Basketball analytics for the Lakers • Diana Ma

Diana Ma (Data Scientist, Los Angeles Lakers || CA USA)

“I’ve always really liked basketball, but in high school I despised statistics. I remember blowing off studying for AP Stats because the exam coincided with the NBA playoffs, and obviously the NBA was much more important. But I was always looking at the box scores for the game to see the players’ statistics — to see if I could use them to prove or disprove any theories I had.

When I started undergrad I wanted to become a doctor. One of the pre-med requirements was a stats course which I begrudgingly took, but something clicked and I ended up really liking it. I realized, ‘Oh, I can use this to solve basketball problems!’ I switched over my major, but at that time I didn’t know I could do basketball analytics as a career and figured I’d just do it in my free time. I went into biostats after undergrad and then did a Masters in applied statistics at NYU. While I was there, the director of analytics for the NBA gave a talk, and I was like, ‘Whoa, this job exists?’ My mind was blown. Afterwards, he told me about the Pacers analytics summer internship and said I should apply. I ended up getting it and it was a dream come true. It was my foot in the door to get the position at the Lakers where I am now.

I see our analytics team as a mini consulting company. Ultimately, our goal is to provide information to the coaches, front office, and anyone else in basketball operations so they can easily get insight to help with decisions. The models we create need to be interpretable to build trust in the prediction — you don’t want a black box telling you to pick a player in a draft but you don’t know why. The story we tell with the data is really important.

There are only a handful of women working in basketball statistics and we desperately need more — the best way to get involved is to use publicly available data to solve a sports problem you’re interested in and try to attend sports conferences to build a network. You could even get hired based on some visualization you post on NBA analytics Twitter! There’s no fixed formula to work in sports analytics — the only necessary ingredient is to always be curious.”



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