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Interview: Philip Maymin, NYU on Why Sports should Embrace Analytics?


We discuss how the increasing use of Analytics will change the game of basketball, the concern of Analytics ruining the game, significant trends, advice and more.



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.

First part of interview: Philip Maymin on How Optical Analytics will Revolutionize Basketball

Here is second and last part of my interview with him:

Anmol Rajpurohit: Q5. How do you see Optical Analytics changing the game of basketball in next 2-3 years?

Philip Maymin: Our current box score will soon seem to be a relic of a more innocent bygone era. There will be far more interesting columns, and many more of them. Even the idea of identical rows might eventually disappear, since for most players, many fields will just be zero, or not particularly interesting.

Ultimately we’ll see more focus on what I think are the two most basic questions you want to ask about a player: what does he do really well, and what does he do really often? The optical data allows you to give much more nuanced answers.

I think we will also soon see the next evolution in measures of player performance. We will have optical counterparts to things like PER, Win Shares, Wins Produced, etc. All of the current methods of player evaluation will seem quaint in hindsight. I think even plus-minus and its extensions might be fine-tuned: perhaps a probability or expected value weighting per possession will give more useful and less noisy results.
Player_Evaluation
As a result of all those changes, I think eventually the market for players will change, and there will be a greater focus on total contributions to a team’s chances of winning than simply paying for total point or rebound production. Agents will want to use the optical data to make a case for their clients. Perhaps once colleges start collecting optical data too, we will be able to see which skills translate to the pros and which do not. Basketball

Hopefully, we will see a lot more experimentation on the court. Some on-court plays require a certain amount of randomness already. It’d be nice to see some randomness in lineups too. Does the cost outweigh the benefit? Without running the experiment it is hard to find out.

AR: Q6. What do you think about the concern of some NBA players and fans that increasing use of Analytics can potentially ruin the 'true essence' of basketball as a game?

PM: This isn't new in sports, let alone athletic sports. The same was once even said for chess. Allowing people to train on computers will ruin the beauty of the game! People will stop playing! In fact, chess has become much more interesting and championship games much more dramatic. The way Magnus Carlsen plays is, and should be, inspirational to all humans. Without computers, Carlsen wouldn't have had much of a chance against the hegemony of Russian players.

In the same way, analytics makes basketball democratic. It can help an unknown player realize his full potential by helping him to prioritize and structure his training and get rid of hard to identify weaknesses. It can help a small-market team break into the oligarchy of NBA championship teams. And as in chess, it can make basketball much more interesting.

Plus, the concern that any voluntary activity can ruin basketball’s true essence is very insulting and disrespectful to the robustness and beauty of basketball’s true essence!

Analytics_Ruining_GameNo team is forced to use analytics, any more than they are forced to conduct practices or have morning shootarounds before a game. But if it helps, why shouldn't they seek every advantage? The point is to use any legal means to go out and get that ring. What’s the alternative? Ban analytics? That’s like banning thinking.

If the results from analytics are more boring basketball games, then rule changes will and should be forthcoming. But it seems as if the analytics approach tends to create better games: high-scoring, fast-paced games with lots of threes, lots of dunks, and lots of passing; in short, lots and lots of highlights. If you see a team where a single player consistently brings the ball up, dribbles it to death, dances around a bit, then takes a contested long-two, you’re probably not watching an analytically minded team. And you’re not watching one that is likely respecting the true essence of the game of basketball.

AR: Q7. Based on your experience as the co-editor-in-chief of the Journal of Sports Analytics, which of the current trends in Sports Analytics do you believe would make a lasting (or maybe even, disruptive!) impact?

Journal of Sports AnalyticsPM: There are a lot of machine learning applications now, because standard libraries and tools are now widely available and easy to use. And it is being applied at a breakneck speed to all sorts of sports analytics issues, which is good. I think that will run its course as it has in every industry until it is no more exciting than a linear regression. However, I do think it is in general more useful than a linear regression, so I imagine it will eventually become the standard approach to much of analytics. Part of the machine learning applications are analyzing existing data to draw interesting and useful conclusions, and part of it is actually generating new data for further analysis. I think both approaches will make a lasting impact.

AR: Q8. What is the best advice you have got in your career?

PM: Be a master of all trades and skills rather than a narrow specialist.

AR: Q9. What advice would you give to people aspiring for a career in Sports Analytics? Are there any specific university programs that you would recommend?

PM: Be useful. Make useful things. Build your human capital. Learn many skills. Go to the annual Sloan AdviceSports Analytics Conference and other conferences, at least virtually. DVR games and skip commercials, except for the important ones, because Twitter will ruin the ending for you. Be kind to people. Write. Code. Produce. Apply the same analytical thinking you would use on sports to help you manage your own career. Have a backup plan. Watch and allow yourself to genuinely enjoy the Top Ten plays every day. Sleep. Eat healthy whenever you can because you’ll end up bingeing at arenas. Apply for every job opening you reasonably can and sign up for updates. Take advantage of Sloan’s career resources.

AR: Q10. What are your favorite books (or blogs) on Analytics? What do you like to do when you are not working?

PM: I like team-specific ones a lot, even if not all of their posts are quantitatively analytical. With so many eyeballs and neurons focusing on a specific team, you can get a lot of great insight that you can’t always find in a more general blog.

When I’m not working I like to spend time with my family! Gotta go! :)

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