Baskteball In Game Interactions with Neo4j

The NBA has enjoyed explosive growth in recent years; so much so that its TV deal, currently fetching $930 million annually from ESPN and Turner, will raise that number to $2.6 billion beginning

next season, a 180 percent increase. In addition to its globalization, nutritional advancement, and technological progress, the quality of play itself has been consistently climbing season after season. Much of this trend can be attributed to team staffs making better decisions about personnel, playing time, play style, matchups, lineups, and the like. And as much as Barkley and other old-school players would like to minimize its impact, it is undeniable that the best teams who make the best decisions have a common underlying focus: data.

Hard data, and how to interpret it (or “analytics”). Finding patterns and adjusting accordingly is crucial in any field. It is certainly no less applicable in basketball, whether it be within your own team, your opponents, or player prospects. All this data can be easily and efficiently stored within a graph database, where anything can be a node. Players, coaches, teams, games, stats, possessions, arenas, management, even gear – these are just some of the things that can relate with each other to have an impact on the ultimate goal of

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