Like most sports, basketball games are noted by the teams, players and their respective game events. Each game is characterized by a set of events that occurs during each unit of time, and are performed by a single or a set of players who together constitute a team- two of which make up a game. Even more interesting than these are the outcomes of each game, be it team-based, player-based or just event-based. The collection of information about these characteristic features and outcomes can be useful in the definition and establishment of solid relationships between these features and thus making the analysis and interpretation of such information easier. From these analysis and interpretation, more factors affecting the outcomes of each game, team or player can be determined, and thus artificially generated. The determination and artificial generation of all factors affecting the performance of each player or team, or those affecting the occurrences of each type of event during a game can help in the fore disclosure of each game event before the game takes place. This can be done in the process called machine learning where computers are fed with some algorithms used to analyze information about teams and players based on their previous performances, and with patterns existing in this information (upon which the algorithm is drawn), the machine will be able to evolve intelligent decision.