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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.

By Anmol Rajpurohit@hey_anmol, Nov 21, 2014.

Dr. Philip Z. MayminPhilip_Maymin is Assistant Professor of Finance and Risk Engineering at the NYU School of Engineering. He is also the founding managing editor of Algorithmic Finance and the co-founder and co-editor-in-chief of the Journal of Sports Analytics. He has also been an analytics consultant with several NBA teams.

He holds a Ph.D. in Finance from the University of Chicago, a Master's in Applied Mathematics from Harvard University, and a Bachelor's in Computer Science from Harvard University. He also holds a J.D. and is an attorney-at-law admitted to practice in California.

He has been a portfolio manager at Long-Term Capital Management, Ellington Management Group, and his own hedge fund, Maymin Capital Management.

He has also been a policy scholar for a free market think tank, a Justice of the Peace, a Congressional candidate, and a columnist for American Banker, the Fairfield County Weekly and LewRockwell.com. He is also an award-winning journalist and the author of Yankee Wake Up, Free Your Inner Yankee, and Yankee Go Home. He was a finalist for the 2010 Bastiat Prize for Online Journalism.

Here is my interview with him:

Anmol Rajpurohit: Q1. How do you define "Optical Analytics"? Why is it important for NBA?

NBAPhilip Maymin: Optical analytics extracts useful information from the high-frequency tracking data which reports the court location of all ten players and the ball, twenty-five frames per second, using an array of video cameras above the court. This is a new and very hot area for basketball decision makers because last year was the first year the data was available to all 30 NBA teams, under a groundbreaking deal between the league and STATS LLC, the innovators and providers of the data.

I believe this kind of data will ultimately prove to be as significant as the three-point line and shot clock in its effect on basketball. It is the most fundamental data: play-by-plays and box scores can in principle all be derived from the optical data. It lets you answer some questions that are otherwise impossible to even address: How does the average small forward’s shooting percentage from the left elbow depend on his distance to the nearest two defenders? Which players have the fastest first step? Who sets the hardest picks?

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