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A predictive analytics model to predict football schemes during a game, depending on various input criteria and using data from sensors inserted into players' shoulder pads and hard-won insights from Todd Steussie, an ex-player and VP of PotentiaMetrics, who took on the Product Owner role on the project
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ScoutSight’s proprietary similarity algorithm provides fans with access to player stats combined with a powerful algorithm to understand how this year's NFL Draft prospects compare to current NFL players
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Custom Cascading filters to provide data cleansing by detecting and replacing 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
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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
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OLAP cubes in SSAS to store data for quick querying and analyzing terabytes of data, enabling fast, consistent, and interactive access to it
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AWS integration to establish complex workflows between distributed systems, and guarantee solid data delivery to thousands of fantasy football fans
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API implementation to connect the data sources with the end-user iOS application
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An easy-to-use iOS ScoutSight application as a reporting system for data analytics; a responsive and clean user-friendly design
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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
