●
Predictive Analytics model to predict football schemes during a game, depending on various input criteria; processed the data from sensors inserted into players' shoulder pads; adopted hard-won insights from Todd Steussie, an ex-player and VP of PotentiaMetrics, who took on the Product Owner role on the project.
●
ScoutSight’s proprietary similarity algorithm provides fans with unparalleled access to player stats combined with a powerful algorithm to understand how this year's NFL Draft prospects compare to current NFL players.
●
Custom Cascading filters to provide data cleansing: to detect and replace gaps, duplicates, and irrelevant data. Since the format of NFL data sources varies from season to season, a critical part of the project was to filter all available datasets to unify data.
●
ETL to fetch out all the data on players, their characteristics, scores, events, and games from multiple heterogeneous systems, and transform the data into a proper format for further querying and analytical purposes.
●
OLAP cubes in SSAS to store data for quick querying and analyzing terabytes of data. It allows gaining insight into the information through fast, consistent, and interactive access.
●
AWS Integration to establish complex workflows between distributed systems, and guarantee solid data delivery to thousands of fantasy football fans.
●
API to connect the data sources with the end-user iOS application.
●
An easy-to-use iOS ScoutSight application as a reporting system for data analytics; a responsive and clean user-friendly design.
●
Load testing against the IIS server with a huge number of streams, queues. In order to gauge the performance of the app, more than 1,000 users were involved.