Podcast Episode #1 – Justin Newman – Pittsburgh Pirates

In the very first episode of the Sport Analytics Podcast, our host Amrit Vignesh sat down with Justin Newman, the newly appointed Head of Player Analysis for the Pittsburgh Pirates. Justin takes us on a fascinating journey through his career in Major League Baseball, from his early days as a Baseball Informatics Intern, to managing the Pirates’ Research and Development team, to his current role as Head of Player Analysis. Justin shares exclusive insights into the day-to-day responsibilities of his various roles, the challenges of transitioning into leadership, and his vision for the future of baseball analytics. He also dives deep into the technical side, discussing the predictive models his team relies on, the programming languages and tools they use, and standout projects that shaped his career.

Whether you’re an aspiring sports analyst, a baseball fan, or simply curious about how data shapes decisions in professional sports, this episode is packed with valuable advice and inspiration. Learn what it takes to break into the field, how to build a standout portfolio, and which skills Justin looks for when hiring new analysts. If you’re passionate about sports analytics or looking to kickstart your career in the industry, this is an episode you won’t want to miss!

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Amrit (Host): Welcome to the first episode of the Sport Analytics Podcast. Today we’re joined by Justin Newman, Head of Player Analysis at the Pittsburgh Pirates. Justin, how are you doing?

Justin: I’m doing great. Thanks for taking the time to chat with me.

Amrit (Host): It’s an honor to have you on our first episode. We’d love to go through your journey. Could you start by walking us through your career path with the Pirates? You started as a Baseball Informatics Intern and have been with the organization for around eight years now. How did your responsibilities evolve with each promotion leading up to your current role as Head of Player Analysis?

Justin: Sure. Starting out as an intern, I was in more of a support role—helping others with queries and smaller analyses. I also had independence to work on my own projects and share them with the group. After my internship, I became a Draft Analyst, where I spent most of my time. That role involved direct interaction with Amateur Scouting and full ownership of the process, from coding to communicating with end users. Over the last year or so, I’ve shifted into more of a management and leadership position. I have a great team who handle the bulk of the work, and I provide guidance and guardrails so they can excel.

Amrit (Host): That sounds really interesting. You’ve held various roles within the organization. What were the biggest challenges when transitioning from one role to the next, especially from a quantitative analyst position into management?

Justin: Going from intern to full-time was tough. I was only a year out of college and found myself in the draft room presenting on players. I made a lot of mistakes and learned as I went. Transitioning from an individual contributor to a manager was another big leap. There’s no set playbook, so I relied on my instincts and core principles to help people grow and to impact decisions across the organization.

Amrit (Host): Great insight. Can you describe a typical day in your previous roles and how your focus changed over time, particularly now that you’re part of Research and Development?

Justin: As a Draft Analyst, I spent the offseason coding new metrics, tools, and models to improve our draft model. In-season, I translated those model results for scouts—highlighting intriguing players we hadn’t scouted or those we needed to see again. Now, as Head of Player Analysis, I spend much of my day in meetings and working with colleagues to create frameworks for player write-ups. I also assist my team with tricky player evaluations and improving existing models. I’m lucky to have a group I can learn from, and hopefully I help them along the way.

Amrit (Host): Looking ahead, what are your primary goals for the department, and what innovations do you hope to implement?

Justin: Our main goal is to make decision-making easier for everyone—coaches, coordinators, scouts, and the GM—by targeting our analysis and reports in a way that streamlines decision-making. We also want to ensure consistency across all evaluations (player development, draft, pro evaluation, international) by using the same core principles.

Amrit (Host): Can you share the types of predictive models your team builds most often, and how they shape organizational decisions?

Justin: Our models range from computer vision to simple linear regressions, plus more sophisticated machine learning like neural networks or Bayesian approaches. Our data science team leads the more involved, long-term modeling efforts. My group collaborates by advising on baseball-specific aspects, then applies and communicates the findings to end users. We also do some shorter-term research that informs the data science team’s work.

Amrit (Host): Which programming languages and tools are most important for your workflow? Any emerging technologies that excite you?

Justin: R and Python are standard for data science and analysis. SQL is also essential. If you’re proficient in at least one language, that’s a strong start. As for emerging tech, ChatGPT is fascinating. I’ve tested the latest version, and it’s impressive how it can provide code suggestions, conceptual insights, and highlight potential pitfalls.

Amrit (Host): Do you have a favorite project or analysis that was a game changer for the Pirates?

Justin: The draft model is my favorite. It combines data like player age, performance, batted-ball tracking, and scouting reports to predict Major League value. It’s been rewarding to watch our decision-making evolve around that model. We now have a systematic process for setting our board and determining when to deviate from the model’s rankings.

Amrit (Host): What advice would you give to aspiring analysts hoping to break into Major League Baseball or professional sports?

Justin: There are many ways to add value. In the early days, you had to be a jack of all trades, but now there’s room for specialization. You don’t have to be an expert in everything if you excel in one specific area. Find your superpower and lean into it.

Amrit (Host): When hiring, what types of projects or portfolio pieces catch your attention the most?

Justin: Any published research is a plus for data science roles. For analyst roles, creativity in understanding the game really stands out. Some candidates run their own analytics blogs with unique takes on in-game strategy or player evaluation, and that originality can make a strong impression.

Amrit (Host): For someone just starting, which areas of analytics or programming should they focus on mastering first?

Justin: Build a solid programming foundation—any language is fine, because it’s really about learning logical structures. On the statistics side, fields like econometrics or applied quantitative social sciences are great at teaching how to handle confounding variables, which is crucial in baseball analytics.

Amrit (Host): Our platform focuses on educating and certifying aspiring analysts. From your perspective, what skills or topics should be emphasized in certification courses?

Justin: There are a ton of methodologies to teach, but I’d highlight problem-solving and a strong understanding of data limitations. Great analysts understand the data they’re working with and can clearly explain any limitations or caveats in their work.

Amrit (Host): Are there any common skill gaps you notice in candidates, and how can programs like ours address those?

Justin: Highly technical applicants might lack the baseball perspective to apply those methods effectively, while those with strong baseball backgrounds might need more robust coding or modeling skills. Filling those gaps with R, Python, and SQL is usually the key.

Amrit (Host): How has the field of baseball analytics evolved since you started, and where do you see it going in the next five years?

Justin: Early on, it was about basic sabermetric principles like valuing walks. Then teams focused on data engineering once pitch-tracking technology became universal. After that, modeling and data science took center stage. Looking ahead, biomechanics and creating proprietary data-collection technologies could be the next big frontier.

Amrit (Host): What lessons about leadership and teamwork would you share with others looking to grow in this field?

Justin: Care about people, invest in their growth, and create a supportive environment. Ambition is fine, but sustainable success relies on ensuring your team doesn’t burn out and has opportunities to develop.

Amrit (Host): Any final words for aspiring analysts or advice for us as we keep building this podcast and platform?

Justin: Put your work out there. Conduct independent research, build models, and share findings that offer a fresh perspective. That’s how you catch a team’s attention.

Amrit (Host): Thanks so much, Justin. Your advice and experience are invaluable. And thank you to everyone tuning in to the first episode of the Sport Analytics Podcast. We’ll see you next time.