant in each game-like scenario, and wherein at least the first participant and the second participant in the game-like scenario are competitors;
• evolving the predictive model population of neural network models by a genetic algorithm until the game-like scenario outcomes predicted by the predictive model population of neural network models satisfy a success criterion determined as a function of a spread set by an oddsmaker and a Game Spread Ratio (GSR) calculated for each game-like scenario, wherein determining the success criterion is satisfied comprises identifying an error window between:
• a first probability that a predictive model's outcome prediction differs from a metric predetermined as a function of the measured performances of at least the first participant and the second participant and a second probability the metric is incorrect, and wherein the members of the predictive model population that satisfied the success criterion are designated as members of the parent generation which will be used to create the next generation, and wherein the parent generation is filled to replace models that did not satisfy the success criterion with neural network models determined as a function of randomized data;
• and,
• providing a user with access to the evolved predictive model population of neural network models to predict a future game-like scenario outcome determined as a function of historical data input, to provide the user with a recommended betting choice from among at least the first participant and the second participant, wherein the recommended betting choice is based on predicted outcomes selectively filtered as a function of the measured performances of at least the first participant and the second participant in each game-like scenario.

A process to model a game-like scenario and predict a game-like scenario outcome, the process comprising:

• constructing a predictive model population comprising a plurality of neural network models each configured to predict a game-like scenario outcome, wherein the game-like scenario comprises a sporting event, and wherein each of the plurality of neural network models is configured to predict the game-like scenario outcome determined as a function of a weight value governing the influence of a neural network input on a neural network output, and wherein each weight value is initialized to a value determined as a function of a category of statistical data associated with the game-like scenario, and wherein the game-like scenario outcome is determined as a function of the measured performances of at least a first participant and a second participant in each game-like scenario, and wherein at least the first participant and the second participant in the game-like scenario are competitors;
• evolving the predictive model population of neural network models by a genetic algorithm until the game-like scenario outcomes predicted by the predictive model population of neural network models satisfy a success criterion, wherein the success criterion is determined as a function of a spread set by an oddsmaker and a Game Spread Ratio (GSR) calculated for each game-like scenario, and wherein the members of the predictive model population that satisfied the success criterion are designated as members of the parent generation which will be used to create the next generation, and wherein a gene in the genetic algorithm represents one neural network weight associated with data predictive of one game-like scenario participant's performance in the game-like scenario, and wherein the parent generation is filled to replace models that did not satisfy the success criterion, and wherein some of the replacement neural network models are determined as a function of the parent generation, and wherein some of the replacement neural network models are determined as a function of randomized data;
• and,
• providing a user with access to the evolved predictive model population of neural network models to predict a future game-like scenario outcome determined as a function of historical data input, to provide the user with a recommended betting choice from among at least the first participant and the second participant, wherein the recommended betting choice is based on predicted outcomes selectively filtered as a function of the measured performances of at least the first participant and the second participant in each game-like scenario.