Methods of Developing Predictive Analytics from Progressions of Comparative Analyses of Base case vs. hypothetical Alternative cases

摘要

A system and methods for producing and modeling predictive data analytics for forecasting future performance in the context of multi-dimensional, sub-datasets via an automated back-end application computer server, comprising: (a) at least one internal data source storing data collected by the enterprise; (b) at least one third-party data source external to the enterprise; (c) a data store containing electronic records created in accordance with data from both the internal data source and the third-party data source, each electronic record representing an association for an entity in connection with a plurality of relationships, wherein each electronic record contains a set of record characteristic values; (d) the back-end application computer server, coupled to the data store, programmed to: (i) search, fetch, and access the electronic records in the database using a uniquely defined a team and player Identity (ID) numbering algorithm to associate, arrange, store, retrieve, compare, and manipulate player profiles containing data and analytics to associate and track player statistics by their team+position+order on roster depth charts to enable “apple-apple” (i.e., same player position, same player depth on roster depth chart) comparison and substitutions of player statistics between teams, (ii) automatically designate a first sub-set of the set of record characteristic values of each electronic record as fixed effect variables, (iii) automatically designate a second sub-set of the set of record characteristic values of each electronic record as random effect variables, (iv) generate, by a data analytics mixed effect predictive model based on the fixed effect variables and the random effect variables, a future performance estimation value.;In one of several embodiments, the present invention delivers predictive sports player/team fit scores via underlying roster modeling based on 87% historically accurate predictive hard and soft skills analytics (controlled for historically pooled leaguewide data, including, but not limited to, National Basketball Association (NBA), National Football League (NFL), Major League Baseball (MLB), National Hockey League (NHL), Major League Soccer (MLS), and English Premier League (EPL) data on age, injury, minutes played, and load management).