The NSWIS Data Science team have won the Sports Analytics & Data Award at the Australia and New Zealand Sports Technology Awards (ANZSTA).

The Awards recognise and celebrate excellence in data, digital and technology across the Sports, Media, Entertainment, Health and Technology landscapes.

Up against fellow nominatees from Empirics, BoomStatAnalysis, Lumin Sports and SportScientia, #TeamNSWIS took out the award at the ANZSTA Dinner in Melbourne last night.

Senior Data Scientist Ric Porteous, Data Scientist George Wehbe and Data Systems Administrator Stevie Lillis make up the current Data Science team at the NSW Institute of Sport.

 

Winning Entry – “Pipeline: Data Science for NSWIS”

In 2018, as part of its core digital strategy, NSWIS invested in creating it’s very first Data Science team. The primary objective of this team to enable NSWIS to use data to inform its decision-making with respect to athlete development and progression as well as decisions surrounding investment in sport.

A major component of the Data Science team’s strategy is the commissioning and implementation of our Data Science platform, Pipeline.  Pipeline is a way for the Data Science team to deploy bespoke Data Science “data products / apps” that connect users to a range of disparate data sources and present customised visualisation to gain insights. The platform, for the first time, enables consistent evidenced based decision making at both an operational (day-to-day athlete decisions) and at a strategic level based on customised longitudinal analysis.

Specifically, the platform delivers value in four key areas;

  1. Process Automation to increase productivity / efficiency of report generation
  2. Databasing to properly centralise data to enable longitudinal analysis
  3. Multiple data source connectivity enriching the information that is processed and,
  4. Insight deployment enabling self-service analytics and evidenced based decision making

Currently, the platform is focused on the four value drivers above. However, the platform is also set up to enable the deployment of predictive analytics models to drive proactive and prescriptive decision making in the future.

The ultimate aim of the project was to provide a system which would enable the organization to, in the long-term, maximise the value of Data Science by making strategic and operational decisions based on the collected data, while, in the short-term, simultaneous delivering immediate value by providing a mechanism to improve operational efficiency and data collection practices.

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