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What Is Basketball Data?

Basketball data collects and analyzes information about players, teams, and more. Most data tracks individual and team performance, from physical attributes to team net rating. However, match viewership, coach performance, player salary, and other data abounds.

Where Does Basketball Data Come From?

Most of this data comes from official channels like the NBA and sports media. Local leagues, coaches, and players also generate plenty of data—though much of it may be protected.

A new source of this data is wearable technology. These wearable sensors can track player movement in real time and easily exports that information to software systems for analysis.

What Types of Columns/Attributes Should I Expect When Working with This Data?

The most common type of basketball data attributes concern players. This can include anything from the number of passes and assists per season, comparison of their performance from one team to another, or even BMI.

Other common data attributes include viewership numbers, college applications in comparison to college basketball season performance, number and type of sponsorships, and so on.

What Is Basketball Data Used For?

Of course, coaches and players use this data to design training programs and prepare for games. Team managers and coaches use the data to make decisions on drafting new players or trades.

Basketball fans, due to their sheer numbers, rival coaches and players for the main users of this data. In addition to arguing player performance and to placing bets, fans use NBA data to play in fantasy leagues. Many fans also use training data and wearable technology to improve their own basketball performance.

Many commercial ventures also use this data when they want to appeal to basketball fans.

How Should I Test the Quality of This Data?

A quality basketball dataset fits its intended purpose, whatever that may be. In other words, a fan playing fantasy basketball uses a different dataset than an amateur player in a neighborhood league.

As with any dataset, users must ensure they collect complete and accurate data then cleanse and evaluate it.

Interesting Case Studies and Blogs to Look Into

NBA Stats
Proballers

Tangible Examples of Impact

“Basically, it’s a compression shooting sleeve for the arm containing sensors that capture the shooting motion for athletes and provides feedback on their form,” Henderson said.

The information gets compiled by the JSleeve sensors, and the player can access the data through a mobile device app for feedback and analytics on their form.

“They get an animated arm visual of their shot and can see if their elbow is too low or they’re having other form issues,” Henderson.

Chicago Tribune: Column: If practice makes perfect, JSleeve shooting aid developed by Aurora Christian graduate Jeremy Henderson could be big

Relevant datasets

iSports API Basketball API

by iSportsAPI

iSports API Basketball API tracks 35,000+ matches in 300+ leagues in 50+ countries. They deliver Stats, Odds, a Historical Database, and more

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