Three-point shooting, Steph Curry, and coming up with stories. If you feel like doing your own analysis to investigate hypotheses or discover insights at any level, RDF graph’s got your back. Case in point: The NBA.

With the NBA playoffs in full swing, we are used to having statistics nuggets thrown into game coverage. While it has been argued that not every aspect of the game should be purely data driven, sports analytics can be fun for fans as well as a useful tool for organizations.

The NBA has come into organizing analytics hackathons, asking participants to propose novel ideas in terms of both the game itself as well as its business side. Projecting the impact of hypothetical rule changes or predicting the entertainment value of games are some examples of ideas investigated in this context.

You don’t have to be the NBA, or professional media, or a sports organization with a dedicated analytics team to do some analysis of your own. But some problems that are hard to tackle at any level may be approachable via flexible graph data modeling.

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