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Interview: Philip Maymin, NYU on How Optical Analytics will Revolutionize Basketball

We discuss the emergence of Optical Analytics, its impact on NBA, challenges in integrating Optical Analytics into game strategy, the trade-off of analytical insights vs gut instinct and the impact on fan engagement.

AR: Q2. Why do you think it's hard to get started on integrating Optical Analytics into a basketball team's game strategy? What are the toughest challenges?

PM: The optical analytics are not always simple. Traditional analytics are simple: points, rebounds, assists. You can see how it changes over time, you can rank players, and you can compare to historical greats.

Some of the optical stuff is relatively simple: drives, contested rebounds, hockey assists, etc. Those will be integrated by all teams quite soon, I believe.

But the more advanced questions are sometimes hard to formulate in the typical analytics tools available to coaches. That’s the toughest and most important and valuable challenge.

Basketball_Analytics_CoachEvery team has at least one member of the coaching staff who gets lots of ideas from watching the games that he wants to check. Is this player really dogging it on defense, or is that an illusion? Who are the league leaders in getting the ball up the floor, or slowing down transition offense, or closing out on wing shooters? And how do our guys compare to them? Which pick-and-roll partners are most effective vs. which ones are we actually using most often? What areas of the post generate the most consistent offense, and does it come from kick-outs or fouls or plain old field goal effectiveness? What situations cause the most defensive lapses?

The toughest challenge is not just getting answers to them quickly, but presenting them with an interactive tool through which they can ask other or deeper questions, filter by game outcome, or shot clock remaining, or quarter, or any other reasonable parameter.

If it takes a week to get an answer to a question, eventually they will ask no more questions. It has to be immediate, interactive, and visual.

There are technical challenges in doing that properly but if it is done right, if an answer to a brand-new unique question can be answered just as quickly as opening up a web page, that’s suddenly an enormous advantage. Then you have not merely an optical analytics report, or even a tool, but a complete system.

Coaches no longer need to rely solely on theory and their own experience: they can become experimental scientists overnight and examine the sum total of basketball experience in an objective and timely way.

That means the skills needed for analytics are no longer purely statistical but also include data science, programming, visual interface design, cloud storage and computation, quality assurance and usability testing, automation and error handling and recovery, and in general a far more polished and production-level approach. Except for the rare case when those skills happen to coincide in a single person, this can be a hiring and/or training challenge for analytics departments more used to producing regular statistical reports.

Some teams consider outsourcing some analytics to third-parties who offer similar services simultaneously to multiple teams, but ultimately a team’s chief analytics officer can’t really be outsourced any more than its general manager. They need to be an integral part of just one team. And yes, I think teams should eventually appoint chief analytics officers once they realize that analytics is not just another viewpoint among many, but can actually be a comprehensive framework for a team’s overall decision making.

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