#131 Decision-Making Under High Uncertainty, with Luke Bornn

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Apr 30, 2025

Luke Bornn, a pioneer in sports analytics and co-founder of Zealous Analytics, shares insights from his rich background in statistics and sports. He dives into the evolution of player tracking data and the role of Bayesian methods in decision-making amid uncertainty. There are personal stories about mentorship in sports and statistics, along with lighthearted tales of family gaming. Bornn discusses the complexities of statistical models and their impact on player acquisition and injury prediction, emphasizing the need for interdisciplinary approaches.

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ANECDOTE

Jazz Unites Sports Analysts

Patrick Ward and Sonny Mato bonded unexpectedly over jazz guitar despite their sports analytics careers.
Their shared musicianship created an intense connection that lasted hours after discovery.

INSIGHT

Player Acquisition as Portfolio Optimization

Player acquisition resembles portfolio optimization, balancing cost and performance under budget constraints.
Bayesian probabilities quantify player value relative to contracts, improving acquisition decisions.

ADVICE

Use Distributions to Guide Decisions

Focus on distributions of future player performance in dollars for robust decision making.
Use computational tools to reduce human bias in player evaluations and negotiations.

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Takeaways:

Player tracking data revolutionized sports analytics.
Decision-making in sports involves managing uncertainty and budget constraints.
Luke emphasizes the importance of portfolio optimization in team management.
Clubs with high budgets can afford inefficiencies in player acquisition.
Statistical methods provide a probabilistic approach to player value.
Removing human bias is crucial in sports decision-making.
Understanding player performance distributions aids in contract decisions.
The goal is to maximize performance value per dollar spent.
Model validation in sports requires focusing on edge cases.
Generative models help account for uncertainty in player performance.
Computational efficiency is key in handling large datasets.
A diverse skill set enhances problem-solving in sports analytics.
Broader knowledge in data science leads to innovative solutions.
Integrating software engineering with statistics is crucial in sports analytics.
Model validation often requires more work than model fitting itself.
Understanding the context of data is essential for accurate predictions.
Continuous learning and adaptation are essential in analytics.

Chapters:

11:58 Transition from Academia to Sports Analytics

20:44 Evolution of Sports Analytics and Data Sources

23:53 Modeling Uncertainty in Decision Making

32:05 The Role of Statistical Models in Player Evaluation

39:20 Generative Models and Bayesian Framework in Sports

46:54 Hacking Bayesian Models for Better Performance

49:55 Understanding Computational Challenges in Bayesian Inference

52:44 Exploring Different Approaches to Model Fitting

56:30 Building a Comprehensive Statistical Toolbox

01:00:37 The Importance of Data Management in Modeling

01:03:21 Iterative Model Validation and Diagnostics

01:06:53 Uncovering Insights from Sports Data

01:16:47 Emerging Trends in Sports Analytics

01:21:30 Future Directions and Personal Aspirations

Links from the show:

Luke’s website: http://www.lukebornn.com/
Luke on Linkedin: https://www.linkedin.com/in/lukebornn/
Luke on Wharton Moneyball: https://knowledge.wharton.upenn.edu/podcast/moneyball-highlights/luke-bornn-part-owner-of-ac-milan/
LBS #108 Modeling Sports & Extracting Player Values, with Paul Sabin: https://learnbayesstats.com/episode/108-modeling-sports-extracting-player-values-paul-sabin

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