What Big Data Can Learn from the NBA


94Fifty smart sensor basketball photo courtesy 94Fifty.

On Thursday, February 13, Grantland’s Zach Lowe wrote an article on the latest technological development in professional basketball: measuring biometric information in game settings. Four D-League (the NBA’s developmental league) teams will start using one ounce sensors fitted on player jerseys to start measuring metrics such as heart rate speed, and position. These sensors are currently available from one of three companies: STAT Sport, Zephyr, and Catapult. These sensors are not new to professional basketball, as nearly two-dozen NBA teams already use these devices. However, NBA teams currently only use these sensors in practice settings, rather than in game-time situations.

There are a couple of interesting Social Big Data lessons that professional basketball could potentially learn from this experiment that every Big Data expert should be interested in finding out.

First, consider one of the quotes from the Grantland article:

“As the research-and-development arm of the NBA, the NBA D-League is the perfect place to unveil innovative performance analytic devices in-game,” said NBA D-League president Dan Reed.

This concept of an R&D product where you collect more data in an experimental setting is one that many technology companies could start to use. For instance, does your core cash cow product have a corresponding R&D product that can be tinkered with without affecting your revenue? This is a good role for your freemium or single user product. (Heck, Facebook does this for their core platform, although DataHive does not recommend the level of iteration that Facebook provides unless you have a monopoly or duopoly of your core market.) This new use of heart rate and other physical information will provide insights on team tactics and performance if used correctly, thus leading to not just Big Data, but interactive and social Big Data where each player’s metrics are dependent on each other.

Second, and more interestingly from a tactical perspective, this measurement will allow basketball teams to more closely align physical effort with results. It is easy to simply believe that hustle and effort lead to better results, but these metrics may actually show that a lack of hustle could be one of several things. It could be a health issue or laziness or it could be good strategy in saving energy for key moments. Hustle and physical movement should not be measured in isolation, but in context of results. If a “clutch” player ends up moving less than an average player or saves exertion for peak moments, the economics of movement may actually state that excessive “hustle” is detrimental to performance. These sensors may also show that specific team tactics lead to greater efficiency, just as our analysis of shot taking shows how important it currently is to take shots from within 4 feet of the rim or on the sides of the three point line.

From a business perspective, most of us do not put out the physical effort of a professional athlete for a prolonged basis at work. But do we waste time and energy by going in the wrong direction? Are we getting stressed because our managers are not telling us the right information? There is a key challenge of understanding how to use this information productively rather than punitively. It can be easy to fall into the trap of simply stating that more time at work equates to greater productivity, but it may actually be that after a certain point, the error rate or lack of clear thinking outweighs the incremental productivity that would be expected. Follow the real business metrics rather than pure resource utilization.

However, as this occurs, one of the biggest challenges will be to translate sports analytics to business analytics. Keeping score is very easy in a rule-based sports environment, but more difficult in a business environment when it can often be difficult to define KPIs. Based on personal experience and interviews with multiple basketball analysts, DataHive has found that the academics and number crunchers conducting this analysis are largely unaware of the value that these findings could provide in the sports world. Although business analysts can quickly see how the heat and activity maps associated with basketball could translate into greater retail, field, and manufacturing success, one of the great challenges is that the sports analysts currently doing this work do not understand how their work could be translated to other fields. In our role of supporting Social Big Data for Human Insight, DataHive serves as a Sports Data Whisperer that wrangles the findings and techniques used in the sports world and brings them to the business world.

DataHive’s principals have long believed that the structured world of sports serves as a natural testing ground for the predictive, geolocated, and biometric data that is being introduced to the corporate world. Video feed metadata and sensor-based data are the Next Big Things in Big Data and it is only a matter of time before the corporate world follows suit. Regardless of your personal interest in sports, Big Data professionals should keep track of the surveillance and sensor data being used in the basketball world to see how this controlled setting provides potential insight for future enterprise technology efforts.

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