The System
Three powerful AI methodologies — Genetic Algorithms, Neural Networks, and Monte Carlo Simulations — combine to produce data-driven sports predictions.
Genetic Algorithms
A method for solving optimization problems based on a natural selection process that mimics biological evolution.
Process
An initial database of factors is created
Each factor is evaluated for its 'fitness' — how well it fits desired outcomes
Selection discards bad factors, keeping only the best in the population
Crossover creates new factor sets by combining aspects of selected factors
Mutation makes small random changes to individual factors
The process repeats until a termination condition is reached
"Although randomized, GAs are by no means random — they exploit historical information to direct the search into the region of better performance within the search space."
Neural Networks
An artificial system that captures and represents complex input/output relationships, inspired by the human brain.
Process
A neural network acquires knowledge through learning
Knowledge is stored as connection weights
The Multilayer Perceptron (MLP) model maps inputs to outputs using historical data
The trained model produces outputs when desired outcomes are unknown
"The true power of neural networks lies in representing both linear and non-linear relationships, learning them directly from the data being modeled."
Monte Carlo Simulations
A computerized mathematical technique that accounts for risk in quantitative analysis and decision making.
Process
Models a range of possible outcomes
Calculates probabilities for each outcome
Shows extremes, conservative results, and everything in between
Used across finance, engineering, insurance, and sports forecasting
"First used by scientists working on the atom bomb, named after the Monaco resort town renowned for its casinos."
GA vs. Neural Networks
"Find a sequence of moves with a given set of chess pieces that will lead to checkmate."
"Find all of the ways a three move sequence in chess will lead to checkmate."