Youth sports organization gains insight into participation with health scoring model
Learn how the development of a health scoring model allowed an organization to gain insight into their leagues' health and why athlete participation was declining.
Client background
The client is a national not-for-profit youth sports organization with millions of players and more than one million volunteers. Their mission is to increase engagement in youth sports by focusing less on the competitive aspects and more on community engagement and the athletic and life lessons learned by participating in sports in an athlete’s local community.
The business challenge
For the last decade, the organization has seen a significant drop in participation, due to an increase in participation in more competitive sports leagues. They wanted to understand factors driving participation so they could determine which ones benefit participants the most. The organization also lacked visibility into strategic efforts and how that impacted sports enrollment.
Strategy and solution
To get to the root of the participation problem, Baker Tilly worked with the client’s key stakeholders and determined that individual local leagues were the main participation driver/influence. To give the organization a 360-degree view of the individual leagues' health within the conglomerate of leagues, the Baker Tilly team built a custom machine learning algorithm to determine what factors drove participation.
This led to the development of an initial scoring model that represented the overall league health using the client’s readily available data. The health score was broken into four components: participant engagement, volunteer engagement, parent/league satisfaction and financial health.
Along with the scoring model, Baker Tilly:
- Created an initial dashboard for viewing the new league health score and scoring models
- Developed the new dashboard leveraging future state solution components, specifically Azure Data Factory, Azure SQL, Azure ML and Power BI
- Enabled the organization’s users to perform ad-hoc analysis using Power BI and/or Excel
As result of this project, the organization now has a strong foundation for adding additional data to increase league health scores in the future, and to support organizational data-driven decisions with the goal of improving overall participation and reach.