1. Building data systems and models that inform decision-making.

2. Working with the GM, head coach, five analysts (draft, evals, tracking data, coaching support).

3. Tools of practice: R, Sequel, Python.

4. “As a group it’s important that the message we have is the same across the board. As a group we meet and discuss what are the pillars about what we want to measure, what we want to study.”

5. “I want to hire people who are fully living the data that they’re working with.”

6. How data use has changed.

7. Data discussions with the coaches.

8. Using data to put players in the best spot to succeed. Individualizing their programs.

9. The complexities of basketball – and the possibilities of geospatial data.

10. How much variance in the league is there in how teams use data? Impacts of leadership.

11. Obstacles in getting data fully integrated into basketball operations.

12. AI: How can it make day-to-day decisions more efficient? How can we leverage large data systematically?

13. Game theory.

14. Collaborating with Troy and other members of the support team to learn about players: bringing different expertise’s together.

15. The Bucks Analytics Hackathon. Bringing in fresh eyes. Identifying young talent.

16. The sports data science pipeline.

17. How does TJ continue learning? Talking to people from other sports, different industries. Monitoring broader advances in the industry – learning the new tools.

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