Podcast Episode #4 – Riley Leonard – Fanduel

In our fourth episode of the Sport Analytics Podcast, host Amrit Vignesh sits down with Riley Leonard, a Data Scientist in Sports Modeling & Innovation at FanDuel who’s also worked in the Boston Red Sox baseball analytics department. Riley shares how he built robust models to price MLB odds in real time, balanced fast-paced sports betting challenges, and leveraged the same pitch-by-pitch data used in MLB front offices.

Riley also delves into how his academic background in behavioral economics influenced his baseball research and gave him a unique lens on batting decisions. From applying advanced R packages for predictive modeling to communicating insights with non-technical teams, Riley’s journey highlights the intersection of analytics, player evaluation, and sports betting innovation.

Whether you’re fascinated by the fantasy baseball aspect of an MLB front office or curious about real-time sports betting operations, don’t miss Riley’s insights into predictive modeling, robust data pipelines, and bridging academia with high-stakes professional environments.

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📧 For inquiries or collaborations, contact Dave Yount at dave@sportanalytics.com.

Amrit (Host): Welcome to the fourth episode of the Sport Analytics Podcast. Today, we’re joined by Riley Leonard, a data scientist at FanDuel who has also worked with the Boston Red Sox in baseball analytics. Riley, how are you doing?

Riley: I’m doing great. Thank you for having me.

Amrit (Host): Awesome. Could you start by describing your role at FanDuel as a Data Scientist in Sports Modeling and Innovation? What does a typical day look like, and how do you handle tasks in such a fast-paced sports environment?

Riley: Sure. Sports betting is all about probabilities, so my job is to create and refine predictive models that generate those probabilities for MLB odds. In the off-season, we’re focused on maintaining and improving the models we already have. During the season, we deal with the day-to-day chaos of live bets and constantly updating odds. A big part of my job is making sure those models can handle real-time data and remain accurate under pressure.

Amrit (Host): Which programming languages or tools do you rely on most, and do you have any favorites that streamline your workflow?

Riley: I work exclusively in R. A lot of my colleagues use Python as well, but I’ve chosen to deepen my R expertise rather than branch out. We rely heavily on publicly available StatCast pitch-by-pitch data—it’s the same data I used in my academic projects and with the Red Sox, which is pretty amazing. Having all that data publicly accessible is a huge advantage for anyone looking to get into baseball analytics.

Amrit (Host): Live betting lines can shift by the minute. What are the biggest challenges with real-time model updates and ensuring data integrity under those circumstances?

Riley: It’s a completely different ballgame compared to static academic projects. If something breaks or the data pipeline hiccups during a live game, you can’t just pause everything. We put a lot of effort into off-season testing and edge-case handling. Debugging and robustness become top priorities because the stakes are high—mistakes can directly affect the sportsbook and our customers.

Amrit (Host): How do you collaborate with other teams at FanDuel, such as risk management or product, to ensure data insights actually get integrated into the business?

Riley: My closest collaborators are probably our traders, who handle bets in real time. They’re on the front lines, and they might not be familiar with all of our data processes. Good communication is crucial—explaining model outputs clearly, receiving feedback from people who watch a lot of games, and continuously refining our methods. It’s very much a two-way street.

Amrit (Host): You also interned with the Red Sox in baseball analytics. What were your main responsibilities, and how did you contribute to the team?

Riley: I focused on player evaluation—helping project and value different players, whether for the draft, trades, or contract extensions. The Red Sox have multiple analytics groups focusing on in-game strategy, player development, or biomechanical data. My role was a bit more traditional: how do we quantify a player’s long-term potential and put a value on it? It was a fantastic learning experience.

Amrit (Host): What surprised you about stepping into a Major League front office?

Riley: I expected a super high-pressure environment, but everyone was incredibly welcoming. It’s still very intense around trade deadlines and such, but day to day, it felt collaborative and open. Another surprise was seeing how it’s somewhat reminiscent of fantasy baseball—negotiating trades with other GMs, doing extensive prospect research. The stakes are much higher, of course, but there’s a parallel in how data drives decisions.

Amrit (Host): How do you communicate findings to coaches and execs on tight deadlines without overwhelming them with too much detail?

Riley: Communication is as vital as model accuracy in sports. You have scouts and coaches with different expertise—some are less versed in stats, but they have tons of domain knowledge from watching players. You need a common language. That means simplifying outputs, focusing on key takeaways, and respecting their insights. Good communication is absolutely essential for driving data-informed decisions.

Amrit (Host): You’ve seen both a baseball R&D department and a sports betting company from the inside. What stands out as the biggest contrast, and are there any surprising overlaps?

Riley: The biggest overlap is the data itself—StatCast pitch-by-pitch data is central to both. The main difference is the scope of predictions. In a front office, you might project a player’s performance over many years. In betting, it’s very short-term: what’s going to happen in this game, this inning, or even this at-bat? Same data, but very different time horizons and model goals.

Amrit (Host): Your master’s thesis at Cornell dove into cognitive biases in MLB swing decisions. What drew you to that topic, and what were your main takeaways?

Riley: I was studying behavioral economics and wanted to merge it with sports analytics. Swing decisions are split-second judgments, which makes them ripe for biases like loss aversion and the representativeness heuristic. My research confirmed that even elite athletes are human and susceptible to these cognitive biases, especially under time pressure.

Amrit (Host): At Reed and Cornell, you had a broad social science foundation. Which skills or lessons from your education do you use most at FanDuel and with the Red Sox?

Riley: The liberal arts approach at Reed taught me to explore many subjects, which ultimately helped me bring social science ideas into analytics. Writing academically also sharpened my communication skills. Having a technical foundation is crucial, but so is cross-disciplinary thinking.

Amrit (Host): How do you compare academic research pace and goals to a professional sports analytics setting?

Riley: They’re almost polar opposites. In school, you might work on the same dataset for months. In the pros, data is live and can change by the minute. Each environment has its challenges—one is long-term, the other is more chaotic but exciting in real time.

Amrit (Host): You’ve publicly shared several analytics projects. How crucial is a public portfolio for students or job seekers in this field?

Riley: I think it’s huge. Personal projects let you apply your skills, explore what excites you, and show potential employers what you can do. It’s one thing to list R or Python on a resume—it’s another to demonstrate a real model or data viz project. It’s also a great way to learn by doing and to get feedback from the community.

Amrit (Host): If you were designing an ideal sports analytics curriculum, what would you emphasize technically and otherwise?

Riley: Fluency in at least one programming language—R or Python—is essential. But communication skills are equally important: data visualization, report writing, and basic storytelling. Many programs focus on coding and stats, which is great, but bridging the gap with strong communication skills really sets you apart.

Amrit (Host): Which emerging tech or methods excite you most, whether in baseball analytics or sports betting?

Riley: MLB continues to evolve with new data streams like biomechanics and advanced tracking. I find that really exciting—motion capture data could transform how we view player development and injury prevention. It’s moving fast, and there’s a lot of potential for innovation.

Amrit (Host): Any final words of wisdom for aspiring analysts wanting to follow in your footsteps?

Riley: Treat sports analytics as both science and art. It’s not just about building accurate models—it’s also about conveying insight and appreciating the fun side of sports. Take diverse classes, pursue passion projects, share your work, and develop communication skills. Above all, enjoy the process. Sports are games; they should be fun.

Amrit (Host): Thanks so much for the great insights, Riley. It’s been a pleasure talking to you.

Riley: Thank you for having me. I’ve really enjoyed it.